A physical sciences network characterization of nonmalignant and metastatic cells

SUPPLEMENTARY MATERIAL

-materials and methods

-supplementary figures and captions

Authors: The Physical Sciences – Oncology Centers Network1

PS-OC Network

1. Principal Investigator: Paul C.W. DaviesSenior Co-Investigator: William M. GradyArizona State University Physical Sciences-Oncology CenterArizona State University, Tempe, AZ

2. Principal Investigator: Michael L. ShulerSenior Co-Investigator: Barbara L. HempsteadCornell University Physical Sciences-Oncology CenterCornell University, Ithaca, NY

3. Principal Investigator: Franziska MichorSenior Co-Investigator: Eric C. HollandDana-Farber Cancer Institute Physical Sciences-Oncology CenterDana-Farber Cancer Institute, Boston, MA

4. Principal Investigator: Robert A. GatenbySenior Co-Investigator: Robert J. GilliesH. Lee Moffitt Cancer Center & Research Institute Physical Sciences-Oncology CenterH. Lee Moffitt Cancer Center & Research Institute, Tampa, FL

5. Principal Investigator: Denis Wirtz2

Senior Co-Investigator: Gregg L. SemenzaJohns Hopkins University Physical Sciences-Oncology CenterJohns Hopkins University, Baltimore, MD

6. Principal Investigator: Alexander van OudenaardenSenior Co-Investigator: Tyler JacksMassachusetts Institute of Technology Physical Sciences-Oncology CenterMassachusetts Institute of Technology, Cambridge, MA

7. Principal Investigator: Mauro FerrariSenior Co-Investigator: Steven A. CurleyThe Methodist Hospital Research Institute Physical Sciences-Oncology CenterThe Methodist Hospital Research Institute, Houston, TX

1 Comprehensive list of authors and affiliations appear at the end of the paper.2 To whom correspondence should be addressed. E-mail: wirtz@jhu.edu.

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8. Principal Investigator: Thomas V. O’Halloran Senior Co-Investigator: Jonathan D. LichtNorthwestern University Physical Sciences-Oncology CenterNorthwestern University, Chicago, IL

9. Principal Investigator: Robert H. AustinSenior Co-Investigator: Thea TlstyPrinceton University Physical Sciences-Oncology CenterPrinceton University, Princeton, NJ

10. Principal Investigator: Peter KuhnSenior Co-Investigator: Kelly J. BethelThe Scripps Research Institute Physical Sciences-Oncology CenterThe Scripps Research Institute, La Jolla, CA

11. Principal Investigator: Jan LiphardtSenior Co-Investigator: Valerie M. WeaverUniversity of California-Berkeley Physical Sciences-Oncology CenterUniversity of California-Berkeley, Berkeley, CA

12. Principal Investigator: W. Daniel HillisSenior Co-Investigator: David B. AgusUniversity of Southern California Physical Sciences-Oncology CenterUniversity of Southern California, Los Angeles, CA

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Supplementary Information: Methods

I. Morphology

A. Differential Interference Contrast Microscopy

Breast cancer cell lines were grown on number 1.5 cover glass (Carl Zeiss, Thornwood, NY), nuclei were stained with DAPI (Serotec, Oxford, UK), cells were washed with PBS and then mounted on microscope slides using Fluoromount G (Southern Biotech, Birmingham, AL). Cells were imaged using Köhler illuminated Nomarski differential interference contrast (DIC) optics with a Zeiss 63X oil immersion 1.40 NA plan apochromat lens on a Zeiss Axiovert 200M microscope (Carl Zeiss, Thornwood, NY). The aspect ratios of the cells were measured using IMAGE J software (National Institutes of Health, Bethesda, MD). The Jarque-Bera test was used to establish the normality of the aspect ratio data for each of the cell types.  A 1-way ANOVA on the aspect ratio data was performed; a p-value of 0.001 was obtained.

B. Single-cell Tomographic Imaging and 3D Morphometry

3D imaging of cells was accomplished by optical CT using the Cell-CTTM instrument (VisionGate, Inc.,

Phoenix, AZ) that provides isotropic spatial resolution of 350 nm.  Cells were fixed with CytolLyt (Cytyc,

Malborough, MA) and stained with 6.25% (w/w) hematoxylin and 1% (w/w) eosin (Electron Microscopy

Sciences, Hatfield, PA), dehydrated with ethanol, and embedded into a carrier gel (Smart Gel, Nye

Lubricants, Fairhaven, MA). Cells were imaged sequentially by flowing the carrier gel through a rotating

glass capillary. For each cell, a volumetric image was generated by acquiring 500 projections taken at

angular intervals of 0.72 degrees around the cell and subjecting them to mathematical reconstruction

algorithms 1. Prior to reconstruction, we removed background noise and aligned the projections to remove

pattern noise artifacts and to compensate for mechanical jitter and run-out of the capillary, respectively.

3D image processing algorithms 2 were used to quantify morphological parameters such as nuclear

sphericity. One hundred volumetric images of each cell type were analyzed and statistical analysis was

performed by two-tailed unpaired t-test.

C. Partial Wave Spectroscopy

PWS instrumentation and measurement techniques have previously been described 3. For cell culture,

25,000 cells were plated in each chamber of two-well sterile glass chamber slides (Lab-Tek Chamber

Slide System, NUNC, Rochester, NY) and incubated for 6 h. Cells were fixed with 70% cold ethanol

(v/v) overnight prior to PWS measurements. All experiments were carried out for early passages 2-5.

