QUANTITATIVE DETECTION OF LIVER-RELEVANT BIOMARKERS

BY SERS-IMMUNOLABALED GOLD NANOPARTICLES

BY

WILLIAM MARK PAYNE

A Thesis Submitted to the Graduate Faculty of

WAKE FOREST UNIVERSITY GRADUATE SCHOOL OF ARTS AND SCIENCES

In Partial Fulfillment of the Requirements

for the Degree of

MASTER OF SCIENCE

Biomedical Engineering

July, 2015

Winston-Salem, North Carolina

Approved By:

Aaron M. Mohs, PhD, Adviser and Co-Chair

Adam R. Hall, PhD, Co-Chair

Nicole H. Levi, PhD

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DEDICATION AND ACKNOWLEDGEMENTS

I first acknowledge my adviser, Dr. Aaron Mohs, for his support and guidance throughout this

first year of my graduate school career. His instruction, not only in research but also in learning

the ropes to becoming a scientist, has helped me to develop skills I will use throughout my

career. I would like to thank Dr. Adam Hall, for his direction and for hosting me during the later

months of my time at Wake Forest. His generous support and time have helped me not only to

progress in this project, but also gain exposure to other areas of nanoscience. I would also like to

thank Dr. Nicole Levi, for her guidance and willingness to serve on my advising committee; her

comments and critiques on my thesis research were invaluable to preparing a well-developed

argument. I would also like to thank Dr. Sneha Kelkar, for her guidance, especially during the

first months of my time at Wake, and whose help and example prepared me for the difficulties of

finishing this project. I thank Dr. Tanner Hill, whose academic and morale support has been

invaluable during my first year. Lastly, I dedicate this thesis to my parents, whose support,

guidance, and encouragement has brought me through many difficult times and shaped my

vision, education, and determination for a career in science.

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TABLE OF CONTENTS

List of Figures ................................................................................................................................ iv

List of Abbreviations ...................................................................................................................... v

Abstract .......................................................................................................................................... vi

Chapter 1: Introduction ............................................................................................................... 1

1.1 Lab-on-a-Chip Technology ................................................................................................... 1

1.2 Theory of Surface-Enhanced Raman Spectroscopy and Surface Plasmon Resonance ......... 6

1.3 Motivation, Hypothesis, and Project Specifics ................................................................... 18

Chapter 2: Synthesis of SERS-Immunolabeled Gold Nanoparticles ..................................... 26

2.1 Introduction ......................................................................................................................... 26

2.2 Materials .............................................................................................................................. 26

2.3 Methods ............................................................................................................................... 27

2.4 Results ................................................................................................................................. 31

2.5 Conclusions ......................................................................................................................... 38

Chapter 3: Quantitaive Detection of Human Serum Albumin ............................................... 39

3.1 Introduction ......................................................................................................................... 39

3.2 Materials .............................................................................................................................. 39

3.3 Methods ............................................................................................................................... 40

3.4 Results and Discussion ........................................................................................................ 43

3.5 Conclusion ........................................................................................................................... 51

Chapter 4: Conclusion and Future Directions ......................................................................... 52

References ..................................................................................................................................... 55

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LIST OF FIGURES Chapter 1: Introduction

Figure 1-1: Light Interaction with a Bond ................................................................................................... 7

Figure 1-2: Carbon Dioxide and its Raman Activity ................................................................................. 10

Figure 1-3: Localized Surface Plasmon Resonance (LSPR) ..................................................................... 12

Figure 1-4: Electromagnetic Field “Hotspots” from Nanoparticles of Different Shapes .......................... 16

Figure 1-5: Nanoparticle Dimer Interaction .............................................................................................. 17

Figure 1-6: The Ordinary Raman Spectrum of DSNB deposited on a Silica Nitride chip ........................ 20

Figure 1-7: The SERS Spectrum of the DSNB-labeled Nanoparticle Complex ........................................ 21

Figure 1-8: The 3D Structure of DSNB Chemisorbed onto a Gold Surface .............................................. 23

Chapter 2: Synthesis of SERS-Immunolabeled Gold Nanoparticles

Figure 2-1: Reaction Scheme for the Synthesis of DSNB ......................................................................... 27

Figure 2-2: Reach Scheme for the Synthesis of SERS-immunolabeled Gold Nanoparticles .................... 29

Figure 2-3: Experimental and Literature Raman Spectra of DSNB. ......................................................... 32

Figure 2-4: Raman Spectrum of DSNB-functionalized Gold Nanoparticles ............................................. 33

Figure 2-5: Comparison of Raman Spectra from 250 nm and 40 nm Gold Nanoparticles ........................ 33

Figure 2-6: Fluorescence Spectrum of Secondary Antibody-labeled Nanoparticles ................................. 34

Figure 2-7: Absorbance Spectra of Nanoparticles ..................................................................................... 35

Figure 2-8: DLS Sizing of Nanoparticles .................................................................................................. 36

Figure 2-9: TEM Sizing of Gold Nanoparticles before Modification ........................................................ 37

Figure 2-10: TEM Comparison of Nanoparticles before and after Modification ...................................... 38

Chapter 3: Quantitative Detection of Human Serum Albumin

Figure 3-1: Calibration Curves of 40 nm and 250 nm Gold Nanoparticle ................................................. 44

Figure 3-2: Spectroscopy in a Fluidic Device ............................................................................................ 45

Figure 3-3: Raman Spectra to Evaluate PEGylation Quenching ............................................................... 46

Figure 3-4: Qualitative Detection of Human Serum Albumin ................................................................... 47

Figure 3-5: Optimization of Nanoparticle Concentration .......................................................................... 48

Figure 3-6: Dynamic Range and Limit of Detection for Nanoparticle Detection ...................................... 49

Figure 3-7: TEM Imaging of Analyte-Induced Nanoparticle Aggregation ............................................... 50

Chapter 4: Conclusions and Future Work

Figure 4-1: Anti-GST labeling of Gold Nanoparticles .............................................................................. 53

v

LIST OF ABBREVIATIONS

AuNP: Gold Nanoparticle BSA: Bovine Serum Albumin DCC: 1,3-dicyclohexylcarbodiimide DCM: Dichloromethane DLS: Dynamic Light Scattering DNBA: 5,5’-dithiobis(2-nitrobenzoic acid) DSNB: 5,5’-Dithiobis(siccinimidyl-2-nitrobenzoate) ELISA: Enzyme-Linked Immunosorbent Assay ERL: Extrinsic Raman Label GST: Glutathione S-Transferase HSA: Human Serum Albumin IR: Infra-Red LSPR: Localized Surface Plasmon Resonance NHS: N-Hydroxysuccinimide NP: Nanoparticle PEG: Poly(ethylene glycol) SERS: Surface-Enhanced Raman Spectroscopy SERS EF: Surface-Enhanced Raman Spectroscopy Enhancement Factor SPR: Surface Plasmon Resonance TEM: Transmission Electron Microscopy THF: Tetrahydrofuran µTAS: Micro-Total Analysis System

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ABSTRACT

Lab-on-a-chip technology has the potential to rapidly change the way experiments are conducted

in a variety of fields ranging from medicine to environmental science. Specifically, sensors,

detectors, and monitoring devices are increasingly being miniaturized to perform many

experiments or measurements on a single chip. In this research, we develop an immunolabeled

gold nanoparticle complex capable of detecting liver organoid biomarkers intended for use in a

microfluidic device. Human Serum Albumin (HSA) and α-Glutathione S-Transferase (α-GST)

are liver biomarkers that indicate liver health and damage respectively. Herein we demonstrate

detection of the liver organoid biomarkers at nanomolar concentrations. Through plasmonic

coupling induced by aggregation in the presence of analyte, the SERS signal obtained from the

nanoparticles is dramatically increased. Furthermore, detection is demonstrated in a simple

fluidic device to show the feasibility of implementing an optimized SERS-immunolabeled

nanoparticle for translational application.

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CHAPTER 1

INTRODUCTION Modern medical research demands increasingly rapid, sensitive, and inexpensive sensors and

detectors for a variety of different systems. Medical diagnostics, pharmaceutical research, and

even implanted medical devices rely on the development of complex detection mechanisms.

Lab-on-a-chip technology aims to improve the efficiency of a wide range of biomedical

processes, offering increased control and using less material. Microfluidic devices and

microreactors hold promise in miniaturizing many industrial, point-of-care, and other laboratory

processes. Specifically, microfluidic and lab-on-a-chip strategies offer a way to miniaturize

biomedical sensors to facilitate faster detection of various biomarkers in both point-of-care and

research settings.

Microfluidic biosensors employ a range of detection methods including electrochemical

mechanisms and optical mechanisms such as fluorescence, absorption, and vibrational (Raman

or IR) spectroscopy. Each detection mechanism has inherent advantages; however, optical

methods show the most promise for innovative devices due to the potential for greater specificity

and reuse. Since each molecule has a unique optical fingerprint, spectroscopic mechanisms can

be used to detect analyte with great specificity.

