Ultrasound in Medicine and Biology. 24 (4), 587–595.
Kovacs, G. T. A., 2003. Electronic Sensors with Living Cellular Components. Proceedings of the
IEEE. 91 (6), 915–929.
Levsky, J. M., and Singer R. H., 2003. Gene Expression and the Myth of the Average Cell. Trends in Cell Biology. 13 (1), 4–6.
Luong, J. H. T., 2003. An Emerging Impedance Sensor Based on Cell–Protein Interactions:
Applications in Cell Biology and Analytical Biochemistry. Analytical Letters. 36 (15), 3147–
3164.
Pancrazio, J. J., Whelan, J. P., Borkholder, D. A., Ma, W., and Stenger, D. A., 1999. Development
and Application of Cell–Based Biosensors. Annals of Biomedical Engineering. 27 (6), 697–
711.
9
Pepe, J., Rincon, M., and Wu, J., 2004. Experimental Comparison of Sonoporation and
Electroporation in Cell Transfection Applications. Acoustics Research Letters Online. 5 (2),
62–67.
Song, J., Tata, D., Li, L., Taylor, J., Bao, S., and Miller, D. L., 2002. Combined Shock-wave and
Immunogene Therapy of Mouse Melanoma and Renal Carcinoma Tumors. Ultrasound in Medicine & Biology. 28, 957–964.
Sui, G., Orbulescu, J., Ji, X., Gattás–Asfura, K. M., Leblanc, R. M., and Micic, M., 2003. Surface
Chemistry Studies of Quantum Dots (QDs) Modified with Surfactants. Journal of Cluster Science. 14 (2), 123–133.
Tolstonog, G. V., Belichenko–Weitzmann, I. V., Lu, J. –P., Hartig, R., Shoeman, R. L., Traub, U.,
and Traub, P., 2005. Spontaneously Immortalized Mouse Embryo Fibroblasts: Growth
Behavior of Wild–Type and Vimentin–Deficient Cells in Relation to Mitochondrial Structure
and Activity. DNA and Cell Biology. 24 (11), 680–709.
Tschoep, K., Hartmann, G., Jox, R., Thompson, S., Eigler, A., Krug, A., Erhardt, S., Adams, G.,
Endres, S., and Delius, M., 2001. Shock Waves: A Novel Method for Cytoplasmic Delivery of
Antisense Oligonucleotides. Journal of Molecular Medicine. 79, 306–313.
Veiseh, M., Veiseh, O., Martin, M. C., Bertozzi, C., Zhang, M., 2007. Single–Cell–Based Sensors
and Synchrotron FTIR Spectroscopy: A hybrid System towards Bacterial Detection.
Biosensors and Bioelectronics. 23 (2), 253–260.
Verma, M. I., and Somia, N., 1997. Gene Therapy–Promises, Problems, and Prospects. Nature.
389, 239–242.
10
Chapter 2
CELL MEMBRANE AND CELL JUNCTIONS
2.1. General Structure and Functions of Cell Membrane
Cell Membrane, also known as plasma membrane, is a phospholipid bilayer that
forms a two–dimensional fluid and separates the aqueous fluid of the cell’s interior and
outside environment [Alberts et al., 2004]. Proteins of various kinds are inserted into or
through the phospholipid bilayer, which are responsible for most of the functions. The
three–dimensional fluid mosaic model of a cell membrane, revealed by freeze–fracture
electron micrographs, is illustrated in figure 2–1 [Silverthorn, 2007]. The general
functions of the cell membrane are: (i) physical isolation or barrier: it separates
intracellular fluid from the surrounding extracellular fluid; (ii) regulation of molecule
exchange with the environment: it controls the entry of ions and nutrients into the cell,
the elimination of cellular wastes, and release of products from the cell; (iii)
communication between the cell and its environment: it contains proteins that enable
the cell to recognize and respond to molecules or to changes in its external environment;
and (iv) structural and mechanical support: it contains proteins that hold the
cytoskeleton and cell shape.
2.2. Cell Membrane Dynamics
Cell membranes are known to be selectively permeable. The lipids and proteins
embedded in the cell membrane usually determine which molecules will enter or leave
the cell. Water, oxygen, carbon dioxide, and lipids, can move easily across most cell
membranes. However, ions (K+, Na+, Cl–, Ca2+, etc.), most polar molecules, and large
molecules (such as proteins), cannot enter the cell easily. As summarized in figure 2–2,
two properties of a molecule influence its movement across cell membranes: the size of
11
the molecule and its lipid solubility [Alberts et al., 2004; Silverthorn, 2007]. Very small
molecules and those that are lipid soluble can cross directly through the phospholipid
bilayer. But larger or less lipid–soluble molecules requires specific mechanisms for
being transported and membrane proteins are the usual mediators in transporting these
molecules. Different transport mechanisms across cell membranes are: (i) diffusion, (ii)
protein–mediated transport, (iii) vesicular transport, and (iv) transepithelial transport.
Figure 2–1. The fluid mosaic model of a biological membrane. The cell membrane is a double
layer of phospholipid molecules. Proteins of various kinds are inserted into and through the
phospholipid bilayer. After [Silverthorn, 2007].
12
Figure 2–2. Transport across the pure phospholipid layer. The rate at which a molecule diffuses across the cell membrane depends on its size and lipid solubility. The smaller the molecule and the less favorable interactions with water (i.e., the less polar it is), the more rapidly the molecule diffuses across the phospholipid bilayer. Many molecules that the cell uses as nutrients are also too large and polar to pass through the phospholipid bilayer. After [Albert et al., 2004].
2.2.1. Diffusion across the Cell Membrane
Substances that are hydrophilic and can dissolve in water are usually lipophobic and
cannot easily cross the cell membrane. Lipophilic molecules can pass through the
phospholipid bilayer and move by diffusion, i.e., the passive movement of molecules
down a chemical concentration gradient from an area of higher concentration to an area
of lower concentration. According to the Fick’s law of diffusion, the rate of diffusion
across the cell membrane is directly proportional to the membrane permeability (the
ability of the diffusing molecule to dissolve in the lipid layer of the cell membrane), the
magnitude of concentration gradient across the cell membrane, the surface area of the
cell membrane, and inversely proportional and inversely related to the thickness of the
cell membrane and the size of diffusing molecules respectively [Silverthorn, 2007].
13
2.2.2. Protein–Mediated Transport and Cell Membrane Potential
The vast majority of solutes, either lipophobic or electrically charged, cross cell
membranes with the aid of membrane proteins, a process known as protein–mediated
transport. Among various types of transport proteins, channel proteins form open,
water–filled passage way and regulate the movement of substances through them.
Channel proteins can be classified into open channels (leak channels or pores) and gated
channels. In gated channels, the opening or closing of the channel can be controlled by:
(i) intracellular messenger molecules or extracellular ligands (chemically gated
channels), (ii) electrical state of the cell (voltage–gated channels), or (iii) a physical
change, such as increased in temperature or a force that put tension on the membrane
and pops the channel open. Once the channel is opened, molecules and solute can cross
the cell membrane down the concentration gradient. Most ion channels are gated and
the passage of ions is controlled by these mechanisms as illustrated in figure 2–3.
Figure 2–3. Gated ion channels that respond to different types of stimuli. Depending on the type of ion channel, the gates open in response to a change in the voltage
difference across the membrane (A), to the binding of a chemical ligand to the channel, outside (B) or inside the cell (C), or the mechanical stimulation (D). After [Albert et al., 2004].
14
Carrier proteins, unlike channel proteins, do not form a continuous connection or
passage between the intracellular and extracellular fluid as shown in figure 2–4. They
bind to substrates and then change their conformations in order to transport the
molecules across the cell membrane. As illustrated in figure 2–5, carrier proteins can be
classified into: (1) uniport carriers: a single solute is transported across the membrane,
(2) symport carriers: one solute and another solute are transported simultaneously or
sequentially in the same direction, and (3) antiport carriers: one solute and another
solute are transported simultaneously or sequentially in the opposite direction.
If protein–mediated transport moves solutes and molecules down their
concentration gradient, the process is known as passive transport. On the other hand, if
protein–mediated transport requires energy from ATP or another outside sources (such
as light) and moves a substance or molecule against its concentration gradient as
illustrated in figure 2–6, the process is known as active transport. Only carrier proteins
can carry out active transport, but both carrier proteins and channel proteins can carry
out passive transport.
Figure 2–4. Schematics of a carrier protein mediating the passive transport. The
carrier protein changes its conformation from state A to B upon binding the solute in
order to transport it across the cell membrane down the concentration gradient. After
[Albert et al., 2004].
15
Figure 2–5. Different types of carrier proteins (schematics). The carrier proteins
transport solutes across the cell membrane in several different ways: a single solute
(uniports), one solute and another solute simultaneously or sequentially in the same
direction (symports), or in the opposite direction (antiports). After [Albert et al., 2004].
Figure 2–6. Protein mediated active and passive transport. The vast majority of
solutes, either lipophobic or electrically charged, cross cell membranes by passive or
active transport with the aid of membrane transport proteins. Passive transport, in the
same direction as a concentration gradient, occurs spontaneously, whereas active
transport (transport against a concentration gradient) requires an input of energy.
After [Albert et al., 2004].
16
2.2.2.1. The Resting Membrane Potential
Most organic compounds and ions carry a net electrical charge. Potassium (K+) is a
major cation within cells, and sodium (Na+) dominates the extracellular fluid. Chloride
ions (Cl–) mostly remain with Na+ in the extracellular fluid, whereas phosphate ions
(H2PO4–, HPO4
2–, and PO43–) and negatively charged proteins are also the major anions
of the intracellular fluid. Protein–mediated diffusion and active transport of ions across
the cell membrane create an electrical gradient, with the inside of cell being negative
relative to the extracellular fluid. This electrical gradient between the extracellular fluid
and the intracellular fluid is known as the resting membrane potential difference.
Moreover, the active transport of positive ions out of the cell also creates a concentration
gradient. The combination of electrical and concentration gradients is called an
electrochemical gradient and the movement of an ion across the cell membrane is
influenced by the electrochemical gradient for that ion. The membrane potential that
exactly opposes the concentration gradient of an ion is known as the equilibrium or
Nernst potential [Malmivuo 1995; Silverthorn, 2007]. In most excitable cells,
potassium ion (K+) is the primary ion that determines the resting membrane potential
and changes in membrane permeability to ions such as K+, Na+, Ca2+, or Cl– will alter the
resting membrane potential and create electrical signals in excitable cells such as
neurons and myocytes.
2.2.3. Vesicular Transport
Large macromolecules and particles are brought into cells by endocytosis: the
process by which cells absorb particles or molecules from outside the cell by engulfing it
with their cell membrane as shown in figure 2–7. The opposite of that process is
excocytosis in which particles or molecules leave the cells. There are three main types of
endocytosis: (1) phagocytosis: the process by which cells ingest large objects such as
bacteria or viruses (In this process, the cell membrane folds inwards, enclosing the
bacteria or virus in a pocket, and then engulfs the object by pinching it off); (2)
17
pinocytosis: the process by which solutes and proteins are up–taken; (3) receptor–
mediated endocytosis: the process by which proteins or solutes are locked onto
receptors/ligands in the cell membrane and then the particles are engulfed.
Figure 2–7. Endocytosis. Food particles are taken into the cell via endocytosis into a vacuole. Lysosomes attach to the vacuole and release digestive enzymes to extract nutrients. The leftover waste products of digestion are carried to the plasma membrane by the vacuole and eliminated through the process of exocytosis. After [Alberts et al., 2009].
2.2.4. Transepithelial Transport
Transporting solutes and molecules across epithelial cells, also known as
transepithelial transport, uses a combination of active and passive transport. Epithelia
in the intestine and kidneys have different membrane proteins on their apical and
basolateral surfaces. This polarized distribution of transporters results in one–way
movement of certain molecules across the epithelium. The movement of glucose from
the lumen of the kidney tubule or intestine to the extracellular fluid, as illustrated in
figure 2–8, is an example of directional movement across a transporting epithelium.
Moreover, larger molecules also cross epithelia by a process known as transcytosis,
which includes vesicular transport.
18
Figure 2–8. Transepithelial transport. Two types of glucose carriers enable gut
epithelial cells to transfer glucose across the gut lining. Glucose is actively transported
into the cell by Na+–driven glucose symports at the apical surface, and it is released
from the cell down its concentration gradient by passive glucose uniports at the basal
and lateral surfaces. These two types of glucose carriers are kept segregated in the
plasma membrane by the tight junction. After [Alberts et al., 2004].
2.2.5. Osmosis and Tonicity
The concentration gradient of particles (ions or intact molecules) across the cell
membrane also induces the movement of water across the cell membrane. This
phenomenon is called osmosis. The cell volume changes when it is placed in the
solution that has different osmolarity (the number of particles per liter of solution) than
that of intracellular liquid. Tonicity of a solution is a measure of the osmotic pressure of
two solutions separated by a selectively permeable membrane. Tonicity of a solution is
also used to describe the change in cell volume, which occurs when the cell is placed in
19
that solution. Cells swell in hypotonic solutions as the water molecules move into the
cell (figure 2–9) and shrink in hypertonic solutions as the water molecules are drawn out
of the cell. If the cell does not change size at the equilibrium, the solution is isotonic
(both the solution and intracellular liquid have equal concentration of solutes). If the
concentration of solutes in intracellular fluid is higher than that of extracellular fluid,
cells use different tactics such as pumping out ions or periodically ejecting the water that
moves into the cell to avoid osmotic swelling [Alberts et al., 2004].
Figure 2–9. Cell swelling in hypotonic solution. Diffusion of water molecules across
cell membrane is known as osmosis. If the concentration of solutes inside a cell is higher
than that outside, water molecules will move in by osmosis, causing the cell to swell. If
the difference in solute concentration is great enough, the cell will burst. After [Alberts
et al., 2004].
2.3. Cell Junctions
Cells assemble into the larger units called tissues. The cells in any tissue are held or
linked together by specialized connections called cell junctions and by other support
structures. Types of tissues range from simple tissues containing only one cell type, such
as the lining of blood vessels, to complex tissues containing many cell types and
extensive extracellular material, such as connective tissue. The cells of most tissues work
together to achieve a common purpose.
20
2.3.1. Extracellular Matrix
Extracellular matrix, also referred to as matrix, is extracellular material synthesized
and secreted by the cells of a tissue. It plays a vital role in many physiological processes,
ranging from cell growth and development to cell death. There are two basic
components in the extracellular matrix: proteoglycans and insoluble protein fibers.
Proteoglycans are glycoproteins, which covalently bound to polysaccharide chains.
Insoluble protein fibers such as collagen, fibronectin, and laminin provide strength and
anchor cells to the extracellular matrix. Attachments between the extracellular matrix
and proteins in the cell membrane or the cytoskeleton are one means of communication
between the cell and its external environment. The amount of extracellular matrix in a
tissue is highly variable (For instance, nerve and muscle tissue have very little matrix,
but the connective tissues, such as cartilage, bone, and blood, have extensive matrix).
2.3.2. Cell–Cell and Cell–Matrix Junctions and Adhesions
General functions of cell junctions are: communication, occlusion, and anchorage.
Cell–cell or cell–matrix adhesions are mediated by cell adhesion molecules (CAMs),
which are membrane–spanning proteins responsible both for cell junctions and for
transient cell adhesions. CAMs are essential for normal growth and development (e.g.,
growing nerve cells creep across the extracellular matrix with the help of nerve–cell
adhesion molecules, or NCAMs). Cell adhesion helps white blood cells escape from the
circulation and move into infected tissues, and it allows clumps of platelets to cling to
damaged blood vessels [Silverthorn, 2007].
2.3.2.1. Cell–Cell Junctions
There are three types of cell junctions: gap junctions, tight junctions, and anchoring
junctions (as shown in figure 2–10). Gap junctions allow chemical and electrical signals
21
to pass directly from cell to cell. They create cytoplasmic communication bridges
between adjoining cells. Cylindrical proteins, known as connexins, form passageways
that are able to open and close regulating the movement of small molecules and ions
through them.
Tight junctions are occluding junctions that prevent the movement of material
between cells. They are usually formed with the help of proteins called claudins and
occludins to fuse cell membranes of adjacent cells together. Tight junctions in the
intestinal tract and kidney prevent most substances from moving freely between the
external and internal environments [Albert et al., 2004; Silverthorn, 2007]. They also
create the blood–brain barrier that prevents potentially harmful substances in the blood
from reaching the extracellular fluid of the brain [Silverthorn, 2007].
Anchoring junctions hold cells to each other or to the extracellular matrix. CAMs are
essential in cell adhesion and in anchoring junctions. Cell–cell anchoring junctions take
the form of either adherens junctions or desmosomes. Adherins joins an actin bundle in
one cell to a similar bundle in a neighboring cell. Desmosomes attach to intermediate
filaments of the cytoskeleton and are the strongest cell–cell junctions. In vertebrates,
desmosomes are created by CAMs called cadherins, which connect with one another
across the intercellular space.
22
Figure 2–10. Types of cell–cell junctions. (a) A tight junction in intestinal epithelial
cells, (b) an anchoring junction in intestinal epithelial cells, and (c) a gap junction in
heart muscle. After [Silverthorn, 2007].
23
2.3.2.2. Cell–Matrix Junctions
There are two types of cell–matrix anchoring junctions: focal adhesion and
hemidesmosomes. Cell adhesion molecules called integrins are used in cell–matrix
junctions. In focal adhesions, the integrin ties intracellular actin fibers to different
extracellular matrix proteins such as insoluble cellular fibronectin as shown in figure 2–
11(C). Fibronectins, synthesized and deposited by the cells, play a very important role in
the adhesion of many cell types and the formation of extracellular matrix [Garcia et al.,
1997; Ruoslahti, 1984]. Cell adhesion strength also increases linearly with adsorbed
fibronectin surface density [Garcia et al., 1997]. In hemidesmosomes, integrins anchors
intermediate fibers (keratin filaments) of the cytoskeleton to fibrous matrix proteins
such as laminin as shown in figure 2–12.
Integrins also play an important role in cell signaling. The external attachments the
integral molecules make can activate intracellular signaling cascades [Albert et al., 2004;
Craig, 1996; Juliano, 1993; Silverthorn, 2007]. These cascades regulate many biological
processes such as cell adhesion, cell migration, cell growth, cell differentiation, cellular
aggregation, and gene expressions [Albert et al., 2004; Bernstein, 1994; Craig, 1996;
Juliano, 1993; LaFlamme, 2008; Ruoslahti, 1987].
24
Figure 2–11. (A) Diagram and (B) electron micrograph of a fibronectin molecule. It has a
collagen–binding site and a cell–binding site (or an integrin–binding site). (C) Focal adhesion:
integrin is anchored inside the cell to the cytoskeleton and externally via fibronectin to the
extracellular matrix. After [Alberts et al., 2004].
Figure 2–12. Schematic of a hemidesmosome junction. Mediated by integrins, it
anchors the keratin filaments in an intestinal epithelial cell to basal lamina. After
[Alberts et al., 2004].
25
2.4. Chapter Appendix
2.4.1. Diffusion across the Cell Membrane
2.4.1.1. Simple Diffusion
For small and non–polar molecules, the rate of simple diffusion across the cell
membrane can easily be expressed by a modified Fick’s Law of diffusion (equation 2–1)
and is directly proportional to the concentration gradient of the diffusing molecules
across the cell membrane [Silverthorn, 2007].
Rate of Diffusion ∝
Membranepermeability
⎛
⎝⎜
⎞
⎠⎟ ×
Surface
area
⎛
⎝⎜
⎞
⎠⎟ ×
Concentrationgradient
⎛
⎝⎜
⎞
⎠⎟
Membranethickness
⎛
⎝⎜⎞
⎠⎟
(2–1)
Figure 2–13 shows the linear relationship between the rate of diffusion and the
concentration gradient for simple diffusion of small and non–polar molecules across the
cell membrane. The slope depends on the membrane permeability of diffusing
molecules, the thickness, and the surface area of cell membrane.
Figure 2–13. The function of the rate of diffusion of small and non–polar molecules
across the cell membrane. The relationship between the rate of diffusion and the
concentration gradient is linear.
26
2.4.1.2. Facilitated Diffusion
Facilitated diffusion involves a limited number of carrier proteins in the cell
membrane. Therefore, there will be a maximum rate of diffusion when all the carrier
proteins are saturated. The modified Fick’s law cannot be used to describe the rate of
diffusion in this case. The rate of diffusion will increase with increasing solute
concentration but asymptotically approach to the maximum saturation rate that is
limited by the number of transport or carrier proteins in the cell membrane. The affinity
of transport or carrier proteins to diffusing molecules determines how quickly they
become saturated.
The kinetic of facilitated diffusion is similar to that of simple enzyme–catalyzed
chemical reaction. For instance, the kinetic of unidirectional transport of glucose from
the outside of a cell inward via the glucose transporter 1 (GLUT1) can be described as
follows:
Glucoseout
+ GLUT1Km⎯ →⎯← ⎯⎯ Glucose−GLUT1 Vmax⎯ →⎯⎯ Glucose
in+ GLUT1
where “Glucose–GLUT1” represents the GLUT1 transport protein in the cell membrane
with a bound glucose. By a similar derivation used to arrive at the Michaelis–Menten
equation, the initial diffusion rate, ν, for glucose molecules into the cell, which is
facilitated by the GLUT1 transporters, can be approximated as follows [Lodish et al.,
2004]:
ν =V
max
1 + Km
C
(2–2)
In equation 2–2, Vmax is the rate of transport or diffusion when all molecules of
GLUT1 in the cell membrane are saturated. The Km approximates the affinity of GLUT1
to the glucose. The lower the Km, the more tightly the glucose binds to the GLUT1, and
27
the greater the transport or diffusion rate at a fixed concentration of glucose outside the
cell membrane. At low concentrations of glucose, the glucose molecules will pass
through the carrier proteins in a way similar to that of simple diffusion. However, at
high solute concentrations of glucose, all the transport proteins are occupied with the
glucose molecules and further increasing the glucose concentration outside of the cell
membrane will not change the rate of diffusion. Figure 2–14 shows the relationship
between the rate of diffusion and the concentration gradient for facilitated diffusion.
Figure 2–14. The function of rate of facilitated diffusion of molecules across the cell
membrane. The relationship between the rate of diffusion and the concentration
gradient is not linear. The rate of diffusion asymptotically approaches to the maximum
diffusion rate limited by the number of transport proteins in the cell membrane. The Km
determines how quickly the transport proteins become saturated.
2.4.2. Active Transport across the Cell Membrane
Active transport is the pumping of molecule against the concentration gradient
across the cell membrane by a trans–membrane protein pumps. The proteins are highly
specific for the molecule to be transported. They are also ATPase enzymes as they
catalyze the splitting of ATP to ADP + phosphate (Pi) and use the energy released from
the reaction to change their conformation as well as pumping the molecule. In general,
the rate of active transport increases with increasing of ATP molecules and of the
28
number of protein pumps. Unlike simple and facilitated diffusion, the active transport
might still have a high transport rate even when there is no concentration difference of
molecules across the cell membrane (i.e, being independent of concentration gradient).
Active transport only stops when cellular respiration stops. The Na+–K+ pump and
H+/Sucrose pump are examples of trans–membrane protein pumps.
2.4.3. Nernst Equation
For the cell membrane that is permeable to only one ion, the equilibrium membrane
potential (Reversal Potential) that opposes the concentration gradient of the ion across
the cell membrane can be described by the Nernst Equation♠:
Eion(in mV )
=RT
zFln
ion⎡⎣⎢⎤⎦⎥out
ion⎡⎣⎢⎤⎦⎥ in
(2–3)
where R, T, F, and z are the universal gas constant, absolute temperature in Kelvin, the
Faraday constant or number of coulombs per mole of electrons, and the electrical change
on the ion respectively. [ion]out and [ion]in represent the concentrations of ions outside
and inside of the cell respectively. The Nernst Equation can be used to calculate the
equilibrium potentials for any biologically relevant ion. The estimated ion
concentrations and equilibrium potentials for Na+, K+, Cl–, and Ca2+ in mammalian cells
are given in table 2–1.
♠A complete derivation of Nernst equation can be found in [Malmivo, 1995].
29
Ion Extracellular Fluid Intracellular Fluid Eion @ 37 °C (Referenced from extracellular fluid)
K+ 5 mM (normal range: 3.5–5) 150 mM –90 mV
Na+ 145 mM (normal range: 135–145) 15 mM +60 mV
Cl– 108 mM (normal range: 100–108) 10 mM (range: 5–15) –63 mV
Ca2+ 1 mM 0.0001 mM +122 mV
Table 2–1. The estimated ion concentrations and their respective equilibrium potentials in
mammalian cells. After [Silverthorn, 2007].
2.4.4. Goldman–Hodgkin–Katz (GHK) Equation
The Nernst equation may be used to determine the equilibrium potential for a single
ion. However, The actual cell membrane is permeable to multiple ions. The equilibrium
or resting membrane potential depends on the relative permeability of ions, and thus,
Goldman–Hodgkin–Katz (GHK) equation must be used [Malmivo, 1995]. For n
monovalent positive ionic species and m negative ionic species, the equilibrium potential
can be described as♥:
Vm
=RT
Fln
PCi
+ Ci+⎡⎣⎢⎤⎦⎥outi=1
n∑ + PAj− A
j−⎡
⎣⎢⎤⎦⎥ inj=1
m∑P
Ci+ C
i+⎡⎣⎢⎤⎦⎥ ini=1
n∑ + PAj− A
j−⎡
⎣⎢⎤⎦⎥outj=1
m∑
⎛
⎝
⎜⎜⎜⎜⎜⎜⎜
⎞
⎠
⎟⎟⎟⎟⎟⎟⎟⎟⎟ (2–4)
The GHK equation includes respective membrane permeability values for
monovalent ions since each ion’s contribution to the membrane potential is proportional
to its membrane permeability. If the membrane is not permeable to an ion, the
permeability term for that ion will be zero, and the related term drops out of the
equation. Also, divalent ions are not included in the GHK equation since resting cells are
♥ A complete derivation of GHK equation can be found in [Malmivo, 1995].
30
not usually permeable to divalent ions. For mammalian cells at rest, it is usually
assumed that Na+, K+, and Cl– are the three major ions that influence membrane
potential. Based on the equation 2–4, the GHK Equation for cells that are permeable to
Na+, K+, and Cl– is:
Vm
=RT
Fln
PK + K +⎡⎣⎢⎤⎦⎥out
+ PNa+ Na+⎡⎣⎢
⎤⎦⎥out
+ PCl−
Cl−⎡⎣⎢⎤⎦⎥ in
PK + K +⎡⎣⎢⎤⎦⎥ in
+ PNa+ Na+⎡⎣⎢
⎤⎦⎥ in
+ PCl−
Cl−⎡⎣⎢⎤⎦⎥out
⎛
⎝
⎜⎜⎜⎜⎜⎜
⎞
⎠
⎟⎟⎟⎟⎟⎟⎟ (2–5)
If divalent ions (e.g., Ca2+, Mg2+, etc.) are to be considered, the GHK equation must
be modified [Chang, 1983; Egebjerg, 1993; Lewis, 1984; Mayer, 1987].
2.4.5. Osmotic Pressure
Osmosis is the phenomenon of solvent flowing through a semipermeable membrane
to equalize the solute concentrations on both sides of the membrane. Osmotic pressure,
in general, is a colligative property of a solution equal to the pressure that, when applied
to the solution, just stops osmosis [Gammon, 1999]. In other words, it is the pressure
that must be applied to a solution to prevent inward flow of solvent molecule across the
semipermeable membrane. The osmotic pressure of a solution can be estimated by:
Π = iMRT (2–6)
where i, M, R, and T are the dimensionless van ’t Hoff factor (for NaCl in water, i is 1
since it dissociates completely to Na+ and Cl–), the molarity of the solution, the universal
gas constant, and the absolute temperature in Kelvin.
31
References
Alberts M. B., Bray, D., Hopkin, K., Johnson, A., Lewis, J., Raff, M., Roberts, K., and Walter, P.,
2004. Essential Cell Biology. 2nd Edition, Taylor & Francis Group, New York.
Alberts M. B., Stein, W. D., Laskey, R. A., Bernfield, M. R., Staehelin, L. A., Slack, J. M. W., 2009.
Cell. Encyclopædia Britannica Online. 20 October 2009. <http://www.britannica.com/EBchecked/topic/101396/cell>.Bernstein, L. R., and Loitta, L. A., 1994. Molecular Mediators of Interactions with Extracellular
Matrix Components in Metastasis and Angiogenesis. Current Opinion in Oncology. 6, 106–
113.
Chang, D. C., 1983. Dependence of Cellular Potential on Ionic Concentrations: Data Supporting a
Modification of the Constant Field Equation. Biophysical Journal. 43, 149–156.Craig, S. W., and Johnson, R. P., 1996. Assembly of Focal Adhesions: Progress, Paradigms, and
Portents. Current Opinion in Cell Biology. 8, 74–85.
Egebjerg, J., and Heinemann, S. F., 1993. Ca2+ Permeability of Unedited and Edited Versions of
the Kainate Selective Glutamate Receptor GluR6. Neurobiology. 90, 755–759. Gammon, E., 1999. General Chemistry, 6th Edition, Houghton Mifflin Company, Boston.Garcia, A. J., Ducheyne, P., and Boettiger, D., 1997. Cell Adhesion Strength Increases Linearly
with Adsorbed Fibronectin Surface Density. Tissue Engineering. 3 (2), 197–206.
Juliano, R. L., and Haskill, S., 1993. Signal Transduction from the Extracellular Matrix. The Journal of Cell Biology. 10 (3), 577–585.
LaFlamme, S. E. and Kowalczyk, A., 2008. Cell Junctions: Adhesion, Development, and Disease.
(6) Gold is biocompatible with cells, i.e., cells can adhere and function on gold
surfaces without evidence of toxicity.
The possible mechanism of the formation SAMs from disulfides is an oxidative
addition of the S–S bond to the gold surface [Ulman, 1996]. This chemisorption reaction
of dialkyl disulfides on gold can be describe as:
RS–SR +
�
Aun0 ⇒ RS–Au+⋅
�
Aun0
In the alkanethiol case, the reaction of gold and thiol group is considered to be an
oxidative addition of the S–H bond to the gold surface, followed by a possible reductive
elimination of the hydrogen [Ulman, 1996]. The reaction can be describe as follows:
R–S–H +
�
Aun0 ⇒ R–S–Au+⋅
�
Aun0 + ½ H2 (?)
The bonding of the thiolate group to the gold surface is very strong (homolytic bond
strength is approximately 40 kcal mol-1 [Dubois, 1992]).
38
3.1.2. Alkylsilane Monolayer
Alkylsilane monolayers are the second most popular system. However, the relevant
mechanisms differ substantially from those of thiols on gold. One of the typical
examples is the irreversibly binding of a trichlorosilane or a similar headgroup (e.g.,
alkoxysilane or aminosilane) to a hydroxylated surface (for instance, the oxide surface of
silicon) [Sagiv, 1980]. This different chemisorption process of n–Octadecyl–
trichlorosilane (C18H37SiCl3) (or OTS) on the glass surface is shown in figure 3–2. In this
process, first, the side–groups (chlorines or other) are split off, and a strongly bound
network of the head groups is generated. Next, adsorbates are immobilized on the
surface due to the formation of hydrolytic bonds with hydroxyl (–OH) surface groups.
Finally, the self–assembly occurs due to the formation of polysiloxane that is connected
to surface silanol groups (–SiOH) via Si–O–Si bonds. The growth of alkylsilane–based
SAMs is very unique since it involves an irreversible covalent cross–linking step that is
essential for the desirable properties of this class of SAMs, including their chemical and
mechanical robustness and the stability of the final monolayer on a variety of substrates.
Alkylsilane monolayers can also be prepared from the solution. However, high–
quality SAMs are not simple to produce since the adsorption process is sensitive to
temperature, pH, and water content in the solution [Kessel, 1991; Schreiber, 2000;
Schwartz, 2001]. Carefully–controlled experimental conditions and protocols are also
required in the preparation of solution that contains active components [Huang et al.,
1994; Kessel, 1991; Schrieber, 2000; Xiao et al., 1995] and the surface of the substrate
requires pretreatment with either plasma [Parker et al., 1989] or chemicals [Carson,
1989].
39
Figure 3–2. Chemisorption of n–Octadecyltrichlorosilane (OTS) on glass surface.
After [Sagiv, 1980].
Since substrates used in the formation of alkylsilane monolayers are amorphous, the
packing and ordering of alkyl chains in the monolayer are determined by the underlying
structure of the surface polysiloxane chain [Schreiber, 2001]. From a physical
perspective, alkylsilane monolayers have less well–defined structures compared to gold–
alkylthiolate monolayers (no long–range order in crystalline sense), and the
comparatively low mobility of the monolayer molecules on the surface due to the strong
and localized binding of the Si–O–Si network (This also implies that the structure
cannot easily be changed by annealing.) [Schreiber, 2000; Ulman, 1996]. However,
alkylsilane monolayers are considered to be more robust [Schreiber, 2001].
40
3.2. SAM–based Biofunctionalized Systems and Surfaces
One of the most exciting properties of self–assembled monolayers is that they can be
modified in order to generate surfaces with biologically relevant functionalities. Several
strategies have been developed to generate biofunctionalized surfaces. One of the most
widely used strategies is to exploit the concept of lock–key recognition in protein
binding. In this strategy, the mixed or patterned SAM, containing two or more
constituent molecules, is terminated with specific functional groups or ligands to which
receptors can be attached, serving as one part of a lock–key pair to specifically bind
biomolecules of interest (as illustrated in figure 3–3). These types of SAMs are
employed as: (i) model systems to study fundamental aspects of the interactions of
surfaces with biomolecules [Jung et al., 2000; Lahiri et al., 1999], (ii) interfaces for
molecular recognition in biosensing applications [Frederix et al., 2003; Subramanian et
al., 2006; Jung et al., 1999], and (iii) model surfaces to study cell–surface interaction
[Houseman and Mrksich, 2001; Kato and Mrksich, 2004; Roberts et al., 1998].
Another strategy is to exploit the end group chemistry of SAM–forming molecules or
constituents as shown in figure 3–4. In this strategy, the mixed or patterned SAM is
formed by two or more SAM constituent molecules that are terminated with different
functional or end groups. For instance, a mixed SAM formed by two SAM constituents,
which presents structurally well–defined hydrophobic end groups at surfaces that are
otherwise terminated with hydrophilic background group, is employed as a model
surface to study physisorption of protein molecules onto the surface [Ostuni et al.,
2003]. In addition to mixed SAMs, substrates that are biofunctionalized with
homogeneous SAM are also used as model surfaces to study the surface–mediated
cellular activities as well as the biocompatibility of materials [Barbosa et al., 2003;
Barbosa et al., 2004].
41
Figure 3–3. Schematic illustration showing the reversible adsorption of a receptor to a mixed SAM presenting a ligand. The mixed SAM comprises of two different constituent molecules. One constituent is terminated with either a biological ligand (or a reactive site for linking to a biological ligand) and the other constituent is terminated with a functional group that resists the nonspecific adsorption of biomolecules and biological cells. The fraction of ligands on the surface is related to the mole fraction of the SAM constituents in the solution used to form the mixed SAM. Adapted from [Lahiri et al., 1999; Love et al., 2005; Mrksich et al., 1995].
Figure 3–4. Schematic illustration showing the physisorption of a protein to a patterned SAM formed by two different constituents. One constituent of the patterned SAM is terminated with a hydrophilic functional group and the other constituent is terminated with a hydrophobic functional group. Adapted from [Love et al., 2005; Ostuni et al., 2003].
42
3.2.1. Common Methods for Preparing Mixed and Patterned SAMs
3.2.1.1. Coadsorption from Solutions of Mixed SAM Constituents
Mixed self–assembled monolayer can be formed by either coadsorbing mixed
alkanethiols from the solution directly onto the metal surface (especially gold) [Chapman
et al., 2000; Heeg et al., 1999; Laibinis et al., 1991; Yang et al., 1997] or coadsorbing
mixed alkylsilanes directly onto silicon surfaces [Heise and Menzel, 1997; Heise et al.,
1998]. In general, the solution that is used to form the mixed SAM usually comprises of
two or more SAM constituents. One of the SAM constituents will be terminated with
specific functional groups or ligands and the rest are terminated with end groups that
present the background. The fraction of functional groups or ligands on the surface after
the SAM formation is related to the mole fraction of the SAM constituents in the solution
[Love et al., 2005].
3.2.1.2. Microcontact Printing (µCP)
The Microcontact printing (µCP) is a method for patterning SAMs on surfaces that is
operationally analogous to printing ink with a rubber stamp on the paper [Kumar et al.,
1994; Mrksich and Whitesides, 1995; Love et al., 2005; Smith et al., 2004; Xia and
Whitesides, 1998]. SAMs form in the regions of contact between a topographically
patterned stamp (usually made of polydimethylsiloxane (PDMS)), which is wetted with
reactive chemical ‘ink’ consisting of molecules that form SAMs, and the bare surface of a
metal, metal oxide, or semiconductor. The stamp is cast from the master template
usually fabricated by standard microfabrication techniques that involves
photolithography and reactive ion etching (RIE).
When forming patterned SAMs on the substrate, the inked or wetted stamp is
brought in contact with the surface and, then, left for a few seconds (5–10 sec) before it is
removed. The molecules are transferred to the surface only at those regions where the
43
stamp contacts the surface. Due to the autophobicity of the SAM to its ink liquid, the
lateral spreading across the surface is prevented [Biebuyck and Whitesides, 1994;
Mrksich and Whitesides, 1995; Xia and Whitesides, 1998; Xia et al., 2001]. The lateral
dimensions of the SAMs formed depend on the dimensions of relief features on the
stamp (the lateral dimension as small as ~50 nm can be generated). After the stamp is
removed, another SAM can be formed in the remaining bare regions of the surface by
either immersion of the substrate into a different solution for a few minutes (~1–10 min)
or application of a second stamp wetted with a different “ink”. The latter approach
requires a good alignment between the second stamp and the substrate, which can easily
be achieved via µCP instruments. Multiple copies of stamps can be cast from the master
template and each stamp can be used hundreds of times without any loss of quality of the
printed patterns. Hence, the µCP can produce patterned SAMs at relatively low cost.
The general procedure for patterning SAM using µCP is summarized in figure 3–5.
44
Figure 3–5. Procedure for patterning thiol-based SAMs using µCP. Photolithography or other
standard microfabrication techniques generates a master template containing features of the
pattern to be reproduced (a). A polydimethylsiloxane (PDMS) prepolymer is poured onto the
master pattern, allowed to cure (b), and then peeled away from the master (c). The stamp is
wetted with alkanethiol solution or “ink” (d), and is used to transfer the alkanethiol to the
surface (e); this transfer forms a patterned SAM (f). Exposing the gold substrate to a solution
that contains different alkanethiol derivatizes the bare regions (g). Adapted from [Mrksich and
Whitesides, 1995; Xia and Whitesides, 1998].
45
3.2.1.3. Substrates Having Surface Patterns Made of Different Materials
Patterned SAMs can be formed on a substrate that presents two–dimensional surface
patterns formed by different materials. This type of approach is usually employed to
exploit the existing two–dimensional surface patterns in microfabricated devices for
biosensing applications [Arya et al., 2009; Asphahani et al., 2008; Veiseh et al., 2001;
Veiseh et al., 2002]. As existing surface patterns are formed by different materials,
different types SAM systems can be formed successively by directly adsorbing the
different SAM constituents onto the surface in different solutions.
3.3. Removal of Self–Assembled Monolayers
SAM–coated devices and substrates in biosensing applications are usually recycled
and, thus, the removal of self–assembled monolayers from surfaces is usually required.
Most common methods for SAM removal are:
(1) Wet chemical etching of SAM with piranha solution (concentrated H2SO4 + 30%
H2O2) [Guo et al., 1994] or Nano–strip™ solution.
(2) Desorption of SAM by electrochemical potential cycling in aqueous solution
[Canaria et al., 2006; Guo et al., 1994; Jiang et al., 2003]. This method is
usually employed for the removal of thiolate–based SAM formed on gold or
metal surfaces.
(3) Photolysis or photo–oxidation with UV light [Huang and Hemminger, 1993;
Hutt and Leggett, 1996; Lewis and Tarlov, 1995; Tarlov et al., 1993]: Oxidation
of an alkanethiolate layer by UV light results in sulfite and sulfate species (RSOx)
that are weakly adsorbed and can easily be rinsed from the surface with polar
solvents.
