Comparison of Data Analysis Techniques for 3D Assessment of Gap Junctional Intercellular Communication
David Reich
Dr. Jeffrey Morgan LaboratoryDr. Elena Oancea, 2nd Reader
In partial fulfillment of the requirements for Honors, Sc.B. Biochemistry and Molecular Biologyat Brown University in Providence, RI, April 2017.
Contents:
Abstract 1Background 1Materials and Methods 9Results and Discussion 11Conclusions 32Appendix 35Sources Cited 36
With great gratitude to Elizabeth Leary for her guidance in this document and over the last three years, and to Jeffrey Morgan, Claire Rhee, Susan Hall, and Alison Xie for all their mentorship and assistance
Abstract
The fields of pharmaceutical development and toxicity testing have historically relied on
2D monolayer and animal model – based testing, but these techniques can be inaccurate,
causing harm to humans during clinical trials, or, in particularly dangerous cases, after mass-
market approval. 2D monolayer systems suffer from the biological oversimplifications they
make relative to in vivo conditions; while animal and human biologies are often significantly
different. We intend to develop a high-throughput methodology for the use of 3D microtissue
spheres for pharmaceutical and toxicity testing, and to apply this methodology in the
assessment of the effects of gap junction and efflux pump activity on model drug permeation
into tissue. Microtissues can already by formed and imaged in a high-throughput, high-content
manner, but this process results in a large quantity of data that requires a clear strategy for
analysis. In this paper, we explore a set of five techniques for data analysis of model drug
permeation into 3D tissue culture, quantitatively comparing and evaluating the five techniques.
We determine that a truly three-dimensional strategy, based on a series of concentric
measurement shells within the spheroid, is the most effective.
BackgroundOver the past fifteen years, research and development funding in the American
pharmaceutical industry has doubled to at least 58.8 billion USD [PhRMA, 2016] while the
average number of new drugs approved every year has declined since the 1990s [Hay 2014].
At the same time, there are over 75,000 toxicologically untested commercial chemicals in the
U.S.A. [Judson 2009], with approximately 2,000 chemicals introduced each year [NTP, 2017],
and testing a single substance’s toxicity can require millions of dollars and 2-3 years [Judson
2009]. In addition to high costs in money and time, preclinical pharmacological testing and
toxicological screening frequently suffer from a lack of predictive power. For example, in
pharmaceutical testing, fewer than 12% of drugs that pass all preclinical screens and enter
1
Phase I human testing are eventually approved by the FDA [PhRMA, 2016]. Because of these
issues, there is a need to develop new models and techniques for chemical and drug testing
that are facter, cheaper, quicker, and more predictive than the existing models.
Current assays for predicting the action of a chemical substance in the human body rely
primarily on two methodologies: Two-dimensional (2D) in vitro monolayer testing and in vivo
animal models.
2D monolayer testing is the examination of the effects of substances on a single layer of
cells cultured on a flat plastic petri dish. Such techniques have been of critical importance in the
history of biomedical and biochemical research, providing an inexpensive, well-understood
platform for a wide range of experiments. They enable testing of a wide range of cell types,
allow experimenters to carefully control many aspects of the cellular environment, and facilitate
microscopy, patch-clamp measurements, and other assessments of cellular activity and state.
However, despite their low cost and ease of use, 2D monolayers fail to mimic the complexity of
the human in vivo environment. Intercellular interactions and cell-matrix interactions are critical
to understanding cell behavior because such interactions deeply affect many signaling
pathways [Kleinman 2003], and in a monolayer these interactions are replaced by biologically
irrelevant cell-plastic interactions [Achili 2012]. Furthermore, 2D models lack the gradients of
oxygen, nutrients, and waste that regulate many aspects of biological activity and the diffusion
of drug molecules into tissue [Antoni 2015]. 2D tissue culture on plastic also causes flattening
and remodeling of cell structure, which is known to alter gene expression, causing de-
differentiation of phenotype [Vergani 2004].
In vivo animal models represent another stage of conventional preclinical testing and
toxicological screens. Drug testing in living animals can effectively demonstrate bioavailability,
pharmacokinetics, metabolism, and excretion of substances, and can identify possible sites of
toxicity outside the active therapeutic site of the drug [Dorato 2007]. However, animal models
are expensive in time and money, making them inaccessible to many researchers, and ethical
2
concerns dictate avoidance of animal models whenever possible. Despite these advantages,
extrapolation from animal models to humans is inadequate because of issues with
bioavailability, and functional biology. Bioavailability of various drugs, for example, does not
always correlate between animal species and humans [Shanks 2009]. There exist significant
functional differences between species in terms of drug and toxin metabolism, plasma
composition and drug-protein partitioning, kidney activity, and excretion rate [Astashkina 2012].
For a specific example, all mammals share the cytochrome P450 pathway for the elimination of
toxic substances, but the particular rates and the particular biochemical transferase pathways
are different in humans and in other animal species, possibly leading to critical errors in toxicity
testing [Smith, 1991]. Despite some physiological relevance, thorough tests of both rodent and
nonrodent models failed to predict 29% of incidents of human toxicity [Olson, 2000]. This
failure to predict human toxicity can have fatal effects: for example, a drug by the trade name of
Troglitizone was approved by the F.D.A. for treatment of diabetes in 1997, having given no
indications of toxicity in animals, and was withdrawn from the market a year later after a patient
died of acute liver failure associated with Troglitizone hepatotoxicity.
As an alternative to 2D monolayers and in vivo models, scientists have begun to employ
3D tissue culture techniques. Relative to monolayers, these techniques allow for human cells to
be used in more physiologically relevant ways that include extracellular matrices generated by
the cells themselves, life-like diffusion gradients, and the exchange of intercellular signals in 3D.
Relative to animal models, they allow for more specific modeling of particular human organ
systems without the biological differences that exist between species. However, 3D models
have certain disadvantages: they require the use of relatively novel, and often technically
challenging, methods; they have often not been fully characterized; they present an upper limit
for tissue size because, beyond a certain radius, oxygen will be unable to diffuse into the center
of the tissue; and they present issues of imaging inside and through tissue that are not present
for 2D monolayers.
3
There exist a variety of techniques for 3D tissue culture, but they can all be divided into
two categories: those that do, and those that do not, rely upon scaffolding material [Lovitt,
2014]. Scaffold-based techniques are those in which a scaffolding matrix material is mixed with
cells and the two are distributed together into a volume of tissue culture. These techniques rely
on laboratory-produced extracellular matrix, which can be expensive, lead to low cell densities
[Knight, 2015], and in which it can be difficult to precisely interact with the cultured tissue
[Breslin, 2013]. Scaffold-independent techniques are those in which cells are used alone. In
these methods, cells adhere to one another and excrete their own extracellular matrix [Knight,
2015]. These methods are limited to those types of cells capable of generating matrix. Specific
types of scaffold-free techniques include hanging-drop experiments, agitation-based
approaches, and low-adhesive plates. Hanging-drop experiments are those in which liquid
media containing cells is suspended in droplets from a flat surface. Cells self-adhere into
spheroids within the droplets. These experiments suffer from the requirement of great physical
precision and the difficulty of changing media, which prevents the addition of a test substance
after the spheroid has formed. Agitation-based approaches keep the container of the cell media
in constant motion, prevention adhesion to the container and forcing the cells to self-assemble.