1.1 Lab-on-a-Chip Technology

Micro-Total Analysis Systems

Automation of the drug discovery process is not a new concept. Many large pharmaceutical

companies have invested billions of dollars to acquire robotics and other equipment to automate

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as much of the synthesis, analysis, and high-throughput screening required for modern drug

discovery. Among these innovative approaches is the use of microfluidic (or continuous flow)

systems to automate chemical synthesis and characterization at a small scale [1]. Systems made

of sequential microfluidic instruments capable of performing various types of synthesis,

separation, and characterization, are known as micro-total analysis systems (µTAS) [2].

The use of microfluidic systems can reduce the amount of reagents used below the volumes

possible with standard pipetting, as well as automating and synchronizing the characterization

process [1-5]. Similarly, greater control over experimental conditions such as temperature is

afforded through the use of microfluidics [3, 6]. These advantages over conventional techniques

should increase throughput and efficiency in the drug discovery process [1, 6]. While significant

research has been aimed at developing microfluidic techniques for drug development, many

improvements still need to be made, and miniaturization of many analysis techniques has yet to

be realized.

High-throughput screening is the process of screening potential drug compounds for biological

efficacy as fast as possible [3-8]. Using cell or biomolecule assays, researchers look for

significant difference or change in viability to evaluate early drug candidate potential. Changes

could be observed from mechanisms as obvious as cell death or as minute as slight changes in a

given biomolecule structure. Specifically, this process could be further optimized through the use

of microfluidic detectors or sensors that detect a change in cellular environment. Techniques

such as the enzyme-linked immunosorbent assay (ELISA) and electrochemical detectors are

often used in these high-throughput settings; however the push for more effective and more

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sensitive mechanisms is relentless. Through the development of better sensors, the drug

discovery process could be improved and further automated to save time and resources.

Similar to the analysis of drugs candidate molecules, high-throughput screening can also help to

determine the effects and potency of toxins or agents of biological warfare. In defense-oriented

projects, sensors could be used to detect warfare agents before lethal levels are reached. All of

the described processes could benefit from the development of more advanced sensing and

detection technologies, and microfluidic sensors offer versatile methods for next generation

devices [1, 6].

SERS Lab-on-a-chip technology

Surface Enhanced Raman Spectroscopy (SERS) has grown as a detection technique in recent

years due to the high sensitivity and specificity [9-11]. SERS is the phenomenon of increased

Raman light scattering intensity observed when a (polarizable) molecule is placed near a metal

substrate. While the specifics of SERS as a detection technique will be discussed in a later

section, it is important to note that SERS has been demonstrated as a viable means of single-

molecule detection and the use of antibodies also enables bioselectivity [12-14]. The utility of

SERS in a fluidic device is derived from the ability to use colloidal suspensions or deposited

films, which can allow for inexpensive, small SERS-based devices for detection [10].

The first use of SERS in a fluidic device that proved to be effective and translationally realistic

was presented by the Porter group in 2003 [15]. In this work, the authors implemented a gold

substrate, coated with a monolayer of antibodies, to capture Raman-labeled gold nanoparticles in

4

solution. Only the gold nanoparticles were labeled with Raman reporters, which would in turn

only bind to the capture substrate in the presence of analyte (Prostate-specific antigen in this

particular work). While the authors were able to demonstrate femtomolar detection and high

specificity, the devices were not reusable. This research began modern SERS-based biosensor

research.

SERS research aimed at developing biosensors continued to search for methods of detection

without the use of a label molecule. For example, some groups developed devices that simply

used SERS-active metals as a deposited layer inside the device. While reusable, this method is

less specific than labeled antibody detection mechanisms. An effective example of such devices

include a device by Xu, et al. who patterned a grating onto a silicon chip using standard

photolithography [16]. Here, the authors developed a noble metal substrate grating that could be

tuned to a given wavelength, allowing for surface-enhanced resonance Raman spectroscopy.

Analysis was only possible with intrinsically strong Raman scattering molecules, such as dyes,

and was not specific or suitable to biomolecule detection.

Work similar to that of Porter’s group has been aimed at detecting cancer biomarkers. Several

groups have sought to improve cancer diagnostic technology through the use of SERS [13, 17-

20].Using a gold capture substrate and an elaborate mixing system, Lee, et al. performed the

detection of alpha-fetoprotein, a hepatocellular carcinoma biomarker [21]. While the detection

relied on the same sandwich assay as previous research, the detection of analyte in serum

performed completely on a single device was a step forward for SERS-based microfluidic

sensors.

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Further research was aimed at developing a reusable SERS microfluidic sensor. Capture

substrates cannot be implemented, limiting any SERS technique to the use of colloidal

suspensions of gold or silver nanoparticles of various sizes or shapes. The use of sols has been

demonstrated both with and without the use of microfluidic devices [9, 17, 21-23]. Implementing

SERS with colloidal suspensions in a fluidic device rely on either intrinsically strong Raman

scattering molecules as the analyte or artificially aggregating the nanoparticles in solution [10,

11]. Research presented by Zhou, et al. implemented valves to collect gold nanoparticles at the

site of laser excitation. The nanoparticle aggregation would then allow the deposition of analyte

(bovine serum albumin), which was shown to have an increasing signal proportional to

concentration [19]. For further example of novel device implementation, Strehle, et al.

demonstrated that intrinsically strong Raman scattering molecules, such as crystal violet, could

be detected through the use of a segmented flow microdevice [24]. Segmented flow devices can

reduce analyte buildup which would lead to signal artifacts. The downfall to these kinds of

devices lies in the lack of specificity; to detect target analytes in biological or non-homogenous

solution, greater selectivity is paramount. Modern SERS-based biosensor research is driven to

develop reusable devices with bioselectivity and high sensitivity.

Cutting-edge SERS-based biosensor research leverages developments in nanoparticle geometry,

computational modeling, and improvements in the understanding of SERS and plasmon

resonance to create detection mechanisms with improvements over previously mentioned

methods [25-27]. Specifically, new nanoparticle geometries, such as nanorods, nanoflowers, and

nanostars offer unique morphologies that exhibit enhanced plasmonic and electromagnetic

6

properties [25, 28]. The nanoparticle interactions lead to “hotspots,” where electromagnetic

fields are strong and SERS enhancement is dramatically stronger than on the surface of a

spherical particle. For example, gold nanostars have protruding points from the surface,

presenting a tip enhancement known as the “lightning rod” effect, but also a hotspot in the trough

between protrusions [29]. Furthermore, complex interactions between particles result in even

stronger field enhancements between nanoparticle aggregates, which could be exploited for

plasmonic coupling and therefore a detection mechanism similar to the ones presented in this

thesis [30-32].

1.2 Theory of Surface-Enhanced Raman Spectroscopy and Surface Plasmon Resonance

Raman Scattering Basics

Raman spectroscopy was first discovered in 1928 by C. V. Raman, who observed the scattering

phenomenon from sunlight. Essentially, when light interacts with a molecule, light is either

reflected, absorbed or scattered. Raman spectroscopy is concerned with the scattering of light,

which is a much weaker phenomenon than reflection or transmission. Usually, scattered light

maintains the wavelength of the incident light, which is known as Rayleigh scattering. However,

vibrations in highly polarizable bonds can cause the wavelength of scattered light to shift; Raman

spectroscopy looks at these changes in wavelength (known as Raman shift). Figure 1-1 shows a

simple diagram of light interactions with a molecule, particularly scattering. It should be noted

that light scattering is very weak, and only a small percentage of that light undergoes Raman

scattering.

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Figure 1-1: Light interaction with a bond. Light can either be reflected, absorbed (transmission),

or scattered. Scattering is very weak, and Raman scattering is much weaker than Rayleigh

scattering [33]. In the diagram, v0 is the wavenumber (cm-1) of the incident light, I0 is the

intensity of the incident light, v is the change in wavenumber due to scattering, Is is the intensity

of scattered light, α is the diameter of the molecule, and λ is the wavelength of the incident light.

The discovery of Raman scattering earned the Nobel Prize in Physics in 1930, and many

scientists anticipated that this discovery would lead to many breakthroughs. However, due to the

difficulty in building instruments for Raman spectroscopy, the breakthroughs never came and

research in the field was very limited until the invention of lasers in the 1960s. Research on

Raman spectroscopy exploded in the late 1970s due to a discovery by Fleischmann in 1974,

where a dramatic signal increase was observed when pyridine was adsorbed onto a silver

electrode [34]. This serendipitous observation was the discovery of Surface-Enhanced Raman

8

Spectroscopy (SERS), which began a revolution in the field of Raman spectroscopy and classical

electromagnetism [35]. A burst of research followed seeking to mathematically derive and

explain the enhancements beginning with Van Duyne’s work in 1977, eventually leading to

several theories including chemical and electromagnetic enhancement factors [36]. Ultimately,

the discovery of SERS re-established Raman scattering as a viable method not only for structural

spectroscopy, but also laid the foundation for application in future translational research such as

the biosensors described in this project.

Raman spectroscopy is a vibrational spectroscopy method very similar to infrared (IR)

spectroscopy [14]. Raman and IR spectroscopy are analogous; the two techniques operate with

the same wavelength range of light, depend on vibrational actions of chemical bonds, and can

give insightful information on chemical structure [14]. Fundamentally, the two techniques are

related in that they are complementary; typically, bonds active in IR spectroscopy are inactive in

Raman spectroscopy and vise-versa. This relation is known as the mutual exclusion principle.