(4) Ozonolysis [Norrod and Rowlen, 1998; Zhang et al., 1998; Zhang et al., 1999]: O3
(ozone) oxidizes the sulfur head–groups of alkanethiol SAMs, similar to
oxidation process in photolysis, to generate polar solvent strippable products.
46
(5) Thermal desorption [Delamarche et al., 1994; Liu et al., 2002]: Thiolate–based
SAMs usually desorb from the surface at temperatures above 100 ˚C (Note:
Long hours of thermal treatment are usually required).
(6) Plasma cleaning or stripping [Raiber et al., 2005]: Oxygen or hydrogen plasma
is usually used. During the cleaning process, chemically reactive plasma gases
react with the SAM and form volatile products that are stripped and swept away
by the continuous gas flow inside the chamber of plasma cleaning equipments.
No additional rinsing with polar solvents is required.
Of these methods, the first two methods are wet chemical processes, which could
contaminate the substrate by the reagents used and, thus, are very unfavorable in some
applications. The most undesirable disadvantage of the piranha treatment or Nano–
strip™ treatment is that it induces recrystallization of the gold layer [Twardowski and
Nuzzo, 2002] and may even cause delamination of the gold layer by etching away the
frequently used adhesion layers. Although the electrochemical potential cycling is a wet
chemical process, it has some advantages in cellular biosensing where immobilized cells
on the SAM–based bio–functionalized electrodes can be released noninvasively [Jiang et
al., 2003]. It can be noticed that the UV/O3 oxidation often requires an additional wet
cleaning step with polar solvents and thermal desorption is not suitable for devices and
substrates that cannot be exposed to high temperature environments for long period of
time. Therefore, cleaning with hydrogen or oxygen plasma is more favorable in some
cases as it does not require additional wet rinsing step and is very simple as well as less
harmful to carry out in significantly shorter time period compared to other methods.
47
References
Arya, S. K., Solanki, P. R., Datta, M., and Malhotra, B. D., 2009. Recent Advances in Self–
Assembled Monolayers Based Biomolecular Electronic Devices. Biosensors and Bioelectronics. 24, 2810–2817.
Asphahani, F., Thein, M., Veiseh, O., Edmondson, D., Kosai, R., Veiseh, M., Xu, J., and Zhang, M.,
2008. Influence of Cell Adhesion and Spreading on Impedance Characteristics of Cell–Based
Sensors. Biosensors and Bioelectronics. 23, 1307–1313.
Bain, C. D., Troughton, E. B., Tao, Y., Evall, J., Whitesides, G. M., and Nuzzo, R. G., 1989.
Formation of Monolayer Films by the Spontaneous Assembly of Organic Thiols from Solution
onto Gold. Journal of the American Chemical Society. 111, 321–335.
Barbosa, J. N., Barbosa, M. A., and Aguas, A. P., 2003. Adhesion of Human Leukocytes to
Biomaterials: An in vitro Study Using Alkanethiolate Monolayers with Different Chemically
Functionalized Surfaces. Journal of Biomedical Materials Research. 65A (4), 429–434.
Barbosa, J. N., Barbosa, M. A., and Aguas, A. P., 2004. Inflammatory Responses and Cell
Adhesion to Self–Assembled Monolayers of Alkanethiolates on Gold. Biomaterials. 25, 2557–
2563.
Biebuyck, H. A., and Whitesides, G. M., 1994. Autophobic Pinning of Drops of Alkanethiols on
Gold. Langmuir. 10, 4581–4587.
Biebuyck, H. A., Bain, C. D., and Whitesides, G. M., 1994. Comparison of Organic Monolayers on
Polycrystalline Gold Spontaneously Assembled from Solutions Containing Dialkyl Difulfides
or Alkanethiols. Langmuir. 10, 1825–1831.
Canaria, C. A., So, J., Maloney, J. R., Yu, C. J., Smith, J. O., Roukes, M. L., Fraser, S. E., and
Lansford, R., 2006. Formation and Removal of Alkylthiolate Self–Assembled Monolayers on
Gold in Aqueous Solutions. Lab on a Chip. 6, 289–295.
Carson, G., and Granick, S., 1989. Self–Assembly of Octadecyltrichlorosilane Films on Mica.
Journal of Applied Polymer Science. 37, 2767–2772.
Chapman, R. G., Ostuni, E., Yan, L., and Whitesides, G. M., 2000. Preparation of Mixed Self–
Assembled Monolayers (SAMs) That Resist Adsorption of Proteins Using the Reaction of
Amines with a SAM That Presents Interchain Carboxylic Anhydride Groups. Langmuir. 16,
6927–6936.
Delamarche, E., Michel, B., Kang, H., and Gerber Ch., 1994. Thermal Stability of Self–Assembled
Monolayers. Langmuir. 10, 4103–4108.
48
Dubois, L. H., and Nuzzo, R. G., 1992. Synthesis, Structure, and Properties of Model Organic
Surfaces. Annual Review of Physical Chemistry. 43, 437–463.
Frederiz, F., Bonroy, K., Laureyn, W., reekmans, G., Campitelli, A., Dehaen, W., and Maes, G.,
2003. Enhanced Performance of an Affinity Biosensor Interface Based on Mixed Self–
Assembled Monolayers of Thiols on Gold. Langmuir. 19, 4351–4357.
Guo, L.-H., Facci, J. S., McLendon, G., and Mosher, R., 1994. Effect of Gold Topography and
Surface Pretreatment on the Self–Assembly of Alkanethiol Monolayers. Langmuir. 10, 4588–
4593.
Heeg, J., Schubert, U., and Küchenmeister, F., 1999. Mixed Self–Assembled Monolayers of
Terminally Functionalized Thiols at Gold Surfaces Characterized by Angle Resolved X–ray
Photoelectron Spectrocopy (ARXPS) Studies. Fresenius’ Journal of Analytical Chemistry.
365, 272–276.
Heise, A., and Menzel, H., 1997. Grafting of Polypeptides on Solid Substrates by Initiation of N–
Carboxyanhydride Polymerization by Amino–Terminated Self–Assembled Monolayers.
Langmuir. 13, 723–728.
Heise, A., Stamm, M., Rauscher, M., Duschner, H., and Menzel, H., 1998. Mixed Silane Self–
Assembled Monolayers and Their in situ Modification. Thin Solid Films. 327–329, 199–203.
Houseman, B. T., and Mrksich, M., 2001. The Microenvironment of Immobilized Arg-Gly-Asp
Peptides Is an Important Determinant of Cell Adhesion. Biomaterials. 22, 943–955.
Haung J., and Hemminger, J. C., 1993. Photooxidation of Thiols in Self–Assembled Monolayers
on Gold. Journal of the American Chemical Society. 115, 3342–3343.
Huang, M., Schneiderman, E. D., Novotny, M. V., Fatunmbi, H. O., and Wirth, M. J., 1994. Self–
Assembled Alkylsilane Monolayers for the Preparation of Stable and Efficient Coatings in
Capillary Electrophoresis. Journal of Microcolumn Separations. 6, 571–576.
Hutt D. A., and Leggett, G. J., 1996. Influence of Adsorbate Ordering on Rates of UV
Photooxidation of Self–Assembled Monolayers. Journal of Physical Chemistry. 100, 6657–
6662.
Jiang, X., Ferrigni, R., Mrksich, M., and Whitesides, G. M., 2003. Electrochemical Desorption of
Self–Assembled Monolayers Noninvasively Releases Patterned Cells from Geometrical
Confinements. Journal of the American Chemical Society. 125, 2366–2367.
Jung, L. S., Nelson, K. E., Campbell, C. T., Stayton, P. S., Yee, S. S., Luna, V. P., and Lopez, G. P.,
1999. Surface Plasmon Resonance Measurement of Binding and Dissociation of Wild–Type
and Mutant Streptavidin on Mixed Biotin–Containing Alkylthiolate Monolayers. Sensors and Actuators B. 54, 137–144.
49
Jung, L. S., Nelson, K. E., Stayton, P. S., and Campbell, C. T., 2000. Binding and Dissociation
Kinetics of Wild–Type and Mutant Streptavidins on Mixed Biotin–Containing Alkylthiolate
Monolayers. Langmuir. 16, 9421–9432.
Kato, M., and Mrksich M., 2004. Using Model Substrates to Study the Dependence of Focal
Adhesion Formation on the Affinity of Integrin–Ligand Complexes. Biochemistry. 43, 2699–
2707.
Kessel, C. R., and Granick, S., 1991. Formation and Characterization of a Highly Ordered and
Well–Anchored Alkylsilane Monolayer on Mica by Self – Assembly. Langmuir. 7, 532–538.
Kumar, A., Biebuyck, H. A., and Whitesides, G. M., 1994. Patterning Self–Assembled
Monolayers: Applications in Materials Science. Langmuir. 10, 1498–1511.
Lahiri, J., Issacs, L., Crzybowski, B., Carbeck, J. D., and Whitesides, G. M., 1999. Biospecific
Binding of Carbonic Anhydrase to Mixed SAMs Presenting Benzenesulfonamide Ligands: A
Model System for Studying Lateral Steric Effects. Langmuir. 15, 7186–7198.
Laibinis, P. E., Fox, M. A., Folkers, J. P., and Whitesides, G. M., 1991. Comparisons of Self–
Assembled Monolayers on Silver and Gold: Mixed Monolayers Derived from HS(CH2)21X and
HS(CH2)10Y (X, Y = CH3, CH2OH) Have Similar Properties. Langmuir. 7, 3167–3173.
Lewis, M., and Tarlov, M., 1995. Study of the Photooxidation Process of Self–Assembled
Alkanethiol Monolayers. Jounal of the American Chemical Society. 117, 9574–9575.
Liu, G., Rodriguez, J. A., Dvorak, J., Hrbek, J., and Jirsak, T., 2002. Chemistry of Sulfur–
Containing Molecules on Au(111): Thiophene, Sulfur Dioxide, and Methanethiol Adsorption.
Surface Science. 505, 295–307.
Love, J. C., Estroff, L. A., Kriebel, J. K., Nuzzo, R. G., and Whitesides, G. M., 2005. Self–
Assembled Monolayers of Thiolates on Metals as a Form of Nanotechnology. Chemical Reviews. 105 (4), 1103–1169.
Mrksich, M., Grunwell, R. J., and Whitesides, G. M., 1995. Biospecific Adsorption of Carbonic
Anhydrase to Self–Assembled Monolayers of Alkanethiolates That Present
Benzenesulfonamide Groups on Gold. Journal of the American Chemical Society. 117,
12009–12010.
Mrksich, M., and Whitesides, G. M., 1995. Patterning Self–Assembled Monolayers Using
Microcontact Printing: A New Technology for Biosensors? Trends in Biotechnology. 13 (6),
228–235.
Norrod, K. L., and Rowlen, K. L., 1998. Removal of Carbonaceous Contamination from SERS–
Active Silver by Self–Assembly of Decanethiol. Analytical Chemistry. 70, 4218–4221.
50
Nuzzo, R. G., and Allara, D. L., 1983. Adsorption of Bifunctional Organic Disulfides on Gold
Surfaces. Journal of the American Chemical Society. 105, 4481–4483.
Ostuni, E., Grzybowski, B. A., Mrksich, M., Roberts, C. S., and Whitesides, G. M., 2003.
Adsorption of Proteins to Hydrophobic Sites on Mixed Self–Assembled Monolayers.
Langmuir. 19, 1861–1872.
Parker, J. L., Cho, D. L., and Claesson, P. M., 1989. Plasma Modification of Mica: Forces between
Fluorocarbon Surfaces in Water and a Nonpolar Liquid. Journal of Physical Chemistry. 93,
6121–6125.
Raiber, K., Terfort, A., Benndorf, C., Krings, N., and Strehblow, H. –H., 2005. Removal of Self–
Assembled Monolayers of Alkanethiolates on Gold by Plasma Cleaning. Surface Science. 595,
56–63.
Roberts, C., Chen, C. S., Mrksich, M., Martichonok, V., Ingber, D. E., and Whitesides, G. M.,
1998. Using Mixed Self–Assembled Monolayers Presenting RGD and (EG)3OH Groups to
Characterize Long–Term Attachment of Bovine Capillary Endothelial Cells to Surfaces.
Journal of American Chemical Society. 120, 6548–6555.
Sagiv, J., 1980. Organized Monolayers by Adsorption. 1. Formation and Structure of Oleophobic
Mixed Monolayers on Solid Surfaces. Journal of the American Chemical Society. 102 (1), 92–
98.
Schreiber, F., 2000. Structure and Growth of Self–Assembling Monolayers. Progress in Surface Science. 65, 151–256.
Schrieber, F., 2004. Self–Assembled Monolayers: from ‘Simple’ Model Systems to
Biofunctionalized Inferfaces. Journal of Physics: Condensed Matter. 16, R881–R900.
Schwartz, D. K., 2001. Mechanisms and Kinetics of Self–Assembled Monolayer Formation.
Annual Review of Physical Chemistry. 52, 107–137.
Smith, R. K., Lewis, P. A., Weiss, P. S., 2004. Patterning Self–Assembled Monolayers. Progress in Surface Science. 75, 1–68.
Subramanian, A., Irudayaraj, J., and Ryan, T., 2006. A Mixed Self–Assembled Monolayer–Based
Surface Plasmon Immunosensor for Detection of E. coli O157:H7. Biosensors and Bioelectronics. 21, 998–1006.
Tarlov, M. J., Burgess, Jr., D. R. F., and Gillen, G., 1993. UV Photopatterning of Alkanethiolate
Monolayers Self–Assembled on Gold and Silver. Journal of American Chemical Society. 115,
5305–5306.
51
Twardowski, M., and Nuzzo, R. G., 2002. Chemically Mediated Grain Growth in Nanotextured
Au, Au/Cu Thin Films: Novel Substrates for the Formation of Self–Assembled Monolayers.
Langmuir. 18, 5529–5538.
Ulman, A., 1996. Formation and Structure of Self–Assembled Monolayers. Chemical Reviews.
96, 1533–1554.
Veiseh, M., Zhang, Y., Hinkley, K., and Zhang, M., 2001. Two–Dimensional Protein
Micropatterning for Sensor Applications Through Chemical Selectivity Technique.
Biomedical Microdevices. 3 (1), 45–51.
Veiseh, M., Zareie, M. H., and Zhang M., 2002. Highly Selective Protein Patterning on Gold –
Silicon Substrates for Biosensor Applications. Langmuir. 18, 6671–6678.
Veiseh, M., Wickes, B. T., Castner, D. G., and Zhang, M., 2004. Guided Cell Patterning on Gold–
Silicon Dioxide Substrates by Surface Molecular Engineering. Biomaterials. 25, 3315–3324.
Veiseh, M., Veiseh, O., Martin, M. C., Asphahani, F., and Zhang, M., 2007. Short Peptides
Enhance Single Cell Adhesion and Viability on Microarrays. Langmuir. 23, 4472–4479.
Xiao, X., Liu, G., Charych, D. H., and Salmeron, M., 1995. Preparation, Structure, and
Mechanical Stability of Alkylsilane Monolayers on Mica. Langmuir. 11, 1600–1604.
Xia, Y., and Whitesides, G. M., 1998. Soft Lithography. Annual Review of Materials Science. 28,
153–184.
Xia, Y., Qun, D., and Yin, Y., 2001. Surface Patterning and Its Application in Wetting/Dewetting
Studies. Current Opinion in Colloid & Interface Science. 6, 54–64.
Yang, Z., Engquist, I., Wirde, M., Kauffmann, J. M., Gelius, U., and Liedberg, B., 1997.
Preparation and Characterization of Mixed Monolayer Assemblies Composed of Thiol
Analogues of Cholestrol and Fatty Acid. Langmuir. 13, 3210–3218.
Zhang, Y., Terrill, R. H., Tanzer, T. A., and Bohn, P. W., 1998. Ozonolysis Is the Primary Cause of
UV Photooxidation of Alkanethiolate Monolayers at Low Irradiance. Journal of the American Chemical Society. 120, 2654–2655.
Zhang, Y., Terrill, R. H., and Bohn, P. W., 1999. Ultraviolet Photochemistry and ex Situ
Ozonolysis of Alkanethiol Self–Assembled Monolayers on Gold. Chemistry of Materials. 11,
2191–2198.
52
Chapter 4
QUANTUM DOTS IN BIO–IMAGING AND BIO–LABELING
4.1. Overview and Features of Quantum Dots
Quantum dots (QDs) are semiconductor nanocrystalswith typical sizes ranging from
2 to 10 nm. They are composed of periodic groups of II–VI, III–V, or IV–VI materials.
A quantum dot represents the three–dimensional quantum box within which its excitons
are confined in all three spatial dimensions. The electrons in this type of confinement
are described as a zero–dimensional electron gas when they are present in the
conduction band and, consequently, the density of states (DOS) for the electron gas is a
discrete function [Prasad, 2004]. Quantum dots are often described in terms of the
degree of quantum confinement. A strong quantum confinement regime represents the
case when the size of the quantum dot is smaller than the exciton Bohr radius of the
semiconductor material being used. In this case, the energy separation between the
sub–bands is much larger than the exciton binding energy and the electrons and holes
are largely represented by the energy states of their respective sub–bands. Also, the
effective energy bandgap of semiconductor quantum dots in the strong quantum
confinement regime is significantly larger than that in the bulk semiconductor [Prasad,
2004]. On the other hand, in a weak quantum confinement regime, the size of the
quantum dot is much larger than the exciton Bohr radius of the semiconductor material
being used and the energy separation between the sub–bands is comparable to or less
than the exciton binding energy [Prasad, 2004].
The absorption spectrum of quantum dots is continuous and broad, as shown in
figure 4–1, due to the overlapping of a series of absorption peaks that get larger at
shorter wavelengths. The quantum dots will not absorb light that has a wavelength
53
longer than that of the first exciton peak, also referred to as the absorption onset.
Material compositions and the size of the quantum dot determine the wavelength of the
first exciton peak and those of all other subsequent peaks. The emission spectrum has a
Gaussian shape and the peak occurs at a slightly longer wavelength than the absorption
onset as shown in figure 4–1. This energy separation phenomenon is known as the
Stoke’s shift. The size of the energy bandgap governs the emission frequency of a
quantum dot and can be controlled by adjusting the size of the quantum dot [Prasad,
2004]. It is, therefore, possible to precisely tune the emission of monochromic light
from a quantum dot. The bandwidth of the emission spectra, usually quantified by the
full width at half maximum (FWHM), stems from the temperature (thermal broadening),
the natural spectral line width of the quantum dots (natural broadening), and the size
distribution of the population of quantum dots within a solution or matrix material
(inhomogeneous broadening).
The efficiency of quantum dots is determined by a measure known as Quantum Yield
(QY), the ratio of photons emitted to photons absorbed. QY is mainly controlled by the
nonradiative transition of electrons or holes between energy levels and nonradiative
recombination of electrons and holes. It is established that capping a core quantum dot
with a shell (several atomic layers of an inorganic semiconductor with a wider bandgap
than the core semiconductor) improves photoluminescence quantum yield by
passivating surface nonradiative recombination sites or surface defects [Dabbousi et al.,
1997; Hines and Guyot–Sionnest, 1996]. In addition, quantum dots passivated with
inorganic shell structures are more robust. One of the examples is CdSe cores
overcoated with a layer of ZnS (shown in figure 4–2).
54
(A)
(B)
Figure 4–1. (A) Absorption (Abs) and emission (Em) of six different QD dispersions.
The black line shows the absorption of the 510–nm–emitting QDs. The wavelength of
lowest absorption (or the absorption onset) for the 510–nm QD is ~450 nm. (B) Photo
demonstrating the size–tunable fluorescence properties and spectral range of the six QD
dispersions plotted in (A) versus CdSe core size (r stands for the radius of CdSe core).
All samples were excited at 365 nm with a UV source. For the 610–nm–emitting QDs,
this translates into a Stokes shift of ~250 nm. After [Medintz et al., 2005].
55
Figure 4–2. Transmission Electron Micrographs (TEM) of (A) one “bare” CdSe
nanocrystallite and (B) one CdSe nanocrystallite with a 2.6 monolayer ZnS shell. After
[Babbousi et al., 1997]. (C) Artist’s rendering of CdSe quantum dot and CdSe/ZnS core–
shell quantum dot.
The surface chemistry of colloidal quantum dots can be modified with a variety of
molecules via cap–exchange/ligand–exchange [Gill et al., 2006; Hwang and Cho, 2005;
Pinaud et al., 2004; Sui et al., 2003; Wang et al., 2007 (a); Xu et al., 2006], cross–
linking chemistry [Bruchez et al., 1998; Chan and Nie, 1998; Wang et al., 2007 (b)], in
situ functionalization [Wang et al., 2008], or receptor modification [Palaniappan et al.,
2006]. By modifying the surface with appropriate molecules, quantum dots can be
dispersed or dissolved in nearly any solvent, solution, or matrix. This molecular
coupling ability greatly increases the flexibility of quantum dots for different types of
environments and applications.
56
4.2. QD Synthesis and Capping
The widely used approach in synthesizing high–quality nearly monodisperse
colloidal quantum dots is based on the pyrolysis of organometallic precursors by
injection into a hot coordinating solvent [Murray et al., 1993]. The synthesis begins with
the rapid injection of organometallic precursors into a hot coordinating solvent (e.g.,
mixture of trioctylphosphine/trioctyl phosphine oxide, TOP/TOPO) to produce a
temporally discrete homogeneous nucleation. Slow growth and annealing are then
continued in the coordinating solvent resulting in uniform surface derivatization and
regularity in core structure. The prepared nanocrystals have a broad size distritution
and optimization approaches such as size–selective precipitation are usually employed to
Weller, 1993; Dabbousi et al., 1997; Guzelian et al., 1996; Gaponik & Rogach, 2008;
Weller, 2003]. The size–selective precipitation method exploits the difference in
solubility of smaller and larger particles. A typical procedure is as follows: a sample of
prepared nanocrystals with a broad size distribution is dispersed in a solvent and a non–
solvent is added dropwise under stirring or sonication until the initially optically clear
solution becomes slightly turbid. As the largest nanocrystals in the sample exhibit the
greatest attractive van der Waals forces, they tend to aggregate before the smaller
particles resulting in flocculation. Separation of supernatant and flocculate by
centrifugation produces a precipitate enriched with the largest nanocrystals. This
precipitate can be re–dispersed in any appropriate solvent. The next portion of non–
solvent is then added to the supernatant to isolate the second size–selected fraction, and
so on. The procedure can be repeated several times and allows for obtaining up to 20 or
more size–selected fractions from the initial crude solution. Moreover, each size–
selected fraction can be subjected again to the size selection to further narrow the size
distribution. Size–selective fractions of quantum dot nanocrystals can be characterized
via absorbance & photoluminescence spectra shown in figure 4–3.
57
Figure 4–3. Absorbance and photoluminescence of the size–selected fractions of the
thioglycolic acid–capped CdTe nanocrystals. The spectra of initial crude solution are
highlighted in bold line. All size–selected fractions possess sharp excitonic transitions in
the absorption spectra which is a direct evidence of their narrow particle size
distributions. After [Gaponik and Rogach, 2008].
Only a few compounds can act as the coordinating solvents and this limits the
synthesis of high–quality nanocrystals in most cases [Qu et al., 2001]. It has been
demonstrated that high–quality monodisperse colloidal nanocrystals can be
synthesized by rapidly injecting reagents into a hot non–coordinating solvent [Yu et
al., 2002; Yu et al., 2003 (a); Yu et al., 2003 (b); Yu et al., 2004; Xu et al., 2006]. In
this alternate approach, the size of nanocrystals can be tuned over a wide range
under controlled conditions such as the injection temperature, the initial molar ratio
of chemicals in a non–coordinating solvent, and the growth time. The growth is
monitored with visible/Near Infrared (NIR) absorption spectroscopy in order to
obtain the desired size [Xu et al., 2006]. This method allows producing highly
monodisperse nanocrystals without any further size–selective precipitation process.
58
Purified core nanocrystals can be further overcoated with a layer of wider–bandgap
semiconducting material for improved quantum yields. In a traditional approach,
appropriate organometallic precursors are injected dropwise into the vigorously stirring
heated coordinating solution with core nanocrystals [Dabbousi et al., 1997; Hines and
Guyot–Sionnest, 1996; Peng et al., 1997]. The precursors dosage must be pre–
determined based on the initial size of core nanocrystals and the desired final thickness
of the shell structure. On the other hand, for the core nanocrystals prepared through the
routes of non–coordinating solvent reaction systems, another widely used approach has
been developed based upon the Successive Ion Layer Adsorption and Reaction (SILAR)
[Li et al., 2003; Xu et al., 2006]. In this second approach, the shell is designed to grow
epitaxially, one monolayer at a time, by alternate syringe–injection of predetermined
dosages of cationic and anionic precursors into the heated reaction mixture with core
nanocrystals. The alternate injection of precursors eliminates the homogeneous
nucleation of shell materials in the solution and ensures the homogeneous monolayer
growth of shell precursors onto core nanocrystals. The number of syringe injections
depends on the final desired thickness of the shell (i.e., the number of monolayers of
shell structure). As the core–shell structure grows upon each set of two successive
syringe–injections, the amount of precursors required for the subsequent shell growth
increases with the size of the core–shell structure and, thus, the first and subsequent
dosages of cationic and anionic precursors are pre–determined, respectively, based on
the initial size of core nanocrystals and subsequent sizes of core–shell structures. In
monitoring the shell growth, absorption and photoluminescence measurements are
carried out on aliquots taken after every two successive injections. The schematic of
general experimental setup for quantum dots synthesis is illustrated in figure 4–4.
59
Figure 4–4. Schematic of experimental setup for quantum dot synthesis. Solvent is heated up and stirred in a three–neck flask. A thermocouple is inserted to precisely control the temperature of the solvent. When the desired injection temperature is
reached, precursors are injected into the solvent with a syringe. The temperature is lowered to growth temperatures after the injection and the growth and annealing of nanocrystals is continued in the solvent.
4.3. Surface Derivatizations
In order to phase transfer quantum dots prepared using high–temperature routes,
surface functionalization with ligands, either through cap exchange, a process mainly
driven by mass action, or by encapsulating the original nanocrystals in a thick
heterofunctional organic coating, driven mainly by hydrophobic absorption onto the
TOP/TOPO–capped quantum dots [Medintz et al., 2005]. These ligands mediate both
the colloidal nanocrystal solubility in their respective solvents and serve as basis for
further surface derivatizations and modifications. Additionally, capping ligands insulate,
passivate, or protect the quantum dot surface from deterioration.
60
4.4. QD Bioconjugates for Bio–Imaging & Bio–Labeling
In order to study the complex spatio–temporal interplay of biomolecules from the
cellular to integrative level, researchers traditionally use fluorescent labeling for both in
vivo cellular imaging and in vitro assay detection [Miyawaki, 2003]. However, the
intrinsic properties of organic and genetically encoded fluorophores, which generally
have broad absorption/emission profiles [Miyawaki, 2003] and low photo–bleaching
thresholds, have limited their effectiveness in long–term imaging and ‘multiplexing’
(simultaneous detection of multiple signals) without complex instrumentation and
processing [Schröck et al., 1996].
The first biological uses of water–soluble biocompatible quantum–dot fluorophoes
demonstrated that unique properties of quantum dots could overcome the limitations of
traditional molecular dyes [Bruchez et al., 1998; Chen and Nie, 1998]. These properties
includes: high quantum yield, high molar extinction coefficients [Dabbousi et al., 1997;
Leatherdale et al., 2002], broad absorption with narrow, symmetric photoluminescence
(PL) spectra spanning from UV to near–infrared, large effective Stokes shifts, high
resistance to photobleaching (shown in figure 4–5), and exceptional resistance to
photo– and chemical degradation [Alivisatos, 2004; Mattoussi et al., 2002; Murphy,
2002; Niemeyer, 2001; Parak et al., 2003]. For example, in comparison with organic
dyes such as rhodamine, colloidal semiconductor quantum dots such as CdSe/ZnS core–
shell QDs are 20 times as bright, 100 times as stable against photobleaching, and one–
third as wide in spectral linewidth [Chen and Nie, 1998]. The ability to size–tune
fluorescent emission as a function of core size and the broad excitation spectra allow
multi–color labeling (shown in figure 4–6) by exciting mixed quantum dot populations
at a single wavelength significantly far from their respective emissions. Additionally, the
flexibility of QDs to surface modifications and further derivitization renders a wide range
of bio–labeling and –imaging applications.
61
(A)
(B)
Figure 4–5. (A) Quantum dot resistance to photobleaching. Top row: nuclear
antigens were labelled with QD 630–streptavidin (red), and microtubules were labeled
with AlexaFluor 488 (green) simultaneously in a 3T3 cell. Bottom row: Microtubules
were labeled with QD 630–streptavidin (red), and nuclear antigens were stained green
with Alexa 488 (green). (B) Quantitative analysis of changes in intensities of quantum
dot 608–streptavidin and Alexa 488–streptavidin. After [Wu et al., 2003].
62
Figure 4–6. Pseudocolored image depicting five–color QD staining of fixed human
epithelial cells. Cyan corresponds to 655–nm Qdots labeling the nucleus, magenta 605–
Qdots labeling Ki–67 protein, orange 525–Qdots labeling mitochondria, green 565–
Qdots labeling microtubules and red 705–Qdots labeling actin filaments. After [Medintz
et al., 2005].
It is suggested that quantum dots are cytocompatible and do not cause adverse
effects on cell viability, morphology, function, or development over the duration of the
experiments (from several hours to several days) at concentrations optimized for
labeling efficiency [Michalet et al., 2005]. In addition, ligand–coated core–shell
quantum dots (e.g., mercaptoacetic–acid–coated ZnS–capped CdSe QDs) show
significant less interference with cell viability or function than core CdSe due to an
improved protection of core quantum dot’s surface from oxidation [Derfus et al., 2004].
CdSe/ZnS core–shell quantum dots are the best available and widely used QD
fluorophores for biological applications because the chemistry and synthesis are mostly
refined and optimized [Medintz et al., 2005]. Furthermore, after surface modification/
functionalization with appropriate cap or ligand, CdSe/ZnS quantum dots can be
conjugated to various types of biomolecules depending on labeling applications.
63
CdSe/ZnS core/shell QDs prepared using high–temperature routes have no intrinsic
aqueous solubility, thus, phase–transfer to aqueous solution requires surface
functionalization with hydrophilic ligands that can also serve as a basis for further
derivatizations as well as conjugating biomolecules. A representative list of caps/ligands
and the general QD–dispersal strategies are provided in Table 4–1. In general, there are
three major strategies to functionalize the surface of quantum dots in bio–imaging and –
labeling applications [Medintz et al., 2005]:
(1) The first strategy uses the cap exchange method and involves the substitution of
the native TOP/TOPO with bifunctional ligands, each presenting a surface
anchoring moiety to bind to the inorganic quantum dot surface (e.g., thiol
group) and an opposing hydrophilic end group (e.g., hydroxyl, carboxyl, etc.) to
achieve water–compatibility. These bifunctional ligands include an array of
thiol and phosphine mono– and multidentate ligands (Table 5–1 a, b) [Chan
and Nie, 1998; Goldman et al., 2002; Mattoussi et al., 2000; Mitchell et al.,
1999; Uyeda et al., 2005].
(2) The second strategy involves formation of polymerized silica shells
functionalized with polar groups, which insulate the hydrophilic quantum dot
(Table 5–1 c) [Bruchez et al., 1998; Gerion et al., 2001].
(3) The third method preserves the native TOP/TOPO on the quantum dots and
uses variants of amphiphilic ‘diblock’ and ‘triblock’ copolymers and
phospholipids to tightly interleave/interdigitate the alkylphosphine ligands
through hydrophobic attraction, whereas the hydrophilic outer block permits
aqueous dispersion and further derivatization (Table 5–1 d, f, g) [Ballou et al.,
2004; Dubertret et al., 2002; Gao et al., 2004; Mattheakis et al., 2004; Osaki et
al., 2004; Pellegrino et al., 2004; Wu et al., 2003].
64
Table 4–1. Schematic of generic quantum dot (QD) solubilization and biofunctionalization (a–h). Biofunctionalization (second panel from top) uses caps/ligands to provide three functions. Linkage to the QD (pink), water solubility (blue) and a biomolecule linking functionality (green). Examples of surface–capping strategies and the mechanism of interaction with the QD and the aqueous environment. For the cap exchange (top right) excess thiolated cap displaces the original TOP/TOPO organic coating by binding the ZnS surface with the thiol group and imparting hydrophilicity with the charged carboxyl (or other functionalities) yielding water–soluble colloidal QD dispersions. After [Medintz et al., 2005].
65
Biofunctionalization can be achieved by linking a biomolecule (e.g., protein, DNA,
etc.) after appropriate ligands are attached to the quantum dot (the second panel from
the top in Table 4–1). A wide range of biological applications of QD–bioconjugates has
been demonstrated. Some of the noteworthy applications are listed below:
• Labeling antidepressant–sensitive, human and Dorsophila serotonin
transporters (hSERT, dSERT) expressed in HeLa and HEK–293 cell membrane
with serotonin–labeled CdSe nanocrystals (SNACs) and serotonin–linker–arm–
conjugated CdSe/ZnS core–shell nanocrystals (LSNACs) [Rosenthal et al., 2002].
• Mapping the expression dynamics of the cytokine receptor [i.e., interleukin–2
receptor–α (IL–2Rα)] in Jurkat T cell by time–lapsed labeling with anti–IL2Rα–
conjugated quantum dots after activating the cell with Phorbol Myrastoyl Acetate
(PMA) and ionomycin [Warnement et al., 2006].
• Identifying the presence of respiratory syncytial virus (RSV) and monitoring the
progression of infection in HEp–2 cell monolayer cultures over time via labeling
F and G proteins, present at the surface of the virion particles in lipid bilayer
envelope, with antibody–conjugated QDs [Bentzen et al., 2005].
• Long–term multiple–color imaging of live mammalian (HeLa) cells or
Dictyostelium discoideum (AX2) cells via receptor–mediated endocytic uptake of
transferrin–conjugated merceptoacetic–acid–capped quantum dots and selective
labeling of cell surface proteins with antibodies–conjugated dihydrolipoic acid–
capped quantum dots [Jaiswal et al., 2003].
• Long–term labeling and tracking (up to 22 days) of human mesenchymal stem
cells (hMSCs) with RGD–conjugated CdSe/ZnS quantum dots during
proliferation and multilineage differentiations into osteogenic, chondrogenic, and
adipogenic cells [Shah et al., 2006; Shah et al., 2007].
66
• Strain– (i.e., gram–positive or gram–negative) and metabolism–specific
microbial labeling of a wide variety of bacteria (including pathogenic bacteria)
and fungi [Kloepfer et al., 2003]: for microorganism strain, specific components
of polysaccharide structures in the cell wall are targeted with lectin–conjugated
CdSe quantum dots; for pathogenic bacteria, transferrin receptors, present and
active on the microorganism’s surface, are targeted with transferrin–conjugated
CdSe quantum dots.
• Specific and simultaneous labeling of the cancer marker Her2, cytoskeleton
components, and nuclear antigens with QD–streptavidin bioconjugates [Wu et
al., 2003]:
− For the cancer marker Her2 over–expressed on the surface of fixed and
live human breast cancer cells (SK–BR–3), either QD–streptavidin probes,
coupled with a humanized anti–Her2 antiboidy and biotinylated goat anti–
human IgG (immunoglobulin G), or QD–IgG probes, coupled with
monoclonal anti–Her2 antibody, are used.
− In staining cytoskeleton fibers in mouse 3T3 fibroblast cells, QD–
streptavidin conjugates in combination with biotinylated phalloidin are
used for F–actin and, for microtubules, monoclonal anti–α–tubulin
antibody, biotinylated anti–mouse IgG, and QD–streptavidin are used.
− For nuclear antigens in human epithelial cells, QD–streptavidin
conjugates, coupled with human anti–nuclear antigen (ANA) antibodies
and biotinylated anti–human IgG, are used.
67
4.5. Chapter Appendix
4.5.1. Synthesis of CdSe/ZnS Core/Shell Quantum Dots
4.5.1.1. Synthesis of Core CdSe Nanocrystals
Core CdSe nanocrystals can be synthesized via the pyrolysis of the organometallic
precursors (dimethylcadmium [Me2Cd] and trioctylphosphine selenide [TOPSe]) in a
coordinating solvent (trioctylphosphine oxide [TOPO]) [Murray et al., 1993]. The brief
procedure is as follows: The precursors are rapidly injected into the heated coordinating
solvent at temperatures ranging from 340 to 360 °C, and the initially formed small dots
are grown at temperatures between 290 and 300 °C. The CdSe quantum dots are then
collected as powders via size–selective precipitation [Murray et al., 1993] with methanol
and then re–dispersed in hexane for further process.
4.5.1.2. Growth of ZnS Shell over CdSe Nanocrystals
In overcoating CdSe quantum dots with ZnS shell, diethylzinc [ZnEt2] and
hexamethyldisilathiane [(TMS)2S] are used as the Zn and S precursors [Dabbousi et al.,
1997]. The amounts of precursors needed to grow a ZnS shell of desired thickness for
each CdSe sample is usually determined as follows: First, the average radius of the CdSe
quantum dots is estimated from TEM (Transmission Electron Microscope) or SAXS
(Small Angle X–ray Scattering) measurements. Next, the ratio of ZnS to CdSe necessary
to form a shell of desired thickness is calculated based on the ratio of the shell volume to
that of the core assuming the core/shell structure is spherical and taking into account of
the bulk lattice parameters of CdSe and ZnS. The actual amount of ZnS that grows onto
the CdSe cores is generally less than the amount of precursors added due to incomplete
reaction of the precursors and to the loss of some material on the walls of the reaction
flask during the addition.
68
The brief overcoating procedure is as follows: First, the CdSe quantum dots dispersed
in hexane is transferred into the TOPO/TOP (trioctyl phosphine oxide/
trioctylphosphine) solution, and the solvent is pumped off. Next, the TOPO/TOP
solution containing CdSe quantum dots is heated. The precursors (ZnEt2 & (TMS)2S)
dissolved in TOP are then added dropwise to the vigorously stirring reaction mixture at
temperatures ranging from 140 °C to 220 °C over a period of 5–10 min [Dabbousi et al.,
1997]. After the addition is complete, the reaction mixture is cooled to 90 °C and left
stirring for several hours. A small aliquot of butanol is also added to the mixture to
prevent the TOPO from solidifying upon cooling to room temperature.
The overcoated particles can be stored in the growth solution to ensure that the
surface of the dots remains passivated with TOPO/TOP. Prepared quantum dots can be
recovered in powder form later by precipitating with methanol in order to re–dispersed
into a variety of solvents. The prepared quantum dots are characterized by optical
Sui, G., Orbulescu, J., Ji, X., Gattás–Asfura, K. M., Leblanc, R. M., and Micic, M., 2003. Surface
Chemistry Studies of Quantum Dots (QDs) Modified with Surfactants. Journal of Cluster Science. 14 (2), 123–133.
Uyeda, H. T., Medintz, I. L., Jaiswal, J. K., Simon, S. M., and Mattoussi, H., 2005. Synthesis of
Compact Multidentate Ligands to Prepare Stable Hydrophilic Quantum Dot Fluorophores.
Journal of American Chemical Society. 127, 3870–3878.
Wang, S., Jarrett, B. R., Kauzlarich, S. M., and Louie, A. Y., 2007 (a). Core/Shell Quantum Dots
with High Relaxivity and Photoluminescence for Multimodality Imaging. Journal of American Chemical Society. 129 (13), 3848–3856.
Wang, Q., Xu, Y., Zhao, X., Chang, Y., Liu, Y., Jiang, L., Sharma, J., Seo, D. –K., and Yan, H.,
2007 (b). A Facile One–Step in situ Functionalization of Quantum Dots with Preserved
Photoluminescence for Bioconjugation. Journal of American Chemical Society. 129, 6380–
6381.
Wang, Q., Liu, Y., Ke, Y., and Yan, H., 2008. Quantum Dot Bioconjugation During Core–Shell
Synthesis. Angewandte Chemie International Edition. 47, 316–319.
Warnement, M. R., Faley, S. L., Wikswo, J. P., Rosenthal, S. J., 2006. Quantum Dots Probes for
Monitoring Dynamic Cellular Response: Reporters of T Cell Activation. IEEE Transactions on Nanobioscience. 5 (4), 268–272.
Weller, H., 1993. Colloidal Semiconductor Q–Particles: Chemistry in the Transition Region
Between Solid State and Molecules. Angewandte Chemie International Edition. 32, 41–53.