Nuclear disorder was quantified in approximately 60 cells for each cell line. All p values were calculated

using standard t-tests. The effect-size between two groups was calculated from the average disorder

strength, Ld(g)

and its standard deviation, σ(g )

:

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Effect−size=μ1−μ2

√σ12+σ

22

where μ1 and σ 1 can be thought as the Ld(g)

and σ(g )

for the MCF-10A cell population. Similarly, μ2 and

σ 2 can be assumed to be Ld(g)

and σ(g )

for the MDA-MB-231 cells. This treatment takes into account the

statistical significance of the average disorder strength difference (i.e., ΔLd , for MCF-10A and MDA-

MB-231). To minimize sample variability, the MDA-MB-231 cell Ld values were normalized with respect

to MCF-10A values. For example, if the Ld of MCF-10A is 1, the Ld of MDA-MB-231 cells will be 1.80.

D. Matrix Stiffness

Cells were trypsinized and plated on ECM-crosslinked polyacrylamide gels (2,000 cells/18 ml gel). MCF-

10A cells were grown in 3D in media containing 2% (v/v) recombinant basement membrane (rBM) as

described 4 and MDA-MB-231 cells were grown in 3D in Dulbecco's Modified Eagle Medium with high

glucose, 10% fetal bovine serum and 2% rBM. ECM-crosslinked polyacrylamide gels were prepared as

previously described 5 and 6. Cells were processed for immunofluorescence with the following antibodies

and reagents: polyclonal Ki67 (Abcam, Cambridge, MA), Alexa Fluor 488 conjugated phalloidin, and

Alexa Fluor 594 rabbit IgGs (Invitrogen, Carlsbad, CA).Cells were visualized using a scanning confocal

laser attached to a Zeiss LSM 510 fluorescence microscope and images were recorded at 63X

magnification (numerical aperture 1.4). Cell proliferation was measured by calculating the percentage of

cells with Ki67-labeled nuclei. On average at least 120 cells where counted per condition. The Ki67 cell

proliferation data were statistically analyzed using a two-tailed unpaired t-test.

E. CD44 and lipid raft distribution assays

Cells were detached using 5-10 mL of enzyme-free cell dissociation buffer (Invitrogen, Carlsbad, CA) for

5 min (MDA-MB-231) or 15 min (MCF-10A) to preserve the cell surface antigen presentation. Complete

media was added once the cells detached, cells were washed twice in 1X Dulbecco’s phosphate-buffered

saline (DPBS) without Ca2+ or Mg2+ (Invitrogen)) and centrifuged at 1,000 RPM at 4 °C for 5 min. Cells

were then incubated over ice with mouse FITC-conjugated anti-CD44 antibody clone G44-26 (BD

Biosciences, San Diego, CA) and Alexa Fluor 555 Vybrant® Lipid Raft Labeling Kits (Invitrogen) as well

as appropriate isotype controls for 45 min. Cells were washed three times with 1mL of 1X DPBS without

Ca2+ or Mg2+ to remove unbound antibody. The lipid raft kit uses an antibody specific for cholera toxin

subunit B (CT-B) that binds to the pentasaccharide chain of plasma membrane ganglioside GM1, which

selectively partitions into lipid rafts.

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Prior to TIRF imaging, cells were fixed in cold 4% (v/v) paraformaldehyde (Electron Microscopy

Sciences, Hatfield, PA) over ice for 30 min. TIRF and epifluorescence images were taken with a Nikon

Ti-E/B automated inverted microscope with Perfect Focus and automated TIRF angle and equipped with

a CFI APO 100X/1.45 oil TIRF objective lens and an EMCCD Detector. A 488-nm Argon laser was used

to detect CD44 FITC-conjugated antibody. A 561-nm laser was used to detect Alexa Fluor 555 Vybrant®

Lipid Raft dye. Areas of fluorescence in the TIRF images were analyzed using ImageJ (National

Institutes of Health) and plotted using Prism 5.0b for Microsoft (GraphPad Software, San Diego, CA).

Two-tailed unpaired t-tests were employed with a significance level of α=0.05 where applicable. Each

experiment was conducted in triplicate.

I. Motility and Mechanics

A. 1D, 2D and 3D Migration

1. Measurement of Maximum Displacement

Maximum displacement from the origin was taken as the distance between the starting position of a cell

and the furthest point from the starting position that it reached at any time during the 16.5 h experiment.

One value was obtained for each cell measured, and these values were averaged to give the value reported

herein.

2. 1D Migration: Device Preparation and Time-lapse Microscopy

The 13 micron-wide silicon microchannels were patterned and etched to a depth of 20 m using standard

photolithography techniques and a Deep RIE etcher (Unaxis 770, Plasmatherm, St. Petersburg, FL). The

device was cleaned under an oxygen plasma treatment for 1 min before a slab of cured PDMS about

500 m thick was placed over one-half of the device. While the device was still hydrophilic, the micro-

channels were coated through capillary action with 50 g/ml fibronectin (Sigma, St. Louis MO) in

1X PBS solution. The device was washed with 1X PBS and placed inside a petri dish containing fresh

growth medium. Cells were seeded on top of the device at a density of 100,000 cells/cm2. The presence of

the PDMS slab prevented cells from settling down on top of the microchannels. After an incubation

period of 8-12 h, the slab was removed and the petri dish placed inside a 5% CO 2 and 37 °C temperature

controlled microscope incubator. A computer-controlled microscope recorded a picture of the array of

channels every 5 min for 8 h. After 8 h, a 7.5 nM dose of paclitaxel was added to the media, and images

were recorded for an additional 8 h. Resulting images were processed using ImageJ in order to highlight

the motile cells in each image. A particle tracking algorithm written in MATLAB (Mathworks, Natick,

MA) was used to reconstruct the motion of each cell inside the microchannels. The instantaneous speed of

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a cell was averaged over the duration of the experiment to obtain the average speed. The maximum radial

displacement over the course of the experiment was used to compute the invasion distance.