For example, water is a strongly IR active, but a relatively weak Raman scattering molecule. The

differences between these Raman and IR spectroscopy can be used to strategically determine

chemical properties and structural information for a wide variety of compounds.

Raman activity depends on molecular polarizability. Mathematically, Raman activity can be

described by the expression:

0 ( 1-1 )

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Here, α denotes molecular polarizability, and q refers to the molecular displacement in 3-

dimensional space [33]. As bonds stretch or bend, the bond will be Raman-active if the

polarizability of that molecule in the different 3-dimensional conformations changes, and the

scattering intensity will be larger as change in polarizability increases [14]. Additionally,

aromatic functional groups will scatter more intensely than aliphatic groups due to the greater

change in molecular polarizability [14]. This relationship is more easily understood in the

following figure, detailing the conformational and polarizability changes in a carbon dioxide

molecule. This figure shows the polarizability ellipsoid of the molecule, where the vibration will

be Raman-active if the size, shape, or orientation of the ellipsoid changes during vibration.

10

Figure 1-2: Diagram of carbon dioxide and its Raman activity. The stretching modes are

presented, of which only v1 is Raman active due to the change in the polarizability. For modes v2

and v3, the polarizability does not change (visible in that the ellipsoids are the same for each

11

conformation q), and thus the bonds are inactive in Raman. The graphs represent the change in

polarizability in terms of Equation 1-1; evaluating the change in polarizability at the origin

determines if a bond will be Raman-active, and as demonstrated by the graphs, the v1 mode is

Raman active while v2 and v3 (both demonstrated by the second graph), are Raman-inactive [33].

Surface Plasmons

SERS is an extension of Raman spectroscopy, so it follows naturally that the theoretical basis of

SERS is similar to that of Raman spectroscopy. The key difference is the presence of a metal

substrate, most often one of the coinage metals such as silver, gold, and copper. However, SERS

has been observed on a number of metal substrates including platinum, sodium, potassium,

indium, and rhodium [14]. Although Raman scattering is significantly weaker than fluorescence,

the use of metallic substrates, especially nanoparticles, results in signal increases that rival the

sensitivity of fluorescence detection. The dramatic enhancement seen on metal substrates is due

to the presence of localized surface plasmons: oscillations of electrons at the interface between a

solid and a solvent or gas made possible by the lattice structure of the metal [37, 38]. Plasmonics

is an entirely separate field in itself, but it should be noted that the electromagnetic interaction

between the metal and an adsorbed or chemisorbed molecule is largely responsible for SERS

enhancement, especially at SERS hotspots.

The interaction between surface plasmons and light is the foundation of the field of plasmonics,

and a popular area of research for nanotechnology. Since surface plasmons are only observable

(and therefore exploitable) on nanoscale materials, most research focuses on particles or nano-

architectures below 100 nm in size [37]. Furthermore, the phenomenon of surface plasmon

12

resonance, where the wavelength of incident light is focused on a resonant frequency, is an

important property in nanomaterials research. The surface plasmon resonance frequency is size

dependent, which enables a lot of information to be obtained about a given nano-structure from

absorption spectroscopy. Figure 1-3 shows a diagram of surface plasmons and interaction with

light.

Figure 1-3: A diagram of localized surface plasmon resonance (LSPR). Surface plasmons

oscillate on the surface of the metal, and interact with incident light. The wavelength at which

the surface plasmons resonate with incident light is known as the surface plasmon resonance

(SPR) frequency. The interaction between light and surface plasmons can offer extensive

information on the nanoscale architecture of the metal substrate [39, 40].

SERS Enhancement Theory

SERS enhancement largely depends on two key factors: chemical enhancement and

electromagnetic enhancement. The electromagnetic enhancement factor is considered to

contribute most of the SERS enhancement observed, with enhancement values on the order of

104 or higher [14]. The chemical enhancement contributes on the order of 102 [14], and in special

scenarios SERS can reach enhancement factors up to 1014 [12, 41, 42]. The overall SERS

enhancement factor is considered to be additive. Calculating the SERS enhancement over

13

standard (non-SERS) Raman spectroscopy is not straightforward, as there are numerous factors

that must be considered when numerically calculating SERS enhancement [42]. Values for SERS

EF have been reported as high as 1014 for single-molecule SERS, in which the authors took

advantage of SERS hotspots in colloidal suspensions [12]. Although true EF values are a source

of controversy, the literature agrees that SERS enhancement can be high enough to detect single

molecules and rival the sensitivity of fluorescence.

In developing a SERS-based sensor for any application, the goal is to show an increase in Raman

scattering with an increasing concentration of a given analyte. In the case of intrinsically strong

Raman scattering molecules, increasing signal is easily obtained as Raman scattering intensity is

proportional to the concentration of the molecule. However, for molecules that are weak Raman

scatterers or for biological molecules, more developed methods are required. To develop a

detection scheme for such molecules, it is important to consider which factors might increase

Raman scattering. Equation 1-2 shows a full expression for the factors affecting Raman

scattering:

∝ ∙ ∙ | | ∙ | | ∙ ( 1-2 )

Where PSERS is the SERS signal obtained, N is the number of Raman-scattering molecules

involved, IL is the excitation intensity of the laser, is the contribution from the

electromagnetic enhancement of the substrate, is the contribution from any chemical

enhancement of the adsorbate, and is a parameter to describe the increase Raman cross

section of the adsorbed molecule over ordinary (non-SERS) Raman spectroscopy. In this

equation, every parameter except for the excitation intensity of the laser can be leveraged as a

mechanism to observe an increase in SERS signal and potentially translated into a detection

14

mechanism. As previously mentioned, the most common and simple way to observe an increase

in SERS signal proportional to a given analyte is to simply increase the number of molecules, N,

participating in SERS. This mechanism works well for intrinsically strong Raman scattering

molecules, as the concentration is directly proportional to SERS intensity. Increasing N may also

be used with an extrinsic Raman label, as demonstrated in research presented by Porter’s group.

In theory, causing an increase in the molecules participating in SERS (N) proportional to analyte

in any way would cause an increase in SERS signal proportional to analyte concentration. This is

demonstrated by Cao and Porter, and was the first mechanism for using SERS as a biosensor [15,

43].

Manipulating the other parameters in Equation 1-2 requires the use more intricate detection

mechanisms. The remaining three parameters, , , and involve manipulating the

electromagnetic, chemical, and quantum mechanical properties of a detection system,

respectively. Manipulation of each of these parameters can result in novel and highly effective

detection systems, further detailed in the next paragraphs.

The electromagnetic parameter depends almost entirely on the substrate, whether a colloidal

suspension (metallic nanoparticles), metal-plated surface, or a combination of nanoparticles and

a capture substrate. The key physical properties to manipulate in this strategy are the surface

plasmons of the metal. The electromagnetic enhancement has been well studied since the very

discovery of SERS, beginning with Van Duyne in 1976 [36, 44]. Significant effort was invested

in determining the exact mechanism of electromagnetic enhancement, including studies of

surface architecture on the nanoscale, inter-particle interactions, single particle theory, particle

15

shape and size, and particle-substrate interactions. The mathematical theory of SERS produced a

renaissance of classical electromagnetism research, aiming to discover the physical phenomena

responsible for the dramatic enhancement of SERS, and seeking to develop translational

applications of this phenomenon [35].

The importance of size and shape of colloidal suspensions for SERS cannot be understated.

Work by Schatz in 2004 produced some diagrams of the electromagnetic hotspots on

nanoparticles of various sizes, and included a diagram of interaction between two spherical

nanoparticles [45, 46]. Figure 1-4 helps to visualize the electromagnetic fields of nanoparticles,

and Figure 1-5 demonstrates the electromagnetic interaction of nanoparticle dimers. It is

important to note, however, that mathematical investigation of the size and shape importance for

SERS has been a highly active area of research since the discovery of SERS, demonstrated by

investigators such as Aravind, Moskovits, Van Duyne, and Schatz [47-57]. Ultimately, the

relationship between particle size and electromagnetic enhancement can be described by

Equation 1-3 [14].

∝ ( 1-3 )

Here, E is the electric field magnitude at the surface of the spherical nanoparticle, E0 is the

magnitude of the incident field, εm is the dielectric constant of the metal (which is wavelength

dependent), and ε0 is the dielectric constant of the solvent or local environment around the

nanoparticle. It should be noted that the dielectric constant will be greatest when 2 , a

maximization that is observed at the localized surface plasmon resonance peaks in absorbance

spectroscopy [14].

16

Figure 1-4: Electromagnetic field “hotspots” seen from nanoparticles of different shapes. Image

(a) demonstrates the electromagnetic hotspot at the tip of a triangular nanoparticle, which is

similar to the strategy used for atomic force microscopy-SER spectroscopy. Image (b) shows a

“capped” nano-triangle, and (c) and (d) show a nanorod and a nano-ellipsoid. Images (c) and (d)

clearly demonstrate the electromagnetic enhancement seen at the tips of the nanoparticles, an

17

enhancement known as the lightning rod effect. The resonant wavelengths are listed for each

particle type at the bottom right of each image [45].