Weller, H., 2003. Synthesis and Self–Assembly of Colloidal Nanoparticles. Philosophical Transactions of the Royal Society A. 361, 229–240.
Wu, X., Liu, H., Liu, J., Haley, K. N., Treadway, J. A., Larson, J. P., Ge, N., Peale, F., and Bruchez,
M. P., 2003. Immunofluorescent Labeling of Cancer Marker Her2 and other Cellular Targets
with Semiconductor Quantum Dots. Nature Biotechnology. 21, 41–46.
81
Xu, J., Cui, D., Zhu, T., Paradee, G., Liang, Z., Wang, Q., Xu, S., and Wang, A. Y., 2006. Synthesis
and Surface Modification of PbSe/PbS Core–Shell Nanocrystals for Potential Device
Applications. Nanotechnology. 17, 5428–5434.
Yu, W. W., and Peng, X., 2002. Formation of High–Quality CdS and Other II–VI Semiconductor
Nanocrystals in Noncoordinating Solvents: Tunable Reactivity of Monomers. Angewandte Chemie International Edition. 41 (13), 2368–2371.
Yu, W. W., Qu, L., Guo, W., and Peng, X., 2003 (a). Experimental Determination of the
Extinction Coefficient of CdTe, CdSe, and CdS Nanocrystals. Chemical Materials. 15, 2854–
2860.
Yu, W. W., Wang, Y. A., and Peng, X., 2003 (b). Formation and Stability of Size–, Shape–, and
Structure–Controlled CdTe Nanocrystals: Ligand Effects on Monomers and Nanocrystals.
Chemical Materials. 15, 4300–4308.
Yu, W. W., Falkner, J. C., Yavuz, C. T., and Colvin, V. L., 2004. Synthesis of Monodisperse Iron
Oxide Nanocrystals by Thermal Decomposition of Iron Carboxylate Salts. Chemical Communications. 20, 2306–2307.
Zhang, J. Z., 1997. Ultrafast Studies of Electron Dynamics in Semiconductor and Metal Colloidal
Nanoparticles: Effects of Size and Surface. Accounts of Chemical Research. 30, 423–429.
82
Chapter 5
CELLULAR AND CELL–BASED BIOSENSORS
5.1. Measurement of Cellular Activities and States
Traditionally, electrical activity of cultured cells has been studied by employing
micropipette or micro–wire electrodes. In these techniques, the cells to be monitored
are seeded on a dish placed on a microscope stage, and micromanipulators are used to
position the recording electrode. Several different techniques (figure 5–1) such as
whole–cell patch recording, intracellular recording, and extracellular recording have
been used depending on the type of electrical signal to be measured.
Figure 5–1. Classical electrical activity recording techniques: for intracellular recordings and monitoring of membrane impedance characteristics, a micropipette is brought into contact with a cell and a light suction is applied forming a tight seal. This technique is known as the “whole–cell patch” technique. The micropipette can be used to puncture the cell membrane and access the intracellular fluid. This method allows direct measurement of intracellular voltage as well as “intracellular” recordings. For “extracellular” recordings, a micropipette or micro wire is positioned in close proximity to the cell. Adapted from [Borkholder, 1998].
In whole–cell patch recording, the micropipette is brought into contact with the cell
membrane with a light suction applied to form a tight seal (figure 5–2). The interior of
the pipette is usually filled with saline solution. A micro–wire is placed in contact with
this solution and conducts electrical current to the high input–impedance amplifier.
83
This setup allows for the observation of currents flowing through a small patch of
membrane, of activities of ion channels, and the determination of the impedance
characteristics of the cell membrane. However, an instable contact between the micro–
pipette tip and the cell membrane limits the duration and efficiency of the experiment.
Figure 5–2. Patch–clamp recording is used to monitor ion channel activity. Because of the extremely tight seal between the mouth of the micropipette and the membrane, current can enter or leave the micropipette only by passing through the channels in the patch of cell membrane covering its tip. Recordings of the current through these channels can be made with the patch still attached to the rest of the cell, as in (A), or detached, as in (B) (the advantage of the detached patch is that it is easy to alter the composition of the solution on either side of the membrane to test the effect of various solutes on channel behavior). The micrograph (C) shows a nerve cell from the eye held in a suction pipette (the tip of which is shown on the left) while a micropipette (upper right) is being used for patch–clamp recording. (D) The circuitry for patch–clamp recording. After [Albert et al., 2004].
84
In order to directly measure the transmembrane potential, an intracellular recording
(relative to a distant reference electrode) is made by carefully inserting a micropipette
electrode through the membrane (figure 5–1b). Although this method allows for a large
signal (~100 mV) to be recorded as well as recordings of action potentials (AP), it also
suffers from a mechanically fragile connection or weak sealing, which makes long–term
recordings difficult. Additionally, in some cases, the damage caused by impalement of
the cell compromises the intracellular ionic composition, which in turn can affect the
intrinsic action potential firing rate [Breckenridge, et al., 1995].
The micropipette or micro–wire can be positioned in close proximity to the cellular
membrane to record the extracellular Action Potentials (AP) (figure 5–1c). However, the
measured signal is on the order of tens to hundreds of micro–volts (µV) relative to a
distant reference electrode immersed in the culture media. Although this passive and
non–invasive monitoring technique does not adversely affect cellular functions, it is still
limited due to the significantly smaller AP signal amplitudes as well as the recorded
signal shape that is vastly different from the actual transmembrane potential.
All of the classical techniques described above rely on the optical observation of the
cells for positioning of a recording electrode and significant human skills/interactions,
and, thus, usually result in fragile interfaces that limit the duration and efficiency of the
recordings. The experiment can be time–consuming and throughputs is significantly
low since recordings can only be taken from one or a few cells at a time. In addition, it is
almost impossible to study the network activity of excitable cells. Therefore, biological–
device platforms that provide high–yield and long–term recordings are required.
5.2. Requirements of Biosensors
When developing biological devices and instrumentations for monitoring the cellular
activities and states, it is necessary to design with many criteria in mind:
biocompatibility, maintenance of the physiochemical environment (temperature, pH,
85
etc.), maintenance of sterility during cell growth, methods of sample introduction, a
transducer for monitoring the desired signal, electronics for extraction and acquisition of
the electrical signal, and packaging, which facilitates insertion of the cell culture system
into the measurement electronics while protecting the living system from the external
environment [Borkholder, 1998]. These requirements often trade off against each other
and require compromise for the best overall solution. Biocompatibility is usually the
most important consideration in developing biological devices.
5.3. Cellular and Cell–Based Biosensors
Cellular and cell–based biosensors have been implemented using microorganisms
and mammalian cells as sensing or recognition elements. Biosensors incorporating
microorganisms have been used particularly for environmental monitoring of pollutants
and those incorporating mammalian cells offer insight into the physiological effect of an
analyte on the cells [Pancrazio et al., 1999]. There are several approaches and methods
for transduction of cellular and cell–based sensor signals. Some of these approaches
include measures of fluorescence, metabolism, impedance, intracellular potentials,
extracellular potentials, and Raman spectra.
5.3.1. Cellular Microorganism–Based Biosensors
Some analytes, such as toxic pollutants, can activate microorganism pathways
involved in metabolism or nonspecific cell stress, resulting in the expression of one or
more genes [Belkin et al., 1997] or causing changes in metabolic activities. Therefore,
biological sensors, where microorganisms are incorporated as sensing elements, can be
used to detect such activities and changes, which are indicative of the presence of toxic
analytes. In one of the approaches, changes in extracellular pH due to effluents from the
microorganisms have been used as means of analyte detection [Rainina et al., 1996]. For
instance, detection of formaldehyde has been performed using immobilized yeast
86
coupled with pH–sensitive field effect transistors, where changes in metabolism of yeast
due to the present of formaldehyde were detected via extracellular acidification rates
[Korpan et al., 1993]. Furthermore, a wide range of organophosphorus neurotoxins such
as pesticides and chemical warfare agents can be detected using recombinant E. coli
cells, which have been engineered to express the enzyme organophosphate hydrolase
[Rainina et al., 1996]. In this approach, resulting changes in extracellular pH due to
protons being generated during the enzymatic hydrolysis of organophosphorus
neurotoxins by organophosphate hydrolase are measured with pH electrodes.
The use of bacteria as sensor elements has a unique advantage as bacteria are
amenable to genetic manipulations and can be engineered to generate an easily assayed
signal in response to specific toxicants. The observed response will indicate the existence
of a toxic compound, as well as providing some insightful information on its nature. For
instance, bacteria can be genetically engineered such that a bioluminescent reporter gene
is fused to the promoter sequence of a gene of the relevant metabolic pathway [Belkin et
al., 1997]. The promoter reacts to the presence of toxicants and turns on the expression
of the bioluminescent reporter gene, which can be detected optically. These modified
bacteria have served as cell–based sensor elements for the detection of napthalene and
its metabolic product (salicylate) [Heitzer et al., 1994; King et al., 1990], benzene
[Applegate et al., 1998], toluene [Applegate et al., 1998; Burlage et al., 1994], mercury
[Selifonova et al., 1993], and middle chain alkanes such as octane [Sticher et al., 1997].
5.3.2. Fluorescence Assays of Cellular Function
Fluorescence imaging has been an invaluable tool for monitoring changes in the
concentrations of ions and protein expression related to cellular signaling [Dunn et al.,
1994; Tsien, 1988]. Most fluorescent reagents being developed for cellular and cell–
based assays are based on the combination of molecular biology, fluorescent probe
chemistry, and protein chemistry. For instance, reporter gene constructs, such as Green
87
Fluorescent Protein (GFP), have been implemented in genetically engineered
mammalian and non–mammalian cell types [Giuliano et al., 1997; Zysk et al., 1998] to
achieve measures of cell functions. Furthermore, the technology also offers High–
Throughput Screening (HTS) [Fernandes, 1998]. For example, an HTS system such as
Fluorometric Imaging Plate Reader (FLIPRTM), commercially available from Molecular
of membrane potential and intracellular calcium mobilization [Kuntzweiler et al., 1998;
Sullivan et al., 1999].
Figure 5–3. (A) Typical layout of a Fluorometic Imaging Plate Reader (FLIPRTM) (96–well mode). It generally consists of an incubated cabinet with integrated 96–channel pipettor and fluorometer. An argon laser is usually used to excite fluorophores in a 96–well microtiter plate and the emitted fluorescence is imaged by a cooled CCD camera. The image data from each well of the microtiter plate for each time point is processed and presented in real time on the
computer screen (Note: the enclosure on the right of the tabletop houses the optics, the cell incubation chamber, and the multi–well pipettor. The water–cooled argon laser is seen toward the rear). After [Groebe et al., 1999]. (B) A schematic representation of the general components of the FLIPRTM system. Depicted are pipettor head for disposable tip loading and multi–well fluid addition, assay and compound addition plates, water–cooled argon laser for fluorescent excitation/illumination, and a cooled CCD camera for fluorescence data collection.
88
Despite the obvious advantages of fluorescent techniques, loading fluorescent dyes
into the cells is usually considered a potentially invasive treatment. In addition, analytes
of interest must be examined for autofluorescence to determine the feasibility of cellular
fluorescent assays for resolution of small effects. Therefore, other cell–based approaches
are more preferable in some cases.
5.3.3. Cellular Biosensors Based on Cell Metabolism
One category of cellular biosensors relies on the measurement of cell metabolism.
For instance, the Cytosensor® microphysiometer, developed by Molecular Devices [Parce
et al., 1989], as illustrated in figure 5–4, makes use of a light–addressable
potentiometric sensor [Hafeman et al., 1988] to measure extracellular pH, which is
correlated with cellular metabolic activity [Owicki et al., 1992]. This microphysiometer
has been used with a wide range of eukaryotic cells including primary central nervous
system neurons [Brown et al., 1997; Raley–Susman et al., 1992], hepatocytes [Cao et al.,
1998], neutrophils and endothelial cells [Gronert et al., 1998], and cells expressing
transfected receptors [Baxter et al., 1994]. However, the microphysiometer could not
discriminate different activations of a variety of receptors in cells, regardless of the signal
transduction method [Owicki et al., 1990]. For instance, measurements of metabolism
from primary neurons revealed that gamma–aminobutyric acid (GABA), an inhibitory
neurotransmitter, and kainic acid, an excitatory neurotransmitter, both cause an
elevation in cell metabolism and a subsequent elevation in extracellular acidosis [Brown
et al., 1997; Raley–Susman et al., 1992]. Therefore, this type of sensing approach usually
requires proper interpretation of data and parallel/control experiments in the presence
of known receptor antagonists that eliminate specific receptor responses.
89
Figure 5–4. Light–addressable semiconductor sensors. (a) A silicon plate with a
surface insulator of oxynitride (shaded with diagonal lines) in contact with an
electrolyte is photo–responsive to the light emitting diodes A, B, C, and D. The resulting
alternating photocurrent I in the external circuit depends on the applied bias potential Ψ
and the surface potential (electrochemical potential at electrolyte–oxynitride interface).
Different chemistries located on different regions of the insulating surface produce
variations in the local surface potential that can be determined by selective illumination
with one or another of the light–emitting diodes. This configuration can be employed as
microphysiometer cell–based biosensor that relies on metabolic rate for analyte
detection. The electrolyte–oxynitride interface provides a Nernstian response to pH or
cell membrane potential changes. The n– or p– type silicon insulated with oxynitride,
which is pH sensitive, will be photo–responsive to light produced by one or more light–
emitting diodes, resulting in different photocurrents depending on pH level. (b) For
high–sensitivity measurements of enzyme activity, it is advantageous to localize the
enzyme molecules in a small volume so that they are present at a high effective
concentration. Volumes as small as a nano–liter can be achieved with a configuration
such as the one illustrated. The controlling electrode acts as a piston. It can be raised to
allow fluid to be passed over the sensor surface and then lowered to create the small
reaction volume. After [Hafeman et al., 1988].
90
5.3.4. Cellular Biosensors Based on Electrical Impedance
The membranes of biological materials including cells exhibit dielectric properties
[Gordon et al., 1989] (i.e., it exhibits capacitive and resistive properties due to
phospholipid bilayer and transmembrane ion channels respectively). Hence, by
culturing cells over one or more planar electrodes, the effective electrode impedance
changes, permitting a noninvasive assay of cell adhesion, spreading, and motility
[Giaever and Keese, 1986; Mitra et al., 1991]. The impedance spectroscopy on cell–
covered electrodes has been used as a mean to address and monitor the behaviors of
non–excitable cell types such as macrophages [Kowolenko et al., 1990], endothelial cells
[Tiruppathi et al., 1992], fibroblasts [Giaever and Keese, 1993], and etc. One of the
impedance measurement techniques known as Electric Cell–Substrate Impedance
Sensing (ECIS) can reveal the dynamic of cellular morphological changes and
movements in response to hormones, enzymes, or chemical compounds (Figure 5–5 and
5–6) [Arndt et al., 2004; Giaever and Keese, 1991; Giaever and Keese, 1993; Noiri et al.,
1997; Smith et al., 1994; Wegner et al., 2000]. Furthermore, impedance measurements
from sensors that employ Inter–Digitated Electrode Structure (IDES) can also reveal
growth and morphological behavior of growing cells [Ehret et al., 1997; Wolf et al.,
1998]. From the perspective of cell–based assays and biosensing, the emphasis of these
impedance spectroscopy is on monitoring of time and concentration–dependent
variations in the impedance of cultured cells, which are incorporated as a sensor
element, in the presence of chemical compounds or toxins [Curtis et al., 2009; Tlili, et
al., 2003]. Not only does the use of living cells as sensor elements provide the high
sensitivity for a broad range of biologically active substances that affect the response or
behavior of cells, the approach can also offer insight into the physiological effect of these
substances on the cell metabolism.
91
Figure 5–5. A simplified schematic of the Electric Cell–Substrate Impedance Sensing (ECIS) instrumentation. Impedance of the small electrode is measured with a lock–in amplifier in series with a 1–MΩ current limiting resistor. The measured fluctuations in the real and imaginary voltage are displayed for a 1–hr experiment with confluent WI–38 VA13 fibroblasts using a 4–kHz, 1–V source. The in–phase and out–of–phase voltage values are approximately proportional to the respective changes in resistance and capacitive reactance of a RC circuit that represents the cell–covered electrode. After [Giaever and Keese, 1991].
92
Figure 5–6. Impedance measurements using ECIS technique showing: (A) an increase and fluctuations in effective resistance of the electrode due to cell spreading and cell micromotion respectively; (B) a sharp decrease and a transient rise in effective impedance of the electrode due to cell wounding after electroporation and cell healing/migration respectively. After [Applied BioPhysics, Inc., 2009].
93
5.3.5. Cellular Biosensor Based on Intracellular Potential
It is known that membrane excitability determines the control of secretion in
neurons and of contraction in myocardiocytes. Thus, analytes that affect membrane
excitability in excitable cells are expected to have profound effects on an organism.
Furthermore, the nature of the changes in excitability can yield physiologic implications
for the organism’s response to analytes. Classically, direct monitoring of cell membrane
potential can be achieved through patch–clamping techniques. For instance, repetitively
firing neurons from the visceral ganglia of the pond snail has been used to quantitatively
assess the concentration of a model analyte, serotonin [Skeen et al., 1990].
A neuroblastoma glioma cell line, NG108–15, can be used as sensing elements to a
variety of toxins. Under serum–free media conditions, NG108–15 cells usually express a
neuronal phenotype (figure 5–7) [Ma et al., 1998; Nirenberg et al., 1983] and the
capability of spontaneous firing [Kowtha et al., 1993]. In bullfrog sympathetic ganglion
neurons, both VX (O–ethyl s–2–NN–diisoprophlamin ethyl methylphosphono–
fluoridate) and GD (soman; O–pincolyl methylphosphonofluoridate) have been shown to
increase membrane excitability in a manner consistent with voltage–gated Ca2+ channel
blockade [Heppner et al., 1991; Heppner et al., 1992]. Therefore, these cells can be
employed as sensing elements to rapidly detect chemical warfare agents. As shown in
figure 5–7, spontaneous firing in NG108–15 cells was significantly affected by exposure
to chemical warfare agents: GD and VX. The effects of these two organophosphate
agents were distinctive; whereas GD irreversibly depolarized the resting membrane
potential (RMP), whereas, VX reversibly hyperpolarized the resting potential, resulting
in a loss of excitability. These records illustrate the utility of excitable cells as sensor
elements with sensitivity to chemical warfare agents.
However, the invasive nature of intracellular recording significantly limits the
robustness of this approach for biosensor applications. High throughput screening is
94
almost impossible for most techniques used can only access one or a couple of cells at a
time. Furthermore, excitable cells assemble into coupled networks rather than acting as
isolated elements. Neurons propagate information via synapses and myocardiocytes
form a syncitium via gap junctions. As a result, the ability to simultaneously monitor
two or more cells are imperative and sensors platforms that can provide such
functionalities are required in order to make measurements of membrane excitability
and cell coupling.
Figure 5–7. Neuroblastoma–glioma (NG108–15) cells as electrical detectors of chemical
agents. Left panel: typical NG108–15 cells cultured under serum–free conditions after 21 days
in vitro exhibiting neuronal phenotype. Right panel: differential effects of the chemical
warfare agents VX (O–ethyl s-2-NN-diisopropylamin ethyl methylphosphonofluoridate) and
GD (soman; O-pincolyl methylphosphonofluoridate) on spontaneous, rhythmic firing measured
using standard glass microelectrodes filled with 3 M KCl as described by [Kowtha et al., 1993].
In contrast to GD, VX was completely reversible. Dotted line indicates the zero membrane
potential level. Adapted from [Pancrazio et al., 1999].
95
5.3.6. Cellular Biosensors Based on Extracellular Potential
Couplings of neurons and cardiac myocytes are inaccessible via intracellular
recording that relies on glass micropipettes or patch clamp techniques. Furthermore,
due to the invasive nature and low throughputs, traditional intracellular recording
techniques are limited to a few applications. As a result, planar microelectrode arrays
have emerged as a powerful device for long–term recording of network dynamics.
Extracellular microelectrode arrays have been used as a noninvasive, long–term, and
high–yield approach to monitor electrical activity in excitable cells such as neurons and
myocytes [Connolly et al., 1990; Gross et al., 1995; Thomas et al., 1972]. A system of
microelectrode arrays presents a grid for data acquisition from networks of electrically
active cells [Egert et al., 1998].
A system of microelectrode arrays incorporated with amplifier/stimulator CMOS
chips can accomplish both extracellular recordings and stimulations [Pancrazio et al.,
1998 (a)]. Extracellular recording potentials from the microelectrodes are usually the
second or third derivative of the action potential [Connolly et al., 1990; Pancrazio et al.,
1999]. Not only can a Microelectrode Array (MEA) system record the action potentials,
but it can also allow the quantification of action potential propagation velocity by
revealing the spike delay between proximal microelectrode sites (Figure 5–8) [Pancrazio
et al., 1998 (b); Pancrazio et al., 1999]. Furthermore, planar microelectrode arrays have
been used to record extracellular potentials from tissue slices [Meister et al., 1994;
Stoppini et al., 1997]. The technique also allows monitoring the effect of chemical
compounds on the electrical activity of neurons (Figure 5–9) [Pancrazio et al., 1999].
96
Figure 5–8. Planar multi–electrode arrays permitting noninvasive, simultaneous recordings from excitable tissue for measurement of action potential propagation. Left panel: Microelectrode Array (MEA) with 36 microelectrode sites that are 10 µm in diameter. Right panel: Simultaneous recording from a monolayer of embryonic day 11 chick myocardiocytes after 2–3 days in vitro. The monolayer exhibited regular beating at a rate of 1–2 Hz, where spike delays between proximal microelectrode sites allow quantification of cardiac propagation velocity. After [Pancrazio et al., 1999].
Figure 5–9. Culture of spinal cord neurons on MEA for toxicological evaluation. Left panel: Phase contrast image of embryonic day–15 rat spinal cord neurons cultured for 18 days on a microelectrode array. Cells were cultured under serum–free defined media conditions on artificial self–assembled monolayer substrates of aminosilanes. Right panel: Addition of glutamate (50 µM) greatly augmented spike activity. Administration of an organophosphate,
diisopropylfluorophosphate (DFP; 25 µM), revealed a marked ablation of spontaneous firing, illustrating the utility of neurons cultured on microelectrode arrays for detection of toxic compounds. After [Pancrazio et al., 1999].
97
5.3.6.1. Analytical Methods
In order to analyze cultured neuronal networks from MEA recordings, cross
correlations between multi–electrode channels and interspike interval (ISI) variances
have been usually evaluated. Both simulation and experimental findings suggested that
the interspike interval variance discriminated periodic bursting from asynchronous
firing, whereas the cross correlation between multi–electrode channels is indicative of
the synchronization among the neurons in the network [Bove et al., 1997; Canepari et al.,
1997]. Other statistical methods used are multivariate analysis, which includes linear
discriminant analysis [Gochin et al., 1994], and principal component analysis (PCA)
[Nicolelis and Chapin, 1994], which can take into account the activity of all the recorded
neurons simultaneously and incorporate spatio–temporal features into the analysis. For
instance, PCA allows construction of the activity of neuron populations by the weighted
addition of the integrated activity of the population, resulting in the structure underlying
the collective neuronal behavior [Takahashi, 1996]. Furthermore, reconstruction of the
first principal component from recordings of cortical, thalamic, and brain stem activity
was used to more reliably track oscillatory episodes than recordings from any single
neuron in the population [Nicoleis et al., 1995]. For analyte detection, minimal sets of
parameters, such as burst amplitude, duration, and frequency, must be determined to
perform risk/threat assessment. Multichannel analysis is also benefited from advances
in nonlinear dynamics, which can potentially distinguish deterministic behavior from
random fluctuations. For example, nonlinear time series analysis of the correlation
dimension from multielectrode electroencephalograph (EEG) records has been shown to
yield a prognostic indicator for seizure activity [Elger and Lehnertz, 1998]. Hence, these
analysis methods are not just limited to recordings from cultured neuronal networks.
98
5.3.7. Raman Spectroscopy Cell–Based Biosensors
The Raman spectrum of a cell represents an information–rich “fingerprint” of the
overall biochemical composition of the cell, thus different toxic agents that initiate
different cellular responses and biochemical changes produce distinct changes in the
Raman spectra [Notingher, 2007]. Furthermore, time–course Raman spectra can be
acquired from live cells maintained in physiological conditions without the need of
invasive procedures. One of the variations of Raman spectroscopy, known as Surface
Enhanced Raman Spectroscopy (SERS), renders huge potentials in biosensing as it
enhance signals by orders of magnitudes and can detect some molecule vibration modes,
which cannot be detected by traditional Raman Spectroscopy [Culha et al., 2003; Isola et
al., 1998; Kneipp et al., 2006].
In Raman micro–spectroscopy (traditional or SERS), a spectrometer is coupled to an
optical microscope to enable both excitation and collection of Raman spectra (Figure 5–
10). Additionally, the high–quality of optical microscopes makes it possible to obtain
measurements with diffraction–limited spatial resolution (approximately half
wavelength of the excitation laser). Raman micro–spectroscopy can be used to monitor
the molecular changes in a single cell after the cell is exposed to a toxic chemical or a
drug (Figure 5–11) [Notingher et al., 2004; Owen et al., 2006; Verrier et al., 2004; Yao et
al., 2009]. Raman spectra or second derivative averaged Raman spectra can present
important biochemical changes taking place during cell death, such as the degradation of
proteins, DNA breakdown, and the formation of lipid vesicles. Furthermore, Raman
spectroscopy can be used as a noninvasive method to distinguish cells at different stages
of the cell cycle by the spectral differences between cells in different stages of the cell
cycle, which are caused by variations in DNA vibrations [Notingher et al., 2003].
99
Figure 5–10. Experimental and instrumentation setup for Raman micro–
spectroscopy measurements of cells. Adapted from [Notingher, 2007].
Figure 5–11. Raman spectra of viable (spectrum a) and dead (spectrum b) human
lung derived (A549) cells. The arrows indicate the positions where the most
significant changes, related to the degradation of proteins, DNA breakdown, and
the formation of lipid vesicles, occur. After [Notingher et al., 2003].
100
Raman spectroscopy cell–based biosensor platforms have promising future in High
Throughput Screening (HTS) of drugs. For instance, Raman spectra from individual
cancer cells such as human medulloblastoma (DAOY) cells are used as sensing elements
to access the chemotherapeutic–agent–induced cellular changes [Buckmaster et al.,
2009]. DAOY cells were immobilized on the cell trapping pads presented on the surface
of patterned single–cell biosensor platform (figure 5–12b). The cell immobilization was
mediated by surface–chemistry–mediated adhesion and spreading (figure 5–12a).
Common chemotherapeutic agent (e.g., etoposide) is administered at different doses and
confocal Raman micro–spectroscopy was taken from individual cells over the time
course of 12 hours. Resulting spectra shows drug–induced changes at the single–cell
level such as reduction in the concentrations of DNA and protein contents. The time–
course spectra can also reveal cells that exhibit resistance to drug (figure 5–13), which is
of special interest in the development of higher efficacy chemotherapeutic drugs.
Figure 5–12. (a) Schematic of the final surface modification of gold–patterned silicon
oxide substrate for subsequent single–cell adhesion. (b) Optical differential interference
contrast (DIC) image of patterned DAOY cells on the surface–modified 20µm × 20µm
gold squares with 40–µm spacing between squares. After [Buckmaster et al., 2009].
101
Figure 5–13. Raman spectra for two representative cells acquired prior to (0 h) and
every 12 hours after etoposide exposure. (a) Raman spectra of a single cell that died
after 36 hours. (b) Raman spectra of a single cell that showed resistance to etoposide
and was viable over the course of the experiment. After [Buckmaster et al., 2009].
102
References
Alberts M. B., Bray, D., Hopkin, K., Johnson, A., Lewis, J., Raff, M., Roberts, K., and Walter, P.,
2004. Essential Cell Biology. 2nd Edition, Taylor & Francis Group, New York.
Applegate, B. M., Kehrmeyer, S. R., and Sayler, G. S., 1998. A Chromosomally Based tod–luxCDABE Whole–Cell Reporter for Benzene, Toluene, Ethylbenzene, and Xylene (BTEX)
Sensing. Applied and Environmental Microbiology. 64 (7), 2730–2735.
Arndt, S., Seebach, J., Psathaki, K., Galla, H. –J., and Wegener, J., 2004. Bioelectrical
Impedance Assay to Monitor Changes in Cell Shape During Apoptosis. Biosensors and Bioelectronics. 19 (6), 583–594.
Baxter, G. T., Young, M. –L., Miller, D. L., and Owicki, J. C., Using Microphysiometry to Study the
Pharmacology of Exogenously Expressed m1 and m3 Muscarinic Receptors. Life Sciences. 55
(8), 573–583.
Belkin, S., Smulski, D. R., Dadon, S., Vollmer, A. C., van Kyk, T. K., and Larossa, R. A., 1997. A
Panel of Stress–Responsive Luminous Bacteria for the Detection of Selected Classes of
Toxicants. Water Research. 31 (12), 3009–3016.
Borkholder, D. A., 1998. Cell Based Biosensors Using Microelectrode. Ph.D. Dissertation.
Stanford University, Stanford, CA.
Bove, M., Grattarola, M., and Verreschi, G., 1997. In Vitro 2-D Networks of Neurons
Characterized by Processing the Signals Recorded with a Planar Microtransducer Array. IEEE Transactions on Biomedical Engineering. 44, 964–977.
Breckenridge, L. J., Wilson, R. J. A., Connolly, P., Curtis, A. S. G., Dow, J. A. T., Blackshaw, S. E.,
and Wilkinson, C. D. W., 1995. Advantages of Using Microfabricated Extracellular Electrodes
for In Vitro Neuronal Recording. Journal of Neuroscience Research. 42, 266–276.
Brown, M. J., Wood, M. D., Coldwell, M. C., and Bristow D. R., 1997. Measurement of GABAA
Receptor Function in Rat Cultured Cerebellar Granule Cells by the Cytosensor
Microphysiometer. British Journal of Pharmacology. 121, 71–76.
Buckmaster, R., Asphahani, F., Thein, M., Xu, J., and Zhang, M., 2009. Detection of Drug–
Induced Cellular Changes Using Confocal Raman Spectroscopy on Patterned Single–Cell
Biosensors. Analyst. 134, 1440–1446.
Burlage, R. S., Palumbo, A. V., Heitzer, A., and Sayler, G., 1994. Bioluminescent Reporter
Bacteria Detect Contaminants in Soil Samples. Applied Biochemistry and Biotechnology. 45,
731–740.
103
Canepari, M., Bove, M., Maeda, E., Cappello, M., and Kawana, A., 1997. Experimental Analysis of
Neuronal Dynamics in Cultured Cortical Networks and Transitions between Different
Patterns of Activity. Biological Cybernetics. 77, 153–162.
Cao, C. J.,Eldefrawi, M. E., Eldefrawi, A. T., Burnett, J. W., Mioduszewski, R. J., Menking, D. E.,
and Valdes, J. J., 1998. Toxicity of Sea Nettle Toxin to Human Hepatocytes and the
Protective Effects of Phosphorylating and Alkylating Agents. Toxicon. 36 (2), 269–281.
Connolly, P., Clark, P., Curtis, S. G., Dow, J. A. T., and Wilkinson, C. D. W., 1990. An
Extracellular Microelectrode Arrays for Monitoring Electrogenic Cells in Culture. Biosensors and Bioelectronics. 5 (3), 223–234.
Culha, M., Stokes, D., Allain, L. R., and Vo–Dinh, T., 2003. Surface–Enhanced Raman Scattering
Substrate Based on a Self–Assembled Monolayer for Use in Gene Diagnostics. Analytical Chemistry. 75 (22), 6196–6201.
Curtis, T. M., Widder, M. W., Brennan, L. M., Schwager, S. J., van der Schalie, W. H., Fey, J., and
Salazar, N., 2009. A Portable Cell–Based Impedance Sensor for Toxicity Testing of Drinking
Water. Lab on a Chip. 9, 2176–2183.
Dunn, K. W., Mayor, S., Myers, J. N., and Maxfield, F. R., 1994. Applications of Ratio
Fluorescence Microscopy in the Study of Cell Physiology. The FASEB Journal. 8, 573–582.
Egert, U., Schlosshauer, B., Fennrich, S., Nisch, W., Fejtl, M., Knott, T., Muller, T., and
Hammerle, H., 1998. A Novel Organotypic long–term Culture of the Rat Hippocampus on
Substrate–Integrated Multielectrode Arrays. Brain Research Protocols. 2 (4), 229–242.
Ehret, R., Baumann, W., Brischwein, M., Schwinde, A., Stegbauer, K. and Wolf, B., 1997.
Monitoring of Cellular Behavior by Impedance Measurements on Interdigitated Electrode
Structures. Biosensors and Bioelectronics. 12 (1) 29–41.
Elger, C. E., and Lehnertz, K., 1998. Seizure Prediction by Nonlinear Time Series Analysis of
Brain Electrical Activity. European Journal of Neuroscience. 10, 786–789.
Fernandes, P. B., 1998. Technological advances in high-throughput screening. Current Opinion in Chemical Biology. 2 (5), 597–603.
Giaever, I., and Keese, C. R., 1986. Use of Electric Fields to Monitor the Dynamical Aspect of Cell
Behavior in Tissue Culture. IEEE Transactions on Biomedical Engineering. 33, (2), 242–247.
Giaever, I., and Keese, C. R., 1991. Micromotion of Mammalian Cells Measured Electrically.
Proceedings of the National Academy of Sciences USA. 88 (17), 7896–7900.
Giaever, I., and Keese, C. R., 1993. A Morphological Biosensor for Mammalian Cells. Nature. 366
(6455), 591–592.
104
Giuliano, K. A., DeBiasio, R. L., Dunlay, R. T., Gough, A., Volosky, J. M., Zock, J., Pavlakis, G. N.,
and Taylor, D. L., 1997. High–Content Screening: A New Approach to Easing Key
Bottlenecks in the Drug Discovery Process. Journal of Biomolecular Screening. 2 (4), 249–
259.
Gochin, P. M., Colombo, M., Dorfman, G. A., Gerstein, G. L., and Gross, C. G., 1994. Neural
Ensemble Coding in Inferior Temporal Cortex. Journal of Neurophysiology. 71 (6), 2325–
2337.
Gordon, L. G. M., Kottra, G., and Frömter, E., 1989. Electrical Impedance Analysis of Leaky
Epithelia: Theory, Techniques, and Leak Artifact Problems. Methods in Enzymology. 171,
642–663.
Groebe, D. R., Gopalakrishnan, S., Hahn, H., Warrior, U., Traphagen, L., and Burns, D. J., 1999.
Use of a Fluorometric Imaging Plate Reader in High–Throughput Screening. Proceedings of SPIE. 3603, 297–306.
Gronert, K., Colgan, S. P., and Serhan, C. N., 1998. Characterization of Human Neutrophil and
Endothelial Cell Ligand-Operated Extracellular Acidification Rate by Microphysiometry:
Impact of Reoxygenation. The Journal of Pharmacology and Experimental Therapeutics. 285,
252–261.
Gross, G. W., Rhoades, B. K., Azzazy, H. M. E., Wu, M. –C., 1995. The Use of Neuronal Networks
on Multielectrode Arrays as Biosensors. Biosensors and Bioelectronics. 10 (6–7), 553–567.
Hafeman, D. G., Parce, J. W., and McConnell, H. M., 1988. Light Addressable Potentiometric
Sensor for Biochemical Systems. Science. 240, 1182–1185.
Heitzer, A., Malachowsky, K., Thonnard, J. E., Bienkowski P. R., White, D. C., and Sayler, G. S.,
1994. Optical Biosensor for Environmental on–line Monitoring of Napthalene and Salicylate
Bioavailability with an Immobilized Bioluminescent Catabolic Reporter Bacterium. Applied and Environmental Microbiology. 60 (5), 1487–1494.
Heppner, T. J., and Fiekers, J. F., 1991. Soman Reversibly Decreases the duration of Ca2+ and
Wolf, B., Brischwein, M., Baumann, W., Ehret, R., and Kraus, M., 1998. Monitoring of Cellular
Signalling and Metabolism with Modular Sensor–Technique: The PhysioControl–
Microsystem (PCM®). Biosensors and Bioelectronics. 13 (5), 501–509.
Yao, H., Tao, Z., Ai, M., Peng, L., Wang, G., He, B., and Li, Y. –Q., 2009. Raman Spectroscopic
Analysis of Apoptosis of Single Human Gastric Cancer Cells. Vibrational Spectroscopy. 50
(2), 193–197.
Zysk, J. R., and Baumbach, W. R., 1998. Homogeneous Pharmacologic and Cell–Based Screens
Provide Diverse Strategies in Drug Discovery: Somatostatin Antagonists as A Case Study.
Combinatorial Chemistry and High Throughput Screening. 1 (4), 171–183.
109
Chapter 6
SONOPORATION: ULTRASOUND–INDUCED PERMEABILIZATION OF CELL MEMBRANE
6.1. Basic Physics of Ultrasound
Ultrasound is cyclic sound pressure with a frequency above 20 kHz, i.e., the upper
limit for human hearing [Shutilov, 1988]. It consists of a mechanical vibration of the
particles or molecules of a material. Within the wave, regular pressure variations occur
with alternating areas of compression, which correspond to areas of high pressure, and
with areas of rarefaction or low pressure zones where widening of particles occurs.
Although each particle moves small distances from its rest position, the vibrational
energy is propagated as a wave traveling from particle to particle through the material.
Sound waves are expressed as sine waves and classified as being longitudinal or
transverse, depending on whether the vibration of each particle is parallel or transverse
to the direction of wave propagation. Although all materials can support the propagation
of longitudinal sound waves, only solids can support transverse waves. Parameters of an
ultrasound wave include frequency, pressure, wavelength, velocity, speed, power, and
intensity. In general, the higher the frequency of the ultrasound, the smaller is the beam
divergence and the narrower is the beam width. Ultrasound pressure, which can be
measured using a microphone in air or a hydrophone in water, is the local pressure
deviation from the ambient equilibrium pressure caused by the ultrasonic wave.
Sound velocity, which is a vector quantity, is the rate at which the vibratory energy is
transmitted in a specified direction of propagation. Acoustic speed, a scalar quantity, is
greater in materials that are more rigid or less compressible. Power and intensity are
measures of the strength of an ultrasound wave. Power is the total amount of energy
110
passing through a surface per unit time. Intensity is the concentration of energy per unit
area (i.e., the energy pass through per unit area per unit time). Therefore, intensity
changes depending on the width of the ultrasound beam. In focused ultrasound beams,
the intensity is greatest at the focus where the beam width is the narrowest. In pulsed
ultrasound beams, the greatest intensity value occurs during the pulse and is zero
between pulses.
How ultrasound traverses a medium is measured by characteristic acoustic
impedance of a medium, which is a material property and depends on the density of the
medium and the propagation velocity of the ultrasound through the medium (i.e., Z0 =
ρ⋅c). Ultrasound is attenuated as it travels through the medium due to beam divergence
(diffraction), absorption, and deflection of acoustic energy out of the beam. The
deflection includes the processes of reflection, refraction, and scattering. An echo is a
reflected wave and its magnitude depends on: (i) the orientation of the reflecting surface
with respect to the sound beam, and (ii) the difference in acoustic impedance between
media on either side of the reflecting surface (i.e., acoustic impedance mismatch). When
an ultrasound beam encounters media of different velocities, the proportion of the beam
that is not reflected but is transmitted undergoes refraction or bending (the change in
the direction of transmitted beam is governed by the Snell’s law). Ultrasound is also
nonionizing, i.e., it does not carry enough energy to completely remove an electron from
an atom or a molecule. However, at sufficiently high intensities, ultrasound can produce
temperature elevation, mechanical effects, cavitation, and chemical effects.
6.2. Ultrasonic Transducers
Ultrasound can be produced and detected using a transducer that employs
piezoelectric materials. In order to generate an ultrasound with a transducer, an
alternating voltage is applied across the piezoelectric structure. The structure will
111
lengthens or shortens depending on the polarity of applied voltage and in proportion to
the strength of the applied electric field. Consequently, this mechanical vibration in the
range of ultrasonic frequencies generates an ultrasound. The reverse effect occurs when
the ultrasound hits the piezoelectric structure, i.e., the mechanical vibrating energy
induced by the ultrasound on the piezoelectric structure is converted into the electrical
energy. The beam pattern of ultrasonic energy emanating from the transducer can be
determined by the dimensions of the transducer, the way it is constructed, the way it is
energized, and the driving frequency [Kossoff, 2000].