3. 2D Migration: 2D Collagen Substrate

2D cell culture plates were prepared by adding soluble rat tail type I collagen in acetic acid (BD

Biosciences, San Jose, CA) to achieve coverage of 32.48 g/cm2 and incubated at room temperature for

2 h. Plates were washed gently with 1x phosphate-buffered saline (PBS) and plated with a low density of

cells. Migration studies were conducted as previously described 7.

4. 3D Migration: 3D Collagen Matrix

Cell-impregnated 3D collagen matrices were prepared as previously described 7. A collagen concentration

of 2 mg/ml was used so that the average matrix pore size (<1 m) was significantly smaller than the cell

body and nucleus. Cell density was kept low (20,000 cells per gel) so as to ensure that only single cell

measurements were taken. Mean and standard error of the mean (SEM) values were calculated and two-

tailed unpaired t-tests were conducted using Graphpad Prism.

B. Traction Force Microscopy

Polyacrylamide gels of specific Young’s moduli (5 kPa) were synthesized as previously described 8 with a

constant ratio of 0.175% (v/v) bis-acrylamide to 7.5% (v/v) acrylamide in the gel solution mixture and

derivatized with 0.1, 10, or 50 g/mL of laminin from Engelbreth-Holm-Swarm murine sarcoma

basement membrane (Sigma, St. Louis, MO). Cells were seeded on polyacrylamide substrates embedded

with 0.5-m fluorescent beads (Invitrogen, Carlsbad, CA), allowed to attach and spread overnight, and

imaged in a custom temperature, humidity, and CO2-controlled environment on a Zeiss Axio Observer

Z1m inverted phase microscope with a Hamamatsu ORCA-ER camera. Traction forces were determined

as previously described 8. Data were analyzed with Tukey’s Honestly Significant Difference test or t-test

after natural logarithm transformation to ensure assumptions of normality and equal variance. Statistical

tests were performed using JMP software (SAS Institute, Cary, NC) and significance was considered

p<0.05.

C. Shear Stresses on Rolling Cells on E-Selectin

1. Cell Preparation

Cells were trypsinized for 3 min (MDA-MB-231) or 15 min (MCF-10A) and then recovered in complete

media in the incubator for 2 h before experiment to ensure normal surface receptor expression. Cells were

washed twice with 1X DPBS, centrifuged at 1,000 RPM at 4 C and resuspended in the flow buffer at a

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concentration of 106 cells/mL. The flow buffer consisted of PBS containing Mg2+ (100 mg/mL) and

saturated with Ca2+. At least 90% viability of cells was confirmed using trypan blue stain.

2. Preparation of Immobilized Protein Surfaces

Recombinant human E-selectin-IgG chimeric protein (R&D, Minneapolis, MN) was dissolved in

1X DPBS to a final concentration of 5 g/mL. The surface was first rinsed with 75% (v/v) ethanol and

then 1X DPBS. The surface was subsequently incubated with 10 g/mL protein-G (EMD Chemicals,

Gibbstown, NJ) solution for 1.5 h, followed by a 2 h incubation with E-selectin chimera then blocked

with 5% (w/v) milk protein in 1X DPBS for 1 h. Control tubes were blocked with 5% milk protein in

1X DPBS for 1 h.

3. Rolling Experiment

Micro-Renathane microtubing (300 m ID; Braintree Scientific, Braintree, MA) was cut to a length of

50 cm, and secured to the stage of the Olympus IX81 motorized inverted research microscope after

surface functionalization as described above. A CCD camera (model no: KP-M1AN, Hitachi, Tokyo,

Japan) and a DVD recorder were used to record experiments for offline analysis. Flow of cell suspension

at a concentration of 106 cells/mL through the microtubes was produced by a syringe pump (KDS 230,

IITC Life Science, Woodland Hills, CA). For both surface conditions, cells were introduced into the

microtubes at a wall shear stress of 1.0 dyn/cm2.

4. Data Analysis

Rolling cells were defined as those observed to translate in the direction of flow with an average velocity

less than 50% of the calculated hydrodynamic free stream velocity. The rolling velocity was calculated by

measuring the distance a rolling cell traveled over a 30-second interval. Videos of rolling cells were taken

at three randomly selected locations along the microtube. The quantity of cells rolling or adherent to the

surface was determined by recording images at 30 randomly selected locations along the microtube. All

errors are reported as SEM values and two-tailed unpaired t-tests were performed using GraphPad Prism.