Figure 1-5: Nanoparticle dimer interaction. The electromagnetic enhancement is strongly

dependent on the wavelength of incident light, as indicated by the resonant wavelength. The

hotspot between particles is what has been used to demonstrate single-molecule SERS detection

[12, 45].

The chemical parameters, and , depend on the Raman scattering molecule. Most

often, these parameters are manipulated using a well-studied Raman reporter molecule (such as

DSNB) as an extrinsic Raman label. The chemical contribution of SERS enhancement begins

with the amplification of the ERL’s naturally strong Raman scattering fingerprint. In coupling or

adsorbing to a metallic particle or thin film, the ERL undergoes a physicochemical transition

further enhancing the Raman scattering. Specifically and as previously mentioned earlier in

Chapter 1.2, the vibration will be Raman-active if the size, shape, or orientation of the bond

ellipsoid changes during vibration, and the SERS intensity will be increased with greater

differences in ellipsoid geometry. Seeking to increase the chemical contribution to SERS will

then focus on increasing the magnitude of the geometric deformations the ellipsoid experiences

during vibrations. An increase in the chemical EF for SERS can be observed as simply as a direct

18

bond between the Raman reporter or a change in a given bond angle resulting from adsorption,

solvent interactions, or other bonds in the molecule.

Because SERS is strongly dependent on the quantum mechanical orientation of the bond

ellipsoid, even small changes in some bonds can be observed. For example, the difference in

peptide bond orientation between α-helices and β-sheets in secondary protein structure serves as

the basis for using Raman spectroscopy to determine the ratio of the two secondary structures in

proteins [33]. Furthermore, structural information can be obtained from aromatic and

heterocyclic molecules by leveraging their 3-dimensional structure [58-60].

Another key element of SERS enhancement that can be leveraged for detection is the interaction

between the substrate and the molecule with special attention to the nanoscale structure. For

example, a grated substrate can be used to investigate the bond angle differences[60]. Moreover,

if a chemical interaction can cause a conformational change in the molecule to reduce the

distance between a Raman-active bond and the metallic substrate, such a change could be

observed as an increase in SERS intensity. Conformational changes to increase SERS signal is

further explored with DSNB in Chapter 1.3.

1.3 Motivation, Hypothesis, and Project Specifics

This thesis is concerned with the development of a SERS-based nanoparticle designed for

implementation in a microfluidic device for the quantitative detection of two specific analytes.

This project is a support technology to complement a larger project aimed at testing drugs or

toxins through a simulated biological system. SERS was chosen as the detection mechanism due

19

to the versatility, reproducibility, lack of photobleaching, and compatibility with water (and

subsequently biological solutions). Additionally, the use of colloidal sols presents opportunity

for re-use of devices, rather than single-use disposable methods, which is an important advantage

for high-throughput and highly automated system designs. Ultimately, the bio-selectivity

afforded through the use of antibodies enables this technology to be easily adapted for detection

of many other biomolecules. As other organoid systems are developed and a fully integrated

system is further pursued, a SERS-based immunoassay, especially when implemented in a

microfluidic device, could provide great extensibility.

SERS of DSNB

The Extrinsic Raman Label DSNB (5,5’-Dithiobis(siccinimidyl-2-nitrobenzoate)) was chosen for

this project because it is a well-studied and highly adaptable Raman reporter. DSNB is the choice

molecule for several SERS research groups aiming to develop SERS-based detection methods

[14, 35]. The ability to add antibodies and the disulfide bond for conjugation to metallic

substrates (especially nanoparticles) render DSNB particularly well disposed for use as a SERS

biorecognition.

The Raman spectrum of DSNB is presented in Figure 1-6, and Table 1-1 presents the peak

assignments. The most important peak in the spectrum is the peak at 1337 cm-1, which is the

symmetric stretching mode of the aromatic nitro group. This group has a very strong peak

intensity, making it ideal for highly sensitive measurements. Similarly, the peaks resulting from

sulfur at 742 cm-1 and 1063 cm-1 contribute strongly to the spectral fingerprint of DSNB.

20

Figure 1-6: The ordinary Raman spectrum of DSNB deposited on a silica nitride chip. The peak

assignments are presented in Table 1-1. The most important peak in this spectrum is the aromatic

nitro group symmetric stretch at 1337 cm-1 which is the peak used in sensing and detection for

DSNB as an extrinsic Raman label. This is an experimentally obtained spectrum, with details

further presented in Chapter 2.

It is noteworthy to compare the ordinary Raman spectrum of DSNB to the SERS spectrum in

Figure 1-7. Several bands shift, most notable the peak at 742 cm-1 and 1063 cm-1 which would

both indicate changes in the carbon-sulfur bond.

21

Figure 1-7: The SERS spectrum of the DSNB-labeled nanoparticle complex. DSNB acts as an

extrinsic Raman label for the detection of a protein analyte (human serum albumin in this work),

selectively bound by an antibody. It should be noted the peaks are very similar to the ordinary

Raman spectrum, with notable shifts due to the reduction of the disulfide bond and the

subsequent bonding with the gold nanoparticle. This is an experimentally obtained spectrum,

with details further presented in Chapter 2.

DSNB Peak (cm-1) DSNB-NP Peak (cm-1) Group Mode 522 522 R-NO2 Rocking 742 714 C-S Stretch (aromatic) 849 850 Tri-substituted benzene CH out-of-plane deform 1063 1076 C-S Stretch (aromatic) 1102 -- C-S Stretch (aromatic) 1150 -- C-C/C-N In-plane Stretch 1337 1338 R-NO2 Symmetric Stretch 1458 1468 C-C Aromatic Stretch 1558 1558 R-NO2 Anti-Symmetric Stretch

22

Table 1-1: Peak assignments for the ordinary Raman spectrum of DSNB and the SERS spectrum

of the DSNB-nanoparticle [33].

The use of DSNB enables a discussion on the relevance of bond angle changes affecting

increased SERS intensity. Specifically, some groups have demonstrated that bond angles,

particularly the bonds between sulfur and the metal substrate, can be manipulated to move a

SERS-active functional group close to the metal substrate [58-62]. Using this unique property of

SERS, highly sensitive measurements based on conformation changes are possible. Exploiting

this concept for biosensing would involve strategically moving the reporter group closer to the

metal substrate. While the biosensor presented in this thesis focuses on plasmonic coupling for

the detection mechanism, future generations of this technology may use a conformational

manipulation approach to detect analyte. A 3D graphic of DSNB on a gold surface is presented

in Figure 1-8 to demonstrate the molecular conformation and potential for structural

manipulation. Since Raman scattering is strongly tied to intramolecular physicochemical

properties, the manipulation of factors such as steric strain, pH, and various bonds within the

molecule could result in an increase in Raman scattering intensity and therefor provide the basis

for an improved SERS-based biosensor.

23

Figure 1-8: The 3D structure of DSNB chemisorbed onto a gold surface. The aromatic nitro

group is the key functional group for SERS; the closer the group is to the metal substrate, the

greater the SER intensity. Since SER enhancement is so strongly dependent on distance from the

metal substrate, a change in even a fraction of an angstrom can cause an increase in SER

intensity.

SERS-Immunolabeled Nanoparticle Detection

Liver organoids have been demonstrated as a potential option for early screening of drug

candidates or toxins [63]. Herein, we aim to quantitatively detect human serum albumin, a

24

biomarker that indicates healthy liver function. Additionally, future work and preliminary data

aim to show the ability to detect other biomarkers using the same mechanism, such as

glutathione S-transferase, a liver damage biomarker in Chapter 4. Ideally, the SERS-

immunolabeled nanoparticle sensor could be used to quantitatively detect the concentrations of

specific biomarkers (expanding beyond liver-relevant biomarkers) for application in regenerative

medicine and drug development.

We demonstrate the detection of human serum albumin using SERS-immunolabeled gold

nanoparticles by allowing the protein to bind to the antibodies on the nanoparticle surface.

Through the use of polyclonal antibodies, it is possible for proteins to bind to more than one

nanoparticle, creating a nanoparticle dimer. As protein-antibody binding continues, the

accumulation of nanoparticles leads to a cluster, which demonstrates an increase in SERS signal

obtained [14, 45, 46, 64]. As detailed in Chapter 1.2, plasmonic coupling and electromagnetic

interaction between nanoparticles results in SERS “hotspots,” amplifying the signal obtained [45,

46, 65, 66]. Since this aggregation is due to the presence of human serum albumin, the increase

in signal will be proportional to the amount of protein present. Therefore, this aggregation effect

can be used to quantitatively detect protein in solution.

SER spectra are obtained both on a silica nitride chip and in a simple, prototype fluidic device.

The silica nitride chip is inert and does not show any Raman signal, providing clean and easily

interpreted SER spectra. However, since the overarching goal of the project is to develop a

microfluidic biosensor, the use of a prototype fluidic device demonstrates the feasibility of using

a SERS-based immunoassay inside a fluidic device. The device background can be subtracted,

25

and due to the inherently sharp peaks obtained in Raman spectroscopy, peak interference is not a

significant concern. Additionally, handling nanoparticle solution and performing spectroscopy

can be more easily accomplished in a fluidic device, where instrument focusing can be more

precise.