The piezoelectric material used in the ultrasonic transducer is a piece of perovskite
crystal (a polarized material). When an electric field is applied across the material, the
polarized molecules will be oriented and align themselves with the electric field,
resulting in induced dipoles within the molecular or crystal structure of the material.
Consequently, this alignment of molecules causes the material to change dimensions
(phenomenon known as electrostriction). Also, the piezoelectric material produces an
electric field when the material changes dimensions as a result of an imposed mechanical
force (phenomenon known as the piezoelectric effect). Tension and compression
generate voltages of opposite polarity in proportion to the applied force. However,
magnitudes of piezoelectric voltages and movements are usually small. Therefore,
mechanical or electrical amplification mechanism is usually integrated to the transducer
system depending on applications. Among various type piezoelectric materials, Lead–
Zirconate–Titanate–based (PZT–based) compounds are widely used in ultrasonic
transducers as it exhibits greater sensitivity and higher operating temperatures.
112
6.3. Acoustic Cavitation
Acoustic cavitation is the formation and/or activity of gas– or vapor–filled bubbles in
a medium exposed to an ultrasound field [Apfel, 1982; Barnett, et al., 1994; Dalecki,
2004; Ter Haar et al., 1986]. According to the crevice model, it is suggested that gas
nuclei, stabilized in crevices of hydrophobic impurities in the liquid, expand and separate
from the impurities to form micro–bubbles as the pressure in the liquid decreases
[Flynn, 1964; Young, 1989]. When the ultrasonic wave passes through the liquid, gas
bubbles of any size will expand at low pressure and contract at high pressure. The
bubble radius varies about an equilibrium value [Dalecki, 2004], and the pulsating cavity
exists for a number of cycles [Flynn, 1982]. If the resulting oscillation in the bubble is
stable (i.e., repeatable over many acoustic cycles) and the amplitude changes in the
bubble size during each acoustic cycle, as with rectified diffusion (i.e., the slow growth of
an oscillating bubble due to a net flow of gas into the bubble over many acoustic cycles)
and resonant bubble motion, is relatively moderate or does not exceed twice the
equilibrium radius, the cavitation is classified as stable or non–inertial cavitation
[Apfel, 1982; Dalecki, 2004]. Such oscillation in response to the ultrasound creates
time–independent second–order quantities [Nyborg, 1982]; these include (i) radiation
pressure: an elevation or a decrease of the steady pressure near the oscillating bubble
relative to that existing in the absence of oscillation; (ii) radiation force: a steady force
on a body near the vibrating bubble, tending to deform the body or make it move; (iii)
radiation torque: a steady twisting action on a particle near the oscillating bubble,
tending to make the article rotate; and micro–streaming: a circulatory motion set up in
the liquid near a vibrating bubble. The velocities and shear rates associated with the
micro–streaming around the bubble are also proportional to the amplitude of the
oscillation [Elder, 1958; Marmottant & Hilgenfeldt, 2003; Nyborg, 1982]. These
bubble–associated small–scale events can occur at ultrasonic frequencies in the lower
megahertz range (1–10 MHz) and even low levels of intensity or pressure [Nyborg, 1982].
113
As the ultrasonic intensity increases, the amplitude of oscillation also increases to a
point at which the inward moving wall of fluid has sufficient inertia that it cannot reverse
the direction when the acoustic pressure reverses, but continues to compress the gas in
the bubble to a very small volume, creating extremely high pressures and temperatures
[Barnett et al., 1994; Brennen, 1995; May et al., 2002]. Such bubbles oscillate about
their equilibrium size, grow until the outward excursion of the surface exceeds a limiting
value (approximately 2 times the initial radius [Flynn, 1982]), and collapse violently.
This type of cavitation is classified as transient, inertial or collapse cavitation. A
small increase in acoustic pressure amplitude can change the bubble response from a
non–inertial cavity to an inertial cavity. The acoustic pressure, at which the transition
occurs, is often called the threshold for inertial cavitation [Dalecki, 2004]. Furthermore,
the collapse cavitation can also occur during the period of a single cycle [Barnett et al.,
1994; Flynn, 1982; Fowlkes & Crum, 1988] or a few cycles, depending on the applied
ultrasonic pressure and the properties of the propagating medium. In general, the
likelihood and intensity of collapse cavitation increases at higher ultrasound intensities
and lower frequency [Apfel & Holland, 1991; Brennen, 1995; Urick, 1983]. Additionally,
for appropriate exposure conditions, a single ultrasound pulse on the order of one
microsecond in duration can also cause a bubble to expand rapidly and collapse violently
[Flynn, 1982]. The size of the bubble and its physical properties, i.e., gas species,
interfacial tension, surface rigidity, etc., also affect this cavitation process [Allen et al.,
2003; Kvikliene et al., 2004; Sboros et al., 2003; Soetanto & Chan, 2000]. The motion
of the bubble in inertial cavitation is highly nonlinear [Flynn, 1982] and various
parameters, including acoustic frequency, pressure amplitude, and initial bubble radius,
determine the violence of the collapse of the inertial cavity.
The collapsed bubble often fragments into smaller bubbles that serve as cavitation
nuclei, grow in size, and eventually collapse again [Brennen, 1995; May et al., 2002].
114
High temperatures, high shear forces, and shock waves are generated by the sudden
collapse of the cavitation bubble. If a sufficiently high temperature is generated (>1500
˚K), the released energy may even cause the emission of light and the formation of
reactive chemical species [Barnett et al., 1994]. Although the amount of energy released
is approximately 100 MeV [Apfel, 1986], the effects of collapse cavitation are
instantaneous and highly localized [Barnett et al., 1994]. Hence, this type of cavitation
concentrates the energy from ultrasound into a small volume [Apfel, 1982]. If the
collapse is near a relatively rigid surface, an asymmetrical collapse will occur, which
ejects a liquid jet at sonic speed toward the surface [Brennen, 1995; Smith, 2007].
Acoustic cavitation phenomena are summarized in figure 6–1.
Figure 6–1. Acoustic cavitation. (a) Acoustic streaming or micro–streaming:
cavitation bubbles can oscillate around their resonant/equilibrium size and generate
radiation pressures and forces. Bubble oscillations can be damped through viscous
dissipation, sound radiation, and thermal conduction [Flynn, 1964; Leighton, 1994;
Young, 1989]. (b) Sonochemistry: sudden collapse of bubbles generates momentary
high temperatures in the bubble core. The hot bubble can induce chemical changes in
the surrounding medium, including free–radical generation. (c) Shock waves: sudden
collapse of cavitation bubbles leads to the formation of shock waves. (d) Liquid
microjets: collapsing bubbles near a surface experience non–uniformities in their
surroundings resulting high–velocity microjets at sonic speed. After [Mitragotri, 2005].
115
6.3.1. Biological and Physical Consequences of Cavitation on Cells
The liquid medium where bubbles are undergoing collapse cavitation is not a healthy
environment for cells as: (i) shock waves and shear forces will be shearing the cell
membrane [Guzman et al., 2003]; (ii) liquid microjets at sonic velocities may be piercing
or lysing the cells; (iii) the free radicals may be interfering with essential biochemical
processes. On the other side, bubbles experiencing mild–stable cavitation have no
negative biological consequences to most cells. Fortunately, somewhere between the
harsh and mild–state cavitation lies the desired realm of cavitational level that produces
bubble–associated activities, which are sufficient to permeabilize plasma membranes
without killing the cells [Pitt et al., 2004]. At this cavitational level, the transport or
uptake of gene products or therapeutic molecules into the cells can be enhanced.
6.4. Sonoporation
Sonoporation is a technique that enhances cell membrane permeability via the
application of ultrasound [Mehier–Humbert & Guy, 2005]. The consensus is that
acoustic cavitation in response of ultrasound exposure is the most probable mechanism
involved in sonoporation. Ultrasound by itself, in the absence of cavitation, has been
thought to have little effect on cells and tissues apart from some heating that may occur
at higher frequencies and intensities [Barnett et al., 1994; Barnett et al., 1997; Nyborg,
2001; Ziskin & Barnett, 2001]. Cells in an environment of cavitation events are
subjected to shear forces from micro–streaming and shock waves. Furthermore, a large
semi rigid cell adjacent to a small cavitating bubble could also induce an asymmetric
bubble collapse, by which a small jet of liquid would impinge directly into the cell,
rupturing the cell membrane.
116
6.4.1. Mechanistic Studies of Plasma Membrane Sonoporation
It has been demonstrated that cavitation activities generated by ultrasound facilitate
cellular incorporation of macromolecules up to 28 nm in radius through repairable
micron–scale disruptions/holes in the plasma membrane with lifetimes of >1 min, which
is a period similar to the kinetics of membrane repair after mechanical wounding
[Schlicher et al., 2006]. Figure 6–2 shows confocal micrographs of prostate cancer cells
(DU 145) exhibiting uptake of calcein, bovine serum albumin, and dextrans of various
atomic weights into the cytosol after ultrasound exposure (20 acoustic pulses each at 24
kHz, 7 atm, and 0.1 s pulse length at 10% duty cycle. The cells were sonicated in buffer
and then incubated in growth media that contained uptake marker molecules). Scanning
Electron Microscope (SEM) and Transmission Electron Microscope (TEM) images
(figure 6–3) confirm the evidence of structural changes in the plasma membrane of
prostate cancer cells (DU 145) due to ultrasound as well as the evidence of healing or
resealing of holes/disruptions in the plasma membrane.
Figure 6–2. Intracellular delivery induced by ultrasound. Confocal micrographs showing a nonsonicated DU 145 cell exposed to calcein (1) and sonicated cells exhibiting uptake of calcein (2), bovine serum albumin (3) and 150 (4), 500 (5) and 2,000 kDa (6) dextrans. Scale bars are 1µm. After [Schlicher et al., 2006].
117
Figure 6–3. Ultrastructural analysis of ultrasound’s effect on plasma membrane. (a) Evidence of structural changes in plasma membrane due to ultrasound: SEM of (a1) nonsonicated DU 145 cell and (a2, a3) sonicated cell, shown at two levels of magnification, with a region lacking characteristic membrane surface topography and revealing exposed cytoskeleton; and (b, c) evidence of repairing wounds: SEM of (b1) nonsonicated cell and (b2, b3) sonicated cell, and TEM of (c1) nonsonicated cell and (c2, c3) sonicated cell, each with membrane disruption associated with numerous vesicles believed to be of intracellular origin and that may facilitate active membrane resealing. Cells were fixed 2 seconds after sonication using EM–grade glutaraldehyde [Castejon et al. 2001]. Scale bars are 1 µm. After [Schlicher et al., 2006].
Furthermore, confocal fluorescent microscopy conducted after labeling plasma
membrane with red–fluorescent (TRITC) wheat–germ lectin (figure 6–4a) and labeling
intracellular vesicles with green fluorescent FM 1–43 (figure 6–4b) have led to the
conclusion that the DU 145 cells reseal holes/disruptions in the membrane using a native
healing response involving endogenous vesicle–based membrane resealing, which is a
energy–dependent process [Schlicher et al., 2006]. Observation of rat mammary
carcinoma cells (MAT B III) and red blood cells under scanning electron Microscope
after insonification with a focused transducer in the presence of ultrasound contrast
agent also shows ultrasound–induced pores in the cell membrane (figure 6–5).
118
Figure 6–4. Confocal fluorescence and brightfield microscopy of intracellular uptake and wound repair. (a) Labeling plasma membrane with red–fluorescent (TRITC) wheat–germ lectin [Sharon and Lis, 1989] and sonicating in presence of calcein, a green–fluorescent transport marker, yields (a1, a2) nonsonicated DU 145 cell with intact membrane labeling and absence of intracellular calcein, which is expected in a normal, viable cell; (a3, a4) sonicated cell with a region of cell membrane removed and uptake of calcein imaged in cells fixed 2 seconds after sonication; and (a5, a6) sonicated cell with intact membrane labeling and uptake of calcein imaged in cells fixed 5 minutes after sonication; and (b) labeling intracellular vesicles with green fluorescent FM 1–43 [Cochilla et al., 1999] and sonicating in the presence of propidium iodide, a red–fluorescent transport marker that stains nuclei, yields (b1, b2) nonsonicated cell containing intracellular vesicles with intact plasma membrane; and (b3, b4) sonicated cell with partially depleted intracellular vesicles and plasma membrane that was breached, as shown by intracellular labeling with propidium iodide. Scale bars shown are 1 µm. After [Schlicher et al., 2006].
Figure 6–5. Direct observation of ultrasound–induced pores by SEM. (A) Rat mammary carcinoma cells (MAT B III), at a concentration of 1 × 106 cells/ml, and (B) red blood cells, at a concentration of 12.4 × 106 cells/ml, were insonified with a focused transducer (2.25 MHz) at a peak negative pressure of 570 kPa, in the presence of UCA (25 particles/cell for MAT B III and 1.2 particles/cell for red blood cells). Cells were observed with a scanning electron microscope after gold sputtering at a magnification of 10,000. After [Mehier–Humbert et al., 2005 (b)].
119
The kinetics of cell membrane permeabilization induced by the sonoporation is a
transient phenomenon that can be studied using the loading of fluorescent molecules of
various sizes [Hallow et al., 2006; Mehier–Humbert et al., 2005 (a); Mehier–Humber et
al., 2005 (b); Juffermans et al., 2006; Korosoglou et al., 2006; Pan et al., 2005; Schlicher
et al., 2006; Sundaram et al., 2003; Zarnitsyn and Prausnitz et al., 2004; van Wamel et
al., 2004; van Wamel et al., 2006] or by measuring changes in ionic conductivity of the
plasma membrane [Deng et al., 2004]. These studies concluded that estimates of the cell
membrane recovery time range from a few seconds to at most a few minutes, with
differing kinetics for small and larger molecules, and some evidence of separate pore
populations that close at fundamentally different rates. Also, the degree of sonoporation
can be varied through changes in ultrasound conditions such as pulse repetition
frequency and ultrasound intensity. Estimates of pore size in the cell membrane, based
on the physical diameter of uptake marker molecules or compounds, are commonly in
the range of 30–100 nm.
It is also suggested that ultrasound exposure induces up to micron diameter
disruptions/wounds in the plasma membrane, but these wounds have a sieve–like
character that only allows passage of molecules considerably smaller than the diameter
of the disruptions/wounds [Schlicher et al., 2006]. Theoretical modeling, supported
with experimental studies, also predicted that membrane wounds would have a 300–nm
radius initially and then would shrink, with a half–life of 20 to 50 seconds [Zarnitsyn et
al., 2008]. The uptake of molecules was shown to occur predominantly by diffusion and
the increasing levels of uptake with decreasing molecular size (figure 6–6) was explained
primarily by differences in molecular diffusivity and, for the larger molecules,
geometrical hindrance within the sieve–like wound [Zarnitsyn et al., 2008].
Furthermore, as shown in figure 6–6, during the cell membrane recovery, intracellular
transport decreased over time after sonication due to resealing of the membrane (Note:
120
This was characterized by post–sonication (5–10 minutes after sonication) intracellular
concentrations of uptake marker molecules that were added into the solution at known
increments of time from 0–240 seconds in 15 seconds intervals after the cells were
sonicated at time = 0 seconds. Experiments for measuring each post–sonication
intracellular concentration were conducted separately) and this decay (or decrease in
intracellular transport) was more rapid for larger molecules. Furthermore, it is also
suggested that the transport of molecules through such porous wounds occurs largely at
the edge of the wounds [Zarnitsyn et al., 2008].
Figure 6–6. Intracellular concentration as a function of time after sonication for calcein (623 Da, diamond), bovine serum albumin (66 kDa, square), and dextrans (150 kDa, triangle; 500 kDa, circle; 2,000 kDa, star). Cells were either sonicated with an uptake marker compound (at time = 0 s) or in buffer alone, after which a marker compound was added at different times after sonication. Intracellular solute concentration, determined by calibrated flow cytometry [Prausnitz et al., 1993], is normalized relative to its extracellular concentration and reported as the average value among viable cells affected by ultrasound. Inset shows expanded view of dextran uptake at short times. After [Schlicher et al., 2006].
121
Ultrasound contrast agents (e.g., Albunex, Optison, Sonovue, etc.), which consist of
elastic and compressible gas–filled microbubbles and serve as artificial cavitation nuclei,
can be used to promote ultrasound–mediated cavitation [Bao et al., 1997; Greenleaf et
al., 1998; Mehier–Humbert & Guy, 2005; Miller and Quddus, 2001]. Bubble implosion
from the contrast–agent–assisted cavitation in response to ultrasound may form
localized liquid jets that would function like a random array of micro–needles, opening
pathways through the cell membrane [Miller, 2000].
Studies on the microbubble–induced cell deformation and enhanced cell membrane
permeability suggest that vibrating microbubbles, which are in the close vicinity to the
cell, poke the cell membrane, forming hydrophobic or hydrophilic pores (as illustrated in
figure 6–7b) [Wamel et al., 2004; Wamel et al., 2006]. In such cases, studies indicated
that non–inertial cavitation–induced mechanisms are strong enough to cause the
transient pore formation in the membrane and this permeabilization lasted for a short
period without affecting cells viability. It is also suggested that pushing and pulling by
microbubble in the close vicinity to the cell, which correlates to the rarefraction and
compression phase of ultrasound, are suggested to be major contributing mechanism in
the formation of pores [Wamel et al., 2006]. It may not be an absolute requirement to
induce transient cavitation to achieve sonoporation in such cases. Furthermore, it was
demonstrated that microbubbles are highly effective at concentrating and focusing low–
intensity long–wavelength ultrasound so that it has profound effects at the micron or
submicron level [Marmottant and Hilgenfeldt, 2003]. Stable and gentle oscillations of
the microbubble without causing the bubble collapse can generate microstreamings and
shear forces that are able to induce disruption of lipid bilayers. These studies indicate
that close proximity between microbubbles and cell membranes facilitates sonoporation,
suggesting that bringing microbubbles nearer to cells using acoustic radiation pressure
and/or by ligand–targeting could improve the efficiency of membrane permeabilization
and/or decrease the required acoustic power and the dependence on transient cavitation.
122
Figure 6–7. (A) Schematic diagram illustrating the effects of acoustic fields of identical frequency but differing intensity on microbubble (MCB) behavior. Low–intensity ultrasound induces oscillation of pre–existing MCB, with a gradual increase in its diameter until a resonant diameter is reached, when stable oscillation occurs (filled circles). (Note: MCB initially grows in size when being insonified primarily because the surface area for dissolved gas to enter the MCB during the expansion (rarefaction) phase is greater than that available for gas to diffuse out of the MCB during the compression phase, a process known as rectified diffusion). At higher intensities, the MCB grows rapidly for a few cycles. Very soon, however, the inertial energy of the fluid surrounding the MCB during the compression half cycle becomes so great that it cannot reverse direction when the next rarefaction half cycle arrives. It continues to rush in and forcibly collapses the MCB, generating highly localized extremes of temperature and pressure. This generates shock waves, free radical production and local heat. After [Newman and Bettinger, 2007]. (B) Proposed model of the oscillating MCB–enforced pore formation in the cell membrane. The pushing and pulling behavior of the microbubble causes rupture of the cell membrane creating a hydrophilic pore allowing trans–membrane flux of fluid and macromolecules. After [Wamel et al., 2006].
123
6.4.2. Gene Therapy Prospects
Sonoporation is used for cell transfection in vitro [Bao et al., 1997; Greenleaf et al.,
1998]. The method offers flexibility and improves transfection efficiency and cell
viability in some cell lines [Pepe et al., 2004]. Although ultrasound covers a broad range
of frequencies and waveforms, continuous sinusoidal or pulsed ultrasound probes at
megahertz frequencies are usually used for sonoporation and lower frequencies (e.g., 20
kHz) are mainly used for cell lysis and disruption [Mehier–Humbert and Guy, 2005].
Piezoelectric–based transducers (Figure 6–8 and 6–9), lithotriper [Bao et al., 1998;
Miller et al., 1998], or clinical imaging system can be used to generate ultrasound [Miller
and Quddus, 2001] for sonoporation of large population of cell suspensions.
Figure 6–8. A schematic representation of the transducer setup used for ultrasound
application to cell suspension. The transducers in this setup were designed by sandwiching
ceramic crystals between two metal resonators of appropriate lengths. A signal generator
along with an amplifier was used to drive the transducers. The electric power applied to the
transducer was measured using a sampling wattmeter. Transducers can be calibrated using
laser interferometry and hydrophone measurements. The transducers were directly immersed
in the cell suspension as shown and the tip of the transducer was located at the center of the
well. After [Sundaram et al., 2003].
124
Figure 6–9. An apparatus used to expose cells with ultrasound. A 35–mm–diameter
1.0–MHz air–backed transducer was placed 2 mm below each of two wells of a six–well
culture plate. The experiment was conducted within a distilled and degassed water bath
maintained at 37 degree Celsius. Water completed the acoustic coupling with the
bottom of the culture plate. After [Greenleat et al., 1998].
Studies also show that the presence of ultrasound contrast agents in the medium
(i.e., the cell suspension) during the sonication significantly enhances the ultrasound–
mediated gene transfection [Greenleaf et al., 1998; Guzmán et al., 2001]. Furthermore,
it was also found that the degree of cell permeability and the level of gene transfection
increase (in a somewhat linear fashion) with ultrasound pressure, exposure time, and
numbers of pulses in the expense of cell viability above the threshold pressure (Figure
6–10) [Greenleaf at al., 1998; Guzmán et al., 2001].
125
Figure 6–10. (A) Transfection rate of living cells after ultrasound exposure as function of average peak pressure of the 20–s burst of 1.0–MHz ultrasound. The DNA concentration was 40 µg/mL and the Albunex concentration was 50 × 106 bubbles/mL prior to exposure. Continuous line is best linear fit. (B) Transfection rate of living cells after exposure plotted against Albunex concentration at the time of exposure. Exposure time is 20 seconds at the indicated average peak pressure. Continuous lines are hand drawn. (C) Transfection rate of live cells after repeated 1–s exposures to ultrasound at the indicated average peak pressure. Prior to each exposure, 50 × 106 microbubbles were added to the 1 mL of media in each well. (D) Transfection rate after 20–s exposure to 1.0–MHz ultrasound at indicated average peak pressure with a microbubble concentration of 50 × 106 bubbles/mL prior to exposure. After [Greenleaf et al., 1998].
Although early transfection results using sonoporation have achieved 10– to 30–fold
increases in transfection efficiency in vitro, technical refinements on important
parameters such as ultrasound intensity, pulse repetition frequency, duration of
exposure (i.e., ultrasound pulse duration), microbubble concentrations, and etc. have
shown enhancements of up to several thousand fold in vitro [Akowuah et al., 2005;
Fischer et al., 2006; Guo et al., 2006; Liang et al., 2004; Mehier–Humbert et al., 2005
(a); Michel et al., 2004; Rahim et al., 2006 (a); Rahim et al., 2006 (b); Zhou et al.,
2005]. Not only are these findings sufficient to encourage studies of ultrasound
enhanced gene transfer in vivo, but also render promising prospects for ultrasound
enhanced gene therapy in the future.
126
References
Akowuah, E. F., Gray, C., Lawrie, A., Sheridan, P. J., Su, C. –H., Bettinger, T., Brisken, A. F.,
Gunn, J., Crossman, D. C., Francis, S. E., Baker, A. H., and Newman, C. M., 2005.
Ultrasound–Mediated Delivery of TIMP–3 Plasmid DNA into Saphenous Vein Leads to
Increased Lumen Size in a Porcine Interposition Graft Model. Gene Therapy. 12, 1154–1157.
Allen, J. S., Kruse, D. E., Dayton, P. A., and Ferrara, K. W., 2003. Effect of Coupled Oscillations
on Microbubble Behavior. The Journal of the Acoustical Society of America. 114 (3), 1678–
1690.
Apfel, R. E., 1982. Acoustic Cavitation: A Possible Consquence of Biomedical Uses of Ultrasound.
British Journal of Cancer. 45 (Suppl. V), 140–146.
Apfel, R. E., 1986. Possibility of Microcavitation from Diagnostic Ultrasound. IEEE Transactions on Ultrasonic, Ferroelectrics, and Frequency Control. 33 (2), 139–142.
Apfel R. E., and Holland C. K., 1991. Gauging the Likelihood of Cavitation from Short–Pulse,
Low–Duty Cycle Diagnostic Ultrasound. Ultrasound in Medicine and Biology. 17 (2), 179–
185.
Bao, S., Thrall, B. D., and Miller, D. L., 1997. Transfection of a Reporter Plasmid into Cultured
Cells by Sonoporation in vitro. Ultrasound in Medicine and Biology. 23 (6), 953–959.
Bao, S., Thrall, B. D., Gies, R. A., and Miller, D. L., 1998. In Vivo Transfection of Melanoma Cells
by Lithotripter Shock Waves. Cancer Research. 58, 219–221.
Barnett, S. B., Ter Haar, G. R., Ziskin, M. C., Nyborg, W. L., Maeda, K., and Bang, J., 1994.
Current Status of Research on Biophysical Effects of Ultrasound. Ultrasound in Medicine and Biology. 20 (3), 205–218.
Barnett, S. B., Rott, H. –D., Ter Haar, G. R., Ziskin, M. C., and Maeda, K., 1997. The Sensitivity of
Biological Tissue to Ultrasound. Ultrasound in Medicine and Biology. 23 (6), 805–812.
Brennen, C. E., 1995. Cavitation and Bubble Dynamics. Oxford University Press, New York.
Dalecki, D., 2004. Mechanical Bioeffects of Ultrasound. Annual Review of Biomedical
Engineering. 6, 229–248.
Deng, C. X., Sieling, F., Pan, H., and Cui, J., 2004. Ultrasound–Induced Cell Membrane Porosity.
Ultrasound in Medicine and Biology. 30 (4), 519–526.
Elder, S. A., 1958. Cavitation Microstreaming. The Journal of the Acoustical Society of America.
31 (1), 54–64.
127
Fischer, A. J., Stanke, J. J., Omar, G., Askwith, C. C., and Burry, R. W., 2006. Ultrasound–
Mediated Gene Transfer into Neuronal Cells. Journal of Biotechnology. 122, 393–411.
Flynn, H. G., 1964. Physics of Acoustic Cavitation in Liquids. Physical Acoustics (editor: Mason,
W. P.), vol. I, part B. Academic Press, New York.
Flynn, H. G., 1982. Generation of Transient Cavities in Liquids by Microsecond Pulses of
Ultrasound. The Journal of the Acoustical Society of America. 72 (6), 1926–1932.
Fowlkes, J. B., and Crum, L. A., 1988. Cavitation Threshold Measurements for Microsecond
Length Pulses of Ultrasound. The Journal of the Acoustical Society of America. 83 (6), 2190–
2201.
Greenleaf, W. J., Bolander, M. E., Sarkar, G., Goldring, M. B., and Greenleaf, J. F., 1998.
Ultrasound in Medicine and Biology. 24 (4), 587–595.
Guo, D. –P., Li, X. –Y., Sun, P., Tang, Y. –B., Chen, X. –Y., Chen, Q., Fan, L. –M., Zang, B., Shao,
L. –Z., Li, X. –R., 2006. Ultrasound–Targeted Microbubble Destruction Improves the Low
Density Lipoprotein Receptor Gene Expression in HepG2 Cells. Biochemical and Biophysical Research Communications. 343, 470–474.
Guzman, H. R., Nguyen, D. X., Khan, S., and Prausnitz, M. R., 2001. Ultrasound–Mediated
Disruption of Cell Membrane. II. Heterogeneous Effects on Cells. The Journal of the Acoustical Society of America. 110 (1), 597–606.
Guzman, H. R., McNamara, A. J., Nguyen, D. X., and Prausnitz, M. R., 2003. Bioeffects Caused
by Changes in Acoustic Cavitation Bubble Density and Cell Concentration: A Unified
Explanation Based on Cell–to–Bubble ratio and Blast Radius. Ultrasound in Medicine and Biology. 29 (8), 1211–1222.
Hallow, D. M., Mahajan, A. D., McCutchen, T. E., and Prausnitz, M. R., 2006. Measurement and
Correlation of Acoustic Cavitation with Cellular Bioeffects. Ultrasound in Medicine and Biology. 32 (7), 1111–1122.
Juffermans, L. J. M., Dijkmans, P. A., Musters, R. J. P., Visser, C. A., and Kamp, O., 2006.
Transient Permeabilization of Cell Membranes by Ultrasound–Exposed Microbubbles is
related to formation of Hydrogen Peroxide. American Journal of Physiology: Heart and Circulatory Physiology. 291, H1595–H1601.
Korosoglou, G., Hardt, S. E., Bekeredjian, R., Jenne, J., Konstantin, M., Hagenmuller, M., Katus,
H. A., and Kuecherer, H., 2006. Ultrasound Exposure Can Increase the Membrane
Permeability of Human Neutrophil Granulocytes Containing Microbubbles without Causing
Complete Cell Destruction. Ultrasound in Medicine and Biology. 32 (2), 297–303.
128
Kossoff, G., 2000. Basic Physics and Imaging Characteristics of Ultrasound. World Journal of Surgery. 24, 134–142.
Kvikliene, A., Jurkonis, R., Ressner, M., Hoff, L., Jansson, T., Janerot–Sjöbert, B., Lukosevicius,
A., and Ask, P., 2004. Modelling of Nonlinear Effects and the Response of Ultrasound
Contrast Micro Bubbles: Simulation and Experiment. Ultrasonics. 42, 301–307.
Liang, H. –D., Lu, Q. L., Xue, S. –A., Halliwell, M., Kodama, T., Cosgrove, D. O., Strauss, H. J.,
Partridge, T. A., and Blomley, M. J. K., 2004. Optimisation of Ultrasound–Mediated Gene
Transfer (Sonoporation) in Skeletal Muscle Cells. Ultrasound in Medicine and Biology. 30
(11), 1523–1529.
Marmottant, P., and Hilgenfeldt, S., 2003. Controlled Vesicle Deformation and Lysis by Single
Oscillating Bubbles. Nature. 423 (6936), 153–156.
May, D. J., Allen, J. S., and Ferrara, K. W., 2002. Dynamics of Fragmentation of Thick–Shelled
Microbubbles. IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control. 49
(10), 1400–1410.
Mehier–Humbert, S., and Guy, R. H., 2005. Physical Methods for Gene Transfer: Improving the
Kinetics of Gene Delivery into Cells. Advanced Drug Delivery Reviews. 57, 733–753.
Mehier–Humbert, S., Bettinger, T., Yan, F., and Guy, R. H., 2005 (a). Ultrasound–Mediated
Gene Delivery: Kinetics of Plasmid Internalization and Gene Expression. Journal of Controlled Release. 104, 203–211.
Mehier–Humbert, S., Bettinger, T., Yan, F., and Guy, R. H., 2005 (b). Plasma Membrane
Poration Induced by Ultrasound Exposure: Implication for Drug Delivery. Journal of Controlled Release. 104, 213–222.
Michel, M. S., Erben, P., Trojan, L., Schaaf, A., Kiknavelidze, K., Knoll, T., and Alken, P., 2004.
Acoustic Energy: A New Transfection Method for Cancer of the Prostate, Cancer of the
Bladder and Benign Kidney Cells. Anticancer Research. 24 (4), 2303–2308.
Miller, D. L., Williams, A. R., Morris, J. E., and Chrisler, W. B., 1998. Sonoporation of
Erythrocytes by Lithotripter Shockwaves in vitro. Ultrasonics. 36, 947–952.
Miller, M. W., 2000. Gene Transfection and Drug Delivery. Ultrasound in Medicine and Biology.
26 (Suppl. 1), S59–S62.
Miller, D. L., and Quddus, J., 2001. Lysis and Sonoporation of Epidermoid and Phagocytic
Monolayer Cells by Diagnostic Ultrasound Activation of Contrast Agent Gas Bodies.
Ultrasound in Medicine and Biology. 27 (8), 1107–1113.
129
Mitragotri, S., 2005. Healing Sound: The Use of Ultrasound in Drug Delivery and Other
Therapeutic Applications. Nature Reviews Drug Discovery. 4, 255–260.
Newman, C. M. H., and Bettinger, T., 2007. Gene therapy Progress and Prospects: Ultrasound for
Gene Transfer. Gene Therapy. 14, 465–475.
Nyborg, W. L., 1982. Ultrasonic Microstreaming and Related Phenomena. British Journal of Cancer. 45 (Suppl. V),156–160.
Nyborg, W. L., 2001. Biological Effects of Ultrasound: Development of Safety Guidelines. Part II:
General Review. Ultrasound in Medicine and Biology. 27 (3), 301–333.
Pan, H., Zhou, Y., Izadnegahdar, O., Cui, J., and Deng, C. X., 2005. Study of Sonoporation
Dynamics Affected by Ultrasound Duty Cycle. Ultrasound in Medicine and Biology. 31 (6),
849–856.
Pepe, J., Rincon, M., and Wu, J., 2004. Experimental Comparison of Sonoporation and
Electroporation in Cell Transfection Applications. Acoustics Research Letters Online. 5 (2),
62–67.
Pitt, W. G., Husseini, G. A., and Staples, B. J., 2004. Ultrasonic Drug Delivery–A General
Review. Expert Opinion on Drug Delivery. 1 (1), 37–56.
Prausnitz, M. R., Lau, B. S., Milano, C. D., Conner, S., Langer, R., and Weaver, J. C., 1993. A
Quantitative Study of Electroporation Showing a Plateau in Net Molecular Transport.
Biophysical Journal. 65, 414–422.
Rahim, A. A., Taylor, S. L., Bush, N. L., ter Haar, G. R., Bamber, J. C., and Porter, C. D., 2006 (a) (a). Physical Parameters Affecting Ultrasound/Microbubble–Mediated Gene Delivery
Efficiency in vitro. Ultrasound in Medicine and Biology. 32 (8), 1269–1279.
Rahim, A. A., Taylor, S. L., Bush, N. L., ter Haar, G. R., Bamber, J. C., and Porter, C. D., 2006 (b). Spatial and Acoustic Pressure Dependence of Microbubble–Mediated Gene Delivery
Targeted Using Focused Ultrasound. The Journal of Gene Medicine. 8, 1347–1357.
Sboros, V., Moran, C. M., Pye, S. D., and McDicken, W. N., 2003. The Behavior of Individual
Contrast Agent Microbubbles. Ultrasound in Medicine and Biology. 29 (5), 687–694.
Schlicher, R. K., Radhakrishna, H., Tolentino, T. P., Apkarian, R. P., Zarnitsyn, V., and Prausnitz,
M. R., 2006. Mechanism of Intracellular Delivery by Acoustic Cavitation. Ultrasound in Medicine and Biology. 32 (6), 915–924.
Smith, N. B., 2007. Perspectives on Transdermal Ultrasound Mediated Drug Delivery.
International Journal of Nanomedicine. 2 (4), 585–594.
130
Soetanto, K., and Chan, M., 2000. Study on the Lifetime and Attenuation Properties of
Microbubbles Coated with Carboxylic Acid Salts. Ultrasonics. 38, 969–977.
Sundaram, J., Mellein, B. R., and Mitragotri, S., 2003. An Experimental and Theoretical Analysis
of Ultrasound–Induced Permeabilization of Cell Membranes. Biophysical Journal. 84, 3087–
3101.
Shutilov, V. A., 1988. Fundamental Physics of Ultrasound (translated by Alferieff, M. E.). 1st
Edition, CRC Press, New York.
Ter Haar, G. R., Daniels, S., and Morton, K., 1986. Evidence for Acoustic Cavitation In Vivo:
Theresholds for Bubble Formation with 0.75–MHz Continuous Wave and Pulsed Beams.
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. 33 (2), 162–164.
Urick, R. J., 1983. Principles of Underwater Sound. 3rd Edition, McGraw–Hill Book Company,
San Francisco.
Van Wamel, A., Bouakaz, A., Versluis, M., and de Jong, N., 2004. Micromanipulation of
Endothelial Cells: Ultrasound–Microbubble–Cell Interaction. Ultraound in Medicine and Biology. 30 (9), 1255–1258.
Van Wamel, A., Kooiman, K., Harteveld, M., Emmer, M., ten Cate F. J., Versluis, M., de Jong, N.,
2006. Vibrating Microbubbles Poking Individual Cells: Drug Transfer into Cells via
Sonoporation. Journal of Controlled Release. 112, 149–155.
Young, F. R., 1989. Cavitation. McGraw–Hill Companies, New York.
Zarnitsyn, V., and Prausnitz, M. R., 2004. Physical Parameters Influencing Optimization of
Ultrasound – Mediated DNA Transfection. Ultrasound in Medicine and Biology. 30 (4), 527–
538.
Zarnitsyn, V., Rostad, C. A., and Prausnitz, M. R., 2008. Modeling Transmembrane Transport
through Cell Membrane Wounds Created by Acoustic Cavitation. Biophysical Journal. 95,
4124–4138.
Zhou, Q. –H., Miller, D. L., Carlisle, R. C., Seymour, L. W., and Oupicky, D., 2005. Ultrasound–
Enhanced Transfection Activity of HPMA–Stabilized DNA Polyplexes with Prolonged Plasma
Circulation. Journal of Controlled Release. 106, 416–427.
Ziskin, M. C., and Barnett, S. B., 2001. Ultrasound and the Developing Central Nervous System.
Ultrasound in Medicine and Biology. 27 (7), 875–876.
131
Chapter 7
DEVICE DESIGNS, FABRICATIONS, AND CHARACTERIZATIONS
This chapter describes chip architectures, designs, fabrications, and characterizations
of Integrated Microelectrode Array (IMA) chips and Ultrasonic Micro–Transducer Array
(UMTA) biochips. IMA chips employ planar microelectrode array architecture and are
used to electrically characterize (by means of electrical impedance) cellular properties,
especially properties of cell membrane, at cellular and single–cell level. Ultrasonic
clear images with an opaque background) were then defined by wet–chemical etching of
exposed chrome followed by photoresist stripping and cleaning. These photomasks are
repeatedly used hundreds of time in the IMA chip fabrication without losing the quality
of transferred patterns.
7.1.2.2. Chip Fabrication
Before any chip fabrication process, the quartz wafer was cleaned with NanoStripTM
solution at 90 ˚C for 10 min, rinsed with DI water thoroughly, and dried using N2 spray
gun. The chip fabrication processes are as follows: Firstly, microelectrodes and
interconnections are created on the wafer. In order to form these structures,
photoresists LOR5A/SPR3012 were spun on the cleaned wafer, UV–exposed (i–line 356
nm) under the photomask consisting of the defined patterns (Karl Suss MA/BA6 contact
photolithography aligner and exposure system was used), and developed. After
removing the residual resists in the exposed regions by an oxygen descum process, a Ti
(125 Å)/Au (4000 Å) twin layer was deposited onto the wafer at a rate of 1.0 Å/s by
electron–beam evaporations (Semicore e–gun evaporator was used. E–gun evaporator is
preferred over thermal evaporator as the way the e–gun evaporators are constructed
usually produces stable deposition rate and pure thin films). The Ti layer was used to
enhance the adhesion between the Au and the quartz substrate. Microelectrodes and
interconnections were then defined by a photoresist lift–off process.
Secondly, interconnections were over–coated with a conformal and electrically–
insulating layer. To create such over–coating, a layer of SiO2 (~1 µm thick) was
deposited by Plasma Enhanced Chemical Vapor Deposition (PECVD). Silane (SiH4) and
Nitrous Oxide (N2O) gas mixture was used. The resulting SiO2 layer was subsequently
annealed/passivated in the furnace at ~250 ˚C for at least 2 hours under the constant
flow of ultra–pure oxygen gas. After the annealing/passivation process, photoresist
135
SPR220 was spun on the wafer, exposed using a second photomask, and developed in
order to form a photoresist etch–mask that covers and protects everywhere except for
the regions above the electrodes. Cell–sized circular openings (of 20–µm, 25–µm, 30–
µm, and 250–µm diameter), known as via holes over sensing microelectrodes, openings
for two large counter electrodes and contact pads through the SiO2 layer were created by
Reactive Ion Etching (RIE) of SiO2 followed by the photomask removal in resist stripper
and subsequent oxygen plasma cleaning. Carbon Tetrafluoride (CF4) and Oxygen (O2)
gas mixture was used to form fluorine–based chemistries in RIE chamber (PlasmaTherm
720) in order to etch SiO2.