Such experiments require specialized equipment and do not offer easy control over spheroid
size. Low-adhesive plate experiments are those in which the surface of a cell culture plate is
coated with a substance to which cells do not adhere. This coating causes the cells to
preferentially form self-assembled 3D tissues which rest upon the plate coating. These
experiments require labor-intensive coating of plates with a nonadherent substance [Breslin,
2013]. These various issues have, historically, prevented 3D tissue culture from being used in
high-throughput assays.
Over the past dozen years, the Morgan lab has pioneered the development and use of
an improved low-adhesive plate technique, known and patented as the 3D PetriDishTM.
4
Fig. 1: Formation of hydrogels for 3D tissue culture. A silicone mold shaped like a shallow cup with, at its bottom, a small platform cover with many small microprojections (A) is filled with molten 2% agarose (B), which is allowed to cool over 10min and is then removed (C) to form a hydrogel with a reservoir or “loading dock”, formed by the platform at the bottom of the mold, in which sit many small microrecesses, formed by the microprojections in the mold, (D) that can be used for 3D tissue culture.
The 3D PetriDishTM itself is a silicone mold (Fig. 1A) that contains a reservoir with a
raised platform, topped with rounded pegs. To form hydrogels, the mold is filled with molten 2%
agarose (Fig. 1B), which is allowed to cool and harden and is removed (Fig. 1C), forming a
hydrogel (Fig. 1D). To form spheroids, a monodispersed cell suspension is dispensed into the
loading dock. Over the course of 30 minutes, cells settle into the micro-recesses due to gravity.
Because cells do not adhere to agarose, they instead self-assemble and aggregate into
spheroids over 24 hours. Spheroid size can be reliably controlled by the concentration of the
cell suspension [Achili 2012, Rago 2009]., The cells of these spheroids recapitulate some
behaviors of corresponding in vivo cell types: experimenters observed heartbeat-like action
potentials in cardiac spheroid microtissues [Desroches 2012]. However, the value of this
technology does not lie in better predictive power than other 3D culture techniques but rather in
higher levels of throughput and lower requirements in terms of equipment and experimenter
training. This value can be further improved with a more high-throughput technology for the
formation of gels and spheroids.
Although the 3D™ Petri Dish technology can reproducibly form a large number of
spheroids of various sizes, it relies on the use of 24-well plates that are unsuitable for high-
5
throughput screening. Therefore, we have adapted the technology for use in 96-well plates that
are amenable to high-throughput, high-content screening.
3D PetriDishTM technology relies on a method for forming agarose hydrogels in 24-well
plates unsuitable for high-throughput imaging. Over the past two years, though, we have
developed a new, similar technique for the formation of hydrogels in a 96-well plate that is
amenable to high-throughput, high-content screening. This technique relies on a high-
throughput mold with a design different from the previous mold. Using this mold, we are
developing a high-throughput assay to assess gap junctional intercellular communication.
Gap junctions are channels between the cytoplasm of adjacent cells that allow
for communication by electrochemical signaling or the exchange of small molecules. Healthy
gap junctions are particularly important in cardiac tissue, where they are critical for the electrical
signaling that regulates contraction and modulates heartbeat [Kanno, 2001]; and in neural
tissue, where they function alongside chemical synapses to provide a foundational level of
neural communication [Connors, 2004]. Gap junction dysfunction has been associated with a
range of diseases including cancer [Czyz 2012]. Electrical coupling is lost in liver tumor cells as
compared with healthy hepatocytes [Loewenstein 1967], and more recent studies have shown
that several non-genotoxic tumor-promoting compounds act by inhibiting gap junctional
intercellular communication (GJIC) [Kalimi, 1984]. However, the modern consensus is that,
although GJIC often inhibits tumorigenesis, gap junction activity can also lead to the growth of
cancer under some circumstances, and certain gap junction proteins are upregulated in certain
cancers - in short, that GJIC plays an important but context-dependent role in cancer biology
[Aasen, 2016].
There exist a variety of techniques for the study of GJIC in 2D, including microinjection,
scrape loading, electroporation, gap-FRAP, preloading assays, radiolabeled nucleotide transfer,
intercellular calcium waves, and dual-cell patch clamp [Abbaci 2008]. Each of these techniques
has its own set of advantages and disadvantages. For example, microinjection, scrape-loading,
6
intercellular calcium waves, and patch clamp techniques involve violation of the cell membrane;
preloading assays and radiolabeled nucleotide transfers are limited to low temporal resolution,
and gap-FRAP can analyze only a limited number of cells at a given time. Overall, these
various techniques are time-consuming and require specialized equipment. We hypothesize
that a move to a 3D model will improve the results of GJIC studies by investigating them in a
more physiologically relevant context in which diffusion, like in human tissue, takes place in 3D
[Mroue 2011].
Previously, the Morgan lab reported a method to study GJIC in 3D spheroids using the
low-throughput hydrogel technique and epifluorescent imaging [Achili, 2014]. Calcein-AM (Fig.
2) was used as a model compound to study GJIC due to its unique properties. Calcein-AM is a
nonfluorescent, uncharged compound capable of passive diffusion across phospholipid bilayers.
Once it is inside a cell, esterases cleave the four acetoxymethyl groups from the esters of the
molecule, leaving it as calcein. The four carboxylic acid groups that are left by the esterases
become ionized at physiological pH, leaving a charged molecule that can no longer diffuse out
of the cell and becomes trapped. The molecule also becomes able to chelate calcium(II) ions,
forming a fluorescent complex with a λex of 470nm and λem of 509nm. Because the calcein-Ca
complex is both small and hydrophilic, it is capable of permeating through gap junctions.
Therefore, in the presence of active gap junctions, fluorescent calcein will diffuse from the edge
to the center of 3D spheroids, while in their absence it will accumulate, trapped, in the outer
layer of cells.
Like gap junctions, the presence and activity of two efflux pump proteins will alter the
uptake and distribution of fluorescent calcein in 3D tissue. These proteins, P-glycoprotein (P-
gp) and multidrug resistance protein (MRP), will both eject molecules of calcein-AM from the
cytoplasm [Olson 2001, Chaisit 2017]. Therefore, if these proteins are expressed and active,
they will slow the uptake and distribution of calcein into 3D spheroids.
7
Fig. 2: Calcein-AM Conversion to Calcein (R = acetoxymethyl group)
The previous study by the Morgan lab relied on the use of agarose hydrogels in 24-well
plates, limiting the capacity of the assay. It also relied on imaging with 2D epifluorescent
microscopy and the use of computational algorithms to predict 3D distribution from 2D data. We
propose to enhance this model by increasing throughput and dimensionality.
Using high-throughput techniques, we can now prepare assays to test more samples more
quickly, and because a new 96-well plate technique is compatible with high-content confocal
microscopy we can measure calcein fluorescence directly at slices within the spheroid. We can
now carry out an assay of GJIC activity in three dimensions, but a major difficulty in designing
such an assay is the development of techniques for data analysis. We image spheroids with
high-content confocal microscopy, generating data that can be used to render the spheroids in
3D digitally. We will examine five different methods for the assessment of GJIC in a 3D
spheroid model. To quantitatively compare these methods, we will use Z-factor analysis to
determine which shows the greatest separation between positive and negative controls.