D. Extracellular Matrix Isolation and Immunofluorescence

Cells were grown in a 12 well plate (tissue culture treated; Nunc, Fisher Scientific) containing 1 12-mm

glass coverslip/well (Fisher Scientific, Pittsburgh PA). Matrix isolation was performed according to an

established protocol 9. For scanning electron microscopy, isolated ECM components were fixed in a

glutaraldehyde/formaldehyde containing buffer (3% (v/v) formaldehyde, 1.5% (v/v) glutaraldehyde,

0.1 M sodium cacodylate, 5 mM MgCl2, 2.3 M sucrose, pH 7.4) for 20 min and washed with 1x PBS.

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Samples were post-fixed with 1% (v/v) osmium tetroxide for 20 min (Sigma, Allentown, PA), followed

by dehydration in ethanol. Samples were critical point dried (Tousimis 795) and coated with

2 nM platinum using a sputter coater (Anatech Hummer 6.2 Sputter Coater, San Diego, CA). Samples

were visualized using a Quanta 200 environmental scanning electron microscope (FEI, Hillsboro,

Oregon). For immunofluorescence staining, MCF-10A and MDA-MB-231 cells were cultured for 6, 9

and 12 d in respective growth media, fixed with 3.7% (v/v) paraformaldehyde for 30 min, washed in

1X PBS and incubated with 50 g/ml FITC-tagged hyaluronan (Sigma) for 1 h. Coverslips were mounted

using 1 drop of fluorescent mounting media (Dako, Denmark).Hyaluronic acid receptors were visualized

using an Olympus BX60 microscope and images were captured and acquired using constant exposure

time in MagnaFireTM (Melville, NY).

E. Hyaluronic Acid Micropatterning

Standard photolithographic techniques were utilized to fabricate silicon masters with 80 m x 80 m

square patterns. PDMS pre-polymer elastomer solution and curing agent (Sylgard 184) were mixed in a

10:1 wt ratio, cast onto the silicon masters and cured overnight at room temperature to form a

complementary elastomeric stamp. Glass substrates were micropatterned with HA using a previously

established protocol 10. Cells were grown to 80% confluence, washed with 1X PBS and digested using

trypsin-EDTA. Cell densities between 0.75 and 1 x10 5 cells were seeded onto each HA patterned

substrate.

After a culture period of 24 h, cells were fixed using 3.7% (v/v) paraformaldehyde for 20 min, washed

with 1X PBS, and incubated with 1% BSA in 1X PBS for 30 min to prevent nonspecific binding. Cells

were washed with 1X PBS and incubated with anti-human CD44 for 1 h (1:100 dilution; Clone A3D8;

Sigma, Saint Louis, MO). After rinsing with 1X PBS, cells were incubated with anti-mouse IgG Cy3

conjugate (1:50; Sigma) for 1 h. Cells were washed with 1X PBS, counterstained with the DNA dye

DAPI (1:1000; Roche Diagnostics) for 10 min, rinsed with 1X PBS, and mounted with glass slides using

fluorescent mounting medium. All substrates were imaged using an Olympus BX60 fluorescent

microscope. To quantify cell adhesion, fluorescently-labeled substrates were analyzed to determine the

percentage of cells (via cell number and cell area) that overlapped HA patterned regions. The total

number of cell nuclei and the number of cell nuclei located directly on HA patterned regions were

counted in the field of view and used to determine cell adhesion (cell # on HA patterns/total cell number).

All image analyses were performed in triplicate experiments with triplicate fields of view. A second

quantification method validated and confirmed these findings: image analysis software (Image J) was

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used to measure cell area using the “freehand selection” tool to outline the respective cell areas. Unpaired

two-tailed t-tests were performed where appropriate.

F. Atomic force microscopy (AFM)

To prepare samples for AFM measurements, cells were plated onto 50-mm glass-bottom dishes (World

Precision Instruments, Sarasota, FL) as described in 11. Cells were incubated with 5 µM SYTO 9 nucleic

acid stain (Invitrogen) for 30 min prior to indentation followed by incubation with 5 µM CellMask Deep

Red lipid membrane stain (Invitrogen) 5 min prior to indentation, or cells were incubated with 5 µM

Nuclear ID Red (Enzo Life Sciences, Plymouth Meeting, PA) and 5 µM Nucleolar ID Green (Enzo Life

Sciences) 30 min prior to indentation.

The combined AFM-Confocal Laser Scanning Microscopy (AFM-CLSM) setup consists of a sample

scanning AFM (MFP-3D Bio, Asylum Research, CA) and a single molecule sensitive CLSM (Microtime

200, PicoQuant, Germany). The optical setup allows fluorescence lifetime imaging microscopy (FLIM)

with two-color excitation (470 nm and 640 nm). The combined AFM-CLSM setup is described in detail

in 12.

Soft silicon nitride AFM probes with nominal spring constants k ≈ 10 pN/nm (MSNL, Veeco Instruments,

Plainview, NY) were used for the indentation experiments. The spring constant of each cantilever was

determined from the thermal noise spectrum13. After the experiment, the probes were imaged with a

scanning electron microscope to determine the tip radius. The AFM tip and the confocal volume were

aligned using the pattern of back-scattered light 12.

After alignment, the AFM tip was fully retracted and the sample stage moved until a cell of interest was

under the tip in the crosshairs of the eyepiece. A two color FLIM image of the cell was acquired while the

tip was still retracted. In the FLIM image, points of interest on the cell were selected. The AFM tip was

moved directly over the first point and force–distance curves were acquired with 2 µm/s approach and

retract speeds in the continuous force curve mode with a trigger force of 600 pN. Approximately 20 such

curves were taken at each point of interest on the cell. After indentation, a subsequent FLIM image was

taken of the cell with exactly the same settings as the preliminary image. The two images were super-

imposed in software (Image J) to determine the extent to which the cell moved during the measurements.