Thirdly, exposed electrodes were coated with a thin Au layer. To achieve such
coatings, photoresists LOR5A/SPR3012 were spun on the wafer, exposed using a third
photomask, and developed in order to form opened patterns on top of exposed
electrodes. After the residual resists in the exposed regions were removed by oxygen
descum process, an Au layer (~1000 Å thick) was deposited onto the wafer at a rate of
0.8 Å/s by electron–beam evaporation. The Au coatings on top of the electrodes (or the
second Au layer) were then defined by photoresist lift–off (this Au coatings on top of the
exposed electrodes provide a micro–rough, amorphous Au surface that was free of
plasma–etch–induced surface impurities and defects and was found to lower the
baseline impedance of Au microelectrodes [Thein et al., 2010; figure 7–5]). The chip
fabrication process is summarized in figure 7–3.
136
Figure 7–3. Summary of fabrication process for Integrated Microelectrode Array (IMA) chip. Top–down microfabrication techniques such as contact photolithography, Physical Vapor Metal Deposition (PVD), lift–off process, Plasma Enhanced Chemical Vapor Deposition (PECVD), and Reactive Ion Etching (RIE) are employed.
7.1.2.3. Dicing
Photoresist SP1827 (a 2.7–µm–thick) was spun on the wafer (front side) in order to
form a protective layer that limits scratching and contamination during dicing process. A
disco saw was used to cut the wafers in order to obtain 12.5 mm × 12.5 mm IMA chips.
After the dicing, photoresist layer was removed using standard resist stripper and
individual chips were cleaned with Nano–StripTM solution at room temperature. Figure
7–4 shows the fabricated IMA chip after being diced.
137
Figure 7–4. Integrated Microelectrode Array (IMA) Chip. (A) Size comparison with a penny.
Each IMA chip has a dimension of 12.5 mm × 12.5 mm. (B) The IMA chip with media reservoir
well mounted. The polypropylene (PP) well is mounted on the IMA chip using biocompatible
glue such as Sylgard®. (C) Bright field image of microelectrode arrays (20–µm, 25–µm, 30–µm,
and 250–µm in diameter). Bright field images of microelectrode: (D) 20–µm, (E) 25–µm, (F)
30–µm, and (G) 250–µm in diameter.
138
7.1.3. Characterization of IMA Chip
Two properties of microelectrode are characterized: 1), surface roughness, which is
an important property for self–assembled–monolayer–based biofunctionalization for
single–cell immobilization (see section 8.2.1) 2), baseline impedance spectrum, which
is an important property used in transductions of electrical signals from IMA–based
sensors in monitoring cellular changes.
First, TappingModeTM Atomic Force Microscopy (AFM) with OTESPA probe tip was
used to characterize the surface roughness of the planar Au microelectrodes. Not only
can AFM with OTESPA probe tip provide the surface topology of the Au microelectrode,
but it can also perform Electric Force Microscopy (EFM) (Note: EFM measurements can
also provide information on whether Au microelectrodes are fully exposed through the
via holes and no residual SiO2 is left on the surface of the electrode after RIE).
In brief, TappingModeTM AFM, a patented technique by Veeco Instruments, is the
most commonly used of all AFM modes that maps surface topography by lightly tapping
the surface with an oscillating AFM probe tip. The cantilever’s oscillation amplitude
changes with sample surface topography, and the topography image is obtained by
monitoring these changes and closing the z feedback loop to minimize them.
TappingModeTM advantage is that it eliminates shear forces usually present in the
traditional contact mode.
However, EFM is a secondary imaging mode derived from TappingModeTM that
measures electric field gradient distribution above the sample surface. EFM is
performed through LiftModeTM, where two–pass/scan technique that separately
measures topography and another selected property (magnetic force, electric force, etc.)
using topographic information to track the probe tip at a constant distance above the
surface. In EFM, a voltage may be applied between the probe tip and the sample. The
cantilever's resonance frequency and phase change with the strength of the electric field
gradient are used to construct the EFM images.
139 �
The AFM scan result (using an OTESPA probe tip) across 30–µm diameter Au
microelectrode is shown in figure 7–5 (the AFM measurement showed that Root Mean
Square (RMS) surface roughness of Au microelectrode is approximately 112 nm).
Figure 7–5. AFM scan image of 30–um diameter Au microelectrode. Root Mean Square (RMS)surface roughness of Au microelectrode is ~112 nm (data shown in green). (Micro–roughness increases the effective surface area that in turn decreases the baseline impedance. However, if the surface is very rough, self–assembled monolayer–based biofunctionalized interface formed on the Au microelectrode for cell adhesion/immobilization will not be homogeneous. Therefore, somewhere between these two extremes, the optimal range of surface roughness exists.)
Second, baseline impedance spectrum for each microelectrode dimension is also
measured in tissue culture media using a Phase sensitive Lock–in amplifier. The
baseline impedance depends on the type of tissue culture media, the contents in the
tissue culture media, and the diameter of the Au microelectrode (or the surface area of
the Au microelectrode). The detailed of baseline impedance spectroscopy including
µm × 50 µm, 75 µm × 75 µm, and 100 µm × 100 µm square pillars) that can extend or
shorten its length along their axial axes (thickness mode oscillations) when the electric
field is applied across the axial direction. These micro–transducer pillars are laid down
on a thin film of silver epoxy coated on the glass substrate. The silver epoxy layer serves
as a common bottom electrode for driving micro–transducers. Epoxy resin is filled in
the space between individual micro–transducers in order to dampen lateral–mode
vibrations and prevent cross–talk between individual micro–transducers [Smith, 1986].
Top surfaces of the micro–transducers are coated with thin Au film, which serve as top
electrodes. These top Au electrodes are individually connected to their respective
bonding/contact pads through Au interconnections. The micro–transducers can be
driven individually by connecting a RF function/signal generator to the top and bottom
electrode in order to produce thickness–mode oscillations. Ultrasounds can be
generated if the micro–transducers are driven by the RF function generator with a
sinusoidal wave of frequencies above 20 kHz.
The micro–transducers are also encapsulated under Parylene C over–coating.
Parylene–C films are pinhole free and posses a relatively high chemical resistance to
most harsh chemicals [Feili et al., 2006]. Furthermore, it is biocompatible [Schmidt et
al., 1988] and also a very good insulator [Loeb et al., 1977]. Parylene–C encapsulation
serves as: (i) an acoustic impedance matching layer between the micro–transducer and
tissue culture media [Hadimioglu and Khuri–Yakub, 1990] (Note: The characteristic
acoustic impedance of Parylene C is ~2.7 MPa.s/m3, which is closer to that of water (~1.5
MPa.s/m3) than those of PMN–PT materials.), (ii) an insulating barrier between
individual top Au electrodes and between Au interconnections, (iii) an insulating layer
143
between tissue culture media and top Au electrodes as well as their interconnections, and
(iv) a physical protection layer for micro–transducer arrays from harsh chemicals as well
as mechanical stress. The surface of the chip (or the surface of Parylene–C coating) also
presents thin Au cell trapping pads, which are located directly above each individual
micro–transducers (Au is biocompatible and cells tend to attach more preferably on Au
surface [Schreiber, 2004]). The bonding pads located at the chip perimeter can be
accessed through via holes. Wires are soldiered onto bonding pads and the common
bottom electrode using silver epoxy and they are sealed under epoxy resin in order to
prevent electrical shorting through the tissue culture media during the experiment. The
whole UMTA biochip can be submerged in tissue culture media and cells can be seeded
and grown on the surface of the UMTA biochip under appropriate cell culture conditions.
The simplified cross–sectional schematic of the UMTA biochip is shown in figure 7–6.
Figure 7–6. Simplified cross–sectional schematic of ultrasonic micro–transducer array
(UMTA) biochip. Micro–transducers can be driven individually to generate ultrasounds. Wires
are soldiered onto the bonding pads and the common bottom electrode using silver epoxy and
they are sealed under epoxy resin in order to prevent electrical shorting during the experiment.
The whole UMTA biochip can be submerged in tissue culture media and cells can be seeded and
grown on the surface of the UMTA biochip under appropriate cell culture conditions.
144
7.2.3. Process Development for UMTA Chip Fabrication
The novel design and architecture of UMTA biochip posed many scientific and
technology challenges in the fabrication. Traditional methods for fabricating small–size
transducer include dicing, molding, and chemical/mechanical polishing etc. [Safari,
1994; Wang et al., 1999 (a)]. The ultimate goal of UMTA biochips is to conduct study on
individual cells immobilized on the cell–trapping pads. This requires the cross sectional
dimension of micro–transducers pillars as small as 10 µm wide. Furthermore, PMN–PT
crystals have finer gain size compared to its counterparts. These requirements and
properties make the designed transducer pillars extremely fragile for traditional dicing
techniques, which can easily cause pillar breakage and chippings. For single crystal
piezoelectric materials, both wet etching and plasma dry etching could be used to
fabricate transducers. Wet etching (using HF and HCl) [Wang et al., 2000] is usually a
cheaper process. However, the most important requirement for ultrasound transducers
in UMTA biochip is the aspect ratio that is greater than 2:1. (The aspect ratio needed for
thickness mode vibrations must be larger than 2:1. This is required so that the
transducer resonates in pure thickness modes, yielding the maximum electromechanical
coupling. High aspect ratio also dampens lateral vibration modes. Both effects are
needed to achieve broad bandwidth and high sensitivity). Therefore, wet chemical
etching of PMN–PT is not suitable due to isotropic nature of etching mechanism.
Therefore, plasma dry etching, which can yield anisotropic etching mechanism and
imposes low mechanical stresses in making fine microstructures, is chosen to fabricate
the micro–transducer pillars. However, a number of major challenges arises in the
fabrication of UMTA biochip, which includes the choice of plasma dry etching technique
and associated preparation processes such as etch–mask deposition and lithography.
145
7.2.3.1. Inductively–Coupled Plasma Dry Etching of PMN–PT Crystals
Plasma dry etching has been shown to be an effective method for machining high–
aspect–ratio anisotropic features in many materials. It is also a relatively gentle
approach compared to mechanical micro–machining methods. Deep Reactive Ion
Etching (DRIE) of PZT ceramic has been investigated by a number of researchers and, in
these studies, either photoresist or metal etch–masks have been used to define the
etching patterns. The chemistries of etchants being used in these studies include
chlorine– and fluorine–based chemistries such as SF6, SF6/N2/Ar, HBr/Ar, CF4, Cl2/CF4,
and etc. [Bale and Palmer, 2001; Chung et al., 2002; Jung and Lee, 2001; Wang et al.,
1999 (b)]. Furthermore, etching depths over 100 microns has been achieved in these
studies [Bale and Palmer, 2001]. Unfortunately, for most of the etch results, the sidewall
angles of the etch profile are less than 80˚ (figure 7–7). In order to achieve pure
thickness vibration modes and dampen lateral vibration modes, the micro–transducers
must posses both high aspect–ratio and nearly vertical sidewall profile (i.e., nearly 90˚
sidewall angles). Traditional RIE systems cannot produce nearly vertical sidewall
profiles in the pattern transfer of high aspect–ratio structures into the wafer.
Figure 7–7. SEM images of array structures produced by a 3–hour RIE with 10% argon in SF6 showing (a) the array and (b) sidewall angle variation. The structure height is 26 µm including the remaining Nickel etch–mask. The dotted white line in (b) shows the nickel/PZT boundary. The RF power was 200 W, process pressure 2 mTorr, and the total gas flow rate was 20 sccm. After [Bale and Palmer, 2001].
146
Because of limitations of traditional RIE systems, new plasma etching techniques
have been developed, such as Reactive Ion Beam Etching (RIBE), Magnetically
Enhanced Reactive Ion Etching (MERIE), Electron Cyclotron Resonance (ECR), and
Inductively Coupled Plasma (ICP). Due to the ability to offer higher ion density and
independent control of ion energy and ion density, ECR and ICP systems can produce
nearly vertical sidewall profiles for high aspect–ratio structures. ICP and ECR systems
operate as simple RIE reactors if the respective high–density plasma source is not
applied. The major difference between ECR and ICP is the way high–density plasma is
generated. In ECR chamber, microwaves are introduced into the chamber resonance
cavity through a wave–guide producing a dynamic electric field to dissociate the gases
and generate the electrons and ions of the plasma that diffuses to wafer surface. Because
of this unique design of the tool, high–density plasmas can be formed at low gas pressure
with low plasma potentials and ion energies. In ICP systems, plasma is formed in a
dielectric vessel encircled by an inductive coil into which RF power is applied. A strong
magnetic field is induced in the center of the chamber, which generates high–intensity
plasma due to the circular region of the electric field that exists concentric to the coil. At
low pressure, the plasma diffuses from the generation region and drifts to the substrate
at relatively low energy [Lee at al., 1998]. Thus, ICP is expected to produce low damage
while achieving high etch rate. Anisotropic profile is obtained by superimposing a
second RF bias on the sample to independently control the ion energy. Therefore, in
chemical and mechanical planarization/polishing, plasma dry etching (inductively–
coupled & conventional RIE), wet chemical etching, and wire bonding.
7.2.4.1. PMN–PT Wafers
The PMN–PT single crystal plates were prepared as wafers and lapped on both sides
and polished on one, then embedded in a 2.5” diameter PZT wafer to avoid the
difficulties in microfabrication process for small samples. Figure 7–13 shows 1.5 cm
diameter PMN–PT crystal embedded in a 2.5” PZT ceramic wafer.
Figure 7–13. PMN–PT (Lead Magnesium Niobate–Lead Titanate) single crystal piezoelectric
disc embedded in a 2.5” PZT ceramic wafer [Courtesy of TRS Ceramics Inc].
155
7.2.4.2. Photomask Fabrication
Three dark–field photomasks (one for top Au electrodes and interconnection, one for
Al etch–mask patterning for via holes, and one for cell–trapping pads) were fabricated.
Photomask fabrication procedures are the same as those described in section 7.1.2.1.
7.2.4.3. Biochip Fabrication
7.2.4.3.1. Micro–Transducer Fabrication
Fabrication of UMTA biochip starts from e–beam deposition of thin Ni seed layer
(~200 nm) film on the polished PNM–PT wafer. To define micro–transducer areas,
positive photoresist (PR) SPR220 was spun on the wafer, exposed using Heidelberg
DWL66 laser lithography tool (doubled–exposure laser lithography), and developed. Ni
etch–mask was grown inside the PR mold by HF pulsed electroplating. After desired
thickness (~12.5 um) is reached, the PR mold was removed by bathing the wafer in
standard resist stripper (EKC 830). The Ni etch–mask grown inside the PR mold and Ni
etch–mask after removal of PR mold are shown in figure 7–14a and 7–14b respectively.
The Ni etch–mask protects the PMN–PT crystal underneath during ICP–RIE etching.
Figure 7–14. Ni etch–mask fabricated by doubled–exposure laser lithography and high–frequency pulsed electroplating. (A) Ni grown by HF pulsed electroplating in the photoresist mold. (B) Ni etch–mask after removing PR (The sample is tilted by 30˚. The actual height is approximately two times FE–SEM measured height, i.e., ~120µm) [Courtesy of An Cheng].
156
Nearly vertical (97–98˚ etch profile) high–aspect–ratio micro–transducer pillars are
created by ICP–RIE etch. The optimized values for bias voltage, ICP coil power,
chamber pressure, and balanced Ar/Cl2 gas mixture are –160 V bias voltage, 1200 W coil
power, 10 mTorr chamber pressure with a mixed gas flow of 25 sccm of Cl2 and 7 sccm of
Ar, respectively. These optimized process parameters gave an etch rate of ~150nm/min
on PMN–PT with ~10:1 etch selectivity over Ni etch–mask. The transducer height is
approximately 110–120 µm. The roughness of the facet as inspected in FE–SEM image
was < 40 nm. After ICP etching, the remaining Ni etch–mask are removed by wet
chemical etching in Ni etchant solution at 40˚C (Type TFG). The resulting micro–
transducer pillars are shown in figure 7–15. The peripheral PZT material was removed
(by chipping off) from the wafer after Ni wet etching.
Figure 7–15. FE–SEM images of: (A) 3 × 3 matrixes of transducers of 25 µm × 25 µm, 50 µm × 50 µm, 75 µm × 75 µm, and 100 µm × 100 µm cross–sectional area; (B) A 3 × 3 matrix of 25–µm transducers with etched depth of ~ 2 × 60 µm = 120 µm; (C) A 3 × 3 matrix of 75–µm transducers; (D) a single 75–µm transducer. The samples are tilted by 30 degrees. The etched depth reached is ~ 2 × 64 µm = 128 µm. [Courtesy of An Cheng].
157
7.2.4.3.2. Epoxy Filling, Wafer Lapping, and Polishing
After micro–transducer pillars are created, PMN–PT disc was placed in the bottom
of a Teflon mold and “EPO–Tech 301” epoxy, which has high elastic modulus and
resistance to high temperature after it was fully cured, was poured into the mold as
shown in figure 7–16A. Epoxy was fully cured overnight (after 24 hours) and the whole
epoxy wafer was taken out of the Teflon mold as shown in figure 7–16B.
Figure 7–16. Epoxy filling and curing process. (A) The PMN–PT disc is placed at the
bottom of the Teflon mold and Epoxy resin is poured to fill the space between the pillars
[Courtesy of An Cheng]. (B) After Epoxy is fully cured, the PMN–PT–disc–embedded
Epoxy wafer is taken out of the mold (Etched–left substrate facing up in the picture
shown). (C) Schematics showing the Epoxy filling and curing process.
158
The remaining etched–left PMN–PT substrate was removed by flat lapping. Figure
7–17A shows the etched–left substrate started being lapped away from the center of the
wafer and figure 7–17B shows the end point at which the etched–left substrate was
almost removed. Once the whole etched–left substrate was removed, all the transducer
pillars will no longer be connected and become individual elements embedded in the
Epoxy wafer. Then, thin Ni film (~200 nm) was deposited on the lapped surface of the
transducer–embedded–Epoxy wafer by e–beam evaporation (PVD) and then the wafer
was mounted on the precision glass substrate using silver epoxy (lapped side facing
down). Once the silver epoxy is cured the wafer will be glued to the precision glass
substrate. Furthermore, silver epoxy layer also serve as a common bottom electrode for
micro–transducers. The remaining epoxy on the top is then lapped away until the tips of
individual pillars were exposed.
In order to improve the quality of the transducer–electrode contact, the device’s top
surface topography, and the flatness and topology of transducers’ top surface, chemical
mechanical planarization/polishing (CMP) followed after lapping process. A two–step
surface CMP was employed. First, the 3–µm Al2O3 lapping powder was used for rough
surface polishing and planarization. Then, 0.1–µm 3M fine slurry was used for fine
surface polishing and the final finishing. Figure 7–17C shows the schematics of entire
flat lapping and chemical mechanical planarization/polishing process on the PMN–PT–
disc–embedded Epoxy wafer. Figure 7–18 compares the surface condition of the micro–
transducer top surface after Al2O3 power planarization/polishing and after 3M fine slurry
polishing (images obtained by FE–SEM). Optical profilometer measurements (with
Wyko NT1100 in VSI (Vertical Scanning Interferometry) mode) on the micro–transducer
top surface showed that RMS surface roughness is less than 1500 Å.
159
Figure 7–17. Flat Lapping and chemical mechanical planarization/polishing (CMP) process. (A)
The etched–left substrate started being removed from the center [Courtesy of An Cheng]. (B) The
end point for flat lapping at which the etched–left substrate is almost removed. (C) Schematics
showing the flat lapping and CMP process.
Figure 7–18. FE–SEM images of PMN–PT transducer top surface and Epoxy resin surface
conditions: (A) After 2 hours of rough polishing with 3–µm Al2O3 lapping powder; (B) After 1.5
hours of fine polishing with 0.1–µm 3M fine slurry. [Courtesy of An Cheng].
160
7.2.4.3.3. Top Electrodes and Interconnection
After lapping and CMP process, the device is ready for photolithography and metal
deposition for top electrodes and interconnection lines. Photoresist SPR3012 were spun
on the cleaned wafer, UV–exposed (i–line 356 nm) under the photomask consisting of
the defined patterns for electrodes and interconnections (Karl Suss MA/BA6 contact
photolithography aligner and exposure system was used), and developed. After
removing the residual resists in the exposed regions by an oxygen descum process, a Ni
(50 Å)/Ti (50 Å)/Au (2000 Å) triplex layer was deposited onto the wafer at a rate of 0.5
Å/s by electron–beam evaporations (Semicore e–beam evaporator was used. The Ni/Ti
twin adhesion layer was used to enhance the adhesion between the Au and the wafer
substrate. Microelectrodes and interconnections were then defined by the photoresist
lift–off process.
7.2.4.3.4. Device Encapsulation with Parylene C Coating
Parylene C film was deposited conformally onto the surface of the wafer by Chemical
Systems). The thickness of the film coating is approximately 15 µm. Next, photoresist
SPR3012 was spun on the cleaned wafer, UV–exposed (i–line 356 nm) under the
photomask consisting of via–hole patterns for contact pads (Karl Suss MA/BA6 contact
photolithography aligner and exposure system was used), and developed. After
removing the residual resists in the exposed regions by an oxygen descum process, an
aluminum (2000 Å) layer was deposited onto the wafer at a rate of 0.5 Å/s by electron–
beam evaporation (Semicore e–beam evaporator was used). Al etch–mask patterns
were, then, defined by a photoresist lift–off process. After that, via holes are created on
the contact pads through the Parylene C coating by RIE etch (O2 plasma was used).
Finally, the Al etch–mask was removed by wet chemical etching with standard Al etchant
in room temperature.
161 �
7.2.4.2.5. Cell–Trapping Pads
In order to create cell–trapping pads on the surface of UMTA biochip, photoresist
SPR3012 was spun on the wafer, UV–exposed (i–line 356 nm) under the photomask
consisting of the defined patterns for cell trapping pads (Karl Suss MA/BA6 contact
photolithography aligner and exposure system was used), and developed. After
removing the residual resists in the exposed regions by an oxygen descum process, a Ti
(50 Å)/Au (500 Å) twin layer was deposited onto the wafer at a rate of 0.5 Å/s by
electron–beam evaporations (Semicore e–beam evaporator was used. The thin Ti layer
was used to enhance the adhesion between the Au and the Parylene C). Cell trapping
pads were, then, defined by the photoresist lift–off process. The surface of the UMTA
biochip is shown in figure 7–19 and the entire device fabrication process is summarized
in figure 7–20.
Figure 7–19. (A) Surface of the UMTA biochip showing 3 × 3 arrays of micro–transducers of four different size (25 µm, 50 µm, 75 µm, and 100 µm) [Courtesy of An Cheng]. (B) A bright field image of a 3 × 3 array of 25 µm × 25 µm transducer (top view) before Au electrodes and interconnections are formed. (C) An DIC image of a 3 × 3 array of 25 µm × 25 µm transducer (top view on the surface of finished UMTA biochip).
A B
C
162
Figure 7–20. Schematic of UMTA biochip fabrication process. Process flow is as follows: 1) Start
from the polished PNM–PT wafer; 2) Thin (200 nm) Ni film is deposited on the PMN–PT wafer
surface as a seed layer by e–beam Physical Vapor Deposition (PVD). Transducer areas are defined on
the seed layer using photoresist mold (double–exposure laser lithography is employed). An Ni etch
hard–mask is grown on the seed layer via HF pulsed electroplating and photoresist mold is removed;
3) Vertical high–aspect–ratio micro–transducer pillars are created by plasma etching (ICP–RIE); 4)
The Ni etch hard–mask is removed. Epoxy resin is filled between the etching pillars and cured; 5) The
remaining PMN–PT substrate is lapped down until all the substrate material is removed (i.e., the
bottom surface of the etched pillars are exposed and the pillars are disconnected); 6) The sample is
then mounted on the precision glass substrate using silver epoxy; 7) The remaining epoxy resin on the
top is lapped down until the top surface of the pillars are exposed. This is followed by chemical
mechanical planarization/polishing (CMP); 8) Au top electrodes and interconnection lines are
deposited using contact photolithography, PVD and lift–off process; 9) Parylene C film is deposited by
Chemical Vapor Deposition (CVD) for acoustic impedance matching and device encapsulation. After
Parylene C depostion, an Ni (or) Al etch mask is created on the surface using contact
photolithography, PVD, and lift–off process; 10) the bonding pads are opened through via holes using
RIE etching and metal etch–mask is removed; 11) cell–trapping pads are deposited on the surface of
Parylene C film, which are located above each micro–transducer, using contact photolithography,
PVD, and lift–off process.
163
7.2.4.2.6. Wire–Bonding and Sealing.
After cell trapping pads are deposited on the Parylene C film, UMTA biochip is ready
for wire bonding. The biochip is mounted on the bottom of the 4” glass petri dish. Wires
were, then, soldered onto contact pads and common bottom electrode with silver Epoxy
and, once the silver Epoxy was fully cured, the solder contacts were sealed under Epoxy
resin in order to insulate them from the tissue culture media.
7.2.5. Characterization of UMTA Biochip
In order to evaluate the performance of UMTA biochip, two properties of micro–
transducers are characterized: 1), ultrasound pressure generated by the micro–
transducer, which is an important property in cell membrane permeabilization at the
cellular level using UMTAs; and 2), resonance frequency of the micro–transducer, which
is an important characteristic in determining and maximizing the power transfer from
transducer driving circuits and instruments to the transducer.
7.2.5.1. Fundamentals of Ultrasounds Generated by the Transducer
In brief, the intensity of ultrasound trespassing through a medium can be estimated
using equation (7–1) if the ultrasound pressure is known (the ultrasound pressure can be
obtained experimentally via a calibrated microphone in air or a hydrophone in water)
[Zagzebski, 1996].
I =
P2
2ρc (7–1)
ρ : Density of the medium (103 kg/m3 for water)
c : Speed of sound (1500 m/s in water)
P : Ultrasound pressure amplitude [Pa].
I : Ultrasound intensity [Watt/m2].
164
Ultrasounds generated from the ideal transducer, which is oscillating in one of its
thickness modes, have two regions: (i) near field region (Fresnel zone) and (ii) far field
region (Fraunhofer zone). Figure 7–21 illustrates the two field regions in the ultrasonic
beam from a single–element, unfocused transducer. The beam of ultrasound remains
well collimated in the near field. However, the beam diverges in the far field region. The
highest ultrasound intensity and pressure occur at the end of the near field region as
illustrated in figure 7–21b.
Figure 7–21. Conceptual illustration of the intensity (or pressure) distribution of
continuous wave (CW) ultrasound: (a) the near– and far–field regions in relation to
the transducer, (b) the field distribution of an ideal transducer source generating a
continuous wave. After [Wells, 1977].
165
The near field length (NFL) and far–field beam divergence angle (θ) of an ultrasound
can be estimated by equation 7–2 and 7–3 respectively [Zagzebski, 1996]. According to
these equations, the NFL and beam divergence angle (θ) mainly depend on the diameter
of the transducer and the wavelength (or frequency) of the ultrasound being generated.
The higher is the frequency (i.e., the smaller is the wavelength), the longer is the NFL
and the smaller is the beam divergence angle (θ). Therefore, in generating the focused
ultrasound with ideal transducers, the two most important parameters are the driving
frequency and transducer’s diameter.
NFL≈
d2
4λ=
d2
4
f
c
⎛
⎝⎜⎜⎜⎜
⎞
⎠⎟⎟⎟⎟
(7–2)
sinθ ≈ 1.22λ
d=
1.22
d
c
f
⎛
⎝⎜⎜⎜⎜
⎞
⎠⎟⎟⎟⎟
(7–3)
d : Diameter of transducer.
c : Speed of sound (in water, it is 1500 m/s)
f : Frequency of ultrasound.
λ : Wavelength of ultrasound.
NFL : Near field length.
θ : Beam divergence angle.
7.2.5.2. Micro–Transducer Performance Testing
7.2.5.2.1. Ultrasound Pressure Measurement Using Hydrophone
A hydrophone was used to measure the acoustic pressure exerted by the ultrasound
generated by individual micro–transducers from the UMTA biochip. Figure 7–22 shows
the simplified schematic of the experimental setup. The UMTA biochip was submerged
into the water tank, which was designed and built for ultrasound exposimetry, and a
hydrophone (75–µm diameter) was mounted to a three–axis–positioning system. The
166
hydrophone was brought near to the surface of the UMTA biochip by the stepper–
motor–controlled positioning system and the surface of the UMTA biochip was scanned
with the hydrophone (the hydrophone was kept at a constant distance above the surface
of biochip) while micro–transducers were being driven by the RF signal generator. The
hydrophone’s signals (voltages) were recorded at various positions across the UMTAs.
The pressure amplitude is calculated by multiplying the recorded signals (voltages) with
the calibrated amplitude coefficient of the hydrophone being used.
Figure 7–22. Simplified Schematic of the experimental setup for measuring the acoustic
pressure generated from the micro–transducers. The water tank was designed and built for
ultrasound exposimetry. Note: the schematic is not drawn to the actual scale.
167
Since biological cells will be seeded on the chip surface instead of being suspended in
the tissue culture media, measuring the acoustic pressure at the surface of the UMTA
biochip must have been more relevant in evaluating the impact of ultrasound generated
by the micro–transducer on the cells. Therefore, the hydrophone should be placed as
close to the top surface of the UMTA biochip as possible. However, the three–axis
motion system used in the setup lacks the fine movements (i.e., micrometer resolution)
due to low stepper–motor resolution and large screw pitch (i.e., coarse threads) on the
positioning shafts. In addition, the extreme small size (75–µm diameter) and the
structure of hydrophone make it very fragile and easy to be broken if its tip crashes on
the surface of the UMTA biochip. Due to these technological issues, the nearest distance
achieved between the hydrophone and UMTA biochip surface was around 5 mm (roughly
estimated by visual inspection through the water tank).
Micro–transducers of three different sizes, i.e., 25–, 50–, 75–µm, are tested. All
transducers were driven by the same signals, i.e., a 30–MHz continuous sinusoidal
signal with 50 mVp–p generated from the RF signal generator. The signal from the RF
signal generator is amplified by the power amplifier (10 dB amplification) and then is fed
to the UMTA biochip in order to drive the micro–transducers. The 30–MHz driving
frequency was chosen in order to increase the Near Field Length (NFL) of the acoustic
field radiated from micro–transducer and also to reduce the beam divergence angle
(Furthermore, the 30–MHz driving frequency also make the NFL of ultrasonic beam
generated from 25–µm transducer approximately equal to the transducer to cells
distance during the site–specific sonoporation. See Chapter 9). For instance, for 30–
MHz ultrasound in water with c = 1500 m/s, λ = 50 µm, and the diameter of transducer
being set at d = 70 µm, the estimated NFL and beam divergence angle are approximately
13 µm and 60°, respectively (using equation 7–2 and 7–3) (Note: equation 7–3 is not
valid and cannot be used for d < 62 µm). Therefore, as the thickness of Parylene C over–
168
coating is approximately 15 µm, the 30–MHz frequency is the optimum frequency that
can bring relatively confined acoustic field at the surface of the UMTA biochip, where the
cells will be seeded and grown for site–specific sonoporation.
During the ultrasound pressure measurement with the ultrasound exposimetry
setup, the hydrophone scanned the 1 cm × 1 cm square area centered at the micro–
transducer being tested. By using the measured results (after voltage data is converted
to pressure) from the hydrophone scanning and using equation 7–1, the acoustic
intensity of the ultrasound across the 1 cm × 1 cm scanning field can be estimated. The
estimated ultrasound intensity at 5 mm distance away from the UMTA biochip surface
when a transducer is activated with the 10dB–amplified sinusoidal signal at 30 MHz
(Note: the signal has 50–mVp–p amplitude before 10dB amplification) is approximately
5.0 ± 0.02 mW/m2 for 25–, 50–, 75–µm transducers. These experimental data are only
used to qualitatively determine whether the UMTA biochip can generate ultrasound. For
precise quantitative measurements, the whole experimental setup needs further
improvements (for instance, precise positioning of the hydrophone with high resolution
positioning system).
7.2.3.2. Electrical Impedance spectrum
To further characterize the micro–transducer performance characteristics, the input
electrical impedance spectra of micro–transducers were obtained using a network
analyzer (Agilent E5100A Network Analyzer). Again, UMTA biochip was also submerged
in water during the measurement. Figure 7–23 shows the input electrical impedance
spectra of 25–, 50–, 75–µm, and 100–µm transducers. The resonance frequency was
found to be 61, 57, 54, and 50 MHz for 100–, 75–, 50– and 25–µm transducers,
respectively. The general concensus is that the higher is the aspect ratio (i.e., diameter
or width/thickness) of the thickness mode transducer, the lower is resonance frequency
169
[Iula et al., 2003]. As all micro–transducers have almost the same thickness and, thus,
the smaller is the micro–transducer’s cross–sectional area, the higher is its aspect ratio
and the lower is its resonance frequency. These data can also be useful in determining
the power transfer from the transducer driving circuit to the transducer. In other words,
electrical impedance mismatch can be calculated and impedance matching circuit can be
built and employed as an interface between the driving circuit and the transducer in
Lee, J., Cho, H., Hays, D. C., Abernathy, C. R., Pearton, S. J., 1998. Dry Etching of GaN and
Related Materials: Comparison of Techniques. IEEE Journal of Selected Topics in Quantum Electronics. 4 (3), 557–562.
174
Loeb, G. E., Bak, M. J., Salcman, M., Schmidt, E. M., 1977. Parylene as a Chronically Stable,
Reproducible Microelectrode Insulator. IEEE Transactions on Biomedical Engineering.
BME–24 (2), 121–128.
Love, J. C., Estroff, L. A., Kriebel, J. K., Nuzzo, R. G., and Whitesides, G. M., 2005. Self–
Assembled Monolayers of Thiolates on Metals as a Form of Nanotechnology. Chemical Reviews. 105 (4), 1103–1169.
Madou, M. J., 2002. Fundamentals of Microfabrication: The Science of Minimization. 2nd
Edition, CRC Press, Boca Raton, Florida.
Maner, A., S. Harsh, S., and W. Ehrfeld, W., 1988. Mass Production of Microdevices with
Extreme Aspect Ratios by Electroforming. Plating and Surface Finishing. 75 (3), 60–65.
Marques, C., Desta, Y. M., Rogers, J., Murphy, M. C., and Kelly, K., 1997. Fabrication of High–
Aspect–Ratio Microstructures on Planar and Nonplanar Surfaces Using a Modified LIGA
Process. Journal of Microelectromechanical Systems. 6 (4), 329–336.
Ohno, I., 1988. Methods for Determination of Electroless Deposition Rate. Proceedings of the Symposium on Electroless Deposition of Metals and Alloys. Honolulu, Hawaii, 129–141.
Park, S. –E., and Shrout, T. R., 1997. Relaxor Based Ferroelectric Single Crystals for Electro–
Mechanical Actuators. Materials Research Innovations. 1, 20–25.
Sagiv, J., 1980. Organized Monolayers by Adsorption. 1. Formation and Structure of Oleophobic
Mixed Monolayers on Solid Surfaces. Journal of the American Chemical Society. 102 (1), 92–
98.
Safari, A., 1994. Development of Piezoelectric Composite for Transducers. Journal de Physique III. 4, 1129–1149.
Schmidt, E. M., McIntosh, J. S., and Bak, M. J., 1988. Long–Term Implants of Parylene–C
Coated Microelectrodes. Medical and Biological Engineering and Computing. 26, 96–101.
Schrieber, F., 2004. Self–Assembled Monolayers: from ‘Simple’ Model Systems to
Biofunctionalized Inferfaces. Journal of Physics: Condensed Matter. 16, R881–R900.
Shung, K. K., Cannata, J. M., and Zhou, Q. F., 2007. Piezoelectric Materials for High Frequency
Medical Imaging Applications: A Review. Journal of Electroceremics. 19, 139–145.
Smith, W. A., 1986. Composite Piezoelectric Materials for Medical Ultrasonic Imaging
Transducers – A Review. 1986 Sixth IEEE International Symposium on Applications of Ferroelectrics. 249–256.
175
Takahashi, C., Takahashi, J., Notomi, M., and Yokohama, I., 2000. Accurate Dry Etching with
Fluorinated Gas for Two–Dimensional Si Photonic Crystals. Materials Research Society Symposium Proceedings. 637, E1.8.1–E1.8.6.
Thein, M., Asphahani, F., Cheng, A., Buckmaster, R., Zhang, M., and Xu, J., 2010. Response
Characteristics of Single–Cell Impedance Sensors Employed with Surface–Modified
Microelectrode. Biosensors and Bioelectronics. 25, 1963–1969.
Todd, B., Flack, W. W., and White, S., 1999. Thick Photoresist Imaging Using a Three
Wavelength Exposure Stepper. Micromachining and Microfabrication Process Proceedings, SPIE. 3874 (40), 1–15.
Ulman, A., 1996. Formation and Structure of Self–Assembled Monolayers. Chemical Reviews.
96, 1533–1554.
Wang, S., Li, J. F., Watanabe, R., and Esashi, M., 1999 (a). Fabrication of Lead Zirconate
Titanate Microrods for 1–3 Piezocomposites Using Isostatic Pressing with Silicon Molds.
Journal of American Ceramic Society. 82 (1), 213–215.
Wang, S., Li, X., Wakabayashi, K., and Esashi, M., 1999 (b). Deep Reactive Ion Etching of Lead
Zirconate Titanate Using Sulfur Hexafluoride Gas. Journal of American Ceramic Society. 82
(5), 1339–1341.
Wang, L. P., Wolf, R., Zhou, Q., Trolier–McKinstry, S., and Davis, R. J., 2000. Wet–Etch
Patterning of Lead Zirconate Titanate (PZT) Thick Films for Microelectromechanical systems
(MEMS) Applications. Materials Research Society Symposium Proceedings. 657, EE5.39.1–
EE5.39.6.
Wells, P. N. T., 1977. Biomedical Ultrasonics. Academic Press, London.
Yamashita, Y., 2001. Piezoelectric Materials in the 21st Century. Proceedings of the 2000 12th IEEE International Symposium on Applications of Ferroelectrics. 1, 139–144.
Yokomizo, Y., Takahashi, T., and Nomura, S., 1970. Ferroelectric Properties of Pb(Zn1/3Nb2/3)O3.
Journal of the Physical Society of Japan. 28 (5), 1278–1284.
Zagzebski, J. A., 1996. Essentials of Ultrasound Physics. 1st Edition, Mosby, Inc., St. Louis.
176
Chapter 8
CELL MEMBRANE CHARACTERIZATION AT THE SINGLE–CELL LEVEL
8.1. Cell Membrane Characterization Using IMA Chip
Characterizing the frequency responses of the cell–electrode heterojunction
impedance has been the fundamental mechanism of signal detection in Electric Cell–
substrate Impedance Sensing (ECIS) [Xiao et al., 2002] and Electrochemical Impedance
Spectroscopy (EIS) [Tlili et al., 2003] techniques. In addition, alterations in the
impedance characteristics of cell–covered microelectrodes due to exposing living cells,
utilized as sensing bio–elements, to biologically active substances [Pancrazio et al., 1999;
Tlili et al., 2003] render potential applications of single–cell–based biosensors in
drug/bacteria detection and identification (chapter 5). In a traditional electrical–
impedance–based biosensing device, a large cell population is often cultured over one
large electrode because of the lack of control over cell isolation and patterning on the
sensor substrate. Although averages of cell properties, such as proliferation, motility,
and cell–cell separation, can be achieved over the cell population by traditional IMA
biosensing schemes, they have been almost impossible to examine individual cells and to
precisely monitor changes in the cell membrane properties of individual cells. In order
to obtain a more fundamental understanding of cell nature and cell metabolism, studies
should also be emphasized on the single–cell level. However, there exists a number of
challenges in single–cell–level studies including the time–consuming and low–yield
manipulation of individual cells, the transduction of weak biological signals through
sensor configurations, and the implementation of proper models on a single–cell level to
analyze and interpret the experimental data.
177
This chapter describes the underlying sensing mechanism of single–cell–based
Integrated Microelectrode Array (IMA) biosensors, which is investigated via
experimental and modeling studies. IMA chips were microfabricated as described in
Chapter 7. The sensitivity and feasibility of IMA chips at single–cell–level sensing
schemes were demonstrated by successful characterizations of 1), cell membrane
properties of mouse fibroblast cells (NIH3T3); and 2), cell–electrode heterojunctional
interface, which are influenced by two types of surface chemistries. Bioinstrumentations
and methodologies employed in this demonstration render a foundation for cell–based
and cellular impedance sensing applications. Not only do theoretical models developed
in this study provide insightful understandings of the working mechanism of cell–based
and cellular impedance sensing, they also reveal the influence of interfacial properties of
cell–electrode–heterojunction on the cellular and cell–based impedance sensing
schemes.