8
Materials and Methods
Agarose Hydrogel Formation
High-throughput molds were designed with modeling software and 3D-printed
(SolidWorks Corp., Concord, MA). They were printed with four rows of eight pegs, each topped
with four conical micro-projections. Using a 96-well glass-bottom plate (Greiner Bio-One North
America, Monroe, NC) and an eight-channel pipettor, 90μL of molten sterile 2% agarose (CAS
9012-36-6, Fisher Scientific, Fair Lawn NJ) in phosphate-buffered saline was dispensed into
each well. A mold was quickly and firmly inserted into the plate such that one peg reached into
each well containing agarose. The agarose was allowed to set for 10 minutes prior to removing
mold. 150μL of serum-free Dulbecco’s Modified Eagle’s Medium (DMEM, 11995-065,
Invitrogen, Carlsbad, CA) containing 1% penicillin / streptomycin (P/S, product #0916702, MP
Biomedicals, Solon, OH) was added to each well and incubated to equilibrate with the hydrogel
overnight. All media was aspirated from the gels immediately before seeding.
Cell Culture and Labeling
KGN cells, a human granulosa cell line, were grown in DMEM with 10% fetal bovine
serum (SH30910.03, HyClone Laboratories, Logan, UT) and 1% P/S at 37oC and 10% CO2 in T-
75 flasks. Before seeding into hydrogels, the cells were stained homogeneously with 5μM
CellTrackerTM Red (C34552, LifeTechnologies, Eugene, OR) for 30min, followed by 15min
incubation with serum-free media.
Seeding of Hydrogels
The CTR-stained cells were passaged, counted, and resuspended to a concentration of
2x10^6 cells / mL (for a desired 1,000 cells/spheroid). 20μL of cell suspension was then
pipetted into each well of the 96-well plate containing the hydrogels, taking care to dispense the
9
suspension into the space immediately above the micro-recesses without letting the pipette tip
directly touch the hydrogel. The cells were allowed to settle into the micro-recesses for 30min
after seeding, and then 130μL of serum-free DMEM +1% P/S was gently added to each well.
The plate was incubated for ~24hr to allow for spheroid self-assembly.
Treatment with CBX, calcein-AM
After 24 hours, the media was aspirated and replaced with either 100μL serum-free
DMEM +1% P/S (negative control) or with 100μL DMEM +1% P/S containing 100μM
carbenoxolone (CBX, CAS 7421-40-1, ThermoFisher). After 4 hours of incubation, the media
was then replaced with either 100μL serum-free DMEM +1% P/S containing 2μM calcein-AM
(C3099, LifeTechnologies) (negative control) or 100μL serum-free DMEM +1% P/S containing
2μM calcein-AM and 100μM CBX (positive control).
Imaging
Immediately after addition of calcein-AM, confocal images were taken using the Opera
Phenix High Content Screening System (PerkinElmer, Waltham, MA). The 20x water-based
objective was used, and Z-stack images were taken every 5μm for a total of 90 Z-slices.
Analysis
3D images were acquired from the Opera Phenix and exported via Harmony
(PerkinElmer) software as TIF files. These files were analyzed using both FIJI (U.S. National
Institutes of Health) and Imaris (Bitplane, Zurich, Switzerland) software. FIJI was used to find
fluorescence intensity along the central axis of the spheroid at each z-slice, and customized
scripts were used with FIJI to measure fluorescence intensity in concentric circles within a single
z-slice. The same data was also analyzed with Imaris 7.7.2 and Imaris 8.2.1. Imaris 7.7.2 was
10
used to convert the raw data to a recognizable Imaris format, and Imaris 8.2.1 was used with a
custom script to create 3D concentric shells the measured the fluorescence intensity of
concentric spheroids moving inward from the periphery of the spheroid and maintaining its
curvature.
Results & Discussion
Results: Spheroids can be formed, dosed with GJIC-altering compounds and model drug, and imaged directly in 96-well plates using a high-throughput micromold system.
The high-throughput micromold consists of four columns by eight rows of conical pegs,
designed to fit into four columns of a 96-well plate (Fig. 3A). Each peg is topped by four conical
microprojections (Fig. 3B). We pipette 90μL of molten agarose into each well of four columns of
a 96-well plate and insert the mold. Once the agarose has set, the mold can be removed,
leaving a microgel in each well (Fig. 3C), and in each microgel lie four microrecesses
corresponding to the microprojections of the mold (Fig. 3D).
11
A
C DH
B
Fig. 3: Formation of hydrogels and cell spheroids with high-throughput technique. Plastic molds (A) with four columns by eight rows of pegs, each peg topped by four microprojections (B), are inverted into a 96-well plate in which each well contains molten 2% agarose. After a 10min cooling period, the molds are removed, and each well is left containing a hydrogel (C) in which lie four micro-recesses (D) (E, side-view diagram showing two of four micro-recesses). Media containing a monodispersed cell suspension is then added to each well (F, side-view diagram) and after 24hr the cells have settled into the micro-recesses and self-assembled into spheroids (G, side-view diagram).
Once the high-throughput gels have been formed in the 96-well plate, they can be
seeded with cells. A mono-dispersed cell suspension is added to each well and, after 24 hours,
the cells settle into the four micro-recesses and self-assemble into spheroids. Spheroid size can
be controlled by altering the seeding density (data not shown), and in this study we used a
concentration of 2x10^5 cells/mL to form spheroids consisting of 1,000 cells each with a
spheroid radius of approx. 80μm in radius.
In confocal imaging, the phenomenon of signal loss due to light scattering at z-depths
within tissue is well known [Dobrucki, 2007]. In order to address this limitation of imaging, we
evenly stained all cells with CellTracker™ Red under monolayer conditions before formation of
spheroids. This homogenous application ensures that any loss of CTR signal is due to light
scattering rather than incomplete staining. In general, signal loss due to light scattering makes
it almost impossible to obtain a usable signal from any region above the equator of a spheroid.
Without fluorescence loss, we might expect an X-Z slice rendering of a spheroid showing
calcein permeation to form the shape of a hollow circle - that is, for the top and bottom halves to
be symmetrical. However, due to fluorescence loss, we see a “salad bowl”-like shape instead.
(Fig. 4)
To determine the feasibility of developing a high-throughput 3D gap junction inhibition
assay, we treated half the spheroids with gap junction inhibitor CBX at 100μM as a positive
control with a four-hour pretreatment and left the other half untreated as a negative control. We
treated all of the cells with calcein-AM as a model drug and began confocal imaging of the entire
plate with the Opera Phenix™ to monitor the uptake and distribution of calcein. All treatment
was done within the wells of the 96-well plate. Imaging the entire plate takes approx. 90min,
12
and as such, spheroids imaged at different times throughout the process represent different
timepoints in the process of calcein permeation into the spheroid.