Ultimately, this work is the beginning of the development of support technology for regenerative

medicine, and enables further research for a more sophisticated detection mechanism. While

nanoparticle synthesis and the qualitative detection presented offer promising results, the

limitations of this work arising from very high concentrations of nanoparticles overwhelmingly

reduce any realistic application. SERS-based detection for biological analytes has been

demonstrated for a number of translational applications, including DNA [67], viral antigens [68],

cancer biomarkers [15, 17, 18, 22, 23], and proteins [13, 17, 69-73], and with an improved

nanoparticle design, this research could lead to a novel, complete device for a quick, accurate,

and sensitive biosensor.

26

CHAPTER 2

SYNTHESIS OF SERS-IMMUNOLABELED GOLD NANOPARTICLES

Introduction

Since proteins are weak Raman scattering molecules, limited by fluorescence and low

specificity, a nanoparticle-based sensor is more ideal that relying on the intrinsic optical

properties of a given protein. In order to develop a nanoparticle-based biosensing device, the

nanoparticle must incorporate an extrinsic Raman label (ERL) and an antibody for

biorecognition. Future generations of this device will be used for detection in cell culture media,

so any increase in SERS signal must be specific. Furthermore, the use of an ERL allows for a

lower limit of detection and a predicable spectral fingerprint. Making use of a well-studied ERL

such as DSNB benefits from established procedures, and enables a focus on the translational

application. In this chapter, a SERS-immunolabeled gold nanoparticle complex is developed,

which shows both a strong Raman scattering signal as well as predilection for biorecognition.

Materials

Spherical gold nanoparticle colloidal solutions (40 nm and 250 nm diameter) were purchased

from Ted Pella, Inc. 5,5’-dithiobis(2-nitrobenzoic acid) (DNBA), N-Hydroxysuccinimide (NHS)

1,3-dicyclohexylcarbodiimide (DCC), Bovine Serum Albumin (BSA), Human Serum Albumin

(HSA), sodium azide, boric acid, and Trisma base were purchased from Sigma-Aldrich (St.

Louis, MO). Tetrahydrofuran (THF) and acetonitrile were purchased from Fisher scientific

(Pittsburg, PA). All water was obtained from either a Barnstead NANOpure Diamond (Thermo

27

Scientific; Waltham, MA) or a MilliQ (Millipore; Billerica, MA) system producing 18.2 MΩ

water. Antibodies were purchased from Abcam (San Francisco, CA).

Methods

Synthesis of DSNB

The first step in synthesizing the immunolabeled gold nanoparticles was the synthesis of 5,5’-

Dithiobis(siccinimidyl-2-nitrobenzoate) (DSNB) for use as the extrinsic Raman reporter. DSNB

can be readily synthesized from commercially available 5,5’-dithiobis(2-nitrobenzoic acid)

(DNBA). The reaction scheme is presented in Figure 2-1.

Figure 2-1: Reaction scheme for the synthesis of DSNB. DNBA is reacted with DCC and NHS

to add a succinimidyl group to the carboxylate group on DSNB.

To synthesize DSNB, 300 mg of DNBA was added to a 25 mL round bottom flask, and the

atmosphere replaced with nitrogen. 10.0 mL of dry tetrahydrofuran (THF) was added to the flask

to dissolve the DNBA. To prepare the reaction flask, 350 mg of 1,3-dicyclohexylcarbodiimide

(DCC) was added to a 50 mL round bottom flask, which was then capped and the atmosphere

replaced with nitrogen. 10.0 mL of dry THF was added dropwise via syringe, and after the DCC

was dissolved, the flask was placed in an ice bath. The previously prepared DNBA in THF

solution was then added dropwise via syringe and the solution stirred for 30 minutes. A solution

28

of N-Hydroxysuccinimide (NHS) in THF was prepared by adding 200 mg of NHS to a 25 mL

round bottom flask, which was then capped and the atmosphere replaced with nitrogen. After the

30 minutes of stirring in the reaction flask, the NHS solution was added dropwise via syringe.

The reaction was allowed to continue overnight.

To purify the DSNB product, vacuum filtration was performed as the byproducts are insoluble in

THF. The precipitate was washed with THF, and the filtrate was collected and placed into a 250

mL round bottom flask. The THF was evaporated using a rotoevaporator for 40 minutes at 60°

C. The dry precipitate was the DSNB product, which was collected from the round bottom flask

and weighed.

Verification of pure product was completed by thin layer chromatography. 1.0 mg of DSNB and

1.0 mg of DNBA were each added to separate samples of 1.0 mL of dichloromethane (DCM). To

prepare for chromatography, 19.0 mL of DCM and 1.0 mL ethanol were added to a 500 mL

beaker which was then covered with a glass dish. The DSNB and DNBA solutions were spotted

onto a TLC plate, and the plate placed in the beaker containing the DCM/ethanol solution.

Synthesis of SERS Immuno-labeled Gold Nanoparticles

To synthesize the immuno-labeled gold nanoparticles, the extrinsic Raman label DSNB was first

conjugated through disulfide reduction. Conjugation was performed by adding 1.0 mL of DSNB

solution (1.0 mg/mL in acetonitrile) dropwise to 3.0 mL of 40 nm gold nanoparticle solution

(9×1010 nanoparticles/mL) and allowed to react for 3-5 hours under constant stirring in a

scintillation vial. The reaction schematic is presented in Figure 2-2. The solution was then

distributed into Eppendorf microcentrifuge tubes, and purified by centrifugation at 16g for 5

29

minutes. The sample supernatant was removed and discarded, and replaced with borate buffer (2

mM, pH 9.0). Centrifugation was performed twice more to ensure all unreacted DSNB was

removed from the sample.

Figure 2-2: Synthesis of SERS-immunolabeled gold nanoparticles. DSNB is conjugated to gold

nanoparticles via disulfide reduction, and the antibody is added to the SERS-nanoparticle by

replacing the succinimidyl group on the DSNB Raman reporter.

To verify conjugation of DSNB, Raman spectroscopy was on a concentrated centrifuge pellet.

The previous centrifugation ensured no excess unreacted DSNB remained. To obtain the

spectroscopy sample, 1.0 mL of DSNB-AuNP sample was centrifuged at 16g for 5 minutes and

concentrated to 50 µL. The centrifuge pellet was then placed on a silica nitride chip and Raman

spectroscopy performed with an integration time of 2 seconds averaged over 4 exposures.

Conjugation of the anti-human serum albumin antibody could then be achieved through

succinimidyl substitution. The nitrogen terminus of an antibody would replace the succinimidyl

group of the DSNB bonded to the gold nanoparticle. This reaction schematic is shown in figure

2.3. Experimentally, 7.0 µL of antibody solution (5.0 mg/mL, Abcam) was added to 1.0 mL of

DSNB-AuNP solution. The solution was allowed to react for one hour, then purified by

centrifugation at 16g for 5 minutes. The sample supernatant was discarded and replaced with

Tris buffer (2 mM, 1.0% BSA, 0.1% NaN3, pH 7.5). The bovine serum albumin (BSA) in the

30

buffer was intended to limit non-specific binding. Centrifugation was performed twice more to

ensure any unreacted antibody was removed from the nanoparticle sample.

As the project progressed, the size of the spherical gold nanoparticles used was increased from

40 nm to 250 nm. This transition was an attempt to increase the signal obtained from the

nanoparticles and facilitate quantitative detection of analyte. Theoretically, the plasmon

resonance should be greater with a larger nanoparticle and plasmonic coupling between

nanoparticles should be observed through the aggregation induced by the presence of analyte,

similar and related to work inspired by Zhou, et al. and as indicated in the introduction [19]. To

compare signal increase, Raman spectroscopy was performed on samples of DSNB-AuNPs of

both 40 nm and 250 nm diameter of concentrations calibrated to the stock concentration for each

particle size (9×1010 NP/mL for 40 nm AuNPs, 3.6×108 NP/mL for 250 nm AuNPs). To obtain

the spectra, 3.0 µL of each sample was placed on a silica nitride chip, and instrument settings set

to an integration time of 2.0 seconds averaged over 4 exposures. As seen in Figure 2.6, the

Raman intensity is substantially higher from the 250 nm nanoparticles than the 40 nm

nanoparticles.

The 250 nm nanoparticles were also functionalized with poly(ethylene glycol) (PEG) by adding

thiol-PEG of length 800 repeat units (Sigma Aldrich) to the nanoparticle during the conjugation

of DSNB as found in reference. Nanoparticle stability became a problem when using the larger

nanoparticles, and the addition of PEG resulted in increased stability and ease of use, especially

in later detection experiments. Experimentally, 1.0 mL of Thiol-PEG solution in DI water (2

mM, Sigma-Aldrich) was added to the DSNB-AuNP reaction 30 minutes after adding DSNB to

31

the gold nanoparticle solution. Although the PEG coating was not visible in TEM imaging due to

the size of the nanoparticles, conjugation could be confirmed by the increased stability observed

from the samples.