If the cell moved more than ~1 µm, then data were excluded. The alignment of the tip was verified as

described above and another cell of interest was located and measured. The measured force distance

curves were analyzed with custom written software (Igor Pro, Wavemetrics) as described in 11.

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G. Ballistic injection nanorheology (BIN)

Cells were seeded on 35-mm plastic dish and grown to ~80% confluence before ballistic injection of

fluorescent 100-nm carboxylated polystyrene particles (Invitrogen, Carlsbad, CA) using a Biolistic PDS-

1000HE particle delivery system (Bio-Rad, Hercules, CA) as previously described 14. In brief,

nanoparticles were placed on macrocarriers and allowed to dry for 2 h. Rupture disks of 1550 psi were

used along with hepta adaptor. After ballistic injection, cells were washed with 1X PBS and incubated for

1 h before re-seeding onto a poly-L-lysine (Concentration/time for PLL coating, Sigma, St. Louis, MO)

treated glass-bottom dish (MatTek, Ashland, MA). Particle-injected cells were allowed to grow overnight

before observation on a microscope. A Nikon TE2000-E inverted microscope equipped with a 60X oil-

immersion, NA 1.4 objective lens and a Cascade 1K camera (Roper Scientific, Tucson, AZ) were used to

acquire the high-speed image of fluorescent particles movement with 30 frames/s for 20 s. Images were

analyzed by custom software in MATLAB (The MathWorks, Natick, MA) to obtain the movement of

particles. The time-averaged mean squared displacement (MSD) were calculated to determine the random

displacement of probe nanospheres by following formula,

MSD( τ )=⟨[ x ( t+τ )−x( t ) ]2+ [ y ( t+ τ )− y ( t ) ]2 ⟩where τ is the time lag and t the is elapsed time.

II. Stress Response and Survival

A. Cell Number and Viability in Low pH,Low Oxygen, and Paclitaxel Treatment

For low pH treatment, complete cell culture growth medium was further supplemented with 25 mM each

of PIPES and HEPES and the pH adjusted to 7.4 or 6.7 with 1N HCL or NaOH. Cells were seeded in

neutral pH (7.4) complete growth medium at 17% oxygen, 5% CO2 and 37 °C, 24 h prior to low pH

treatment. The following day, growth medium was maintained at neutral pH (7.4) or changed to low pH

(6.8) growth media for 72 h.

For low oxygen treatment, complete cell culture growth medium was further supplemented with 25 mM

each of PIPES and HEPES and the pH adjusted to 7.4 with 1N NaOH. Cells were seeded in pH 7.4

growth medium at 17% oxygen, 5% CO2 and 37 °C, 24 h prior to low oxygen treatment. The following

day, cells were either cultured in pH 7.4 growth medium at 17% or 1% oxygen for 72 h.

Cell viability was determined every 24 h using the Invitrogen Live/Dead Viability and Cytoxicity Kit (L-

3224). Images were collected with an automated Zeiss Observer Z.1 inverted microscope through a 5X

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/0.15 N.A. objective using green fluorescent protein and Rhodamine filters. Tiled mosaic images were

captured using the AxioCam MRm3 CCD camera and Axiovision version 4.7 software suite (Carl Zeiss

Inc., Germany). Quantification of viable cells was performed using fully automated methods in Definiens

Developer XD.

For paclitaxel treatment, approximately 4,000 cells/well were plated in a 96-well plate (Corning

Incorporated #29442-056, Lowell, MA) in PS-OC media conditions. The following day, increasing

concentrations of paclitaxel (LC Laboratories #P-9600, Woburn, MA) were added to the respective wells

and allowed to incubate. After a specified period of time, as indicated in Fig. 3f, the CellTiter 96

Aqueous Non-Radioactive Cell proliferation Assay reagent was added (following manufacturer’s

instructions, Promega #G5421, Madison, WI) to the individual wells. Cell viability was determined by

reading the plate at an absorbance wavelength of 490 nm. The results are averages of four wells per

experimental sample.

B. 3D Alginate-based Tumor Models

RGD-modified calcium alginate discs (200 m thick, 4 mm in diameter) were made by suspending cells

in RGD peptide-modified alginate (Protanal LF; FMC Biopolymer, Philadelphia, PA; dissolved, 4%

[w/v], in serum-free DMEM (for MDA-MB-231) or DMEM/F12 (for MCF-10A) at a concentration of

20×106 cells/mL, followed by casting in a machined plexiglass mold and cross-linking with 0.1 M CaCl 2,

as previously published 15. Cell-seeded discs were cultured in 24-well plates (one disc per well) on an

orbital shaker. Culture was performed at 37oC, 5% CO2, and either hypoxic (1%) or ambient (17 ± 1%)

O2, in a controlled atmosphere incubator (Thermo Fisher Scientific Inc., Waltham, MA). Culture media

was changed daily (500 L/disc), with harvest time points at days 1, 3, and 6.