Calcein-AM uptake and conversion to calcein is a dynamic process that will continue to
take place and change over the entire duration of imaging. Regardless of drug treatment,
overall calcein fluorescence will increase over 90min, while CellTracker™ Red (CTR)
fluorescence will remain constant. Furthermore, spheroids treated with CBX show a brighter
overall calcein signal than nontreated controls because KGN cells express the efflux pump P-
gp, for which calcein-AM is a substrate and CBX is an inhibitor. The effect of CBX inhibition of
P-gp is more dramatic at later timepoints. However, because CBX also functions as a gap
junction inhibitor, that larger quantity of calcein at later timepoints will be confined to the
peripheral layers of the spheroid and will prove unable to diffuse into the central areas of the
tissue. At earlier timepoints (Fig. 4A,D) there will only be minimal differences between positive
and negative control conditions because, even with inactive P-gp, calcein shows a relatively
slow rate of diffusion from extracellular fluid into the cytoplasm. However, at later timepoints
fluorescence intensity of calcein will be so bright that it will be difficult to obtain a reliable reading
(Fig. 4C,F).
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A B C
Fig. 4: X-Z Slices of Representative Individual Spheroids. Green: Calcein fluorescence, Red: CTR fluorescence. Data captured with Opera Phenix™ and rendered in FIJI. Spheroids from the negative control (0μM CBX) after 10min (A), 70min (B), and 90min (C), and from the positive control (100μM CBX) after 10min (D), 70min (E), and 90min (F).
In order to determine the best method for analyzing the inhibition of gap junctions, we
set a series of parameters for how to choose which spheroids to analyze: (1) both positive and
negative control spheroids must be imaged at similar times, (2) both positive and negative
control spheroids must be of similar size, (3) the time-point chosen must be within the dynamic
range of calcein uptake and distribution for both the positive and negative controls and cannot
be immediately after addition of calcein-AM. Therefore, we chose to analyze spheroids
approximately 70-minutes after the addition of calcein-AM, since active uptake and distribution
of calcein was still occurring. (Fig. 4B,E)
Results: Several techniques can be used to assess GJIC activity by examining calcein
distribution.
In order to develop an assay successful in predicting whether a compound will inhibit
gap junctions, we must first identify an optimal technique for data analysis. We will seek the
technique that shows the highest separation between out known negative (0μM CBX) and
known positive (100μM CBX) controls because such a technique will also show a high
separation between a negative control and an unknown, gap junction-inhibiting compound,
allowing the clear identification of even relatively weak gap junction inhibitors. We will examine
14
D FE
five potential data analysis strategies: equatorial slices, quartile slices, vertical axes, concentric
spheroids, and concentric hemispheroids. Using each strategy, we will examine the same set of
eight spheroids: four each from the positive and negative controls. Each strategy will seek to
examine the gradient of calcein permeation from the edge to the center of each spheroid. In
order to quantitatively compare between methods, we will use Z-factor analysis to measure the
separation between positive and negative control distributions for each method.
Strategy 1: Equatorial Slices
The first of the five techniques for assessing calcein permeation into the 3D spheroids is
the assessment of fluorescence distribution across equatorial slices of the spheroids. In this
technique, we take the equatorial confocal slice from each spheroid and analyze fluorescence
intensity across the spheroid radii. (Fig. 5A,B,C). To analyze fluorescence distribution across
the slices, we first identified the center of the spheroid section, then used FIJI and a custom
macro to measure the average raw fluorescence intensity across the equatorial slice as a
function of distance from the center (radius) for both calcein and CTR fluorescence (Fig. 5D,E).
Regardless of drug treatment, the brightest calcein fluorescence occurs along the outer
rim of the spheroid. This is due to the nature of calcein-AM uptake, and indeed diffusion of any
sort into tissue: the molecules of calcein must first penetrate into the outer edge of the spheroid
before beginning to diffuse deeper into tissue. As such, a plot of average raw calcein
fluorescence as a function of distance from the spheroid center will exhibit a positive slope.
Additionally, the average calcein signal for CBX-treated spheroids is higher than that for
untreated spheroids, confirming that CBX inhibits P-gp and allows for a greater amount of
calcein-AM to enter the spheroid.
Because the cells were homogenously stained with CTR under monolayer conditions
prior to self-assembly into spheroids, we might expect the CTR fluorescent signal to
homogenous throughout the equatorial slice. However, like the calcein fluorescent signal, the
15
brightest signal is observed on the outer edge of the spheroid. This lack of homogenous signal
is due to the fluorescent loss that occurs during confocal imaging due to light scattering, which
also occurs with respect to the calcein signal. Signal from the center of the spheroid must pass
through more tissue before exiting the spheroid at its bottom than signal from the periphery, and
as such it shows a higher level of fluorescence loss. This signal loss is also occurring
throughout the spheroid with respect to calcein staining, yet it is hidden by the gradient of signal
due to diffusion of dye. Therefore, in order to correct for the loss of signal due to imaging
limitations, we normalized the calcein fluorescence by CTR fluorescence for each spheroid.
(Fig. 5F,G).
After normalizing calcein signal by CTR signal, spheroids in both the positive and
negative control conditions show the brightest relative signal along their edges. Similar to the
data before normalization, CBX-treated spheroids have a stronger overall calcein signal, though
the difference between the groups in the center of the spheroid is minimal. CBX treatment
therefore causes an increase in calcein signal along the edges of spheroids without a
corresponding increase in the center of spheroids, showing that it inhibits gap junction activity.
If CBX were a pure P-gp inhibitor, we would see a curve similar to the negative control but
shifted upward (higher normalized calcein) overall.
In order to use this technique to compare multiple spheroids in each condition, we need
to conduct a normalization by radius to remove the error that would otherwise be introduced by
minor variances in spheroid size. As such, distances along the radius of each spheroid were
divided by the total length of that spheroid’s radius in order to express distances in terms of
percentages rather than μm. When averaging multiple spheroids per experimental group (Fig.
5H), we observed a similar trend: Treatment with CBX increases fluorescent signal along the
spheroid’s edge without a corresponding increase in the center of the tissue.
16
0 20 40 60 800
5000
10000
15000
Calcein and CTR in the Equatorial Slice: 0μM CBX
Radius (μM)
Avg R
aw Fl
oure
scen
ce In
tens
ity
0 10 20 30 40 50 60 70 80-1000
1000
3000
5000
7000
9000
11000
13000
15000
Calcein and CTR in the Equatorial Slice: 100μM CBX
Radius (μM)
Avg R
aw Fl
oure
scen
ce In
tens
ity
0 10 20 30 40 50 60 70 80 900
0.5
1
1.5
2
2.5
3
3.5
4
Adjusted Calcein in the Equatorial Slice: 0μM CBX
Radius (μM)
Norm
alized
Flou
resce
nce I
nten
sity
0 10 20 30 40 50 60 70 80
0
0.5
1
1.5
2
2.5
3
3.5
4
Adjusted Calcein in the Equatorial Slice: 100μM CBX
Radius (μM)
Adjus
ted Fl
oures
cenc
e Int
ensit
y
0 10 20 30 40 50 60 70 80 90 1000
0.5
1
1.5
2
2.5
3
Average Calcein Intensities in Equatorial Slice
0μM CBX100μM CBX
Normalized Spheroid Radius
No
rmalize
d C
alc
ein
Flu
ore
scen
ce
17
A B C
D E
F G
H
Core A
Edge A
Fig. 5: Analysis of calcein permeation into spheroid using the Equatorial Slice strategy shows quantitative difference between positive and negative control conditions. An equatorial slice is taken from a spheroid (A, diagram), visualized (B, red: CTR fluorescence; green: calcein fluorescence), and concentric circles are superimposed on the slice to measure the average raw fluorescence intensity at each radial distance from the center of the spheroid (C). Average raw CTR and calcein fluorescence intensities were measured for all individual spheroids under both negative and positive control conditions (D, negative control; E, positive control. Green: calcein fluorescence intensity, red: CTR fluorescence intensity. Y-axis in arbitrary units designated by FIJI. Note that the same two individual spheroids are also used for all examples in figs. XX, YY, ZZ, and AA.) For both conditions, adjusted calcein intensity is calculated as the ratio of raw average calcein fluorescence to raw average CTR fluorescence at each distance from the center (F, negative control; G, positive control, representative individual spheroids. Green: adjusted calcein intensity). The adjusted calcein intensities are averaged for all spheroids under each condition at all distances between the center of and the edge of the spheroid (H, blue: negative control; orange: positive control. Error bars represent standard deviations at each point.)