Results

Synthesis of DSNB

Chromatography was performed until the solvent front reached about 75% plate height, then

removed and the solvent front marked in pencil. The solvent was allowed to evaporate, and UV

light used to determine sample spots. Rf (retention factor) values were then calculated; the

DSNB sample had an Rf value of 0.98 while the DNBA (starting material) sample showed an Rf

value of 0.0, indicating complete reaction. It should be noted that no other starting materials or

byproducts could remain, as byproducts were precipitated out during filtration and no spots

remained at the origin of the DSNB sample on the chromatography plate.

Verification of the DSNB product was further shown through Raman spectroscopy. To prepare

the spectroscopy sample, 1.5 mg of DSNB was added to 1.0 mL of borate buffer (2 mM, pH 9.0).

A silica nitride chip (4 mm x 4 mm) was placed on a hotplate and heated to 60 °C, and droplets

of DSNB solution were added to deposit DSNB on the chip. 300 µL of DSNB solution was used.

After the solvent had dried, the chip was removed and allowed to cool. Raman spectroscopy was

performed and the spectrum compared to literature spectra as seen in Figure 2-3.

32

Figure 2-3: Experimental Raman spectrum (a) and literature Raman spectrum (b) of DSNB. The

expected peaks are present, indicating successful synthesis of DSNB [15].

DSNB Conjugation

Conjugation of DSNB to the gold nanoparticles was confirmed by Raman spectroscopy after

centrifugation. The data are shown in Figure 2-4, which clearly shows the SERS spectrum of

DSNB when compared to the spectra in Figure 2-3.

33

Figure 2-4: Raman spectrum of DSNB-functionalized Gold Nanoparticles in a centrifuge pellet.

The presence of the DSNB characteristic peaks after centrifugation indicates covalent linkage of

DSNB to the gold nanoparticles.

40 nm vs. 250 nm Gold Nanoparticles

The larger diameter nanoparticles resulted in an increase in SERS signal obtained. This increase

is shown clearly in Figure 2-5. Furthermore, the use of larger gold nanoparticles should result in

an increase in plasmonic coupling, the basis of detection experiments.

Figure 2-5: Comparison of Raman spectra from 250 nm and 40 nm gold nanoparticles labeled

with DSNB.

Antibody Conjugation

Antibody conjugation was confirmed through fluorescence spectroscopy by the addition of a

secondary antibody. Goat anti-Rabbit antibody labeled with Texas Red fluorescent dye was

added to the nanoparticle complex to show the antibody remained after centrifugation.

Experimentally, 7.0 µL of Goat anti-Rabbit Texas Red was added to a 1.0 mL sample of

nanoparticle complex. The sample was then purified by centrifugation to remove excess

34

unreacted secondary antibody three times, replacing the supernatant with Tris buffer.

Fluorescence spectroscopy was performed with the excitation wavelength set to 565 nm. Texas

Red has a fluorescence peak at 613 nm, and the experimental spectra are presented in Figure 2-6.

The presence of the peak at 613 for the sample with the secondary antibody indicates the

conjugation of the anti-HSA antibody to the nanoparticle complex.

Figure 2-6: Fluorescence spectroscopy of secondary antibody-labeled nanoparticle complex

contrasted with nanoparticle control sample. The extra peak at 613 nm indicates the presence of

the fluorophore and therefore the presence of the anti-HSA antibody.

The nanoparticle complex was further characterized by absorption spectroscopy to demonstrate

the lack of aggregation. The surface plasmon peak remains at the same wavelength as

demonstrated in Figure 2-7. Here, a wavelength shift would indicate aggregation; furthermore,

the presence of BSA is shown not to induce aggregation as the nanoparticles are dispersed in a

buffer containing BSA.

35

Figure 2-7: Absorbance spectra of DSNB-functionalized and stock gold nanoparticles. The

consistency of the peak around 525 nm between samples indicates a lack of aggregation.

Lastly, TEM images and DLS sizing graphs of both nanoparticle sizes were obtained. The DLS

data is presented in Figure 2-8, with polydispersity and effective diameter data listed in Table 2-

1. The average size of the nanoparticles from TEM imaging is shown in Figure 2-9. Figure 2-10

demonstrates the comparative differences of 40 nm gold nanoparticles both before and after

conjugation of DSNB and antibodies.

36

Figure 2-8: DLS sizing of nanoparticles both before and after functionalization. Graph (a)

presents DLS for stock 40 nm gold nanoparticles, and graph (b) shows DLS of nanoparticles

after the addition of DSNB and antibodies. Graph (c) shows DLS data for stock 250nm gold

nanoparticles, and graph (d) demonstrates the increase in diameter resulting from the addition of

PEG, DSNB, and antibodies. These data are summarized in Table 2-1.

Sample Effective Diameter (nm) Polydispersity Index Stock 40 nm AuNPs 44.15 0.098 Ab-DSNB-40AuNPs 79.29 0.279 Stock 250 nm AuNPs 249.89 0.112 Ab-DSNB-PEG-250AuNPs 284.71 0.133

Table 1-1: Summary of the dynamic light scattering data comparing functionalized and stock 40

nm and 250 nm gold nanoparticles.

37

Figure 2-9: Sizing of nanoparticles before modification. Images (a) and (b) depict stock 40 nm

nanoparticles. The bottom of the figure shows 250 nm nanoparticles, before (c) and after (d)

functionalization, consistent with DLS data presented in Figure 2-8.

38

Figure 2-10: Comparison of functionalized 40 nm nanoparticles (a) with stock 40 nm gold

nanoparticles (b). In (a), a slight ring is seen around the nanoparticle. The presence of antibodies

is also more apparent in Figure 3-6.

Conclusions

In this section of the thesis, SERS-immunolabeled nanoparticles are synthesized and

characterized. Spherical gold nanoparticles of 40 nm or 250 nm in diameter are functionalized

with a monolayer of an extrinsic Raman label, DSNB. Antibodies could then be conjugated to

the DSNB by replacing the succinimidyl group. These nanoparticles were characterized by

Raman spectroscopy, absorption spectroscopy, transmission electron microscopy, and dynamic

light scattering. In the next section, the synthesized nanoparticles are used for detection

experiments.

39

CHAPTER 3

QUANTITATIVE DETECTION OF HUMAN SERUM ALBUMIN WITH SERS-IMMUNOLABELED GOLD NANOPARTICLES

Introduction

Using the previously developed SERS-immunolabeled gold nanoparticles, detection methods are

presented which show an increase in SERS intensity in the presence of human serum albumin.

Due to the increased aggregation, plasmonic coupling causes a significant increase in SERS

signal proportional to the concentration of analyte. Two experimental methods for obtaining

SERS data are presented, first using a silica nitride chip which shows no background noise, and

second in a simple fluidic device, demonstrating the possibility of using a nanoparticle-based

assay in a more translationally relevant capacity. TEM images also support the theory of

aggregation-induced plasmonic coupling. While this study is limited due to the high

concentrations of nanoparticles, the implementation of SERS nanoparticles in a device and the

confirmation of research on plasmonic coupling enable future, more optimized generations of

this technology.

Materials

Bovine Serum Albumin (BSA), Human Serum Albumin (HSA), sodium azide, boric acid, and

Trisma base were purchased from Sigma-Aldrich (St. Louis, MO). All water was obtained from

either a Barnstead NANOpure Diamond (Thermo Scientific; Waltham, MA) or a MilliQ

(Millipore; Billerica, MA) system producing 18.2 MΩ water.

40

Methods

Concentration Calibration of Nanoparticles

Calibration curves for both 250 nm and 40 nm gold nanoparticles were created using a known

stock concentration and serial dilutions. To create the curve, absorption spectroscopy was

performed on 1.0 mL commercially obtained stock solution of gold nanoparticles in DI water.

The nanoparticle solution was further diluted with water and concentrations calculated for a

serial dilution of samples as shown in Figure 3.1. Once absorbance spectra were obtained for

each sample, a linear regression of absorbance vs. concentration was created. For the remaining

experiments, 250 nm gold nanoparticles were used.

Procedure for Measurements on Silica Nitride Chips

Nanoparticle samples for Raman spectroscopy were prepared by concentrating samples into

smaller volumes by centrifugation and removing the supernatant. A RamanStation400 Raman

Spectrometer (Perkin Elmer) was used to obtain spectra, with the silica nitride immobilized on a

glass slide. The laser wavelength was 785 nm for all Raman experiments. The dimensional

coordinates were kept constant to ensure the laser was focused on each sample the same. Unless

otherwise noted, spectra were obtained with an integration time of 2 seconds and averaged over 4

exposures.

Fabrication of a Simple Fluidic Device

In order to demonstrate viability in a more translatable system, simple fluidic devices were

fabricated. These devices consisted of glass slide, a pre-drilled polystyrene slide, and an adhesive

layer cut into the shape of the fluidic design. Specifically, an exacto knife was used to cut a

41

pattern into the adhesive, including channels and wells, which was then attached to the

polystyrene slide which had been drilled to attach tubing for input and output. Lastly, the glass

slide was added to complete the device. The device is pictured in Figure 3.2.