8.2. Materials and Methods
8.2.1. Surface Modification of IMA Chip and Single–Cell Immobilization
In a single–cell–level impedance biosensor, it is very important that individual cells
should be isolated and selectively immobilized onto individual cell–sized electrodes in a
repeatable, highly–efficient, and controllable manner. Biological signals transduced
from impedance sensors are usually weak and occasionally unobservable (or
indistinguishable to that of control or baseline data) even in multiple–cell level
spectroscopy [Tlili et al., 2003]. A significant factor that accounts for the observed weak
signals is poor cell adhesion on the electrode [Lo and Ferrier, 1998; Tlili et al., 2003].
Additionally, when a probing current is injected through the electrode, poor cell
adhesion also results in larger paracellular currents, i.e., currents that flow between the
bottom (the ventral part) of the cell and the top surface of the substrate (or electrode),
178
and consequently, the measured impedance–related signal does not contain much
information on the cell itself, especially the information about the cell membrane
[Huang et al., 2004; Lo and Ferrier, 1998]. Many efforts have been made to achieve
tight cell–electrode adhesion by selectively and specifically modifying the surface
chemistry of specially designed microelectrodes with dimensions comparable to those of
single cells [Asphahani et al., 2008]. This surface–mediated approach renders
successful isolation and immobilization of individual cells onto the microelectrodes as
well as facilitating the precise control over cell adhesion, cell viability, and cell
morphology in single–cell biosensors [Nam et al., 2004; Veiseh et al., 2004, Veiseh et al.,
2007]. In this approach, the surface chemistries of microfabricated surfaces or biochips
are modified by SAM–based biofunctionalized interfaces and subsequent surface
derivitizations. For instance, the surface chemistry of the IMA chip can be modified in
such as way that gold electrode surface presents biocompatible short peptide (KRGD:
lysine–arginine–glycine–aspartic acid) or fibronectin (extracellular cell adhesion
molecule). On the other hand, surface modification can also make silicon dioxide surface
(i.e., the insulation layer) on the chip present carboxyl functional groups. Then, the
modified SiO2 surface becomes hydrophilic and eventually repels the cells onto the
biofunctionalized gold electrodes. After individual cells are trapped or anchored onto
the sensing or working electrodes, they tend to spread and cover over the sensing
microelectrodes. Furthermore, the modified (or biofunctionalized) surface of gold
microelectrodes also enhances the cell adhesion. Figure 8–1 shows Human Umbilical
Vein Endothelial Cells (HUVEC) immobilized on an Au–square array with SiO2
background. The schematic of surface chemistry modification methods is also illustrated
in figure 8–2.
179
Figure 8–1. Optical micrographs of Human Umbilical Vein Endothelial Cells (HUVEC)
immobilized on gold patterns presented on silicon oxide substrates with gold patterns coated
with (a) fibronectin, (b) physically adsorbed REDVY (Ly–Arg–Glu–Asp–Val–Tyr), and (c)
covalently bound KREDVY (Arg–Glu–Asp–Val–Tyr). The insets show a magnified cell image
for each case to reveal the cell morphology and cell spreading. After [Veiseh et al., 2007].
180
Figure 8–2. A schematic representation of surface molecular engineering of a gold–
silicon dioxide substrate for guided (or surface–mediated) cell immobilization and
adhesion. Adapted from [Veiseh et al., 2004].
8.2.1.1. Protocols for Modification of IMA Chip’s Surface Chemistries
Mouse fibroblast cells (NIH3T3) were immobilized onto microelectrodes through a
lysine–arginine–glycine–aspartic acid (KRGD) short peptide–mediated or fibronectin
extracellular–adhesion–molecule mediated natural cell adhesion process [Asphahani et
al., 2008]. The following materials and chemicals are used: Remover PG (MicroChem,
where ω = 2πf. The lock–in, locked to f, will single out the first component. The
measured signal will be 1.273sin(ωt), not the 2.0 Vp–p. In the general case, the input
consists of signal plus noise. Noise is represented as varying signals at all frequencies.
The ideal lock–in only responds to noise at the reference frequency. Noise at other
frequencies is removed by the low pass filter following the multiplier. This "bandwidth
narrowing" is the primary advantage that a lock–in amplifier provides. Only inputs with
frequencies at the reference frequency result in an output. Lock–in amplifiers, as a
general rule, display the input signal in volts rms. When a lock–in displays a magnitude
of 1.0 Vrms, the component of the input signal (at the reference frequency) is a sine wave
with an amplitude of 1.0 Vrms, or 2.8 Vp–p (the amplitude of the reference is usually set at
1 Vp–p). Thus, in the previous case with a 2.0 Vp–p square wave with the frequency locked
to f, the lock–in would detect the first sine component, 1.273sin(ωt). The measured and
displayed magnitude would be 0.90 Vrms (or 1.273/√2).
8.2.2.2. Instrumentation and Impedance Spectroscopy
In this study, the cell–electrode heterojunction impedance was characterized with
voltage divider circuitry as shown in figure 8–4. Integrated Microelectrode Array (IMA)
chip is used in the setup. The cell–size sensing microelectrode, on which a single
NIH3T3 cell was immobilized, was electrically connected to a function generator (Agilent
33220A) through a current–limiting 1–MΩ resistor and the large counter electrode was
grounded. All the contacts were made on Au bonding/measurement contact pads of the
IMA chip using low parasitic capacitance tungsten probes. A 100–mV peak–to–peak
sinusoidal voltage was applied from the function generator that injected a low–intensity
ac probe current signal, clamped by the limiting resistor, into the interface between the
186
sensing microelectrode and the testing cell. The resulting differential potential
(comprising both magnitude and phase components) across the sensing and grounded
counter electrodes of the IMA chip was lock–in detected with a phase sensitive digital
lock–in amplifier (Stanford Research SR–810) for several frequencies (1, 2, 4, 6, 8, and
10 kHz), which provided an accurate measure of the NIH3T3’s membrane impedance
and cell–electrode heterojunction impedance. The frequency band of interest (1–10
kHz) was determined based on the frequency–dependent dielectric properties of the
biological membrane ensuring that the cell membrane impedance was measured.
Figure 8–4. Schematic of single–cell–electrode characterization experimental setup.
The impedance–related RMS voltages across the sensing electrode (covered with a
single cell) and the large counter electrodes were recorded with a lock–in amplifier over
a frequency range of 1 to 10 kHz. After [Thein et al., 2010].
187
The impedance characterization was performed with a four–step procedure as
follows. First, the spreading resistance of the microelectrodes was measured in the
tissue culture medium prior to cell seeding in order to obtain the baseline data. For the
second and third steps, the impedance measurements were carried out, respectively,
after the surfaces of the microelectrodes were coated with SAM and after subsequent
biofunctionalization via covalently linking fibronectin proteins (or) KRGD peptides. The
final measurement was conducted when the surface–chemistry–modified
microelectrodes were covered with a single NIH3T3 cell after the cell seeding. The entire
set of measurements (cell–free baseline, cell–free SAM–coated, cell–free surface–
chemistry–modified, and single–NIH3T3–covered modified microelectrode) was
recorded separately from a set of chips that consistently demonstrated approximately the
same cell–free baseline impedance characteristics.
8.2.3. Experimental Results
An optical photomicrograph of a single NIH3T3, immobilized on 30–µm diameter
covalently–linked–KRGD–modified microelectrode, is shown in figure 8–5 (inset). The
image shows the high degree of cell spreading and coverage achieved on the modified
microelectrode of IMA chip. In addition, the picture also suggests that the PEG–
modified SiO2 surface (surrounding area) facilitated cell–to–cell isolation by repelling a
single NIH3T3 cell onto the modified microelectrode. The experimental measurements
were carried out based on the protocols described in Section 8.2.2. The recorded root–
mean–square (RMS) voltages across the 30–µm–diameter sensing microelectrode and
large counter-electrode (of both magnitude and phase), which reflect impedances of
different cell–sensing electrode heterostructures at six different operating frequencies (1,
2, 4, 6, 8, and 10 kHz), are plotted in figure 8–5. Each set of data is averaged over eight
measurements taken from separate sensing microelectrodes. It was observed that the
188
reproducibility of the experimental data mainly depends on the degree of cell coverage
and cell adhesion on the sensing microelectrode, which can be precisely controlled by the
surface chemistry modification approaches [Asphahani et al., 2008; Veiseh et al., 2004].
As consistent and reproducible data are observed with higher degree of cell coverage, the
impedance related voltages were recorded from the electrodes onto which the cell covers
approximately >95% of the electrode area. No significant difference in the cell coverage
was observed before and after the recordings.
189
Figure 8–5. Measured impedance–related RMS voltages (magnitude and phase) across
sensing/detecting microelectrodes and large counter electrodes for different cell–electrode
heterostructures at 6 operating frequencies. Inset: a single NIH3T3 cell immobilized on the
30µm–diameter covalently–linked–KRGD–modified microelectrode (The scale bar is 10 µm).
After [Thein et al., 2010].
190
8.2.4. Modeling and Data Fitting
8.2.4.1. Modeling the Cell–Electrode Heterojunctions with the Area–
Contact–Model–Based Distributed Circuit Network
It is known that the cell membrane exhibits dielectric and insulating properties
(capacitance and resistance) [Borkholder, 1998; Pancrazio et al., 1999]. The impedance
of the cell membrane can be modeled as a parallel combination of a capacitor that arises
from the phospholipid bilayer and a variable resistor that represents transmembrane ion
transport proteins [Asphahani and Zhang, 2007]. Figure 8–6 shows the Equivalent
Circuit Model (ECM) of non–excitable cell membrane, which has Cl–, Na+, K+, and Ca2+
transmembrane ion channels. In addition to the cell membrane impedance, the
cytoplasm, which contains ions in the intracellular fluid, is conductive and can be
modeled as a variable resistor [Mossop et al., 2004; Pilwat and Zimmermann, 1985].
Figure 8–6. The equivalent Circuit Model (ECM) of the non–excitable cell membrane, which has Cl–, Na+, K+, and Ca2+ transmembrane ion channels. Cm represents the capacitance of the cell membrane and RCl, RCa, and RNa,K represent the ion channels’ resistances (or channel protein resistances).
It is also important to take into account of the planar electrode impedance in
modeling of the cell–electrode impedance. When an electrode is immersed in the
conductive electrolyte solution such as tissue culture media, according to the
191
electrochemical theory, a current flow is possible by charging and discharging the double
layer at the interface or by oxidizing/reducing substances from the electrode or/and in
the solution [Panke et al., 2008]. Subsequently, a reasonably approximated equivalent
circuit model for the microelectrode–electrolyte interfacial impedance is a parallel
combinatory circuit of a constant phase element (CPE) that represents the double–layer
capacitance and a resistor that represents the charge transfer between the electrode and
electrolyte solution [Franks et al., 2005; Lasseter et al., 2004; Panke et al., 2008].
The circular–shaped planar microelectrodes were used in developing of the
equivalent circuit model (ECM) in this study. It has been reported that there exists a
very thin layer of tissue culture media between the bottom (the ventral part) of the cell
and the top surface of the electrode when a single cell covers over a microelectrode
[Braun and Fromherz, 2004; Huang et al., 2004; Iwanaga et al., 2001; Lo et al., 1995; Lo
and Ferrier, 1998]. When an AC probe current is injected through the microelectrode,
only a portion of it will flow through the cell (transcellular current) while the rest of it
will shunt through the cleft region, i.e., thin tissue culture media layer between the
bottom (the ventral part) of the cell and the substratum (figure 8–7). The ECM model
developed employs the first–order approximate of the radial shunt (or paracellular)
currents flowing outwards from the center of the microelectrode due to the
centrosymmetry [Lo and Ferrier, 1998]. Constriction of shunt current path can be
achieved through tight cell adhesion for the resistance of the shunt current path, i.e., the
sealed resistance, is inversely proportional to the averaged cell–to–substrate separation
distance [Asphahani et al., 2008; Lo and Ferrier, 1998]. There are two different ECM
models that can represent these currents flowing through cell–electrode heterojunction:
(i) point–contact–model–based lumped circuit and (ii) area–contact–model–based
distributed circuit network. The former is the simplified version and the latter is a more
accurate model.
192
Figure 8–7. Schematic of a single cell immobilized over a planar electrode, illustrating
transcellular (dashed) and paracellular current paths (solid), which are interlinked.
After [Thein et al., 2010].
8.2.4.1.1. Point–Contact Model with Lumped Circuit Elements
In the point–contact model with lumped circuit elements as shown in figure 8–8a,
the current flowing out of the microelectrode diverges into two paths: (i) one path
through the cell (a lumped sum of transcellular currents) and the other travels radially
outwards from the center of the microelectrode to the edge (a lumped sum of shunt or
paracellular currents) [Borkholder, 1998]. It is assumed that a cell of radius (rcell) is
centered over a surface–chemistry–modified microelectrode of radius (re) and the cell
completely covers the whole area of microelectrode. It is also assumed that cell–to–
substrate distance (d) is uniform (averaged). A single resistor (Rseal) can be used to
represent the shunt current path (equation 8–1), which is directly proportional to the
radius of the cell (rcell) as well as the resistivity of tissue culture medium (ρsoln), but
inversely proportional to the averaged cell–to–substrate separation distance (or cleft
height) (d) [Borkholder, 1998]. The cross–sectional area of the shunt current path at any
location in the radial direction of the cleft equals the perimeter/circumference of the
circular ring at that location times the averaged cell–to–substrate separation distance.
193
R
seal=
ρsolnL
A= lim
ro→0
ρsoln
2πrddr = lim
ro→0
ρsoln
2πdln
rcell
ro
⎛
⎝⎜⎜⎜⎜
⎞
⎠
⎟⎟⎟⎟⎟ro
rcell
∫ (8–1)
The averaged cell–to–substrate distance (or the thickness of thin tissue culture
media layer) reflects how strong the cell adheres to the substrate [Asphahani et al.,
2008; Thein et al., 2010; Borkholder, 1998]. According to the nature/mechanism of
parallel combination of two impedance elements, Rseal must be on the same order or
much larger than the cell membrane impedance if changes in membrane properties are
to be observed in cell–based biosensors. In other words, the major portion of the
probing current must be transcellular. Therefore, stronger cell adhesion is very
important for single–cell–based sensors in order to have very large sealed resistance.
Cell membrane capacitances (Cm,b & Cm,t) are directly proportional to cell radius (or
the surface area of the cell membrane) and can be estimated by equations 8–1a and 8–1b
(Note: they are averaged and, thus, cell membrane folding is neglected). On the other
hand, ion–channel resistances (Rm,b & Rm,t) are inversely proportional to the cell radius,
which can be estimated by equations 8–2a and 8–2b. Here, the combined top and side
(the dorsal and lateral part) of the cell membrane area is approximated to be one third of
the surface of a sphere (Acell,b, and Acell,t are areas of bottom (ventral) cell membrane and
top (dorsal & lateral) cell membrane respectively. (cm), and (ρm) are specific capacity and
resistivity of cell membrane respectively.).
C
m,b= c
mA
cell ,b= c
m2πr dr = c
mπ r
cell( )2
0
rcell
∫ (8–2a)
C
m,t= c
mA
cell ,t≈ c
m
1
3
⎛
⎝⎜⎜⎜⎜
⎞
⎠⎟⎟⎟⎟
rcell( )2
sinθ dθ dφ0
π
∫0
2π
∫ =4
3
⎛
⎝⎜⎜⎜⎜
⎞
⎠⎟⎟⎟⎟c
mπ r
cell( )2 (8–2b)
Rm,b
=ρ
m
Acell ,b
=ρ
m
2πr dr0
rcell
∫=
ρm
π rcell2
(8–3a)
194
Rm,t =ρm
Acell ,t
≈ρm
13( ) rcell( )2
sinθ dθ dφ0
π
∫0
2π
∫=
3ρm
4π rcell( )2 (8–3b)
Similarly, the circular–shaped surface–chemistry–modified microelectrode CPE
(CCPE,e) is directly proportional to the electrode radius (or the surface area of the
electrode, equation 8–4), and the charge transfer resistance (Re) is inversely
proportional to the electrode radius (or the surface area of the electrode, equation 8–5).
(Ae, cCPE,e, and ρe are the surface area, specific CPE, and resistivity of surface–chemistry–
modified microelectrode respectively.)
C
CPE ,e= c
CPE ,eA
e= c
CPE ,e2πr dr = c
CPE ,eπ r
e( )2
0
re
∫ (8–4)
Re
=ρ
e
Ae
=ρ
e
2πr dr0
re
∫=
ρe
π re2
(8–5)
As illustrated in figure 8–8a, the total measured impedance across the
sensing/working microelectrode and large counter–electrode consists of the surface–
chemistry–modified microelectrode impedance (CCPE,e || Re), the sealed/cleft resistance
(Rseal), the membrane capacitance and ion channel resistance over the microelectrode
(Cm,b and Rm,b), the membrane capacitance and ion–channel resistance of the top and
sides (the dorsal & lateral part) of the cell (Cm,t and Rm,t), cytoplasmic resistance (Rcyto),
the solution resistance (Rsoln) and the counter–electrode impedance (Zc). The large
counter–electrode impedance (Zc) can be negligible (very large surface area compared to
that of sensing/working microelectrode) and the impedance of cell–electrode
heterostructure is mostly dominated by the sealed resistance and the cell impedance.
195
However, the lumped circuit model is a simplified model and does not really reflect
the physical nature of cell–electrode heterojunction as shown in figure 8–7 since the
paracellular currents pathways are normally meshed with currents coming out of the
microelectrodes and those passing through the dorsal cell membrane (transcellular
currents). Therefore, a more accurate area–contact–model–based distributed circuit
network is developed (figure 8–8b) in order to analyze the experimental data.
196
(A)
(B)
Figure 8–8. Schematics of (A) point–contact (or) lumped circuit model, (B) area–contact (or) distributed circuit model of single–cell–covered surface–chemistry–modified microelectrode. After [Asphahani et al., 2008; Thein et al., 2010].
197
8.2.4.1.2. Area–Contact Model with Distributed Circuit Network
The current at any point in the 2D cylindrical cleft disc can be derived using
Kirchhoff’s Circuit Laws as shown in equation 8–6a, where ∇ is the spatial gradient
operator, ρseal is the resistivity of tissue culture media in the cleft region (ρseal/d is the
sheet resistivity of the cleft disc), cCPE is the specific CPE of the electrode, ρe is the charge
transfer resistivity of the electrode, cm and ρm are specific capacity and resistivity of cell
membrane respectively, VJ, Ve, and Vm are the potentials (voltages) at the cell–electrode
heterojunction at the point of interest, the electrode, and the intracellular side of the cell
membrane respectively. Furthermore, β is the fractional coefficient (or phase coefficient)
of the electrode CPE and 0 < β < 1 [Bisquert et al., 2000; Biswas et al., 2006; Guyomar et
al., 2008; Itagaki et al., 2002; Zoltowski, 1998]. This CPE phase coefficient takes into
account of the heterogeneity of the electrode surface and of the bulk material (e.g. water
content in the SAM) and resulting frequency dispersions. The mathematical model in
equation 8–6a and 8–6b represents the mashing nature of paracellular and transcellular
currents in 2D cylindrical cleft disc, i.e., the current along the cleft disc is balanced by the
displacement current through the electrode and by the ionic and displacement current
through the cell membrane.
−∇ ⋅d
ρseal
∇VJ
⎛
⎝⎜
⎞
⎠⎟ = c
CPE
∂βVe
∂t β−∂βV
J
∂t β
⎛
⎝⎜
⎞
⎠⎟ +
1
ρe
Ve−V
J( ) + cm
∂Vm
∂t−∂V
J
∂t
⎛
⎝⎜
⎞
⎠⎟ +
1
ρm
(Vm−V
J) (8–6a)
−1
r
⎛⎝⎜
⎞⎠⎟∂∂r
rd
ρseal
∂VJ
∂r
⎛
⎝⎜
⎞
⎠⎟
⎡
⎣⎢⎢
⎤
⎦⎥⎥−
1
r
⎛⎝⎜
⎞⎠⎟∂∂φ
d
rρseal
∂VJ
∂φ⎛
⎝⎜
⎞
⎠⎟
= cCPE
∂βVe
∂t β−∂βV
J
∂t β
⎛
⎝⎜
⎞
⎠⎟ +
1
ρe
Ve−V
J( ) + cm
∂Vm
∂t−∂V
J
∂t
⎛
⎝⎜
⎞
⎠⎟ +
1
ρm
Vm−V
J( ) (8–6b)
In this modeling analysis, VJ is assumed to vary in radial direction only and the current
flows outwards radially within the cleft disc. Then, the equation is simplified as follows:
198
d
ρseal
⎛
⎝⎜
⎞
⎠⎟ −
∂2VJ
∂r2−
1
r
⎛⎝⎜
⎞⎠⎟∂V
J
∂r
⎡
⎣⎢⎢
⎤
⎦⎥⎥
= cCPE
∂βVe
∂t β−∂βV
J
∂t β
⎛
⎝⎜
⎞
⎠⎟ +
1
ρe
Ve−V
J( ) + cm
∂Vm
∂t−∂V
J
∂t
⎛
⎝⎜
⎞
⎠⎟ +
1
ρm
Vm−V
J( ) (8–6c)
Equation 8–6c is analogous to the following differential equation (equation 8–6d)
employed in Electric Cell–substrate Impedance Sensing (ECIS) model [Lo et al., 1995].
∂2VJ
∂r2+
1
r
⎛⎝⎜
⎞⎠⎟∂V
J
∂r− γ 2V
J+α = 0 (8–6d)
where
γ 2 =ρ
seal
d
1
ze
+1
zm
⎛
⎝⎜
⎞
⎠⎟
α =ρ
seal
d
Ve
ze
+V
m
zm
⎛
⎝⎜
⎞
⎠⎟
z
e= 1 / ρ
e+ j2π f( )β c
CPE( )−1
z
m= 1 / ρ
m+ j2π f c
m( )−1
(8–6e)
here, ze and zm are specific impedance of the electrode and of the cell membrane
respectively. The general solution to the equation 8–6d is [Hildebrand, 1949]:
V
Jr( ) = C
1I
0γ r( ) +C
2K
0γ r( ) + α
γ 2 (8–6f)
where I
0γ r( ) and
K
0γ r( ) are modified Bessel functions of the first and second kinds
with complex arguments, respectively. However, K
0γ r( ) goes to infinity as r goes to
zero, and rcell > r > 0; hence, C2 = 0. Thus, the solution of equation 8–6d or the voltage
at the cell–electrode heterojunction is:
199
V
Jr( ) = C
1I
0γ r( ) + α
γ 2 (8–6g)
The constant C1 can be approximated by applying the following boundary value:
VJ
r = rcell( ) ≈V
m,t= I
probeR
soln=
Vin−V
e
RLimit
⎛
⎝⎜
⎞
⎠⎟ R
soln (8–6h)
where Vm,t is the averaged potential at the extracellular side of the dorsal and lateral part
of the cell membrane, Iprobe is the total current, i.e., combined transcellular and
paracellular currents, injected through the limiting resistor (RLimit) and working
electrode (figure 8–4) (or the total current spreading through the bulk tissue culture
media), Vin is the applied potential from the functional generator (figure 8–4), and Rsoln
is the spreading resistance (figure 8–8a and 8–8b). (Note: the potential at the surface of
the counter–electrode is assumed to be zero since its impedance is neglected).
Inspired by the equation 8–6c (or) 8–6d, the area–contact–model–based equivalent
distributed circuit network can be developed. In this model, currents flowing through
the cell–electrode heterostructure are described by weighted circuit elements. The
lumped circuit elements of point–contact model are broken down into weighted circuit
elements in a distributed network to reflect the true physical nature of the cell–electrode
heterojunction. Therefore, the area–contact–model–based ECM for the single–cell–
covered microelectrode takes into account of shunt/paracellular currents, transcellular
currents, and the probe current from the microelectrode as shown in figure 8–8b
[Borkholder, 1998; Gordon et al., 1989; Weis et al., 1996; Weis and Fromherz, 1997].
As illustrated in figure 8–8b, the cell–electrode heterojunction in this model is
partitioned into a series of peripheral domains or segments with microelectrode and cell
radius increment (∆re and ∆rcell), which are denoted with the subscript number n. Each
200
domain or segment is represented by weighted equivalent–circuit components (Zn’s, Cn’s
and Rn’s). Like in the point–contact–model–based ECM, the counter–electrode
impedance (Zc) is negligible due to its very large surface area as compared to the
sensing/working microelectrode [Borkholder, 1998; Lo et al., 1995; Lo and Ferrier, 1998;
Xiao et al., 2002]. The following assumptions were made in the derivation of formulas
for weighted equivalent–circuit elements (summarized in table 8–1): (1) the cell is
centered over and completely covers the surface–chemistry–modified microelectrode,
and thus, the immobilized cell’s radius (rcell) is equal to the planar microelectrode’s
radius (re); (2) the current spreads through the cytoplasm of the immobilized cell.
Therefore, the total measured impedance across the sensing microelectrode and large
counter electrode consists of the combination of all weighted circuit elements, including
resistance (Rseal,n), weighted cell membrane impedance over the microelectrode (Zm,n),
the total membrane impedance of the top and sides (the dorsal & lateral part) of the cell
(Cm,t||Rm,t), the cytoplasmic resistance (Rcyto), the solution (or spreading) resistance
(Rsoln), and the large counter–electrode impedance (Zc) (can be neglected).
201
Table 8–1. Formulas for weighted circuit elements of the area–contact–model–based distributed circuit network. Weighted circuit elements are functions of their respective model parameters employed in the data–fitting. These parameters include: resistivity of tissue culture media (ρs), averaged cell–to–substrate separation distance (d), charge transfer resistivity of surface–modified microelectrode (ρe), specific CPE of surface–modified microelectrode (cCPE), cell membrane resistivity (ρm), specific cell membrane capacity (cm), resistivity of cytoplasm (ρcyto), and CPE phase coefficient (β). Note: n is an integer. After [Thein et al., 2010].
Weighted Circuit Elements Formulas
Impedance of nth weighted segment of surface–modified microelectrode
Ze,n = 1 Re,n + j2π f( )β CCPE ,n⎛⎝
⎞⎠
−1
(8–7)
Impedance of nth weighted segment of bottom (ventral) cell membrane
Zm,n = 1 Rm,n + j2π f Cm,n( )−1 (8–8)
Impedance of dorsal and lateral cell membranes
Zm,t = 1 Rm,t + j2π f Cm,t( )−1 (8–9)
Shunt/sealed resistance (nth weighted segment)
Rseal ,n =
limro→0
ρsoln d( ) 1 2πr( )drro
1+ 12( )Δre∫ n = 1( )
ρsoln d( ) 1 2πr( )drn− 1
2( )Δre
n+ 12( )Δre∫ n ≥ 2( )
ρsoln d( ) 1 2πr( )drn− 1
2( )Δre
nΔre∫ n = n( )
⎧
⎨
⎪⎪⎪
⎩
⎪⎪⎪
(8–10)
Resistance of nth weighted segment of surface–modified microelectrode
Re,n = ρe 2πrn−1( )Δre
nΔre∫ dr (8–11)
CPE of nth weighted segment of Surface–modified microelectrode
CCPE ,n = cCPE 2πrn−1( )Δre
nΔre∫ dr (8–12)
Resistance of nth weighted segment of bottom (ventral) cell membrane
Rm,n = ρm 2πrn−1( )Δre
nΔre∫ dr (8–13)
Capacitance of nth weighted segment of bottom (ventral) cell membrane
8.2.5.1. Signal Enhancement via Cleft–Thickness Control over
Surface–Modified Sensing Microelectrodes
It is well known that the accuracy of cell–electrode impedance characterization will
be enhanced effectively by reducing the “cleft” thickness at the cell–electrode
heterojunctional interface. When cells are immobilized on planar metal electrodes, cell
adhesion is mediated by protein molecules that protrude from the cell membrane
(integrins, glycocalix, etc.) and those that are deposited on the substrate (extracellular
matrix proteins). These proteins keep the lipid core of the ventral part of the membrane
at a certain distance from the substrate, stabilizing a cleft between the cell and the chip,
which is filled with electrolyte solution [Fromherz, 2003]. The resulting shunt resistance
that arises from the cleft may seriously degrade the actual impedance characterization of
the cell membrane, and, in some cases, even dominate the measured data.
8.2.5.1.1. Influence of Cell Adhesion on the Lock–in Measurement
The estimated averaged cell–to–substrate separation distance for NIH3T3 cell on the
surfaces of both covalently–linked–KRGD–modified & covalently–linked–fibronectin–
modified microelectrodes is in the range of 11–16 nm, which is obtained via numerically
data–fitting the ECM model to the experimentally recorded data in the present study
(figure 8–5) and suggests very tight adhesion between the single NIH3T3 cell and the
surface–chemistry–modified Au microelectrode [Braun and Fromherz, 1998, 2004]. In
comparison, the averaged cell–to–substrate separation distance for fibroblast cells and
rat neural cells on the surfaces of substrates coated with physically adsorbed fibronectin
or laminin was estimated to be approximately 50–100nm according to previous studies
carried out using fluorescent interferometry and voltage–sensitive fluorescent dye
[Braun and Fromherz, 1998; Braun and Fromherz, 2004; Iwanaga et al., 2001; Garcia et
206
al., 1997]. These results, therefore, indicate that covalently linking biocompatible
peptides or cell–adhesion molecules on the substrate surface improves the cell adhesion
significantly. This observation is also consistent with the finding that covalently–
linked–KRGD peptides on the Au surfaces mediates a higher degree of cell spreading
than physically–adsorbed–KRGD peptides and enhances the signal transduction of
impedance–based cellular sensors [Asphahani et al., 2008; Figure 8–10 and 8–11].
Figure 8–10. (A) Epi–DIC images of mouse fibroblast (NIH3T3) single cells patterned on 25µm– and 30µm–detecting electrodes and multiple cells patterned on a 110µm–detecting electrode, which are modified with covalently–bound KRGD peptide. (B) Fluorescent images of nuclei– (blue) and membrane– (green) stained NIH3T3 cells on electrodes of three different sizes and modified with physically absorbed (left, p–electrode) or covalently bound (right, c–electrode) KRGD. The scale bar is 25µm in (A), and 5µm for 25–µm and 30–µm electrodes and 20 µm for 110–µm electrodes in (B). After [Asphahani et al., 2008].
207
Figure 8–11. Voltage magnitudes, measured across the working electrode and the
counter electrode, of cell–free and mouse fibroblast (NIH3T3) cell–covered electrodes
with surface areas of 625µm2 (25µm x 25µm), 900µm2 (30µm x 30µm), and 12,100µm2
(110µm x 110µm). The electrodes were pre–coated with either covalently–bound or
physically–absorbed KRGD peptides. After [Asphahani et al., 2008].
It can be concluded that stronger cell adhesion and higher degree of cell spreading on
the electrode improve the transduction of signal from cell–based biosensor circuitry.
According to the ECM models, the shunt resistance increases with the cell radius (or cleft
disc radius) and is inversely proportional to the averaged cell–to–substrate separation
distance according to the area–contact model. Therefore, stronger cell adhesion on the
surface of the electrode, provided that the cell spreads entirely on the electrode, results
in smaller averaged cell–to–substrate separation distance that, in turn, yields larger
values of shunt resistances. In order to understand and analyze the effect of cell
adhesion on the single–cell–based sensor circuit response, the normalized RMS voltage
magnitudes across the sensing microelectrode (20µm or 30µm in diameter) and the
counter electrode (i.e., v = |Vcell-covered/Vbaseline|), which represent the strength of the
208
single–cell IMA biosensor response, were calculated and plotted (figure 8–12) for
different averaged cell–to–substrate separation distances (d), ranging from 10 to 120
nm, under the probing condition of 1 kHz frequency, 10MΩ current clamping resistance,
and an input impedance (Zin) of 20 GΩ || 25 pF for the lock–in detection (estimated
ECM model parameters from table 8–2 were used in the calculations). The computed
plot shows that when the cell–to–substrate separation distance is reduced from 120 to 10
nm, the single–cell IMA biosensor response, represented by the normalized RMS voltage
magnitude, is enhanced by the factors of approximately 1.8 and 2.4 for 20µm and 30µm
electrodes, respectively.
Figure 8–12. Simulated plot: normalized RMS voltage magnitudes, measured across
the sensing microelectrode and the counter electrode, as a function of averaged cell–to–
substrate separation distance. After [Thein et al., 2010].
209
The fact that the calculated normalized RMS voltage magnitudes for the same type of
cell is smaller in the 20µm–diameter electrode compared to that of the 30µm–diameter
electrode indicates the strong cell adhesion, and hence the minimized cell–to–substrate
separation distance is absolutely essential for smaller electrodes suitable for single–cell–
based sensing schemes, which utilize small–sized biological cells. A plausible
explanation for this phenomenon is that cell–to–substrate separation must be
dominantly smaller in order to obtain a large shunt resistance due to shortening of
paracellular or shunt current paths in smaller electrodes covered with a small–sized cell.
Otherwise, the injected ac current will shunt through the paracellular paths and the
signal becomes hardly distinguishable from that of cell–free baseline.
8.2.5.2. Frequency–dependent characteristics
The ultimate goal of Integrated Microelectrode Array (IMA) chips is to be
implemented in cell–based and cellular sensor configurations. The successful
characterization of cell membrane at single–cell level demonstrated the feasibility of
IMA chips. However, other characteristics such as frequency response, sensitivity, and
signal–to–noise ratio of the sensor configuration are also important. Hence, frequency–
dependent characteristics of IMA chips employed with NIH3T3 cells are investigated.
These characteristics include: (i) frequency–dependent impedance difference between
cell–covered and cell–free electrode, (ii) impedance spectrum of cell–electrode
heterostructures, and (iii) frequency–dependent response characteristics of IMA sensor
configuration (overall circuitry), which is employed with cell–electrode heterostructures.
To investigate the frequency–dependent characteristics of the Integrated–
Microelectrode–Array–based single–cell sensing scheme, firstly, the spectrum of
impedance difference between the single–NIH3T3–covered electrode and the
unmodified cell–free baseline electrode was calculated using the area–contact–model–
210
based circuit network over the frequency range between 100 Hz to 1MHz (the estimated
values from table 8–2 are used). Figure 8–13 plots the spectrum of the computed
impedance difference (ΔZ = Zcell–covered – Zcell–free baseline), which is associated with the
covalently–linked–KRGD–mediated or covalently–linked–fibronectin–mediated cell
adhesion of single NIH3T3 cells on 30µm–diameter microelectrodes and provides a
mean of measure of the cell–electrode–heterojunction interfacial impedance as well as
the cell membrane impedance. The frequency–dependent characteristic of the
magnitudes of the impedance difference (|ΔZ|) approximates to a linear function in log–
scale up to the frequency of 50 kHz. This characteristic is governed by the resistive and
capacitive properties of the cell membrane, the binding–specific cell adhesion molecules
(KRGD or fibronectin) and their regulated cell adhesion, and the intracellular cytoplasm.
The cell membrane becomes more capacitive and leaky at higher frequencies (≥50 kHz)
and, thus, the capacitive elements of NIH3T3 are effectively short–circuited at higher
frequencies [Gordon et al., 1989]. The impedance change due to the present of a single
NIH3T3 on the microelectrode, therefore, becomes frequency independent at high
frequencies (≥50 kHz) as it is dominated by the resistance of cytoplasm (Rcyto). This
resistive nature of the single–NIH3T3–covered microelectrode impedance at high
frequencies is also revealed by the reduced phase angles of the impedance difference in
the spectral diagram (figure 8–13). An important conclusion drawn from the spectrum
analysis is that there exists a suitable frequency band within which the electrical
properties of multiple cellular components become dominant in measured signals and
variations or changes in these properties can be detected by the lock–in technique.
211
Figure 8–13. Simulated plot of frequency–dependent impedance difference,
referenced to the cell–free baseline, after a single NIH3T3 cell was immobilized on the
microelectrode (30µm–diameter) via different surface–chemistry–mediated cell
adhesion processes. After [Thein et al., 2010].
Secondly, the impedance spectra of overall cell–electrode heterostructure is
calculated. The computed spectrum of overall impedance magnitude of single–
NIH3T3–covered microelectrode in figure 8–14 is found to trace along the asymptotic
approximation line of the NIH3T3’s membrane capacitance and the microelectrode
constant phase element (CPE) within the frequency band of 100 Hz to 50 kHz. In
addition, the phase shifts of the impedance due to the presence of a morphologically
controlled single NIH3T3 on the microelectrode, represented by the phase angles of the
212
impedance difference in figure 8–13, are very prominent within the same frequency
band. These characteristics suggest the constriction of shunt currents due to tight cell
adhesion (most of the AC probe current is flowing through the cell) and confirm that the
measured signal in this frequency range is mostly dominated by NIH3T3’s cell
membrane impedance as well as the surface–modified microelectrode’s CPE.
Figure 8–14. Overall impedance magnitude spectrum of cell–electrode
microelectrode, 30µm–diameter). Dashed lines are asymptotic approximation lines
representing dominant electrical properties in the measured data. After [Thein et al.,
2010].
213
Again, the most important aspect of these characteristics for single–cell IMA–based
sensing schemes is that real–time changes and variations in NIH3T3’s cell membrane
properties (such as membrane permittivity and conductivity) due to external or internal
stimuli can be transduced and revealed in this frequency band. The uncovered frequency
band also provides maximum sensitivity in the single–cell IMA–based sensing scheme
and can be utilized to optimize the operation of the sensor circuit in real–time analysis
so that the time needed for impedance recording and processing can be reduced.
Thirdly, the optimal frequency range for the maximum sensor response and
sensitivity in the present study was also investigated by simulating the frequency–
dependent sensor response characteristics of the IMA sensing circuitry. The normalized
RMS voltage magnitudes, v = |Vnon–baseline/Vbaseline|, were calculated (with 1 MΩ current
limiting resistor) over the frequency range between 100 Hz to 1 MHz and plotted for
NIH3T3 cells immobilized on covalently–linked–KRGD– or fibronectin–modified
microelectrodes, respectively, as shown in figure 8–15. A small impedance difference
between the two cell–electrode configurations, resolved by the difference in normalized
RMS voltage magnitude plots, is observed due to two different surface chemistries. This
difference is unambiguously resolved over the frequency range of 1–100 kHz. This
suggests the frequency–dependent impedance–specific sensitivity of the single–cell–
based IMA impedance sensor circuitry. The reduced impedance–specific resolution and
sensitivity at lower (<1 kHz) frequencies, where resistive properties dominate, also
suggests that modifying the surface chemistry with KRGD peptides or fibronectin
proteins alters significantly on the double–layered capacitance of the electrode and cell
membrane capacitance of NIH3T3 (see table 8–2). A plausible explanations are: (i)
different proteins could influence the cell membrane folding and number of focal
adhesion points. Cell membrane folding would increase the cell membrane capacity per
unit area; (ii) different number of charges (or polar molecules) in the protein could affect
214
the equilibrium charge distribution of at the electrode–electrolyte interface; (iii) it is also
known that cell adhesion proteins mediate or trigger excretion of cell metabolites
through cell signaling pathways, which are deposited on the substrate or matrix [Albert
et al., 2004]. Hence, these metabolites dumped into the cleft are expected to affect the
cell–electrode interfacial properties, causing small difference in overall impedances that
can be detected by the IMA sensor configuration.
Figure 8–15. Frequency–dependent response characteristics of Integrated
Microelectrode Array (IMA) sensor configuration after single cell immobilization. After
[Thein et al., 2010].
215
The response of IMA sensor configuration remains constant or frequency
independent at higher frequencies (>100 kHz) as shown in figure 8–15 for the
impedances are mainly contributed by the resistance of the cytoplasm at higher
frequencies. No difference in sensor responses between two different types of modified
electrodes is observed at higher frequencies above 100 kHz. This could also suggest that
the resistance of NIH3T3 cytoplasm remains unaltered in substituting from fibronectin–
mediated to KRGD–mediated cell adhesion. Furthermore, the response characteristics
in figure 8–15 also suggest that the probing frequency must be within 1–100 kHz if the
real–time variations in NIH3T3 cell membrane due to external or internal stimuli are to
be monitored. It is also expected that utilizing different types of cells as a sensing bio–
element and different surface chemistries could shift the frequency band and optimum
operating/probing frequencies. Therefore, a proper equivalent circuit model is necessary
and prior experimental measurement and analysis must be carried out to obtain the
insight knowledge of the contribution of each property of a single cell to the overall
impedance, as well as determining optimum sensor circuit operating/probing
frequencies for single–cell–based biosensing applications (e.g., real–time constant–
frequency analysis of the single–cell response to a given dose of a pharmaceutical agent).