Strategy 2: Quartile Slices
When measuring the average fluorescence intensity across the equatorial confocal slice
of a spheroid with a radius of ~80μm, we are attempting to analyze a slice that as lost a
significant amount of data to light scattering. At a certain level of fluorescence loss, we may be
no longer able to detect signal over the noise of the system. To address this possible issue, we
may either (1) form and assess smaller spheroids, or (2) measure fluorescence intensity across
slices with a lower z-height. We note that in either case, the radius of the slice decreases and
the range between the center and the edge of the spheroid over which we can observe data
shrinks. We have selected the second approach, as it allows us to examine the same set of
spheroids using a new technique.
Overall, the second of our five techniques is conducted almost identically to the first, but
with one major difference: instead of selecting a z-slice from the equator of the spheroid, we
take a slice at the first quartile of the spheroid (Fig. 6A,B) We then conduct the same procedure
as for the previous technique (Fig. 6C) in order to determine the average raw calcein and
average CTR fluorescence intensities at all radii. (Fig. 6D,E)
Both the positive and the negative controls show an increased fluorescent signal
compared to the equatorial slice in the previous technique. This increased intensity is observed
because the quartile slice is subject to less light scattering and fluorescence loss than is the
equatorial slice. Normalization of calcein intensity shows a greater accumulation of calcein on
18
the periphery compared to the core for both positive and negative controls, and shows that CBX
treatment results in an increase in peripheral calcein fluorescence with no corresponding
increase in core calcein fluorescence, indicating that CBX functions as an inhibitor of both P-gp
and GJIC. (Fig. 6F,G) As for the first method, we then average all four spheroids within each
condition (Fig. 6H).
As in equatorial slice analysis, we see a quantitatively notable difference between the
positive and negative controls with a discernable difference in normalized calcein along the
edge, but only a minimal difference, if any is present at all, in the center of the confocal slice.
0 10 20 30 40 50 60 700
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A B C
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alize
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e Int
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Calcein Intensities in Quartile Slice
0μM CBX100μM CBX
Normalized Spheroid Radius
No
rma
lize
d C
alc
ein
Flu
ore
scen
ce
Core
Fig. 6: Analysis of calcein permeation into spheroid using the Quartile Slice strategy shows quantitative difference between positive and negative control conditions. A quartile slice is taken from a spheroid (A, diagram), visualized (B, red: CTR fluorescence; green: calcein fluorescence), and concentric circles are superimposed on the slice to measure the average raw fluorescence intensity at each radial distance from the center of the spheroid (C). Average raw CTR and calcein fluorescence intensities were measured for all individual spheroids under both negative and positive control conditions (D, negative control; E, positive control. Green: calcein fluorescence intensity, red: CTR fluorescence intensity. Y-axis in arbitrary units designated by FIJI. Note that the same two individual spheroids are also used for all examples in figs. 9, 11, 12, and 13.) For both conditions, adjusted calcein intensity is calculated as the ratio of raw average calcein fluorescence to raw average CTR fluorescence at each distance from the center (F, negative control; G, positive control, representative individual spheroids. Green: adjusted calcein intensity). The adjusted calcein intensities are averaged for all spheroids under each condition at all distances between the center of and the edge of the spheroid (H, blue: negative control; orange: positive control. Error bars represent standard deviations at each point.)
Strategy 3: Vertical Axes
The two methods discussed so far have both relied on the extraction of 2D data from a
3D image. Although the 2D data that they use is only available because of 3D confocal
technology that allows us to separately image each Z-slice from a spheroid, it may be argued
that these techniques lack depth. An alternative approach could involve investigation of calcein
permeation along the Z-axis, and such an approach is taken by the following technique.
20
F G
H
Edge A
The third of our five techniques inspects calcein fluorescence intensity along the central
vertical axis of each spheroid. In this technique, we draw a vertical axis from the center of the
spheroid to its bottom (Fig. 7A), ignoring the upper hemisphere of the spheroid: fluorescence
loss renders data from that half of each microtissue virtually unusable. We then use FIJI to take
each of the Z-slices that constitute the lower hemisphere of the spheroid and measure the
fluorescence intensity of CTR and calcein at the center-point of that z-slice. Essentially, we
draw a straight line in 3D from the center of the spheroid to its bottom point and measure
fluorescence intensity along that line (Fig. 7C).
We then follow the same procedure as for the previous two methods: we examine the
raw fluorescence intensity for calcein and CTR along this axis for each spheroid (Fig. 7D,E),
calculate the adjusted calcein intensity along this axis for each spheroid (Fig. 7F,G), and
average the adjusted calcein intensities for all spheroids in the negative and positive control
conditions (Fig. 7H). Again, we see that both conditions show greatly elevated levels of calcein
intensity (raw and normalized) in the edge of the spheroid as compared to its center. We also
see that the CBX increases calcein intensity along the rim of the spheroid (consistent with its
role as a P-gp inhibitor) without correspondingly increasing calcein intensity in the center of the
spheroid (consistent with its role as a gap junction inhibitor).
Note that the peak seen for the 0μM individual spheroid at approx. seven Z-slices from
the edge of the spheroid (Fig. 7D) is somewhat of an outlier, and no similarly large peak was
seen for any other spheroid in the negative control – although the other three spheroids did
exhibit at least a slight peak in that region, reflected in the average (Fig. 7H).
21
0 10 20 30 40 50 60 70 80
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alize
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e Int
ensit
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alize
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oure
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ce In
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ity
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Calcein Intensities along Vertical Axis
0μM CBX
100μM CBX
Normalized Spheroid Radius
No
rmali
zed
Ca
lcein
Flu
ore
scen
ce
Core
Edge
22
AB C
D E
F G
H
Fig. 7: Analysis of calcein permeation into spheroid using the Vertical Axis strategy shows quantitative difference between positive and negative control conditions. A vertical axis is drawn in a spheroid from its center to its lowest point (A, diagram). A X-Z slice of the spheroid is visualized (B, red: CTR fluorescence; green: calcein fluorescence), and a vertical axis is superimposed on the slice to measure the average raw fluorescence intensity at each radial distance from the center of the spheroid (C). Average raw CTR and calcein fluorescence intensities were measured for all individual spheroids under both negative and positive control conditions (D, negative control; E, positive control. Green: calcein fluorescence intensity, red: CTR fluorescence intensity. Y-axis in arbitrary units designated by FIJI. Note that the same two individual spheroids are also used for all examples in figs. 9, 10, 12, and 13.) For both conditions, adjusted calcein intensity is calculated as the ratio of raw average calcein fluorescence to raw average CTR fluorescence at each distance from the center (F, negative control; G, positive control, representative individual spheroids. Green: adjusted calcein intensity). The adjusted calcein intensities are averaged for all spheroids under each condition at all distances between the center of and the edge of the spheroid (H, blue: negative control; orange: positive control. Error bars represent standard deviations at each point.)