Procedure for performing spectroscopy in a fluidic device

Similar to performing spectroscopy on a silica nitride chip, the RamanStation400 was used with

the microscope slide attachment and the laser focused to the measurement well. The dimensional

coordinates were kept constant across samples. Samples were loaded by syringe through the

tubing attached to the device, and spectroscopy performed. Between samples, the device was

washed with buffer solution (Tris buffer, 2 mM, pH 7.5) to clear any residual signal. To calculate

the intensity from the nanoparticles, the peak height at 1337 cm-1 was compared to a

characteristic peak of polystyrene at 1032 cm-1.

Evaluation of PEGylation Quenching of DSNB

Three samples were prepared of each PEGylated and non-PEGylated 250 nm nanoparticle

complexes in different concentrations to investigate any quenching effect that the addition of

PEG might affect. Samples were prepared procedurally identical and as described in Chapter 2,

with the exception that one set of three samples was synthesized without PEG. Raman

spectroscopy was performed on a silica nitride chip, and the spectra compared. This Raman

spectra (Figure 3-3) demonstrates two sets of three samples as representative spectra for the

experiment. This experiment was repeating in triplicate to minimize error, and spectra were

chosen that represented the experimental results most accurately.

42

Qualitative Detection of Analyte

The first experiment to determine if the nanoparticle complex was a viable method of analyte

detection was performed by comparing equal quantities of nanoparticles both with and without

the presence of analyte (human serum albumin). For this experiment, two 1.0 mL samples of

nanoparticles were used, one of which contained 2 mM HSA. The sample containing HSA was

well mixed for one hour, then both samples were centrifuged for 5 minutes at 16g. The samples

were then concentrated to 50 µL by removing the supernatant volume. This experiment was

repeated in triplicate with both 250 nm and 40 nm gold nanoparticles, and a spectrum was

selected and presented that best represented the experimental results.

Nanoparticle Concentration for Analyte Detection

A curve of Raman intensity as a function of nanoparticle concentration, with the presence of

analyte (HSA) and with control samples of bovine serum albumin and no protein present, is

presented to show the optimal nanoparticle concentration for further experiments on quantitative

detection. Three sets of six samples of 0.5 mL were prepared, a sample set of nanoparticle

control (no proteins present), a control sample of nanoparticles with 2 mM bovine serum

albumin (BSA), and a sample of nanoparticles with 2 mM analyte (HSA). Samples were

concentrated by centrifugation to an increasing concentration gradient of nanoparticles. Raman

spectroscopy was then performed to determine the concentration of nanoparticles best calibrated

for observing a signal increase due to the presence of analyte (HSA).

43

Determination of Dynamic Range and Limit of Detection

After calibration of nanoparticle concentration determination, experiments were designed to

determine the linear (dynamic) range of signal increase of Raman scattering intensity in the

presence of HSA. Eight samples containing 1.0 mL of nanoparticle solution of concentration

5.43×108 NP/mL were prepared in a concentration gradient of analyte (HSA) ranging from 50

nM to 100 µM, with control samples of no analyte (0 nM) and saturated (2 mM). Samples were

incubated for an hour at room temperature, and then concentrated by centrifugation at 5g for 2

minutes. The sample supernatant was removed and the samples concentrated to 50 µL. Raman

spectroscopy was performed both on a silica nitride chip as well as in the fluidic device by the

previously described methods.

Results and Discussion

The calibrations curves were constructed as shown in Figure 3-1. The linear regressions of the

absorbance peaks at 525 nm (40 nm nanoparticles) and 616 (250 nm nanoparticles) is included,

which was used to calculate the concentration of nanoparticles according to Beer’s law. The

presented concentration ranges are adequate for experimental samples, as some nanoparticles are

always lost during synthesis procedures. Furthermore, DSNB and biological products do not

interfere with the surface plasmon peaks.

44

Figure 3-1: Calibration curves of 40 nm, (a) and (b), and 250 nm, (c) and (d), gold nanoparticle

solutions. Concentrations were chosen relevant to experimental working concentrations, and

linear regressions constructed to determine the concentration of unknown samples. Calibration

curves were constructed using the plasmon resonance peaks of each size of nanoparticle.

Raman spectroscopy was performed both on a silica nitride chip and in a simple fluidic device.

The silica nitride was used because of its Raman inactivity, Raman spectroscopy could be

performed without any background noise. The simple fluidic device is demonstrated in Figure 3-

2. Although no intricate mixing or processing occurred on the device, the device served as a way

to validate the use of SERS-nanoparticles as a method for detection. Figure 3-2 also

demonstrates the result of detection of the nanoparticles in the device; when compared to the

45

device baseline, the characteristic peaks of DSNB can be seen. This figure demonstrates the

possibility of using fluidic devices with nanoparticles for an integrated biosensor.

Figure 3-2: The simple fluidic device and the result of performing Raman spectroscopy inside

the device. A background scan was performed and the background subtracted from the

nanoparticle solution sample and a subsequent scan of the device with buffer solution to show

negligible residual signal.

PEGylation of nanoparticles increased stability, ease of use, and longevity of dispersion. As

demonstrated in Figure 3-3, the addition of PEG to the nanoparticles did not result in a decrease

in SERS signal. Three different concentrations of nanoparticles show no significant deviation in

SERS intensity with the addition of PEG.

46

Figure 3-3: PEGylation does not appear to significantly alter SERS intensity obtained from the

nanoparticles. These spectra represent the overall trend of an experiment performed in triplicate.

To determine if the SERS-labeled nanoparticles were effective at detecting analyte, a qualitative

analysis experiment was performed. The results are shown in Figure 3-4, in which the sample in

the presence of analyte (HSA) resulted in an increase in Raman scattering intensity at the

symmetric nitro stretch at 1337 cm-1. This experiment confirmed the theory that the presence of

analyte would cause an increase in Raman scattering, providing the basis of quantitative

detection in future experiments.

47

Figure 3-4: Qualitative detection of HSA is demonstrated by the increase in Raman scattering

intensity from nanoparticles in the presence of HSA. The symmetric nitro stretch at 1337 cm-1

from the DSNB conjugated to the gold nanoparticles is the primary peak to observe an increase

in intensity, although all characteristic peaks from DSNB are shown to increase. These spectra

represent the overall trend of an experiment performed in triplicate.

Optimizing the nanoparticle concentration for quantitative detection was the next step in

developing the SERS immunoassay. At lower concentration, a difference is not visible, whereas

higher concentrations show some difference between the sample with the presence of analyte and

the control samples. The results of this experiment are shown in Figure 3-5. It should be noted

that a high concentration of nanoparticles was required to show a difference in signal, which was

a limitation of this method. From these results, it appears the optimal concentration of

nanoparticles is around 1×1010 NP/mL, which is two orders of magnitude higher than the stock

concentrations of nanoparticles.

48

Figure 3-5: Nanoparticle concentration is optimized by finding a concentration where the

difference in intensity observed with the presence of analyte is higher than that of control

samples. In this experiment, concentrations around 3×1010 NP/mL provided the best increase in

signal.

After examining the increase effects in the presence of analyte and determining the optimal

concentration range of nanoparticles for quantitative detection, the dynamic range and limit of

detection was investigated. Theoretically, the Raman scattering intensity should increase with

increasing concentration of analyte (HSA). It is expected that in very low concentrations, the

increase should be statistically insignificant, while a range of concentration increases should

increase signal linearly, and eventually reach a saturation point where the addition of HSA does

not result in an increase in signal. Ultimately, these data should resemble a sinusoidal curve. The

results of this experiment are presented in Figure 3-6.

49

Figure 3-6: The dynamic range and limit of detection graphs. In (a), the experiment was

performed on a silica nitride chip. As detailed in the methods, measurements on a silica nitride

chip were performed by placing a drops of solution onto the chip and focusing the instrument on

the solution. Here, the raw intensity at 1337 cm-1 was used. As expected, little difference is seen

at low concentrations, while a small trend is seen at higher concentrations. However, the large

margin of error limits the conclusions from this experiment. In (b) and (c), the results of the same

experiment performed in a fluidic device are presented, with intensity reported as the ratio of the

peak at 1337 cm-1 (DSNB characteristic peak) and a peak at 1032 cm-1 (a characteristic peak of

polystyrene from the device). The difference in experimental method for obtaining these data

rely on the use of the fluidic device, precisely controlling the volume injected into the device by

using a syringe. The error is much smaller due to the more controlled environment of the device.

50

A slightly linear trend is seen, with intensity gradually increasing as HSA is added. In (c), the

samples are presented in the order of addition, to highlight that residual signal in the device was

not significant, and that the signal gradually increased with each sample.

TEM imaging was performed on 40 nm nanoparticles that had been reacted with HSA. Figure 3-

7 demonstrates the aggregation induced by the presence of analyte. The theoretical basis for our

detection mechanism depends on plasmonic coupling, which is induced by the presence of

analyte.

Figure 3-7: TEM imaging of 40 nm nanoparticles. Image (a) shows a nanoparticle without the

presence of analyte, and (b), (c) and (d) show aggregation resulting from the presence of HSA.

The staining reveals the presence of protein and the antibody coating on the nanoparticles.