1. Cell Viability in 3D Culture

Viability of cells seeded in alginate discs was monitored using live-dead staining. Alginate discs were

submerged in a live/dead staining solution (5 μM calcein-AM and 5 μM ethidium homodimer-1;

Invitrogen, Carlsbad, CA). The submerged discs were incubated for 30 min prior to fluorescence

microscopy. Images were taken on an inverted microscope (Axio Observer; Carl Zeiss;

excitation/emission wavelengths used were 470/525 and 545/605 nm for green and red fluorescence).

ImageJ enabled the merging of green and red fluorescence images. Plots represent mean ± SD. Statistical

significance was assessed with ANOVA and a Tukey post-test, wherein p < 0.05 (*) was considered

significant using Graphpad Prism.

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2. Measurement of O2 Consumption in 3D Culture

Cellular O2 consumption was measured as previously reported 15. Twelve cell-seeded 200 μm-thick

alginate discs at day 6 of either 1% or 17% O2 3D cultures were submerged in 2 mL media in a sealed

glass chamber (Agilent Technologies, Inc., Santa Clara, CA), and kept stirring at 37oC. Media was

initially equilibrated at ambient O2 and 5% CO2, and reduction in O2 level due to cellular consumption

was measured with a dissolved oxygen meter (Innovative Instruments, Inc., Tampa, FL). O2 drawdown

measurements were taken at two-minute intervals for 30 min, and draw-downs were run in duplicate.

Consumption rate was calculated from a linear fit to the O2 level versus time and normalized to total

sample DNA content for comparisons of different conditions.

C. Carcinoembryonic Antigen Expression under Hypoxia

Cells were grown at < 80% confluence under normal (ambient atmosphere supplemented with 5% CO2)

or hypoxic (1% O2 with 5% CO2) conditions for 72 h. To harvest cell singletons, cells were mildly

trypsinized (≤ 5 min, 0.05% trypsin with EDTA) and resuspended to 10 6 cells/mL in cold 1X PBS with

0.1% (w/w) bovine serum albumin. Cells were stained on ice for single-color flow cytometry

(FACSCalibur, BD Biosciences, San Jose, CA) with mouse anti-human CEA primary antibodies (clone

COL-1, BD Biosciences, San Diego, CA) with phycoerythrin-conjugated anti-mouse IgG secondary

antibodies (Vector Labs, Burlingame, CA), or proper isotype controls 16. Mean fluorescence intensities

are equal to the geometric mean of the fluorescence intensity of each collected cell (<2,500 cells per

sample). Data are presented as the mean ± SEM. of n ≥ 3 for all experiments. Two-tailed unpaired t-test

was used to determine statistical significance (p< 0.05).

D. Wound healing assay

Cells were plated in a six-well plate at a density of 10,500 cells/cm2. At confluence, a sterile 200 L

pipette tip was used to scratch 3 parallel lines into the monolayer. The media in each well was refreshed,

and the initial time point was imaged and then every 12 h until confluency in the wound was reached.

E. Single-cell respirometry

Single-cell oxygen consumption rates (OCR) measurements were performed as described elsewhere17.

The technique is based on enclosing individual cell in hermetically sealed glass microwells that contain

an extracellular optical oxygen sensor. Oxygen concentration changes inside of the microwells are

measured as changes in sensor emission intensity. Cells were loaded into microwells utilizing a custom

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built high-precision pump that enables manipulation of sub-nanoliter volumes. After loading cells were

incubated for 18-24 hours in an incubator under 5% CO2 at 37o C in “open” condition, i.e. the wells were

fully open for nutrient and gas exchange with the outside medium. Immediately after incubation the

microwells were sealed be placing a “lid” with integrated oxygen sensor on top. The sensor emission

intensity was measured at time intervals of 5 seconds and oxygen concentration inside of the sealed

microwells was determined using a two-point calibration curve. The OCR values were calculated from

the slope of the oxygen concentration time course.

III. Molecular Signatures for Morphology, Motility, and Survival

A. Proteomics Experiments

1. Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC)

Cells were grown in Advanced DMEM F-12 Flex media (Invitrogen, # MS10033) containing 5% or 10%

(MCF-10A or MDA-MB-231 cells, respectively) dialyzed FBS (10kDa MW cut-off and dialyzed against

0.15M NaCl) and either 13C - lysine or unlabeled lysine for six passages according to the standard SILAC

protocol 18. MCF-10A cells were also supplemented with 0.5 µg/ml hydrocortisone (Sigma; #H-0888), 20

ng/ml hEGF (Invitrogen; #PHG0311), 10 µg/ml insulin (Sigma; #I-0516), and 100 ng/ml cholera toxin

(Sigma; # C-8052). After the completion of six passages in the presence of SILAC media, experiments

were set-up and cells then harvested.

2. Protein Sample Preparation for LC-MS/MS

Cells were washed with 1X PBS to remove cellular debris and residual FBS. Approximately 2 x 107 cells

were lysed in 0.75 ml of 1X PBS containing 1% w/v octyl-glucoside and HALT protease inhibitor

cocktail (Pierce, Rockford, IL #78430) by scraping and needle treating using a 27G1/2 needle. The cell

lysate was centrifuged at 16,000 g, the supernatant of which was passed through a 0.22-µm filter. Cell

lysates were mixed 1:1 using protein quantitation results determined by the bicinchoninic acid assay (i.e.,

MCF-10A lysate from cells grown in light lysine media was mixed 1:1 with MDA-MB-231 lysate from

cells grown in heavy lysine).