Strategy 4: Concentric Spheres
Although the previous technique engages the Z-dimension of the spheroid, unlike
the first two techniques that we examined, it still reduces each spheroid to a single one-
dimensional line. Furthermore, because only ~20 Z-slices lie between the center and
the bottom of the spheroid, this method is only able to perceive at most that number of
data points. As such, our next strategy will attempt to engage the spheroid with a truly
three-dimensional framework, analyzing all available data from a 3D spheroid.
To analyze the entire spheroid, confocal Z-slices were loaded into Imaris
software. Using Imaris, we generate a surface representing the outermost edge of the
spheroid. We then use a distance transformation function to generate another, smaller
surface of the same shape, nested concentrically inside of the first. We repeat the
process for a total of six surfaces, evenly spaced, positioned within one another and all
centered on the center of the spheroid (Fig. 8A). Unlike techniques 1 and 2, which also
make use of concentric shapes, this “outside-in” style of construction ensures that
imperfections in the spheroid’s curvature are maintained throughout analysis, more
faithfully recreating the real potential of diffusion into a spheroid rather than a perfect
sphere.
23
Instead of then calculating the average raw fluorescence at each distance from
the center, as in the previous techniques, we instead calculate the total raw
fluorescence in the space between each pair of shells. We then analyze these values
much like the average raw fluorescence values of previous techniques (Fig. 8B,C),
However, they cannot be used quantitatively to the same extent because they have not
been normalized by volume: with a perfectly homogeneously labeled dye and no signal
loss, we would see a positive slope, as we do for CTR (Fig. 8B,C). We do, though,
observe a leveling-off or slight decrease in the final shell, perhaps due to inclusion of
some empty space in the outermost surface).
As previously, we can then adjust the calcein intensity by dividing by the CTR
intensity. This now serves a double purpose of adjusting both by volume and by
fluorescence loss (Fig. 8D,E). We can then average all spheroids under both the
negative and positive control conditions (Fig. 8F). Again, we observe a higher level of
calcein fluorescence in the edges of spheroids of both conditions, due to calcein
diffusion from extracellular media into the outer layer of cells before it continues,
through gap junctions, into the centers of spheroids. We also observe a higher level of
calcein fluorescence in the edges of spheroids treated with CBX without any
corresponding increase in calcein fluorescence in their cores, indicating that CBX is
functioning as both a P-gp and gap junction inhibitor.
24
1 2 3 4 5 60
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Calcein and CTR along Concentric Shells: 0μM CBX
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Concentric Shell: Distance from Center
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Calcein and CTR along Concentric Shells: 100μM CBX
Concentric Shell: Distance from Center
Norm
alize
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ures
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e Int
ensit
y
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 60
0.5
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1.5
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Calcein Intensities along Concentric Shells
0μM CBX100μM CBX
Concentric Shell: Distance from Center
No
rmal
ized
Cal
cein
Flu
ore
sce
nce
25
B
B C
D E
F
A
Fig. 8: Analysis of calcein permeation into spheroid using the Concentric Shell strategy shows quantitative difference between positive and negative control conditions. A series of six concentric spheroids were drawn inwards from the outer surface of the spheroid (A, diagram). Total raw fluorescence values could then be calculated for calcein and CTR at each of the six shells, for each shell, summing all fluorescence lying within the shell and then subtracting the sum of all fluorescence contained by the next-smallest shell. Total raw CTR and calcein fluorescence intensities at each shell were measured for all individual spheroids under both negative and positive control conditions (B, negative control; C, positive control. Green: calcein fluorescence intensity, red: CTR fluorescence intensity. Y-axis in arbitrary units designated by FIJI. Note that the same two individual spheroids are also used for all examples in figs. 10, 11, 12, and 14.) For both conditions, adjusted calcein intensity is calculated as the ratio of raw total calcein fluorescence to raw total CTR fluorescence at each distance from the center (D, negative control; E, positive control, representative individual spheroids. Green: adjusted calcein intensity). The adjusted calcein intensities are averaged for all spheroids under each condition at all distances between the center of and the edge of the spheroid (F, blue: negative control; orange: positive control. Error bars represent standard deviations at each point.) For B-E, X-axis is given in shell count, which can be approximately converted to radius by multiplication by ~20μm, though for technical reasons precise radii cannot be given.
Strategy 5: Concentric Hemispheres
Although the previous technique is capable of examining all data generated by a 3D
spheroid, not all of this data is necessarily useful. Specifically, due to the limitations of
fluorescent signal loss due to light scattering, fluorescent signal from regions of the spheroid
beyond a particular Z-height may be diminished to levels comparable with systematic noise.
Qualitatively, we observe that areas above the equator of the spheroid show no usable signal,
and their inclusion may in fact introduce higher levels of noise into the analysis stream. In order
to eliminate this noise, we used a strategy very similar to the previous technique but limited our
3D analysis to the lower hemisphere of the spheroid.
To conduct this technique, a slight modification of the previous, we again generate a
series of concentric shells mimicking the curvature of the spheroid edge, but now we bisect
them at the spheroid’s equator and only analyze data from the lower hemisphere: we have
generated a series of concentric hemishells (Fig. 9A). We then conduct the same analysis of
individual spheroids (Fig. 9B-E) and averages for both conditions (Fig. 9F). In this final
technique, we again observe a higher level of calcein fluorescence in the edges of spheroids of
both conditions. We also observe a higher level of calcein fluorescence in the edges of positive-
control as opposed to negative-control spheroids without any corresponding increase in calcein
fluorescence in their cores, indicating that CBX is functioning as both a P-gp and gap junction
inhibitor. Overall, we observe patterns very similar to those of the previous technique, and
26
conclude that perhaps the large regions containing little fluorescent signal above the spheroid’s
equator had, in practice, neither a constructive nor destructive effect on the calculations.
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 60
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nsity
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Calcein and CTR in Concentric Hemihells: 100μM CBX
Concentric Shell: Distance from Center
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sted F
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Calcein Intensities along Concentric Hemishells
0μM CBX100μM CBX
Shell (Inside to Outside)
Ad
j. C
alc
ein
Flu
ore
scen
ce
27
B
B C
D E
F
A
Fig. 9: Analysis of calcein permeation into spheroid using the Concentric Hemishell strategy shows quantitative difference between positive and negative control conditions. A series of six concentric spheroids were drawn inwards from the outer surface of the spheroid and cut so that only the fluorescence data below the spheroid equator was analyzed (A, diagram). Total raw fluorescence values could then be calculated for calcein and CTR at each of the six shells, for each shell, summing all fluorescence lying within the shell and then subtracting the sum of all fluorescence contained by the next-smallest shell. Total raw CTR and calcein fluorescence intensities at each shell were measured for all individual spheroids under both negative and positive control conditions (B, negative control; C, positive control. Green: calcein fluorescence intensity, red: CTR fluorescence intensity. Y-axis in arbitrary units designated by FIJI. Note that the same two individual spheroids are also used for all examples in figs. 10, 11, 12, and 13.) For both conditions, adjusted calcein intensity is calculated as the ratio of raw total calcein fluorescence to raw total CTR fluorescence at each distance from the center (D, negative control; E, positive control, representative individual spheroids. Green: adjusted calcein intensity). The adjusted calcein intensities are averaged for all spheroids under each condition at all distances between the center of and the edge of the spheroid (F, blue: negative control; orange: positive control. Error bars represent standard deviations at each point.)