8.3 Automation & Upgrade on IMA Chip Instrumentations and
Sensor Platforms
8.3.1. Multiplexing and Data Acquisition
Microelectrode arrays are employed in the chip in order to improve the efficiency and
yield of the experiment. Automatic data acquisition and synchronization between
instruments will further improve the efficiency and yields of cellular assays. Therefore,
multiplexing and data acquisition circuitry was constructed and integrated to the IMA
chip platform as shown in figure 8–16. The integrated microelectrode array chip was
216
placed inside the portable chip carrier/holder (custom-built) and wires from the chip
carrier were connected to multiplexer relay switching system (2 Agilent 34901A 20
channel multiplexer (2/4–wire) modules installed in Agilent 34970A data
acquisition/control unit) through current limiting resistors (high tolerance). The
function generator (Agilent 33220A) and lock–in amplifier (Stanford Research System
SR–810) were also connected to the input and output of the multiplexer respectively so
that ac current injection from the function generator and data recording from the lock–
in amplifier can be synchronized. The lock–in amplifier is interfaced to the chip carrier
through unity–gain high–input–impedance pre–amplifiers (in–house built) in order to
reduce the loading effect (Impedances from biological samples are usually high and,
thus, instruments with high–input impedance are always required). Each pre–amplifier
channel includes two–stage cascaded pre–amplifier setup where the signal passes the
first stage of in–amp (differential) and then is fed through the second stage of op–amp
(buffer) to the output (figure 8–16b). Polytetrafluoroethylene (PTFE) coated wires were
used in the multiplexing network in order to reduce parasitic coupling between wires
(PTFE has much lower dielectric constant and much higher bulk resistivity than PVC
elastomer traditionally used in electrical wire insulation). Figure 8–17 shows the test
setup with EG&G 5206 two–phase lock–in analyzer. Automated excitation and data
acquisition is accomplished by connecting the function generator, lock–in amplifiers,
and multiplexers to a PC through RS–232 and IEEE GPIB interface respectively. Data
acquisition from the lock–in amplifier is controlled by LabVIEW (National
Instruments).
217
(A)
(B)
Figure 8–16. (A) Schematics of automated excitation and data acquisition circuitry, which includes a lock–in amplifiers, a multiplexer, a function generator, and a multi–channel pre–amplifier module. (B) Circuit schematics of a unity–gain ultra–high–input–impedance amplifier in each channel of the multi–channel pre–amplifier module.
218
Figure 8–17. A complete multiplexing/addressing setup (a test setup with
EG&G 5206 two–phase lock–in analyzer).
8.3.2. Integration with Portable Tissue Culture Chamber and
Fluorescent Microscope
Biological experiments are usually coupled with microscopic observations (including
fluorescence spectroscopy). In addition, the environment in which the experiment is
carried out must be under desired controlled conditions. Therefore, Olympus upright
microscope (capable of taking fluorescent images) and Nikkon portable tissue culture
chamber are integrated to the IMA sensor platform. The chip carrier with the media
Unity–gainHigh–input–ZPre–amplifiers
ChipCarrierwithCurrentLimitingResistors
FunctionGenerator
MultiplexerControlUnit
EG&G5206TwoPhaseLock–inAnalyzer
219
reservoir, inside which the IMA chip is mounted, can be placed inside the chamber. The
Nikkon tissue culture chamber has a temperature control unit and a gas–flow control
unit. In addition, the environment inside the portable tissue culture chamber is
sterilized. Therefore, this upgraded set up is very relevant for real–time long–term
monitoring of the effects of chemicals (such as drugs or toxins) on the cells as well as
cellular assays using the IMA chip platform.
Figure 8–18. Experiment set–up to monitor real–time responses from individual cells
and multiple–cell colonies upon exposure to toxins and drugs. The inset shows the
modified chip carrier with tissue culture media reservoir placed inside the portable
tissue culture chamber.
220
8.4. Chapter Appendix
8.4.1. Single–Cell–Based Vs. Multi–Cell–Based Impedance Sensing in
Monitoring Cellular Response to Drug Treatment
In order to compare the single–cell–based impedance sensing to multi–cell–based
impedance sensing in monitoring cellular response to drug treatment, human
glioblastoma (U87MG) cells were immobilized (or seeded) on single– and multi–cell
microelectrodes through ligand–mediated natural cell adhesion process (see section
8.2.1). These cancer cells seeded on both types of microelectrodes were then exposed to
an ion channel inhibitor, chlorotoxin (CTX), for up to 16 hrs. The cancer cells exhibited
shape, size, and morphological changes upon exposure to CTX, which can be detected by
cellular impedance sensing technique using IMA chips.
Figure 8–19a show the epi–DIC images of single cells and a monolayer of multiple
cells seeded on the respective electrodes before and after the 5–µM CTX treatment. The
single–cell images (right panels in figure 8–19a) reveal that single U87MG cells
immobilized on the cell–size electrodes shrank or retracted after 16–hour inoculation
with 5–µM CTX. Time–course normalized real impedance (measured at 2.0 kHz) of
single–cell–electrode heterostructures were also obtained for the period of 16–hour
inoculation with 5–µM CTX (figure 8–19b to 8–19e). It can be concluded from the
time–course plots, coupled with epi–DIC images, that the impedances of single–cell–
electrode heterostructures significantly decrease over the period of 16–hour inoculation
with 5–µM CTX due to cell shrinkage or retraction. On the other hand, no or little drop
in the normalized real impedance (measured at 2.0 kHz) of multi–cell–electrode
heterostructure was observed for the same concentration of CTX over the same
inoculation period (figure 8–20a). Furthermore, changes in frequency spectrum of cell–
electrode heterostructure after cell seeding are more prominent in single–cell electrodes
than in multi–cell electrodes (figure 8–2ob).
221
Figure 8–19. (a) Schematic of IMA chip featuring optical epi–DIC micrographs of four single
live U87MG cells on 30–µm working/detecting electrodes (right panel) and one 250–µm
working/detecting electrode hosting a confluent monolayer of live U87MG cells (left panel)
before and 16 hours post 5–µM CTX treatment. Both scale bars represent 30µm. (b)–(e) Time–
course normalized real impedance of the four U87MG single cells exposed to 5 µM CTX over a
period of 16 hours. Real impedance was normalized to the point just prior to CTX inoculation.
[Courtesy of Fareid Asphahani]
222
Figure 8–20. (a) Average normalized real impedance of multiple U87MG cells (blue) and
single U87MG cells (green) in response to 5 µM CTX, as well as to single cells receiving no CTX
treatment (brown). Real impedance was normalized to the point just prior to CTX inoculation.
(b) Frequency spectroscopy plot of impedance magnitude |Z| at a frequency range from 500 to
20 kHz when cell–free electrodes of 30 µm and 250 µm diameters are seeded with U87MG cells.
[Courtesy of Fareid Asphahani]
These experimental findings suggest that there are two intrinsic phenomena that
degrade the signal–to–noise ratio (SNR) of multi–cell–based cellular impedance sensing
technique in monitoring the full toxicity or drug response of the cells. These two
phenomena are: 1), the initial cell coverage on the working electrode; and 2), the effect of
cell–to–cell junction on the cell shrinkage or retraction.
8.4.1.1. The Effect of Initial Cell Coverage on the Working Electrode on the
Transduced Electrical Signals
In order to analyze the effect of initial cell coverage prior to the CTX administration
on the transduced electrical signals, the point–contact–model–based equivalent
electrical circuit of single–cell–electrode heterostructure is modified as illustrated in
figure 8–21. In developing this application–specific equivalent circuit, the following
223
assumptions are made: 1), the cell covers almost 95–98% of the surface–chemistry–
modified microelectrode just prior to the CTX administration and, thus, the electrode
possesses cell–occupied area, A
cellr
cell( ) , and cell–free area, A
freer
cell( ) = Ae
re( )− A
cellr
cell( ) ;
2), the cell adhesion is strong due to ligand–mediated cell adhesion, i.e., very large Rseal
[Asphahani et al., 2008; Thein et al., 2010]; 3), the cell impedance, Zcell, is much larger
than the surface impedance of cell–covered portion of the electrode, Ze,cell, and the
surface impedance of unoccupied portion of the electrode, Ze,free, for the
probing/operating frequency used in this study (i.e., 2.0 kHz); and 4), the spreading
resistance (Rsoln) is much smaller than other electrical components in the ECM.
Figure 8–21. Modified point–contact–model–based equivalent circuit of single–cell–
electrode heterostructure. Rseal is very large due to very tight cell adhesion mediated by
ligands/peptides covalently linked to the SAM–functionalized Au electrode. Zcell is
comparably larger than Ze,ell and Ze,free at 2–kHz operating frequency.
Based on the circuit schematic shown in figure 8–21, the overall impedance of
single–cell–electrode heterostructure can be described as follows:
224
Zhet
jω( ) = Ze,cell
jω( ) + Zcell
jω( )Rseal
Zcell
jω( ) + Rseal
⎛
⎝⎜⎜
⎞
⎠⎟⎟
−1
+1
Ze, free
jω( )⎡
⎣
⎢⎢⎢
⎤
⎦
⎥⎥⎥
−1
+ Rsoln
(8–23)
Furthermore, the surface impedance of cell–occupied portion of microelectrode is:
Ze,cell
jω( ) = ze
jω( )πr
cell2
(8–24)
The surface impedance of unoccupied portion of microelectrode is:
Ze, free
jw( ) = ze
jw( )π r
e2 − r
cell2⎡⎣ ⎤⎦
(8–25)
And the impedance of the cell is:
Zcell
jω( ) = zcell
jω( )πr
cell2
(8–26)
The impedance of sealed resistance (Rseal) and spreading resistance (Rsoln) can be
described by equation 8–1 and 8–20 respectively.
For all operating frequencies, the overall impedance of single–cell–electrode
heterostructure can generally be described by equation 8–23. However, operating at 2–
kHz frequency and having very tight cell adhesion between the cell and the electrode, the
impedance of the single–cell–electrode heterostructure is mainly contributed by the
surface impedance of unoccupied portion (or cell–free region) of the microelectrode, i.e.,
Z
het⎡⎣ ⎤⎦( f =2kHz)
≈ Ze, free
⎡⎣ ⎤⎦( f =2kHz)= z
e⎡⎣ ⎤⎦( f =2kHz)
Ae
re( )− A
cellr
cell( )⎡⎣ ⎤⎦ [Haung et al., 2004].
Therefore, the overall impedance Zhet at 2.0 kHz is inversely proportional to the cell–free
surface area of the electrode or, specifically, is proportional to 1 1 − r
cellr
e( )2⎡⎣⎢
⎤⎦⎥
.
225
For multi–cell–electrode heterostructures, the equivalent impedance is simply the
parallel combination of individual single–cell–electrode domains. Then, the total area
covered by each individual cells can be treated as the single large area covered by a very
large virtual cell, i.e., r
cell ,eff≈ r
cell( )2
i=1
n∑ . Hence, the average percent change in the
mean radius of individual cells due to the CTX toxicity effect is directly correlated to that
in the effective radius. However, the percentage of initial cell–free area just prior to the
CTX administration on the multi–cell electrode is relatively larger than that on single–
cell electrode due to cell–to–cell gap in the monolayer, i.e., r
cell ,effr
e ,multi< r
cellr
e ,single.
Therefore, the initial cell–occupied area on the multi–cell electrode is usually less than
that on the single–cell electrode. The dependence of normalized real impedances, i.e.,
Re Z
hetr
cell( )⎡⎣ ⎤⎦ /Re Zhet,o
rcell ,o( )⎡
⎣⎤⎦ ∝ 1 − r
cell ,or
e( )2⎡⎣⎢
⎤⎦⎥
1 − rcell
re( )2⎡
⎣⎢⎤⎦⎥
, on the initial cell
coverage or the ratio rcell,0/re at 2–kHz operating frequency during the cell shrinkage or
retraction are shown in figure 8–22. The plots show that 15% reduction in cell radius
from initial rcell,o yields different overall decreases in normalized real impedances (or
overall decreases in real impedances, i.e., Re[Zhet,o]–Re[Zhet,ss]) for different initial cell
coverage on the electrode. The trend shows that the higher is the initial cell coverage just
prior to the CTX administration or rcell,0/re ratio, the larger is the decrease in normalized
real impedances for the same degree of cell shrinkages or retractions. Therefore, the
electrical signal transduced via multi–cell–based impedance sensing does not usually
reveal significant decrease in normalized real impedance for the same concentration of
Chlorotoxin administration due to the relatively larger initial cell–free area resulting
from cell–to–cell gaps.
226
Figure 8–22. Influence of initial cell coverage (rcell,o/re) on the normalized real
impedancse during the cell shrinkage or retraction.
8.4.1.2. The Effect of Cell–to–Cell Junction on the Overall Cell Shrinkage or
Retraction
It is hypothesized that the overall cell shrinkage or retraction in the multiple–cell
environment due to Chlorotoxin is relatively less than that in single–cell environment
due to the linkage between adjacent cells in the monolayer by cell–to–cell junctions. In
other words, the adjacent cells are linked or hold together in the monolayer resulting in
relatively less shape, size, and morphological response to CTX. Therefore, it is conceived
that the overall decrease in the normalized impedance of multi–cell–electrode
heterostructure is relatively less than that in the normalized impedance of single–cell–
electrode heterostructure due to lower degree of cell shrinkage or retraction and, thus,
227
the full toxicity effect by CTX may not be observed in the multiple–cell–level
measurements.
It is concluded that both effects, the initial cell coverage on the electrode and cell–
to–cell junctions, contribute to the apparently smaller decrease in the normalized real
impedance observed in multiple–cell–level measurements. These highlight the
importance of single–cell–level measurements for drug and toxicity studies. Although
the suppression of cell–to–cell junction linkages and higher cell confluency may improve
the multiple–cell–level measurement in this study, the single–cell–level measurement
provides much more easier and robust control over the cell manipulation such as initial
cell coverage and adhesion in analyzing the full toxicity effect by CTX on the cell.
228
8.4.2. Time Fractional Derivative
The mathematical model of current travelling through the cleft region (equation 8–
6a and 8–6b) includes fractional derivatives of V
et( )
and V
Jt( ) , which can be solved by
applying the following definition.
The fractional derivative of a function f t( ) is a convolution between
f t( ) and
t β−1 Γ β( ) where
Γ β( ) is the gamma function and β is the order of the fractional derivation
(see equation 8–23). Generally, –1 < β < 1.
∂β f t( )∂t β
=1
Γ β( )t β−1( )∗ f t( )⎡⎣⎢
⎤⎦⎥
=1
Γ β( )t−τ( )β−1
f τ( )dτ0
t
∫ (8–23)
From the spectral point of view, the frequency spectrum of the fractional derivative
of f t( ) can be described as:
F∂β f t( )∂t β
⎡
⎣
⎢⎢⎢
⎤
⎦
⎥⎥⎥= jω( )β F ω( ) (8–24)
where F ω( ) is Fourier transform of
f t( ) .
229
8.4.3. List of Symbols
Acell [µm2] Surface Area of the microelectrode occupied by the cell/cells
Ae [µm2] Surface Area of the microelectrode
Afree [µm2] Surface Area of the microelectrode unoccupied by the cell/cells
β Phase coefficient of CPE.
cCPE [µF/cm2] Specific CPE of surface–modified microelectrode
cm [µF/cm2] Specific cell membrane capacity
CCPE,n [µF] CPE of nth weighted segment of surface–modified microelectrode
Cm,n [µF] Capacitance of nth weighted segment of bottom (ventral) cell
membrane
Cm,t [µF] Capacitance of top (dorsal) and lateral cell membranes
d [nm] Averaged cell–to–substrate separation distance
Δre [µm] Electrode radius increment
ΔZ [Ω] Impedance difference
f [Hz] Applied or excitation frequency in Hertz
Iprobe [A] Total current (paracellular + transcellular) injected
n Number of domains or segments
ω [Radians] Applied or excitation frequency in radians
rcell [µm] Radius of cell
rcell,o [µm] Initial radius of the cell
rcell,avg [µm] Averaged radius of individual cells in the monolayer
rcell,eff [µm] Effective radius of combined area of individual cells
re [µm] Radius of microelectrode
Rcyto [Ω] Resistance of cytoplasm
230
Re,n [Ω] Resistance of nth weighted segment of surface–modified
microelectrode
RLimit [Ω] Current limiting resistor
Rm,n [Ω] Resistance of nth weighted segment of bottom (ventral) cell
membrane
Rm,t [Ω] Resistance of top (dorsal) and lateral cell membranes
9.1. Site–Specific Sonoporation at Cellular Level Using UMTA Chip
Ultrasound–induced cell membrane permeabilization (or sonoporation) has been
used to transfect gene products in vitro (see Chapter 6). A number of researches have
led to the general consensus that sonoporation results from the cavitation activities
induced by the ultrasound. Furthermore, the efficiency of sonoporation can be enhanced
by employing ultrasound contrast agent (UCA) or microbubbles (MCB), which serves as
artificial cavitation nuclei (see Chapter 6). Studies show that the effects of therapeutic
ultrasound on the living tissues is usually non–invasive and relatively mechanical [Pitt et
al., 2004]. Mechanical stresses cause cell membrane stretching, which increases plasma
membrane inter–molecular distances [Valahakis and Hubmayr, 2000]. The cell usually
responds by intracellular lipid trafficking to the plasma membrane in order to prevent
membrane rupture (i.e., cytoprotective mechanisms). When the mechanical stresses are
large enough, the cytoprotective mechanisms fail to prevent the rupture and the plasma
membrane breaks down. The plasma membrane breaks are usually non lethal and that
they are spontaneously resealed by site–directed exocytosis (ATP dependent).
It has been reported that ultrasound–induced perturbations and cavitations can
facilitate or enhance drug delivery processes through several mechanisms:
• The first mechanism is from the oscillatory motion of the fluid driven by the
ultrasound. The oscillating motion of the fluid increases the effective diffusivity
of drug molecules, whether free or bound to carriers [Starritt et al., 1989]. This
237
enhanced transport can occur within blood, cells, and extracellular fluids
[Nyborg, 1982; Rooney, 1970].
• Perturbation of the drug carrier is another impact of ultrasound toward drug
delivery. The shear force in the ultrasound field can help to shear the drug from
the carrier polymer backbone [Guzman et al., 2003; Sundaram et al., 2003], and
then release the drug molecules. For drugs carried inside the liposome vesicle,
when the shear stress generated in ultrasound field exceeds the strength of
vesicle membrane, it will rupture the membrane and release the interior content
[Sundaram et al., 2003].
• The third contribution of the ultrasound to drug delivery relates to the stresses
that pressure wave applied on cells and tissues that result in cell permeabilization
and capillary ruptures. Cells in an environment with cavitation events are subject
to shear forces due to microstreaming, sonic jets, and shock waves (also see
Chapter 6). It is possible that a small jet of liquid would shoot into the cells
directly at sonic speed if a large semi rigid cell were adjacent to a small cavitating
bubble, which undergoes an asymmetric bubble collapse. Such activities can
probably rupture the cell membrane. Likewise, a collapse of a microbubble near
a capillary or blood vessel wall will cause the liquid jet to shoot right into the cell
wall. Such a collapse may be the source of the large amount of extravasation
caused in tissue exposed to ultrasound in the presence of microbubble contrast
agents [Song et al., 2002].
Therefore, by harnessing these mechanisms, the transport of drugs or gene products into
the cells can be enhanced or facilitated. Figure 9–1 illustrates various modes by which
drug delivery can be enhanced using ultrasound.
238
Figure 9–1. Schematic representation of various modes by which drug delivery can be enhanced by ultrasound. A: therapeutic agent (triangles); B: gas bubble undergoing stable cavitation; C: microstreaming around the cavitating bubble; D: collapse cavitation emitting a shock wave; E: asymmetrical bubble collapse producing a liquid jet that pierces the endothelial lining; F: completely pierced and ruptured cell; G: non–ruptured cells with increased membrane permeability due to sonication; H: cell with damaged membrane from microstreaming or shock wave; I: extravascular tissue; J: thin–walled microbubble decorated with agents on the surface; K: thick–walled microbubble with agents in lipophilic phase; L: micelle with agents in lipophilic phase; M: liposome with agents in aqueous interior; N: vesicle decorated with targeting moieties attached to a specific target. After [Pitt et al., 2004].
In all these transfection and drug delivery applications, ultrasound is usually
generated by devices or instruments based on piezoelectric–based transducers.
However, these devices currently lack the functionality of targeting specific locations
with high degree of spatial resolution. Although, varieties of device designs have showed
that ultrasound can be focused to a specific region in the cell suspension or in the tissue
[Rahim et al., 2006 (a); Rahim et al., 2006 (b)], the spatial resolution is still not high
enough to target a small cluster of cells or individual cells (in those designs, cell samples
are placed at the focal point of ultrasound beam generated by complex focused
239
ultrasound transducers). In order to achieve high degree of spatial resolution, a
potential sonoporation device must possess three major functionalities:
(1) In most current applications, the sonoporation device is located relatively far
from the site for ultrasound applications, therefore, higher ultrasound pressure
has to be generated in order to achieve, at least, the threshold pressure at the
desired location in the cell suspension or tissue. Therefore, this compromises
cells or tissues in the surroundings.
(2) The dimensions of the transducers are still relatively large compare to the
dimension of individual cells so that focusing ability at the cellular level is very
poor. Therefore, transducers must be miniaturized.
(3) Current sonoporators are still relatively large for using as medical implants.
Therefore, the devices must also be miniaturized. In addition, the bio–
compatibility of the devices is also important.
Therefore, in this study, Ultrasonic Micro–Transducer Array (UMTA) biochip was
microfabricated (The detail of the device fabrication and characterization are described
in Chapter 7). The UMTA chip employs miniature transducers (cell–size), which are
designed to be located very close to the seeded cells. Due to the proximity of these
micro–transducers to the cells, the intensity of ultrasound, needed to be generated by
the transducers in order to permeabilize cell membrane, can be reduced. Furthermore,
due to its miniature size, the micro–transducer demonstrated renders huge potentials
for the future development of cellular/cell–based assay platforms and ultrasound–based
implants. The feasibility of the UMTA biochip prototype was demonstrated by applying
ultrasound effectively at the specific locations within the cell monolayer of melanoma
cancer cells (LU1205) in order to induce sonoporation at the cellular level. Successful
sonoporation at the cellular level was also confirmed by the facilitated uptake of
nanoparticle quantum dots (QDs).
240
9.2. Materials and Methods
9.2.1. Cell Culturing and Preparation
In order to demonstrate the feasibility of the UMTA biochips, site–specific cell
membrane permeabilization (sonoporation) was carried out on the monolayer of Green
Fluorescent Protein (GFP)–expressing human melanoma (LU1205) cells. The cells will
be seeded and grown on the surface of the UMTA biochip until the monolayer is
achieved. LU1205 cell line was chosen for three reasons: (i) it is an aggressive skin
cancer cells with no current viable therapy and the ultimate goal is to develop UMTA
biochips for ultrasound–enhanced transfection/gene therapy assays of cancer cells at
single–cell/cellular level in vitro; (ii) melanoma is also one of the fastest growing cancers
in the developing world with the incidence having tripled in the last three decades.
Chemotherapy, immunotherapy, and vaccines have produced very limited benefits
especially since the response are typically short–lived, with no significant effect on
overall survivals [Ghosh et al., 2005]; (iii) its very fast growth rate, good morphology,
well–understood proliferation, and robustness to less friendly cell culture environment
[Dothager et al., 2005] make LU1205 melanoma cancer cell line easier to handle so that
the feasibility and working mechanisms of the UMTA biochip can be emphasized. In
addition, GFP–expressing cell line is chosen for fluorescent imaging and mapping.
9.2.1.1. Cell Thawing
A vial of GFP–expressing LU1205 melanoma cancer cell line was taken out of from
the freezer at –80 ˚C, then thawed quickly in a 37 ˚C water bath for 2 minutes until the
medium become liquid. Then cells were transferred into a 100 ml Petri dish with cell
media. The media contains 90% Dulbecco’s Modified Eagle Medium (DMEM), 20%
Fetal Bovine Serum (FBS), and 100 U/ml of penicillin–streptomycin (PS). The whole
transfer process takes place in the sterile hood with air curtain. The total solution
241
volume is 10 ml (9 ml medium + 1 ml cells from frozen vial). The Petri dish was then
placed in the incubator with constant temperature of 37 ˚C and 5% CO2 environment for
6 hours for cells to recover and settle on the surface.
9.2.1.2. Cell Passing and Culturing
After 6 hours of staying in the incubator for cell thawing, the GFP–expressing
LU1205 are ready for passing and culturing on the surface of micro–sized transducer
chip. 30–ml cell culture medium (90% DMEM + 10% FBS + 100 U/ml PS) was warmed
up to 37 ˚C. After washing cells two times with 5 ml of Dulbecco’s Phosphate Buffered
Saline (DPBS) (without Ca and Mg), then 1 ml trypsin was placed into the Petri dish for
approximately 1 minute to detach the cell from the bottom of the Petri dish. After cells
begin to round–up and detach, cell culture media was put into the Petri dish. Before
passing the cell onto the surface of the UMTA biochip using a pipette, medium was
gently agitated to fully detach and break the clumps of the cells. Then, the cells were
transferred onto the surface of the UMTA biochip that is placed at the bottom of 6–in
glass Petri dish. Then, 100 ml of tissue culture media (90% DMEM + 10% FBS + 100
U/ml PS) was added to obtain the total tissue culture media volume of 110 ml in the glass
Petri dish with the UMTA biochip placed at the bottom (Note: the 6–in glass Petri dish,
the UMTA biochip, and peripheral accessories are thoroughly cleaned with ethanol
solution and sterilized under the UV light prior to the cell passing). Then, the glass Petri
dish was put back into the incubator and GFP–expressing LU1205 melanoma cells were
allowed to seed and grow for approximately 36 hours on the device surface until the cells
were approximately 70% confluent. Figure 9–2 shows GFP–expressing LU1205
melanoma cancer cells in the Petri dish after 48 hours of culturing (the cells reached
about 90–100% confluent in this case). Figure 9–3 shows the optical microscope image
of melanoma (LU1205) cancer cells grown on the surface of UMTA biochip after 24
hours of incubation.
242
Figure 9–2. GFP–expressing Melanoma cancer cells (LU1205) after ~48 hours of
incubation in the cell culture medium (90% DMEM + 10% FBS + 100 U/ml PS) at 37 ˚C
and 5% CO2 flow (~90–100 % confluent). The scale bar is 10 µm. The image was taken
with inverted fluorescent microscope.
Figure 9–3. Melanoma LU1205 cancer cells grown on the surface of the UMTA
biochip. (A) Low magnification optical microscope image of the device surface with
gold electrodes and interconnections lines. (B) High magnification showing melanoma
cells growing on top of a 25–µm square electrode with Parylene C film between them.
What underneath the electrode is the micro–sized transducer, which will be driven to
generate ultrasonic waves. The images are taken under an upright microscope.
[Courtesy of An Cheng]
243
9.2.2. Quantum Dots Tracking
Successful sonoporation will create transient holes/disruptions in the cell membrane
and facilitate/enhance the transport of some molecules into the cytoplasm by means of
passive diffusion. Therefore, by tracking and monitoring the concentrations of these
molecules inside the cells, the level of cell membrane permeabilization and the degree of
delivery enhancement can be determined. In this study, Quantum Dots (QDs) were used
as an optical probe to quantify the level of cell membrane permeabilization after LU1205
cells were exposed to the ultrasound generated by micro–transducers. There are six
reasons for choosing QDs:
(1) QDs are biocompatible (non–toxic) and have been used in bio–labeling and bio–
imaging in recent years. They have been tested as “smart” targets for diagnostic
or therapeutic purpose in the areas such as cancer detection and drug delivery
[Rosenthal et al., 2002]. Furthermore, they are stable inside the cell without
causing noticeable change in cell characteristics.
(2) The size of QDs (5–20 nm) is comparable to those of most drug molecules and
gene products such as plasmid vectors [Muller–Borer et al., 2007; Papazoglou et
al., 2004].
(3) QDs have unique properties such as enhanced brightness, narrow emission
intermolecular distances; (C) intracellular lipid trafficking to the plasma membrane in
order to prevent cell membrane rupture (cytoprotective mechanisms); (D) plasma
membrane stress failure (represents the short–term failure of cytoprotective
mechanisms). There is evidence that plasma membrane breaks are nonlethal and that
they are resealed by site–directed exocytosis. After [Valahakis and Hubmayr, 2000].
It is conceived that when an adherent cell on the surface of the UMTA biochip is
subject to mechanical stress due to ultrasonic radiation pressures generated by the
micro–transducer beneath the cell, the plasma membrane undergoes elastic deformation
(i.e., stretching). Above the threshold pressure, stress failure occurs and plasma
membrane ruptures resulting in transient wounds in the cell membrane (figure 9–12)
[Schlicher et al., 2006].
262
Figure 9–12. (A) Cell membrane of adherent cell on the active area of UMTA biochip in normal steady state before being exerted by ultrasonic radiation pressure. (B) Cell membrane is stretched due to radiation pressure/force exerted by ultrasonic wave resulting in the increase in plasma membrane intermolecular distances. Lipid vesicles from intracellular origin travel to the cell membrane to prevent from membrane rupture. (C) Failure and rupture of the cell membrane above the threshold radiation
pressure. Transient wound is created in the plasma membrane. Intracellular lipids travel to the wound for repair (i.e., membrane patching).
Experimental results (control experiment) show that the endocytosis–driven QD–
uptake by LU1205 cells is a slow process (Note: It is also conceived that the smaller is the
hydrodynamic diameter of the QDs being used, the slower is the endocytosis–driven QD
uptake. This is because higher binding energy is required between the surface of QD and
the cell membrane in engulfing QDs due to larger radius of curvature in smaller QD).
Time–lapsed fluorescent microscopic images (figure 9–5b) also confirm that significant
(or detectable) amount of QD uptake was observed approximately 2 hours after QDs
were introduced. Furthermore, according to the data from the flow cytometry (figure 9–
7), only 22 percents increase in QD–uptake (referenced from the baseline or control data
with no QD–inoculation) was observed after 24 hours of incubation in the QD–
suspended tissue culture medium. It is hypothesized that the endocytosis–driven QD–
uptake occurs via G–protein coupled receptor–mediated endocytic pathways (illustrated
in figure 9–13) [Zhang and Monteiro–Riviere, 2009].
263
Figure 9–13. QD nanoparticle endocytic pathway in human epidermal keratinocytes (HEKs). QD uptake mechanism and subcellular localization with 24 inhibitors with different inhibitory functions and protein markers for organelles. Lipid rafts, caveolin 1, clathrin, early endosome, late lysosomes are stained (teal). Inhibitors are located near the targets where they exert their functions. Inhibitory effects are labeled in green, whereas no inhibitory effects are labeled in black. Briefly, QDs were first recognized by lipid rafts (CTX as marker, which binds to ganglioside GM1) and get internalized into early endosome
(EEA1 as the marker), then localized in late endosome (CD63) and remained in the lysosome (LAMP1). QD uptake was through the GPCR and the downstream proteins regulated by G–protein, PLC, and PKC. These inhibitors blocked QD uptake, indicating QD endocytosis may be recognized by specific receptors. The inhibitors for scavenger receptors (PolyI and FCD) greatly blocked QDs. LDL/AcLDL competed with QDs and reduced QD internalization suggesting that LDLR/SR–BI may be the most appropriate receptors of QD uptake. After [Zhang and Monteiro–Riviere, 2009].
On the other hand, after the UMTA biochip generated ultrasounds, QD–uptake by
the sonoporated LU1205 cells is very fast (observed after 3–minute sonoporation) and
the level of QD uptake is very significant since ultrasound–induced cell membrane
permeabilization is followed by the passive diffusion of QDs into the cytoplasm through
264
repairable holes/disruptions. The hydrodynamic size (core/shell + polymer) of QDs
used in the experiment is approximately 15–20 nm in diameter [Dabbousi et al., 1997;
Pons et al., 2006; Smith and Nie, 2008; Tomczak et al., 2009; Ocean Nanotech, LLC]
and, thus, it is concluded that the size of transient pores/holes induced in the membrane
must be much larger than 20 nm in order to transport the significant amount of
CdSe/ZnS QDs into the cell by passive diffusion in 6 minutes of post sonoporation.
The consensus is that after ultrasound application, cell membrane permeability
changes (or disruptions/holes are created in the cell membrane) due to radiation
pressure, acoustic cavitation–induced events, shear stress by micro–streaming, and
oscillating fluid. It was observed that at low or moderate input RF powers (0.5 mW, 1.0
mW, and 2.0 mW), the resulting sonoporation has high spatial specificity (or lateral
resolution). At much higher input RF power (80 mW), the degree of spatial specificity
was significantly reduced although there was a higher level of QD–uptake (figure 9–
10B). This is probably due to the fact that, at higher input RF powers, which correspond
to higher ultrasound pressures, more powerful random cavitations are created and larger
mechanical stresses are also imposed on the cell membrane by larger radiation
pressures, more vigorous micro–streaming, and more powerful fluid oscillations.
Therefore, it is hypothesized that the increase in the number of random cavitation events
and much higher stresses induced from these events also permeabilize cells seeded on
the interspaces between individual micro–transducers resulting in low spatial resolution
of sonoporation. Another reason for resulting low spatial resolution (or low site–
specificity) at high RF powers is that, at high RF powers, pressures from the side lobes of
ultrasonic beam due to radial vibrations of micro–transducers become large enough to
sonoporate cells located on the interspaces. The hypothesized model of enhanced QD–
uptake (or transmembrane QD transport) after sonoporation with UMTA biochip is
illustrated in figure 9–14.
265
Figure 9–14. Hypothesized model of enhanced QD transport (or transmembrane QD
transport) induced by ultrasound generated from the micro–transducer of UMTA
biochip. The size of the hole/disruption in the membrane is concluded to be much larger
than hydrodynamic diameter of quantum dots.
9.4.1. Ultrasound–Induced Wound Model
Ultrasound–induced mechanical stresses produce transient wounds in the cell
membrane [Schlicher et al., 2006]. Unlike electroporation, where the pores are small
(1–10 nm in radius) and short lived (a typical lifetime from milliseconds to seconds)
[Weaver and Chizmadzhev, 1996], the induced wound is large (> 150 nm in radius) and
is resealed in a transient manner with lifetime τw > 1 minute via a spontaneous and active
(ATP– and Ca2+–dependent process) healing response involving endogenous vesicle–
based membrane resealing [McNeil et al., 2000; Schlicher et al., 2006; Terasaki et al.,
1997; Togo et al., 1999; Zarnitsyn et al., 2008; Zhou et al., 2008]. In this process,
intracellular vesicles are recruited to the site of the wound and their lipid material is used
266
to patch the membrane. Due to the presence of cytoskeletons (e.g., Spectrin spacing)
and their associated proteins supporting the cell membrane, the wound cannot be
treated as a complete open hole. Rather, the wound opening behaves like a sieve with
nano–size pores as large as 50–70 nm in radius [Zarnitsyn et al., 2008]. The wound can
be modeled as a circular opening of radius Rw with nano-size pores of radius dpore/2
within the wound (figure 9–15A) [Zarnitsyn et al., 2008].
Figure 9–15. (A) Ultrasound–induced wound model for one wound (Rw is wound radius; j is
QD flux density; r is the distance from the center of the wound to the point of QD–flux
measurement; Deff is effective diffusion coefficient of QDs; dpore is the averaged diameter of
nanopores within the wound; h is cell membrane thickness; t is time; Cin and Cout are the
intracellular and extracellular concentrations of QDs respectively). (B) Ultrasound–induced
wound model for multiple wounds for mass transport calculations (Rw,eff is effective wound
radius; Rw,i is the radius of ith wound; N is the total number of wounds). The circumference of
effective wound is equal to the total circumferences of individual wounds combined as the mass
transport is largely along the wound edges. (C) Geometrical model representing the membrane
wound resealing overtime.
267
For large wounds (i.e., Rw >> dpore/2), the flux density weakly depends on the
number, size, and distribution of nano–size pores within the wound and the transport
through the membrane wounds largely depends on the mass transfer along its edges
[Sneddon, 1966]. Research shows that the transient transport of nanoparticles during
the ultrasound application is statistically insignificant in comparison to the post–
sonoporation process [Zarnitsyn et al., 2008] since QDs are transported predominantly
by passive diffusion in the absence of ultrasound–induced activities. It is hypothesize
that at higher ultrasound pressure, larger wound is created (i.e., larger Rw) as
experimental results show higher concentration of intracellular QDs after the membrane
is completely resealed. For a monolayer of small cluster of cells (as well as more wounds
in the single cell), the wound model is modified and induced wounds are estimated as a
single large circular opening with the circumference equal to the sum of circumferences
of individual wounds (i.e., total effective wound radius is equal to the sum of the radius
of individual wounds, figure 9–15B) since the mass transfer is mostly along the edges.
R
w,eff= R
w,ii=1
N
∑ (9–1)
In this modified model, Rw,eff reflects two extreme regimes (i.e., a few numbers of
very large wounds or many relatively smaller wounds) and any moderate regime in
between. During the resealing process, the porosity and pore size within the wound area
decreases over time due to the fusion of lipid vesicles. The process is modeled as overall
wound area (or Rw,eff) decreases over time with the same pore distribution of smaller
nanopores within the wound (figure 9–15C). As the pore size decreases overtime, the
characteristic diffusion time for QDs through the wound (or) the mean effective wound
lifetime (defined as τw,eff), which largely depends on the hydrodynamic diameter (deff) of
the diffusing molecule, is relatively shorter than the actual lifetime of the wound due to
the geometrical hindrance. Since the wound decays exponentially [Zarnitsyn et al.,
268
2008; Zhou et al., 2008], the mathematical model of time–dependent effective wound
radius for the passive diffusion of QDs is described by equation 9–2, where τw,eff is
employed in lieu of the actual wound lifetime.
Rw,eff t( )= Rw,eff
0 e−
t
τw ,eff (9–2)
9.4.2. Calculation of Peak Acoustic Radiation Pressure
By design, the peak ultrasonic pressure exerting on the cell is estimated to be almost
equal to the peak pressure at the end of Fresnel zone (frequency is adjusted so that NFL
is equal to the transducer to cell distance of ~15µm). As high aspect–ratio PMN–PT
micro–transducers are embedded in the Epoxy resin and piezoelectric coefficient d33 >>
d31, the flexural vibration modes are dampened, leaving the longitudinal modes as the
dominant vibration pattern for the transducer operation. The longitudinal vibrations of
the PMN–PT transducer pillar of length L can be described by the wave equation
(equation 9–3) that has the general solution described in equation 9–4.
∂2ξ x,t( )∂x2
=ργ∂2ξ x,t( )∂t2
(9–3)
ξ x,t( )= Ae
j ωt−kx( ) + Bej ωt+kx( )
(9–4)
where ρ, γ, ξ , ω, and k are the density of PMN–PT, Young’s modulus of PMN–PT,
longitudinal displacement of the micro–transducer, frequency of vibration, and wave
number respectively. The micro–transducer is fixed at one end (attached on the glass
substrate with silver Epoxy) and is mass–loaded at the other end (fluid column above the
transducer), and thus, two boundary conditions can be obtained (equation 9–5 and 9–6)
[Kinsler et al., 1999; Uchino and Giniewicz, 2003].
269
Fixed end (no displacement): ξ 0,t( )= 0 (9–5)
Mass support (strain):
∂ξ∂x
x=L
=−f
Sγ+ d
33E (9–6a)
f = ZM
S∂ξ∂t
x=L
(9–6b)
where f , E , ZM, and S are complex force exerted on/by the micro–transducer, complex
electric field applied across the micro–transducer, characteristic acoustic impedance of
water, and cross–sectional area of the micro–transducer respectively. By applying the
boundary conditions to the general solution of the wave equation, the longitudinal
displacement of the micro–transducer (equation 9–7) and peak acoustic radiation
pressure exerted by the micro–transducers onto the fluid column (equation 9–10) can be
obtained.