3. Cell Treatments

For ROCK inhibitor experiments, heavy labeled cells (13C -lysine) from the MCF-10A and MDA-MB-

231 cell lines were treated with 40 M Y-27632 for 24 h and the corresponding light labeled cells (12C-

lysine) were untreated. For the paclitaxel experiments, MCF-10A light labeled cells were treated with 15

nM paclitaxel (LC Laboratories, Woburn, MA; #P-9600) for 18 h and the heavy labeled cells were

13

untreated. For the MDA-MB-231 line, heavy labeled cells were treated with 120 nM paclitaxel for 18 h

and the light labeled cells were untreated. The respective heavy and light lysates were mixed at a 1:1 ratio

following cell lysis and protein quantitation. Reciprocal experiments were also performed (data not

shown).

4. Protein Identification and Quantification by LC-MS/MS

Protein digestion and identification by LC-MS/MS was performed as described previously 19. Acquired

data was automatically processed by the Computational Proteomics Analysis System (CPAS) 20. The

tandem mass spectra were searched with X!Tandem against version 3.13 of the human IPI database. A

fixed modification of 6.020129 mass units was added to lysine residues for database searching to account

for incorporation of the heavy lysine isotope. All identifications with a PeptideProphet probability greater

than 0.9 were submitted to ProteinProphet and the subsequent protein identifications were filtered at a 1%

error rate 21. Quantitative information was extracted and only peptides with a minimum of 0.90

PeptideProphet score were considered in the analysis. All normalized peptide ratios for a specific protein

were averaged to compute an overall protein ratio.

5. Cytoskeleton Staining

Cells were cultured at 50,000 cells/cm2 in Lab-Tek Chamber Slides (Sigma-Aldrich, St. Louis, MO) in

standard media conditions with or without ROCK Inhibitor (40µM Y-27632) for 2 h (EMD Biosciences,

Gibbstown, NJ; #688000). Cells were then fixed in 4% (w/v) paraformaldehyde and permeabilized using

0.1% (w/w) Triton-X. FITC-conjugated phalloidin (Invitrogen, Carlsbad, CA; #A12379) was used to

stain F-actin and cell nuclei were counterstained using a DAPI mounting medium (Vector Laboratories,

Burlingame, CA; #H-1500). Fluorescent images were acquired using Perkin Elmer spinning disk confocal

microscope with a 63x/1.4 oil immersion lens.

B. Computational Analysis

Starting with the microarray data, we performed network inference using an Inferelator-based inference

pipeline 22 , and visualized the results using Cytoscape 23, a program that enables network visualization

and analysis. We also analyzed the microarray and SILAC data, yielding sets of differentially expressed

genes that we further analyzed with Sungear 24. Sungear enables comparison and investigation of large

data sets. An example is shown in Suppl. Fig. 4. Sungear is connected to The Gaggle Boss 1, which allows

seamless communication and data exchange with an ensemble of other analysis tools including

Cyotscape, FireGoose 25, the Data Matrix Viewer, and the MultiExperiment Viewer 26. This integrated

14

analysis platform allowed for the iterative selection of sub-networks and genes in Cytoscape, followed by

analysis and visual overlays in Sungear, resulting in our final networks and sub-networks.

C. Genomic Analysis

Microarray data were taken from publicly available data sets in the Gene Expression Omnibus (GEO) 27.

MCF-10A data was taken from several different experiment sets: GEO accession numbers GSE4917 28,

GSE678429, GSE8240 30, GSE10070 31, GSE12764 32, GSE1498733, and GSE20285 34. MDA-MB-231 data

were all from one experiment set: GSE260335. These eight data sets were generated on four different

Affymetrix microarray platforms: GSE2603 and GSE4917 on Human Genome (HG) U133A arrays,

GSE6784 on HGU133A 2.0 arrays, GSE8240 on HT HGU133A arrays, and the remaining four on

HGU133 Plus 2.0 arrays. We used MAS5 36 (without quantile normalization) to normalize data in order to

account for the lab-to-lab and platform-to-platform variation. The original full dataset contained 12,000

genes, from these we selected those whose standard deviation across experiments was in the top 25 th

percentile along with the 2,000 genes that were most differentially expression as determined by

Significance Analysis of Microarrays (SAM). This resulted in a final network of 4,619 genes, including

289 transcription factors (TFs).

1. Network inference

We used our network inference pipeline to infer a regulatory network from microarray data characterizing

the signaling networks of MDA-MB-231 and MCF-10A cells (non PS-OC lines). Final networks edges

(depicted as an arrow from regulatory TF to gene) were determined by the voting of multiple methods, to

produce networks of higher quality. The inference pipeline we used here is composed of two methods: 1)

time-lagged Context Likelihood of relatedness (tlCLR) 37 and 2) a modified version of the Inferelator 38.

We combine the results of these multiple methods using a resampling approach. Time-lagged Context

Likelihood of Relatedness (tlCLR) is based on Context Likelihood of Relatedness (CLR) 39 and explicitly

uses the time-series data and mutual information (MI) 40 (a measure of similarity) to assign confidences to

regulatory interactions. The Inferelator learns regulatory dynamics as well as topology by explicitly using

the time-series data to parameterize a linear ordinary differential equation model using an constrained

linear regression and model selection method 41. The modified version of the Inferelator used here uses an

ordinary differential equation model, as in 15, but uses an adaptive constraint 41, which allows the

inference procedure to incorporate previously known regulatory edges, derived here from publicly

available interaction databases.