Results: Quantitative comparison of strategies
Overall, in each technique we observe the same essential result: calcein
fluorescence is clustered at the edge of the spheroid, where it is between three and
eight times stronger than in the center. This reflects the gradual process of calcein
uptake, in which calcein-AM is able to passively diffuse from the surrounding media into
the spheroid before being converted into fluorescent calcein. Furthermore, each
analysis strategy shows that CBX treatment increases peripheral calcein, demonstrating
its ability to block P-gp activity; CBX causes no corresponding increase in central
calcein, indicating that it also blocks gap junctions.
Although we can observe quantitative differences between positive and negative
control groups with each strategy for analysis, we need a metric to compare these
methods with one another. In order to compare all five analysis strategies, we
investigated the percentage of average normalized calcein fluorescence in both positive
and negative control spheroids found in the following locations: (Fig. 10A) The outer
edge of the spheroid; (Fig. 10B) the inner core of the spheroid. We also examined (Fig.
10C) the ratio of the outer edge to the inner core of the spheroid. The “outer edge” is
defined as the region of the spheroid less than a sixth of its radius from its perimeter,
28
while the “inner core” is defined as the region of the spheroid less than a sixth of its
radius from its center.
All three comparisons showed at least some separation between positive and
negative controls for all techniques except the quartile slice. Each technique found a
higher or equal percentage of calcein in the spheroid outer rim for those spheroids
treated with CBX compared to those not (Fig. 10A). Similarly, each technique found a
lower or equal percentage of calcein in the spheroid inner core for those spheroids
treated with CBX compared with those not (Fig. 10B), and a higher or equal ratio of rim
to core calcein for the CBX-treated spheroids (Fig. 10C). However, only concentric
shell and concentric hemishell techniques showed a statistically significant
differentiation between conditions at a P<0.05 level, and then only for assessments of
the percentage of fluorescence in the outer rim and of the ratio between outer rim and
inner core fluorescence.
Equatorial S
lice
Quarti
le S
lice
Vertica
l Axi
s
Concentr
ic S
hells
Concentr
ic H
emis
hells
05
101520253035404550
Comparison of Methods: Rim Percentage
0μM CBX100μM CBX
Perc
en
tage
of
No
rmalize
d C
alc
ein
Flu
ore
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ce in
O
ute
r R
im
29
A
*{ *{
Equatorial S
lice
Quarti
le S
lice
Vertica
l Axi
s
Concentr
ic S
hells
Concentr
ic H
emis
hells
0
24
68
1012
1416
1820
Comparison of Methods: Core Percentage
0μM CBX100μM CBX
Perc
en
tage
of
No
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alc
ein
Flu
ore
scen
ce in
In
ne
r C
ore
Equatorial S
lice
Quarti
le S
lice
Vertica
l Axi
s
Concentr
ic S
hells
Concentr
ic H
emis
hells
0
2
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6
8
10
12
Comparison of Methods: Rim/Core Ratio
0μM CBX100μM CBX
Rati
o o
f O
ute
r R
im t
o In
ner
Co
re
No
rmalize
d C
alc
ein
Flu
ore
sce
nce
Fig. 10: Several comparisons can be made between the various techniques for analyzing calcein permeation into spheroids. Techniques were compared on the basis of the percentage of total normalized calcein found in the outer rim of the spheroid (A,) on the basis of the percentage of total normalized calcein found in the inner core of the spheroid (B), and on the basis of the ratio of normalized calcein found in the outer rim to normalized calcein found in the inner core (C). Blue: negative control, orange: positive control. Error bars represent standard deviations. Asterisks represent significance at a P<0.05 level using a two-tailed two-sample Student’s t-test.
In order to assess the utility of these assays for high-throughput testing, we calculated Z-
factors for each technique under each method of comparison (Table 2) A Z-factor is a
dimensionless statistical parameter intended for the evaluation of the quality of scientific assays,
and is intended particularly for the assessment of high-throughput screens on the basis of the
differentiation of positive and negative control conditions. [Zhang, 1999] The Z-factor is
estimated with the expression 3(σ̂ p+ σ̂n)
|μ̂p− μ̂n|where σ̂ pand σ̂ n represent the observed standard
30
B
C
*{ *{
deviations of the positive and negative control conditions and μ̂p∧ μ̂n represent the observed
means of the positive and negative control conditions. This quantity can be interpreted as
related to the number of standard deviations separating the two means: for example, when the
standard deviations of the positive and negative controls are equal, a Z-factor of 0.5 signifies 12
standard deviations between the means of the positive and negative control conditions. Z-
factors above 0.5 are considered to represent excellent screening tests, Z-factors between 0
and 0.5 to represent marginal assays, and Z-factors below 0 show too much overlap between
positive and negative controls to necessarily be useful. We conducted Z-factor analysis of each
of the five analysis strategies (equatorial slices, quartile slices, vertical axes, concentric shells,
concentric hemishells) with respect to each of the comparison methods (outer rim calcein
percentage, inner core calcein percentage, and outer rim / inner core calcein ratio) (Table 1).
The best observed Z-factor was the outer rim / inner core calcein ratio for the concentric shell
technique, with a Z-factor of -0.83. The percentage of calcein in the outer rim for the concentric
hemishells was the next-best, with a Z-factor of -0.93. All the remaining tests had Z-factors
between -1 and -6, with the concentric shell techniques showing the lowest Z-factors in general.
However, Z-factors are intended to be extremely conservative in their assessment of
assays because they are intended to evaluate high-throughput assays that may screen millions
of compounds. As such, an assay with a Z-factor of 0 or below may still indicate a meaningful
difference in effects between the positive and negative controls. This may be the case for our
concentric shell technique: although these results indicate that it is not to be relied upon as a
sole screening method in a high-throughput context, its significance under a Student’s t-test
shows that it may nonetheless be a useful assay in assessing the effects of potential gap
junction inhibitors in 3D.
31
Z-Factors of Comparison Techniques
Equatorial Slices
Quartile Slices
Vertical Axis
Concentric Shells
Concentric Hemishells
% Outer Rim -5.85 -75.25 -2.72 -1.02 -0.93% Inner Core -1.96 -63.02 -5.96 -1.52 -1.52Outer Rim / Inner Core -2.99 -65.09 -1.95 -0.83 -1.02
Table 1: Z-Factors for various comparison techniques.