ξ x,t( )=d
33E γ sin k
nx( )
2ωn
Zm
sin knL( )− j ργ cos k
nL( )( )n=1
∞
∑ Exp j ωnt−
π2
⎛
⎝⎜⎜⎜⎜
⎞
⎠⎟⎟⎟⎟
⎡
⎣⎢⎢⎢
⎤
⎦⎥⎥⎥ (9–7)
Resonance frequencies are:
f
r ,n=
2n−1
4
⎛
⎝⎜⎜⎜⎜
⎞
⎠⎟⎟⎟⎟
c
L
⎛
⎝⎜⎜⎜⎜
⎞
⎠⎟⎟⎟⎟; n = 1, 2, 3, ... (9–8)
Anti–resonance frequencies are:
f
a ,n=
m
2
⎛
⎝⎜⎜⎜⎜
⎞
⎠⎟⎟⎟⎟
c
L
⎛
⎝⎜⎜⎜⎜
⎞
⎠⎟⎟⎟⎟; m = 1, 2, 3, ... (9–9)
270
The magnitude of the peak acoustic radiation pressure is:
ppeak
ω( ) = d33
E1
γ
⎛
⎝⎜⎜⎜⎜
⎞
⎠⎟⎟⎟⎟
2
+1
ZM
ccot
ωc
L⎛
⎝⎜⎜⎜⎜
⎞
⎠⎟⎟⎟⎟
⎡
⎣⎢⎢⎢
⎤
⎦⎥⎥⎥
2⎧⎨⎪⎪⎪
⎩⎪⎪⎪
⎫⎬⎪⎪⎪
⎭⎪⎪⎪
−1
2
(9–10)
where c is velocity of sound in PMN–PT crystal and E = V / L is an applied ac electric
field. Computed peak ultrasonic radiation pressures exerted by the micro–transducers
on the LU1205 cells at 0.5 mW (0.96 Vp–p), 1.0 mW (1.31 Vp–p), and 2.0mW (1.79 Vp–p) at
30 MHz are 0.21 MPa, 0.29 MPa, and 0.40 MPa respectively [note: the output electrical
impedance of RF signal generator (50 Ω) and input electrical impedance of micro–
transducer (figure 7–23) were taken into account in the calculation]. Equation 9–10
shows that the peak pressure is maximum at resonance frequencies and is minimum (or
zero) at anti–resonance frequencies for a particular E or RF power (figure 9–16). The
normalized peak pressure Vs. frequency plot in figure 9–16b also shows that the quality
factor, Q = Δf −6dB( ) f
1,n, is very high for the 25–µm micro–transducers (characterized
by very sharp peaks at resonance frequencies).
271
(A)
(B)
Figure 9–16. (A) Peak ultrasonic radiation pressure (at the end of NFL) Vs frequency for
different RF powers applied to the micro–transducer. (B) Normalized Peak Pressure Vs
frequency plot showing the bandwidth of multi–mode micro–transducer (S = 25µm × 25µm).
272
9.4.3. Mass Transport through the Effective Wound and Intracellular
Concentration of Quantum Dots
According to volumetric mass–balance equations, the rate of change in concentration
(C) of nanoparticles inside a reservoir of volume V is:
V
dC
dt= J
in− J
out (9–11)
where Jin and Jout are time–dependent rate of transport of nanoparticles into and out of
the reservoir respectively. For one single cell, V = Vcell and since there is no significant
QD transport out of the cell (i.e., Jout = 0), the transport of QDs into the cell through the
wound opening can be described as:
V
cell
dCin
dt= J
inQD (9–12)
where Cin is the intracellular concentration of QDs and JinQD is the time–dependent rate
of transport of QDs (or the time–dependent flux of QDs) into the cell.
According to Fick’s 2nd law of diffusion in the cylindrical coordinate system, the rate
of change in concentration of nanoparticles at any point across the cell membrane can be
described as follows:
∂C r,θ , z, t( )∂t
= Deff∇2C r,θ , z, t( )
= Deff
1
r
∂∂r
r∂C r,θ , z, t( )
∂r
⎛
⎝
⎜⎜⎜⎜⎜
⎞
⎠
⎟⎟⎟⎟⎟⎟+
1
r2
∂2C r,θ , z, t( )∂θ2
+∂2C r,θ , z, t( )
∂z2
⎡
⎣
⎢⎢⎢
⎤
⎦
⎥⎥⎥
(9–13)
where Deff is the effective diffusivity of nanoparticles. For the simplified case in which
the concentration gradient of nanoparticles is only in the z direction, the equation 9–13
can be reduced to:
273
∂C z, t( )∂t
= Deff
∂2C z,t( )∂z2
=∂∂z
Deff
∂C z,t( )∂z
=∇ i Deff∇C z,t( )⎡
⎣⎢⎤⎦⎥
(9–14)
Furthermore, at steady–state, equation 9–14 is reduced to Fick’s 1st law of diffusion as
follows:
∇ i D
eff∇C z,t( )⎡
⎣⎢⎤⎦⎥= 0 ⇒ D
eff∇C z,t( )= const =−J (9–15)
where J is the flux of nanoparticles through the cell membrane wound. As the plasma
membrane is very thin compared to the wound size, it is hypothesized that the diffusion
of QDs through the cell membrane wound is steady–state (i.e., the flux of QDs into and
out of the wound are equal at any time during the diffusion although these fluxes are
changing with time). Then, the time–dependent the flux of QDs into the cell is equal to
the time–dependent flux of QDs through the cell membrane wound. Therefore, the
volumetric mass–balance (equation 9–12) and Fick’s 1st law (equation 9–15) can be
combined as follows:
V
cell
dCin
dt= J
inQD = D
eff∇C z,t( ) = D
effC
outt( )−C
int( )⎡
⎣⎢⎤⎦⎥
(9–16)
where
Deff
=h
Dpore
Spores
+1
4Dout
Rw
+1
4Din
Rw
⎡
⎣
⎢⎢⎢
⎤
⎦
⎥⎥⎥
−1
h : Cell membrane thickness
Rw : Radius of the wound
Dout : Diffusion coefficient of QDs in the extracellular region
Din : Diffusion coefficient of QDs in the intracellular region
Dpore : Diffusivity of QDs in the pore
Spores : Total area of nanopores within the wound
Cout : Extracellular concentration of QDs
274
The effective diffusivity of QDs (Deff) is equal to the inverse of the total diffusion
resistance that is comprised of three individual diffusion resistances connected in series
(one resistance from the system of pores in the membrane of thickness h, the others two
are constriction resistances, which arise from the wound openings on the extracellular
and intracellular side of the membrane). From the Stoke–Einstein equation, diffusion
coefficient of quantum dots in the extracellular fluid (or tissue culture media) can be
described as:
D
out=
kBT
3π ηdeff
(9–17)
where
η : Viscosity (0.00078 Pa.s for DMEM)
deff : Hydrodynamic diameter of QDs
kB : Boltzmann constant
Experimental result show that the ratio Din/Dout is approximately 0.3 [Seksek et al., 1997;
and equation 9–16, the rate of change in intracellular concentration of QDs can be
described by the following differential equation:
Vcell
dCin t( )dt
=h
DporeSpores
+1
4Rw,eff0 Exp − t
τw ,eff
⎡⎣⎢⎢
⎤⎦⎥⎥
1
Dout
+1
Din
⎛
⎝⎜⎜⎜⎜
⎞
⎠
⎟⎟⎟⎟⎟
⎡
⎣
⎢⎢⎢⎢⎢
⎤
⎦
⎥⎥⎥⎥⎥
−1
Cout −Cin t( )⎡⎣⎢
⎤⎦⎥
(9–18a)
Initial condition:
C
int
0( )= 0 (9–18b)
where t0 is the time at which QDs are introduced into the extracellular liquid (for
instance, if QDs are introduced immediately after the sonoporation, t0 = 0).
275
By using separation of variables and applying the initial condition, the time–
dependent intracellular concentration of quantum dot can be obtained (equation 9–19).
Cin t( )= Cout 1−Exp −1
Vcell
⎛
⎝⎜⎜⎜⎜
⎞
⎠
⎟⎟⎟⎟⎟h
DporeSpores
+1
4Rw,eff0 Exp − t
τw ,eff
⎡⎣⎢⎢
⎤⎦⎥⎥
1
Dout
+1
Din
⎛
⎝⎜⎜⎜⎜
⎞
⎠
⎟⎟⎟⎟⎟
⎛
⎝
⎜⎜⎜⎜⎜⎜⎜⎜⎜
⎞
⎠
⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟
−1
dtt0
t
∫
⎡
⎣
⎢⎢⎢⎢⎢⎢
⎤
⎦
⎥⎥⎥⎥⎥⎥
⎧
⎨
⎪⎪⎪⎪⎪⎪
⎩
⎪⎪⎪⎪⎪⎪
⎫
⎬
⎪⎪⎪⎪⎪⎪
⎭
⎪⎪⎪⎪⎪⎪
(9–19)
For the wound opening larger than the thickness (i.e., Spores >> h) and for multiple cells
the equation 9–19 can be simplified as follows:
Cin t( )= Cout 1−Exp −1
Vcells
⎛
⎝⎜⎜⎜⎜
⎞
⎠
⎟⎟⎟⎟⎟1
4Rw,eff0 Exp − t
τw ,eff
⎡⎣⎢⎢
⎤⎦⎥⎥
1
Dout
+1
Din
⎛
⎝⎜⎜⎜⎜
⎞
⎠
⎟⎟⎟⎟⎟
⎛
⎝
⎜⎜⎜⎜⎜⎜⎜⎜⎜
⎞
⎠
⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟
−1
dtt0
t
∫
⎡
⎣
⎢⎢⎢⎢⎢⎢
⎤
⎦
⎥⎥⎥⎥⎥⎥
⎧
⎨
⎪⎪⎪⎪⎪⎪
⎩
⎪⎪⎪⎪⎪⎪
⎫
⎬
⎪⎪⎪⎪⎪⎪
⎭
⎪⎪⎪⎪⎪⎪
(9–20)
9.5. Analysis and Simulations
9.5.1. QD–Uptake and the Size of Membrane Wound
The input RF power applied correlates to the radiation pressure of the ultrasound
generated by the piezoelectric micro–transducers. The higher is the input RF power (or
the higher E ), the higher is the ultrasound pressure generated (equation 9–10 and figure
9–16). Furthermore, ultrasound–pressure dependent QD–uptake can be correlated to
the ultrasound–pressure–dependent cell membrane wound size. The size of the cell
membrane wound is a measure of cell membrane permeability after sonoporation.
9.5.1.1. Ultrasound–Pressure–Dependent QD–Uptake
The intracellular concentrations of quantum dots loaded into the LU1205 cells
seeded on the micro–transducers’ active area can be estimated from the experimental
data (figure 9–9) using equation 9–21, where the sum fluorescent intensity spatial
276
profiles (figure 9–9) are fitted as Gaussian function as follows (note: the reason for using
Gaussian function will be explained in section 9.5.4):
Cin
t = 360s, t0
= 0( )=I
sum( )max
3Exp −
x−µ( )2
2σ 2
⎡
⎣
⎢⎢⎢⎢
⎤
⎦
⎥⎥⎥⎥
−∞
∞
∫ dx
⎧
⎨⎪⎪⎪⎪
⎩⎪⎪⎪⎪
⎫
⎬⎪⎪⎪⎪
⎭⎪⎪⎪⎪
× α (9–21)
where α = 1.0413×10−13 [M/A.U.] is a fluorescence–to–intracellular–QD concentration
conversion factor (an empirical conversion factor) and Isum is the sum fluorescent
intensity per element column in the pixel array (see section 9.2.4.2.). The α is
calculated as follows: it is estimated empirically from QD photoluminescence (PL)
spectra that a 2D quantum–dot layer in each pixel area (~278nm × ~278nm) emits
fluorescence intensity of approximately 1000 A.U. and contains ~196 quantum dots. The
averaged thickness of cell monolayer is estimated to be ~5µm (i.e., the volume occupied
by a portion of cell above each pixel area is: Volume = ~278nm × ~278nm × ~5µm).
Then, the fluorescence–to–intracellular–QD–concentration conversion factor (α
[M/A.U.]) can be obtained by the equation 9–22.
α =
# of quantum dots per 2D layer
1000 × Avogadro's # × Volume (9–22)
Using equation 9–10 (peak ultrasound radiation pressure at the end of Fresnel zone),
equation 9–21, and equation 9–22, the estimated intracellular concentrations of QDs,
which are transported into the cytoplasm of the LU1205 cells (seeded on the 25µm–by–
25µm active area presented by a micro–transducer), after post–sonoporation 6–minute
inoculation with QDs for three applied radiation pressures of 0.21 MPa (0.5 mW), 0.29
MPa (1.0 mW), and 0.40 MPa (2.0 mW) are 7.8 ± 2.3 nM, 22.8 ± 1.6 nM, and 29.9 ± 2.5
nM respectively (see figure 9–17).
277
Figure 9–17. Estimated intracellular concentrations of quantum dots, which were transported into the cytoplasm of the LU1205 cells seeded on the 25µm × 25µm active area presented by a micro–transducer, after 6–minute inoculation with QDs (post sonoporation) for three peak ultrasound radiation pressures applied to the cells.
9.5.1.2. Ultrasound–Pressure–Dependent Wound Size
From equation 9–20, ultrasound–induced initial effective wound radius can be
calculated (using to = 0, τw,eff = 40 s for deff = 20 nm [Zarnitsyn et al., 2008], t = 6 min,
Cout = 100nM, and Cin’s from figure 9–17). The initial effective wound radius is a
measure of cell membrane permeability due to sonoporation. The estimated initial
effective wound radius ( R
w,eff0 ) are 150 ± 45 nm, 460 ± 50 nm and 650 ± 70 nm for
applied ultrasound radiation pressures of 0.21 MPa, 0.29 MPa, and 0.40 MPa
respectively. It is observed that the initial effective wound size increases in a linear
fashion with ultrasonic pressure (figure 9–18). Extrapolated plot shows that the
threshold ultrasound pressure
ppeak
30MHz( )( )th
required to induce enhanced QD
transport into LU1205 cells by sonoporation is ~0.12 MPa. The threshold pressure is the
pressure at which cell membrane undergoes stress failure. Below this threshold, the cell
membrane is still intact and may not be permeable enough for QDs to be transported via
passive diffusion.
278
Figure 9–18. Calculated initial effective wound radius ( R
w,eff0 ) for three peak
ultrasound radiation pressures (0.2, 0.3, and 0.4 MPa). Extrapolated plot shows that
the threshold ultrasound pressure required to induce enhanced QD transport into
LU1205 cells by sonoporation is ~0.12 MPa.
9.5.2. Model–Based Simulations and Calculations
9.5.2.1. Analysis of the Influence of Transport–Model Parameters on the
Enhanced QD–Uptake by the Cells Sonoporated with UMTAs
Equation 9–20 can be used to analyze the influence of ultrasound radiation pressure
and wound healing kinetics on the sonoporation–enhanced transport process of
nanoparticles (e.g. QDs) into the cells and will allow us to precisely control and enhance
the delivery of drugs or gene products into the cells using UMTA–biochip platforms.
This section describes the analysis of the influence of ultrasound radiation pressure
(represented by the initial effective wound radius ( R
w,eff0 ) due to the observed linear
relationship between the low– and medium–range pressure to the wound radius in
figure 9–18), mean effective wound lifetime (τw,eff), and the lag time of QD introduction
following the sonoporation (to) on the sonoporation–enhanced QD–transport process
across the cell membrane (using equation 9–20).
279
First, time–dependent intracellular concentrations of QDs transported into the cells
were computed and for: 1), ultrasound–pressure–controlling regime (or the
wound–healing–limiting regime (figure 9–19B), where the enhanced transmembrane
transport of QDs through the wound is limited by the wound healing dynamics
(represented by the characteristic QD diffusion time or mean effective wound lifetime
(τw,eff)); and 3), different lag times (figure 9–19C). These time–dependent plots of
intracellular QD concentrations (figure 9–19A, 9–19B, 9–19C) show the dynamics and
rate of QD–uptake by the cells after 3–minute sonoporation (or the rate of enhanced
QD–transport through membrane wounds) before the cell membrane is completely
resealed. The time–dependent QD concentrations inside the cells will saturate due to
the wound healing/sealing. The plots also show that the initial QD–transport rate is
determined by the initial effective wound size (or the ultrasound radiation pressure) and
the total QD–uptake is determined by both the wound healing dynamics, i.e., the mean
effective wound lifetime (τw,eff), and the ultrasound radiation pressure.
Figure 9–19. Computed time–dependent intracellular concentrations of QDs transported into
the cells for: (A) different initial effective wound size or radius ( R
w,eff0 ), i.e., ultrasound pressure
dependence; (B) for different mean effective wound lifetimes (τw,eff), i.e., wound healing dynamic dependence; and (C) different lag times (to) for QD introduction following the
sonoporation. τw,eff is fixed at 40s in (A) and (C), R
w,eff0 is fixed at 500nm in (B) and (C). The
extracellular concentration of QDs is kept at 100 nM for all calculations.
280
Second, total concentrations of QDs transported into the cells in ~6 minutes after the
site–specific sonoporation were computed for varying R
w,eff0 , τw,eff, and to. The plots in
figure 9–20A and 9–20B show linear dependence of total QD–uptake, which is also
confirmed experimentally, on the applied ultrasonic radiation pressure onto the cell
membrane (represented by R
w,eff0 ) as well as on the mean effective wound lifetime (τw,eff).
On the other hand, the total uptake of QDs by the cells in ~6 minutes after the site–
specific sonoporation decreases exponentially with increasing lag time (to) (figure 9–
20C) mainly due to the fact that the membrane wound decays exponentially [Zarnitsyn
et al., 2008; Zhou et al., 2008]. These analyses provide better understanding of
sonoporation–enhanced transmembrane transport of nanoparticles and will allow us to
precisely control and enhance the delivery of drugs or gene products into the cells by
UMTA–biochip platforms.
Figure 9–20. Computed total concentrations of QDs transported into the cells in 6 minutes after the sonoporation (or until the wound is completely healed) for: (A) different initial
effective wound size or radius ( R
w,eff0 ), i.e., ultrasound pressure dependence; (B) for different
mean effective wound lifetimes (τw,eff), i.e., wound healing dynamic dependence; and (C)
different lag times (to) for QD introduction following the sonoporation. τw,eff is fixed at 40s in
(A) and (C), R
w,eff0 is fixed at 500nm in (B) and (C). The extracellular concentration of QDs is
kept at 100 nM for all calculations.
281
9.5.2.2. Sonoporation–Enhanced Transport and QD–Uptake
Equation 9–20 describes the time–dependent intracellular concentration of QDs due
to site–specific sonoporation by UMTAs. Therefore, to compute the sonoporation–
induced transmembrane permeability (or transport) enhancement factor (φE) and QD–
uptake enhancement factor (υE), a second time–dependent equation that describes the
dynamic of endocytosis–driven QD–uptake is required and is obtained as follows: the
flow cytometry data related to endocytosis–driven QD–uptake by LU1205 cells are
converted into intracellular concentrations for respective QD–inoculation periods (the
data are coupled with time–lapsed fluorescence microscopy and fluorescence–to–
intracellular–QD–concentration conversion factor (α) is also used). The resulting values
are numerically fitted (3rd–order polynomial fitting in Mathematica®) to obtain the
time–dependent intracellular QD concentrations due to endocytosis (equation 9–22).
Moioli, E. K., Shah, B., Clark, P. A., Stroscio, M., and Mao, J. J., 2006. Quantum Dot Labeling of
Stem Cells during Proliferation and Differentiation. Molecular and Cellular Biomechanics. 3
(4), 153–156.
Muller–Borer, B. J., Collins, M. C., Gunst, P. R., Cascio, W. E., Kypson, A. P., 2007. Quantum Dot
Labeling of Mesenchymal Stem Cells. Journal of Nanobiotechnology. 5 (9).
Nyborg, W. L., 1982. Ultrasonic Microstreaming and Related Phenomena. British Journal of
Cancer. 45 (Suppl. V), 156–160.
Nyborg, W. L., 2001. Biological Effects of Ultrasound: Development of Safety Guidelines. Part II:
General Review. Ultrasound in Medicine and Biology. 27 (3), 301–333.
Papazoglou, E., Edrissi, B., Rezvan, A., and Pourrezaei, K., 2004. Q–Dots as Probes for Dynamic
Observation of Intracellular Pathways. Proceedings of the IEEE 30th Annual Northeast Bioengineering Conference. 231–232.
Pitt, W. G., Husseini, G. A., and Staples, B. J., 2004. Ultrasonic Drug Delivery – A General
Review. Expert Opinions in Drug Delivery. 1 (1), 37–56.
Pons, T., Uyeda, H. T., Medintz, I. L., and Mattoussi, H., 2006. Hydrodynamic Dimensions,
Electrophoretic Mobility, and Stability of Hydrophilic Quantum Dots. Journal of Physical Chemistry B. 110 (41).
Rahim, A. A., Taylor, S. L., Bush, N. L., ter Haar, G. R., Bamber, J. C., and Porter, C. D., 2006 (a). Spatial and Acoustic Pressure Dependence of Microbubble–Mediated Gene Delivery
Targeted Using Focused Ultrasound. The Journal of Gene Medicine. 8, 1347–1357.
290
Rahim, A., Taylor, S. L., Bush, N. L., ter Haar, G. R., Bamber, J. C., and Porter, C. D., 2006 (b).
Zhou, Y., Shi, J., Cui, J., and Deng, C. X., 2008. Effects of Extracellular Calcium on Cell
Membrane Resealing in Sonoporation. Journal of Control Release. 126: 34–43.
292
Chapter 10
CONCLUSIONS AND FUTURE WORK
The work presented has shown and demonstrated the many capabilities and benefits
of microfabrication technology (i.e., the science of miniaturization) in the development
of biochips, which is a growing biotechnology industry. The integrated Microelectrode
Array (IMA) chip, which employs microelectrode array architecture, and the Ultrasonic
Micro–Transducer Array (UMTA) biochip, which employs piezoelectric–based micro–
size transducers array architecture, were successfully micro–fabricated. The process
modules, which were developed, characterized, and integrated for the fabrication of
these chips, includes photolithography, laser lithography, high frequency pulsed
electroplating, physical vapor deposition (PVD), plasma–based reactive ion etching (i.e.,
Reactive Ion Etching (RIE) and Inductively Coupled Plasma (ICP)), plasma enhanced
chemical vapor deposition (PECVD), Chemical Vapor Deposition (CVD), furnace
annealing/passivation, wet chemical etching, chemical mechanical planarization/
polishing (CMP), and wire bonding. However, two processes developed for fabrication of
UMTA biochip will require process optimization in order to improve yields (i.e., more
biochips per processing) and efficiency (i.e., reduction in processing time). These two
process modules are CMP and wire bonding. In this work, these two processes were
carried out mostly by hands using in–house tools and fixtures. The reproducibility (or
precision) was quite poor and the yield was low. Therefore, these two processes should
be optimized for automated processing using industrial–standard precision tools.
It was demonstrated that by integrating with proper instrumentations (such as
phase–sensitive lock–in detection, fluorescent microscopy, etc.), the IMA chip and
UMTA biochips become powerful, robust, and versatile characterization/screening tools.
For instance, bottom–up surface engineering approaches were employed to IMA chips
293
and single–cell level manipulations such as cell separation and cell immobilization on
the working microelectrodes were successfully achieved through surface–mediated
guided cell adhesion. Then, these surface–modified IMA chips, integrated with the
phase–sensitive lock–in amplifier, enable characterization of cell membrane properties
of individual cells immobilized on the working microelectrodes (i.e., cell membrane
characterization at the single–cell level). In these characterization studies, area–
contract–model–based Equivalent Circuit Models (ECMs), which represent the nature of
cell–electrode heterostructure, were employed. These ECMs were used to accurately
interpret the electrical signals transduced from IMA sensor configurations and enabled
successful characterization of the cell membrane properties at the single–cell level.
In the UMTA–biochip–based configuration, an RF signal generator was used to
activate micro–transducers in order to generate ultrasound radiation pressures. These
ultrasound radiation pressures sonoporated (i.e., permeabilized the cell membrane) the
targeted human melanoma cells (LU1205) within the cell monolayer, which is grown on
the surface of the UMTA biochip. Furthermore, fluorescent microscopy and image
processing algorithms were employed to quantitatively estimate the degree of cell
membrane permeability and the level of enhanced nanoparticle (i.e., Quantum Dot)
uptake by the cells due to sonoporation. In these analyses, theoretical models that
represent the nature of ultrasound–inflicted cell membrane wounds and the mechanism
of mass transport of nanoparticles through the wound were employed. These models
enable successful characterization and analysis of sonoporation–enhanced nanoparticle
delivery into the melanoma cells.
Although the working concept of UMTA–biochip–based configuration was
successfully demonstrated in this study, a few methods used to characterize the quality
and performance of micro–transducers require some modifications and upgrades. In
this work, the ultrasound pressures generated by the micro–transducers were estimated
based on model–based calculations and simulations. These calculated values must be
294
confirmed experimentally. In other words, the ultrasonic pressures and intensities
generated by the micro–transducers must be measured experimentally using
hydrophone and the results should be compared with model–based calculations and
simulations. In order to carry out this characterization, the ultrasound exposimetry
setup employed in this study require major modification since the system lacks high–
resolution movement in scanning the hydrophone very close above the UMTA biochip’s
surface. This problem can easily be resolved by installing very high–resolution stepper
motors, high–resolution–pitch positioning shafts, and position sensors to precisely
position and track the hydrophone very close to the top surface of the micro–transducer.
This setup will enable measurement of the actual ultrasound pressures and intensities
generated from the micro–transducers, which are almost equal to the pressures and
intensities at the end of Near Field. Furthermore, RLC networks must be designed and
interfaced between the driving circuit and the micro–transducers in order to minimize
the impedance mismatch so that the power transfer is maximized. This RLC network,
integrated with the multiplexer or the relay switches, will improve the efficiency in
multiplex–driving of individual micro–transducers.
Both experimental and computational results from the cell membrane
characterizations at the single–cell level showed that the IMA–based sensor
configuration provides high sensitivity and can even resolve the subtle differences in the
cell membrane of fibroblast cells (NIH3T3) influenced by different surface chemistries
(i.e., KRGD peptide or fibronectin) being employed on the working microelectrodes.
Moreover, the significance of single–cell–level impedance characterizations in terms of
sensitivity and Signal–to–Noise Ratio (SNR) were also highlighted by comparing the
response signals from single–glioblastoma cell–electrode heterostructure to those of
multi–glioblastoma cell–electrode heterostructure, which were transduced via IMA–
based sensor configuration, upon exposure to toxins (e.g., chlorotoxin). All of these
results render many potential applications of IMA chips in cellular/cell–based sensing.
295
Nevertheless, in the single–cell–level cell membrane characterization study using IMA
chips, only one cell line, i.e., NIH3T3 (fibroblast cell lines), was used as a sensing
element to demonstrate the feasibility of IMA chips in cellular/cell–based sensing. In
order to obtain a complete evaluation on the sensitivity of IMA sensors, impedance
spectroscopy measurements of various cell lines, which possess different densities of
transmembrane ion channels, should be carried out. The impedance measurement
results should be compared with those obtained by traditional patch–clamp techniques.
Depending on the comparison result, the signal transduction and data acquisition
circuitry should be optimized in terms of designs, materials, and/or electronic
components so that such subtle difference in cell membrane properties can be
ambiguously resolved by IMA sensors, offering the highest sensitivity possible.
Furthermore, experimental parameters such as probing frequencies and the degree of
cell spreading and adhesion are also important in optimizing the sensitivity of IMA senor
(or maximizing SNR). Calculations based on the ECM model revealed that there was the
optimum probing frequency bandwidth of IMA sensor configuration where the highest
SNR could be achieved. However, this frequency bandwidth could be shifted if a
different cell line, which has a different density of ion channels in the membrane, was
employed. Moreover, degrees of cell adhesion on a particular type of surface chemistry
could be different for various cell lines. According to the studies on the influence of cell
adhesion and spreading on the IMA sensor’s SNR, differences in degrees of cell adhesion
of various cell lines could affect the sensitivity of IMA sensor configuration. Different
peptides or proteins had to be employed in surface modification methods if the adhesion
of the cell line of interest was poor on the peptides being used. Therefore, impedance
spectroscopy measurements of cell–electrode heterostructures formed with various cell
lines should be carried out in order to evaluate and optimize the IMA sensor response.
Furthermore, in order to evaluate the full potential of IMA chips, real time
monitoring of drug–induced changes such as cellular apoptosis, necrosis, and ion
296
conductivity variations in the cell membrane should be carried out at the single–cell level
using IMA sensing platforms. In these characterization experiments, theoretical models
with parameters, which represent the nature and mechanism of such changes, are
required and, thus, they should be developed for interpretation and analysis of IMA
sensor response. By employing the suitable models, important information regarding
drug–binding kinetics and dose–dependent cell responses to drugs (even at the single
cell level) could be retrieved from the transduced signals. Such investigations will
demonstrate the full potential of IMA chips and will convince many researchers and
scientists in biopharmaceutical and biotechnology industries that IMA sensing platforms
are extremely powerful, versatile, and robust technology for cellular/cell–based drug
screening.
In the site–specific sonoporation experiment using the UMTA biochip, only one type
of cell line (i.e., human melanoma cells (LU1205)) was used for demonstration and study
of sonoporation–enhanced nanoparticle delivery into the cells. However, the threshold
ultrasound pressure required to permeabilize the cell membrane and the mean effective
lifetime of ultrasound–inflicted cell membrane wound could be different if a different
cancer cell line was used. Furthermore, cell viabilities of different cancer cell lines after
sonoporation at a specific ultrasound pressure could also be different. Such varying
properties could result in different level of enhanced nanoparticle uptake in various
cancer cell lines after sonoporation. Therefore, in order to complete the study on the
sonoporation–enhanced nanoparticle delivery into the cells, site–specific sonoporation
of various cancer cell lines should be carried out in the future studies. Additionally, only
one type of quantum dot (carboxylic–acid–derivatized CdSe/ZnS QD) was employed as
an optical probe in the characterization of the mass transfer of nanoparticle through the
ultrasound–inflicted cell membrane wound. In this characterization, the amount of QDs
(estimated from the fluorescence emissions of QDs) transported into the cells was used
297
to access the degree of cell membrane permeabilization after the site–specific
sonoporation. Since the QD diffusion through the wound is also dependent on the size of
QDs, characterizations with various sizes of QDs should also be carried out in the future
studies. In these characterizations, Design of Experiments (DoE) should be developed
and implemented to investigate the QD uptake of a particular cancer cell line for
different sizes of QDs as well as different post–sonoporation lag times after which QDs
are added into the tissue culture media. Not only these characterizations will provide the
information regarding the QD–size–dependent enhanced cellular uptake and mass
transfer through the membrane wound, they also determine the largest size of
nanoparticles that can be transported through the sieve–like wound by passive diffusion
after sonoporation. Moreover, adding QDs (of same size) after different post–
sonoporation lag times (note: experiments must be conducted separately) and
characterizing the resulting intracellular QD concentration using proper mass–transport
models can give information regarding the characteristic diffusion time of a particular
QD for a particular cancer cell line (or the mean effective wound lifetime of a particular
cancer cell line). Therefore, future studies should be emphasized on these
characterizations, which will provide further insight into the sonoporation–enhanced
nanoparticle delivery into the cancer cells. Also, the dynamics of sonoporation–
enhanced QD uptake to that of endocytosis–driven QD intake were contrasted in this
study. It was found that the sonoporation enhanced the uptake of QDs by the cells
significantly. However, it was speculated that the calculated enhancement factors could
vary from one cell line to another cell line as well as from one type of nanoparticle to
another since the rate and mechanism of endocytosis process could differ depending on
the cell line and the type of nanoparticle being used. Therefore, again, a set of
characterizations with the aid of careful Design of Experiments (DoE) should be
conducted in the future studies in order to complete the comparison study.
298
In addition, the site–specific sonoporation study also led to the conclusion that the
ultrasound radiation pressures stretch the cell membrane of adherent cells and when the
exerting pressure is equal to or above the threshold pressure, the cell membrane is
ruptured resulting membrane wounds or openings, which are transient and actively re–
sealed. Owing to the device design and architecture as well as transducer’s miniature
size, the resulting site–specific sonoporation showed high degree of spatial specificity
and resolution (i.e., only targeted cells within the cell monolayer were sonoporated).
With the aid of theoretical models for ultrasound–inflicted cell membrane wound and
for mass transport through the wound openings, ultrasound–pressure–dependent cell
membrane wound size and QD–uptake were also investigated. The results showed that
the higher the ultrasound pressure/intensity, the higher was the level of enhanced QD
uptake due to larger degree of cell membrane permeability (or larger effective wound
size). In this investigation, the effective wound sizes for different ultrasound radiation
pressures were calculated from the experimental data using equations derived from the
models developed. Therefore, in order to observe the true nature and dynamics of cell
membrane wound, in situ monitoring of the cell membrane during and/or after the site–
specific sonoporation should be carried out. Current techniques being used to observe
the cell membrane wounds, which include Scanning Electron Microscopy (SEM),
Transmission Electron Microscopy (TEM), and fluorescence microscopy, require post–
processing (such as sample washing and fixing). Furthermore, fluorescent dyes and
nanoparticles employed are usually invasive for the cells under study. The invasive
nature of these characterizations as well as sample preparation procedures could become
sources of experimental artifacts in characterizing the wound size and wound re–sealing
dynamics. Therefore, the techniques that are less invasive and require little or no post–
processing should be employed to investigate the dynamics of cell membrane during or
after the site–specific sonoporation. For instance, high resolution scanning acoustic
microscopy or atomic force microscopy (using biological AFM probes for liquid
environments) could be employed for imaging of the cell membrane.
299
For long term prospects and impacts, successful cell membrane characterization and
permeabilization as well as successful monitoring of the changes in the cell–electrode
heterostructure at the single–level upon exposure to a toxic chemical (i.e., chlorotoxin)
using micro–fabricated biochips have rendered huge potentials in many applications
such as biosensing, biochemical assay, and transfection assay at multi– or single–cell
level screenings and characterizations. The novel design and architecture of these chips
bring efficiency, improve throughputs, offer multiplexing capability, provide robust
analysis, and overcome the technological challenges and limitations encountered by
many scientists in biopharmaceutical and biotechnology research. For instance, IMA
chips can be implemented in cell–based biosensing applications as efficient and sensitive
biosensors and UMTA biochips can be implemented in drug or gene product assay
platforms as delivery–enhancement sub–systems for improved screening efficiency.
Therefore, not only does the research work conducted in this study have a huge impact
on the biochip industries, it also brings benefits to the biomedical research.
Furthermore, by integrating with state–of–the–art instrumentations, other micro–
/nano–devices such as Micro–Electro–Mechanical Systems (MEMS) and micro–/nano–
fluidic components, and/or micro–array transfer printing systems, the biochips used in
this study can be implemented as sub–systems in next–generation state–of–the–art
cellular and cell–based assay platforms. These platforms will offer high efficiency with
multiplexing capabilities and high screening throughputs for many biochemical assays
and drug discovery research. With the aid of suitable theoretical, analytical, and
computational analysis, not only do these assay platforms bring efficiency and
throughputs, they also become very powerful, versatile, and robust characterization and
screening tools for biotechnology and pharmaceutical industries. Moreover, the
microfabrication processes and device characterization techniques developed in this
study only require minor modifications/optimizations and the technology can easily be
transferred to the industries for large–scale manufacturing. Therefore, it is foreseen that
huge market exists in the near future for the biochips developed in this work.
300
NONTECHNICAL ABSTRACT
The plasma membrane of living cells imposes a physical barrier to most polar and large
molecules. Permeability of the plasma membrane to polar molecules such as ions mainly depends
on the number and the state (either open or closed) of ion channels present in the membrane.
Large molecules such as food particles or drugs are usually absorbed into the cell by a slow
process known as endocytosis. Varying the cell membrane permeability to ions alters the cellular
state (either dead or alive) and activities. In addition, by temporarily breaking or tearing the cell
membrane, the transport of large molecules into the cell can be enhanced.
In assays of drugs that specifically affect the state of ion channels, the efficacy of drugs can be
determined by monitoring the changes in permeability of the cell membrane to the particular ion
of interest and subsequent cellular responses. In these assays, measurements are usually made
on the cell population. However, monitoring the cellular responses at the single–cell level upon
exposure to drugs offers unique advantage. It rules out the influence from cell–to–cell
interactions and is suitable for stochastic model–based assays. Current single–cell–level
techniques such as patch–clamping are inefficient and has very low throughputs.
Successful non–viral gene therapy relies on efficient delivery techniques that transport gene
products (they are large molecules) into the cells. In non–viral transfection and gene delivery, the
cell membrane needs to be broken or torn temporarily to enhance the delivery of gene products.
New enhanced–delivery approaches are being needed in order to improve the efficiency and
throughput in the studies of the genetic events after gene products are transported into the living
cells, i.e., transfection assays.
Biochips, usually consist of arrays of isolated small environments for living cells, are
important emerging tools and enable researchers in biology and medicine to carry out cost–
effective and high–throughput experiments and assays. They are also portable for on–site
experiments and are robust. In this study, two biochips, Integrated Microelectrode Array (IMA)
biochip and Ultrasonic Micro–Transducer Array (UMTA) biochip, were designed and fabricated.
The IMA biochip employs cell–size microelectrode array to measure the permeability of the
cell membrane to ions at the single–cell level. Individual mouse fibroblasts were seeded on the
microelectrodes and electrical impedance of the cell membrane, which represents the membrane
permeability to ions were measured. The IMA biochip architecture has potentials in single–cell
level high through–put cell–based sensing and assays.
The UMTA biochip consists of arrays of piezoelectric micro–transducers employed to
temporarily break or tear the cell membrane at the cellular level. Ultrasonic waves, generated
from micro–transducers, temporarily tore the cell membrane of the human melanoama cells
seeded very close to the transducers. The enhanced transport of large molecules into the cells
after temporarily tearing the cell membrane was confirmed by tracking the transport of quantum
dots, which were used as biocompatible optical probes. The UMTA biochips have high potentials
in non–vial transfection and gene therapy assays at the cellular level as well as in drug screenings
as sub–systems for enhancing the delivery efficiency.
MYO THEIN C U R R I C U L U M V I T A E
Home Address [email protected] College Address348 Blue Course Dr. 212 Earth–Engineering Sciences Bldg.Bldg. 10 Apt. 119 University Park, PA 16802State College, PA 16803 Cellular: (814) 232–0234
EDUCATION
THE PENNSYLVANIA STATE UNIVERSITY, University Park, Pennsylvania, U.S.A. Doctor of Philosophy in Engineering Science and Mechanics (December 2010)
Dissertation: Biochips Employing Microelectrodes and Microtransducers for Characterization and Permeabilization of Cell Membrane at the Whole–Cell and Cellular Level
Cumulative GPA: 3.71/4.00
Bachelor of Science in Electrical Engineering (May 2005) Bachelor of Science in Engineering Science with Honors (May 2005)
Honor Thesis: Modification of the Selectivity of Gold–Nanowire–Based Mercury Sensors by Self–Assembled–Monolayer Functionalization
Cumulative GPA: 3.60/4.00
PUBLICATIONS
Thein, M., Asphahani, F., Cheng, A., Buckmaster, R., Zhang, M., and Xu, J., 2010. Response Characteristic of Single–Cell Impedance Sensors Employed with Surface–Modified Microelectrodes. Biosensors and Bioelectronics. 25 (8), 1963–1969. [doi:10.1016/j.bios.2010.01.023]
Tan, Z., Zhu, T., Thein, M., Gao, S., Cheng, A., Zhang, F., Zhang, C., Su, H., Wang, J., Henderson, R., Hahm, J. –I., Yang, Y., and Xu, J., 2009. Integration of Planar and Bulk Heterojunctions in Polymer/Nanocrystal Hybrid Photovoltaic Cells. Applied Physics Letters. 95, 063510-063513. [doi:10.1063/1.3189083 ]
Buckmaster, R., Asphahani, F., Thein, M., Xu, J., and Zhang, M., 2009. Detection of Drug-induced Cellular Changes Using Confocal Raman Spectroscopy on Patterned Single-cell Biosensors. Analyst. 134, 1440-1446. [doi:10.1039/b900420c]
Asphahani, F., Thein, M., Veiseh, O., Edmondson, D., Kosai, R., Veiseh, M., Xu, J., and Zhang, M., 2008. Influence of Cell Adhesion and Spreading on Impedance Characteristics of Cell-based Sensors. Biosensors and Bioelectronics. 23 (8), 1307-1313. [doi:10.1016/j.bios.2007.11.021]
HONORS AND AWARDS
Penn State College of Engineering Fellowship (2005/2006 academic year) Joseph C. Conway Jr., Memorial Award for outstanding graduate teaching assistant
TEACHING EXPERIENCE
Graduate Teaching Assistant 2006 January – Present Department of Engineering Science and Mechanics The Pennsylvania State University, University Park, Pennsylvania
Graduate Teaching Assistant/Laboratory Demonstrator 2005 June – 2005 December PSU Center for Nanotechnology Education and Utilization The Pennsylvania State University, University Park, Pennsylvania