51

The results for detection of the nanoparticles both in device and on silica nitride chips appear

promising for application. However, upon close examination of the results from the experiments

concerned with nanoparticle calibration and dynamic range, the limitation of our method

becomes very apparent. The nanoparticle concentrations required for any difference in signal is

highly concentrated, up to two orders of magnitude over stock concentrations. At this

concentration, nanoparticle interactions become much more significant and can dominate any

signal-producing effects. Furthermore, since the use of 250 nm gold nanoparticles relies

primarily on plasmonic coupling for any signal increase, the inter-particle interactions interfere

with detection.

Conclusion

Ultimately, with such high concentrations of nanoparticle, this method of detection cannot be

reliable as inter-particle interactions will come to dominate and SERS increase and effects. The

signal increases observed cannot be attributed solely to the presence of analyte. Furthermore, at

such high concentrations, any practical application remains limited. High concentrations of

nanoparticles are prone to precipitation, and would be difficult to react with analyte. Further

implementation in more sophisticated devices would suffer from this limitation.

52

CHAPTER 4

CONCLUSIONS AND FUTURE DIRECTIONS In this project, SERS-immunolabeled gold nanoparticles were successfully synthesized and

characterized. We presented a nanoparticle complex capable of selectively binding to protein

analyte and demonstrating an increase in SERS intensity. Furthermore, this nanoparticle complex

was capable of being detected inside a simple fluidic device. While quantitative detection using

this particular nanoparticle complex was shown to be infeasible due to inter-particle interactions,

some aspects of this project show promising conceptual validity for application to a further

developed and integrated fluidic device.

While some fluidic devices have been designed to use SERS quantitatively as outlined in the

introduction section, most suffer from either lack of specificity or single-use design. Future work

for this project first includes improvement on the nanoparticle complex, which will solve the

reusability problem. While SERS-immunolabeled nanoparticles demonstrate promise in

detecting analyte with specificity and sensitivity as demonstrated in this work and other research,

improvement is needed on the detection mechanism. Improvements of the nanoparticle complex

could result in a more effective and viable SERS-based biosensor. Specifically, research

demonstrated by Yu, et al. used realistic concentrations of nanoparticles to detect protein analyte

via plasmonic coupling [17]. Implementing a nanoparticle complex similar to this research in a

fluidic device could result in a more practical detection system, including taking advantage of the

stronger electromagnetic enhancement observed from other nanoparticle shapes. Further, a

detection mechanism based on intramolecular physicochemical changes could lead to a more

specific, sensitive, and advanced device.

53

After optimization of nanoparticle design, the improvement and practical application of this

technology will stem from implementation in a fluidic device. To develop a fully integrated lab-

on-a-chip biosensor, detection must be performed on cell culture media, and the detection

reaction performed in a reservoir on the chip itself. As previously demonstrated with the simple

fluidic device developed in this work, residual signal does not appear to be significant.

Therefore, performing multiple measurements on a single device will be feasible. Loading a

nanoparticle solution along with a sample to analyze into a reservoir for mixing and incubation,

then moving the sample through the device with the use of a buffer solution will sufficiently

accomplish the fluidics portion of the project. Measurements could then be performed by Raman

spectroscopy.

Lastly, future work on the project would be aimed at developing nanoparticles for measuring

other protein analytes. As mentioned in the introduction, human serum albumin is a biomarker

for liver health; however there are many other biomarkers that could be measured for other

conditions and other organ systems. For example, the biomarker α-Glutathione S-transferase (α-

GST) is a biomarker for liver damage, and can be detected in the same manner as human serum

albumin. Since the antibody conjugation reaction will remain the same for any antibody, SERS-

immunolabeled nanoparticles can be adapted to a range of biological markers. The addition of

the antibody for α-GST is shown in Figure 4-1. Just as the anti-HSA antibody could be

conveniently added, antibodies for other proteins can be added.

Figure 4-1: Labeling of the nanoparticle complex with a secondary antibody to show the

conjugation of the anti-GST antibody. Here, the peak at 610 nm is indicative of the fluorescent

54

label of the secondary antibody. Just as the anti-HSA antibody was shown to be added in Chapter

2, other antibodies, including anti-GST can be added to the nanoparticle complex as well.

Overall, the use of SERS-immunolabeled gold nanoparticles for quantitative detection of

provides an innovative method for biosensing. While some design flaws must be overcome,

integration of SERS-immunolabeled nanoparticles in a novel device could lead to a dynamic,

adaptable method of detection for integrated body-on-a-chip platforms. With improvements to

nanoparticle design, this project enabled progress toward a biosensor device capable of detecting

protein biomarkers for body-on-a-chip research.

55

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59

William M. Payne Curriculum Vitae

Virginia Tech‐Wake Forest University

School of Biomedical Engineering and Sciences

391 Technology Way

Winston Salem, North Carolina

1411 Miller Street

Winston Salem, North Carolina, 27103

wpayne@wakehealth.edu

T: 317‐987‐2399

Education:

2015: MS in Biomedical Engineering (exp. August 2015)

Virginia Tech‐Wake Forest University School of Biomedical Engineering and Sciences

Wake Forest University, Winston‐Salem, NC

Overall GPA: 3.60

2014: BS in Chemistry, Computer Science

Gardner‐Webb University, Boiling Springs, NC

Overall GPA: 3.70 GPA in Major: 3.84

Minors: Biology & Mathematics

Research Experience:

2014‐2015: Graduate Student, Wake Forest University, Biomedical Engineering

2013: Internship ‐ University of North Carolina at Charlotte, Department of Nanoscale Science

2013‐2014: Senior Honors Thesis, Gardner‐Webb University

2012: Database application, engineering, and implementation. Gardner‐Webb University

2012: Internship ‐ Anderson University Software Research Center, Anderson University, Indiana

Laboratory Skills:

‐Analytical Raman and IR spectroscopy; SEM and TEM Microscopy; Absorption spectroscopy; NMR

spectroscopy; HPLC and GC; Mass Spectrometry; Dynamic Light Scattering

‐Synthetic Colloidal nanoparticle chemistry and synthesis; Air‐free and Inorganic chemistry;

Organic synthesis; Polymer chemistry; Bioconjugate chemistry; Acid/Base chemistry

‐Biological Cell culture techniques; in vivo research techniques

‐Nanoscience Gold nanoparticle synthesis and chemistry; Polymeric nanoparticle synthesis and

characterization; Plasmonics; Carbon nanotube functionalization and characterization;

Microfluidic and lab‐on‐a‐chip device fabrication and theory

Computation Science Skills:

Fluent in C, Java, PHP, Ruby, Rails, R, SQL; Proficient in MATLAB, Python, C#, C++ and Open GL;

Database Application design and development; Web Programming and Design; Linux kernel

2

development, programming, and administration; Embedded system design and implementation;

AVR microcontroller programming; Data analytics and statistical analysis; Distributed and

Parallel Computing

Academic Awards/Honors:

Virginia Tech‐Wake Forest University SBES Symposium 3rd place Master’s Presentation 2015

Sigma Zeta Math & Science National Honor Society, Secretary 2013‐14

Gamma Sigma Epsilon Chemistry Honor Society, Vice President 2013‐14, 12‐13

AITP Computer Science Honor Society, Membership Chair 2013‐14, 2011‐12

GWU Honors College

Tri‐Beta Biology Honor Society, Member

Deans List Spring 2011

Honor Roll Fall 2012, Fall 2011, Fall 2010

GWU Academic Achievement Scholarship 2010‐2014

GWU Athletic Scholarship ‐ Men’s Swim Team 2010‐2013

Indiana Master Gardner’s Academic & Character Scholarship 2010‐2011

Conference Presentations:

May 2015: Virginia Tech‐Wake Forest School of Biomedical Engineering and Sciences Symposium,

“Detection of Liver Organoid Biomarkers by SERS‐Immunolabeled Gold Nanoparticles.” Oral

Presentation.

March 2014: Southeast Regional Honors Conference, “Ethical Concerns in the Big Data Revolution of

Implanted Medical Devices and the Internet of Things.” Oral Presentation.

February 2014: GWU LOTS Multidisciplinary Conference, “Ethical Concerns in the Big Data Revolution of

Implanted Medical Devices and the Internet of Things.” Oral Presentation.

November 2013: Southeast Regional Meeting of the American Chemical Society, "Polymer

functionalization of single‐wall carbon nanotubes for nano‐resin water purification." Oral

Presentation.

August 2013: Charlotte Summer Research Scholars Symposium, "Polymer functionalization of single‐wall

carbon nanotubes for nano‐resin water purification." Poster Presentation.

Publications:

Payne, W. M.*; Amburgey, J. E.; Poler, J. C. Journal "Polymer functionalization of single‐wall carbon

nanotubes for nano‐resin water purification" Abstracts, 65th Southeast Regional Meeting of

the American Chemical Society, Atlanta, GA, United States, November 13‐16, SERM‐779 (Oral

Presentation)

Saeed, M.*; Nguyen, A.*; Payne, W. M.*; Amburgey, J. E.; Poler, J. C. Journal "Synthesis and

characterization of doubly functionalized carbon nanotubes for removal of dissolved organics

from water" Abstracts, 65th Southeast Regional Meeting of the American Chemical Society,

Atlanta, GA, United States, November 13‐16, SERM‐841 (Poster Presentation)