Conclusions & Future Directions
These experiments and procedures for data analysis have shown that we are capable of
using molded hydrogels in a 96-well format for high-throughput formation and analysis of 3D
spheroid tissue culture. We used spheroids of similar and reproducible sizes at reproducible Z-
heights from the bottom of the microscopy plate. Treatment of cells with active compounds and
with fluorescent dyes can be accomplished within the plate, as can high-content confocal
imaging. Although in this experiment cells were seeded into the hydrogels manually, previous
trials have demonstrated the compatibility of the 96-well system with the Eppendorf epMotion™
5070, an automated liquid handling system. Such compatibility may be useful the future for
further improving the throughput of an integrated assay.
Because of this ability to reliably form, treat, and image cells in spheroids in a 96-well
plate, we have been able to use a calcein permeation assay to assess gap junctional
intercellular communication within 3D spheroids at high-throughput rates and high-content
conditions. This assay measures the uptake and distribution of calcein as it first permeates into
the outer layer of the spheroid as calcein-AM, is converted into fluorescent calcein-Ca(2+), and
begins to diffuse through gap junctions into cells deeper within the spheroid. The amount of
calcein in the outer periphery of the spheroid is influenced by P-glycoprotein activity, while the
amount that diffuses into the core is influenced by gap junction activity; we can therefore use
calcein fluorescence in these areas as a proxy for the activity levels of these cellular
mechanisms. We conducted such tests on a negative control (untreated spheroids of cells that
32
express both gap junctions and P-gp) and a positive control (spheroids treated with CBX, a
known inhibitor of both mechanisms).
We tested five different quantitative strategies to analyze calcein fluorescence
distribution throughout the spheroids. These five were equatorial slices, quartile slices, Z-axes,
concentric shells, and concentric hemishells. Each showed the same three trends: First, both
the positive and negative controls showed a higher concentration of calcein in the outer rim,
consistent with calcein-AM permeation first into peripheral cells before a slower diffusion of
calcein through gap junctions into the core. Second, the positive control showed an increase of
calcein in the outer rim relative to the negative control, consistent with inhibition of P-gp-
mediated efflux of calcein-AM. Third, the positive control showed no corresponding increase of
calcein in the core, consistent with inhibition of gap junctions: although more calcein was taken
up in total by CBX-treated spheroids, that calcein was unable to permeate into the spheroid
center. These three trends were observed under all approaches to data analysis, and so we
compared the five approaches to determine which shows the greatest separation between
positive and negative controls. We conducted this comparison using Z-factor analysis, a
statistical technique for assessing an assay’s separation between positive and negative controls
intended to determine the assay’s reliability in a high-throughput screen. A Z-factor between 0.5
and 1 is considered to represent a good assay, a Z-factor between 0 and 0.5 is considered a
marginal assay, and a Z-factor below 0 is considered not to be useful for a large-scale
screening procedure.
The first strategy, equatorial slices, was a simplified system assessing in 2D a central X-
Y slice of each spheroid. It provided a straightforward approach, but fails to use all available
information and suffers significantly from fluorescence loss due to light scattering: the signal to
noise ratio in central areas of relatively large spheroids may hardly be useable. It does not
show statistically significant separation between positive and negative controls, and at best
gives a Z-factor of -1.96.
33
The second strategy, quartile slices, hoped to remedy the issue of fluorescence loss by
assessing a 2D slice closer to the base of the spheroid. While the slice did indeed suffer from
lower levels of fluorescence loss, it was least effective technique that we assessed, showing
results with Z-factors below -60 and far from statistical significance. We believe that this may be
because of vertical diffusion of calcein: although the center of the quartile slice may be dozens
of microns from the edges of the slice, it is actually much closer in the Z-direction to the bottom
of the spheroid.
The third strategy, Z-axes, was conducted by assessing levels of calcein fluorescence
from the very bottom to the center of each spheroid. This technique was about as successful as
the first strategy, giving a Z-factor of -1.95 without statistically significant separation between
control conditions. Its largest failing is perhaps its reduction of the dataset analyzed to a single
1D line.
The fourth strategy, concentric shells, analyzed calcein fluorescence as a function of
radius throughout the entire 3D tissue of the spheroid. Although it did not give a very high
resolution after analysis, averaging the data to only six concentric shells, it did incorporate all
available data into these averages and was in fact the only strategy to do so. This technique
gave us our best results, showing statistically significant separation between positive and
negative controls and a Z-factor of -0.83.
The fifth and final strategy, concentric hemishells, shared many of the advantages and
disadvantages of the previous technique, and was conducted identically except for the removal
of data from the upper hemisphere of each spheroid due to belief that in those regions
fluorescence lass had made the signal to noise ratio so low that data would not be useful. This
technique, too, showed a statistically significant separation of conditions, though with a Z-factor
of 0.93 was perhaps not quite as useful as the previous.
Direct comparisons of these techniques show that the concentric shells strategy is the
most effective, and that future experiments should move forward by using this technique to
34
assess gap junction and P-gp activity in spheroid tissue culture. This technique should not be
considered sufficient for a large-scale pharmacological screen. It may be possible, though, to
further refine the technique and hope to increase its utility. Perhaps a different timepoint would
yield clearer results, and perhaps comparisons between timepoints could illustrate the
timecourses of substances that inhibit gap junctions. Higher or lower doses of calcein-AM may
also give clearer results, as might larger or smaller spheroids.
As it stands, the concentric shells assay is capable of differentiating between positive
and negative control conditions, though it should not be relied upon as the sole metric for a
high-throughput screen. Experiments moving forward with this or an improved assay
may be focused on assessing the effects of various compounds believed to inhibit GJIC, P-gp
activity, or both. The same assay may be used for a variety of cell types and environmental
conditions. This assay will enable experimenters to explore in more biologically accurate
conditions the nature of calcein permeation (and drug permeation in general) into tissue, as well
as the effects of various substances on this permeation.
Appendix 1: Exclusion of one spheroid from equatorial slice analysis.
In assessing the four spheroids of the negative control using the equatorial slices
technique, one stood out widely from the others in its level of outer-rim calcein fluorescence.
Closer investigation revealed that this was due to the presence of a small ‘satellite’ spheroid
lying adjacent to the primary spheroid precisely at the height of its equator (Fig. A1). This
satellite spheroid shows a high level of calcein fluorescence, probably because of its very high
surface area to volume ratio: calcein-AM was able to diffuse into the satellite spheroid from all
directions. The satellite was so bright that it noticeably influenced calculations: without the
outlier, analysis resulted in an outer rim / inner core ratio Z-factor of -2.99, with it, the same Z-
factor was -7.52. In choosing to exclude this outlier, we based our decision on the rarity of such
35
a satellite lying in such a position with such an intense calcein fluorescence: in quantitative
observations of other spheroids, this result was very infrequently seen.
We did not choose to exclude this spheroid from the concentric shell and concentric
hemishell techniques: although these techniques (unlike the vertical axis and quartile slice
techniques, these two include the satellite spheroid in the data that they analyze). The decision
to include the this spheroid in those techniques was based on the knowledge that they average
fluorescence intensity over a much larger set of data.
The knowledge that this outlier was excluded from analysis by the equatorial technique
and included by the concentric shell technique, and that the latter nonetheless showed a much
better Z-factor than the former, is perhaps another compelling argument that the concentric shell
technique shows more promise for future experiments.
Table 1: An equatorial X-Y slice of the excluded outlier. The white arrow indicates a small region of very high relative calcein intensity.
36
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