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

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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

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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).

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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,

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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.

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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.

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0μM CBX100μM CBX

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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

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lcein

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ore

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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

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Concentric Shell: Distance from Center

No

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cein

Flu

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sce

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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.

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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

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lice

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l Axi

s

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Comparison of Methods: Rim Percentage

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A

*{ *{

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Comparison of Methods: Core Percentage

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Comparison of Methods: Rim/Core Ratio

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Rati

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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

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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.

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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

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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.

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