2. Problem set up

We denote the expression levels of the genes by x=(x 1 , .. . , x Ng ). We store the C observations of these

genes in an N g xC matrix, where the columns correspond to the experimental observations. These

15

observations can be of two types: time-series data (), and steady state data (). Since we make explicit use

of the time series data in the description of our inference procedure we denote by t 1 , t 2 ,. .. , t K the K time

series observations (columns of ). Our inference procedure produces a network in the form of a ranked list

of regulatory interactions, ranked according to confidence. We refer to the final list of confidences as an

N g xN p matrix , where denotes the possible predictors. Element i,j of represents our confidence in the

existence of a regulatory interaction between and .

3. Core Method 1: Time Lagged Context Likelihood of Relatedness

tlCLR is an MI based method that extends the original CLR algorithm to take advantage of time-series

data 16. The original formulation of CLR was unable to learn directionality of regulatory edges as MI is a

symmetric measure. In the tlCLR algorithm we make explicit use of the time-series data to learn directed

regulatory edges. We describe, in brief, three main steps: 1) model the temporal changes in expression as

an ODE, 2) calculate the MI between every pair of genes, 3) apply a background correction (filtering)

step to remove least likely interactions. We refer the reader to 14 for a thorough description of this method.

We assume that the temporal changes in expression of each gene, , can be approximated by the linear

ODE:

dx i (t )dt

=−α i x i+∑j=1

N

β i , j x j( t ) , i=1 ,. .. , N (1)

where α i>0 is the first-order degradation rate of and the ’s are a set of dynamical parameters to be

estimated. The value of describes the extent and sign of the regulation of target gene by regulator . We

store the dynamical parameters in an N × P matrix, , where N is the number of genes, and P is the

number of possible regulators. Note that is typically sparse (i.e., most entries are 0, reflecting the

sparsity of transcriptional regulatory networks). Later, we describe how to calculate the values by a

modified version of Inferelator. Now we briefly describe how to use the time-series data in the context of

improving the calculation of MI values between a gene and its potential regulator .

We first apply a finite approximation to Eq. 1, for each , and rewrite it as a response vector , which

captures the rate of change of expression in . We pair the response with a corresponding explanatory

variable . Note each is time lagged with respect to the response , i.e. is used to predict . For more details

of this transformation we refer the reader to 14. As a measure of confidence for a directed regulatory

interaction between a pair of genes we use, , where a pair that shows a high MI score (relative to other

pairs) is more likely to represent a true regulatory interaction. Note that I ( y i , x j )≠I ( y j , x i) , making one

16

regulatory direction more likely than the other. We refer to the MI calculated from as dynamic-MI, as it

takes advantage of the temporal information available from time-series data (distinguishing time-series

data from steady-state data). As described above, we calculate and for every pair of genes and store the

values in the form of two N g xN p matrices and , respectively. Note that is symmetric while is not. We

now briefly describe how tlCLR integrates both static- and dynamic-MI to produce a final confidence

score for each regulatory interaction. For a more detailed explanation we refer the reader to 14.

For each regulatory interaction we compute two positive Z-scores (by setting all negative Z-scores to

zero): one for the regulation of by based on dynamic-MI (i.e. based on ), where is the standard

deviation of the entries in the i’th row of . And one for the regulation of by based on both static and

dynamic-MI, where is the standard deviation of the entries in the j’th column of . We combine the two

scores into a final tlCLR score, . Note that some entries in are zero, i.e. is somewhat sparse. For a more

detailed description of tlCLR we refer the reader to 14.

4. Core method 2: Inferelator

We use a modified version Inferelator to learn a sparse dynamical model of regulation for each gene . As

potential regulators of we consider only the P highest confidence (non-zero) regulators. Accordingly, for

each gene, , we denote this subset of potential regulators as . We then learn a sparse dynamical model of

regulation for each as a function of ’s (using Inferelator). We assume that the time evolution in the ’s is

governed by

dx i (t )dt

=−α i x i+∑j=1

N

β i , j x j( t ) , i=1 ,. .. , N which is exactly (eq. 1) with our constraint on the

number of regulators. Adaptive Least Angle Regression 20 is used to efficiently implement an constraint

on , which minimizes the following objective function, amounting to a least-square estimate based on

the ODE (eq. 1): under an adaptive -norm penalty on regression coefficients,

∑j=1

P i

|β i , jθ i , j|≤∑j=1

P i

β i , j( ols)(2)

where, β i , j corresponds to a weight on the predictorx i , j chosen in a data-dependent manner as suggested

in 20. Known interactions are encoded by setting θ i , j<1 when it is previously known that regulates ,

thus making it less likely that β i , j is shrunk out of the model. Note that it is unlikely for any weight of θ

to rescue β i , j if it is not at all supported by the idea (i.e. the constraint takes precedence over the

17

adaptive weight θ i , j ). After applying the adaptive elastic net, we have an N g xN p matrix of dynamic

parameters for each regulatory interaction. We use the percentage of explained variance of each

parameter as described in [22], as confidence in these regulatory interactions. We store these confidences

in . We combine these confidences in a rank-based way, such that each method is weighted equally, as

described in 14, to generate , which represents the our confidence in each regulatory interaction after

running our pipeline one time. We now describe how we resample our network inference pipeline to

generate an ensemble of predicted networks (i.e. lists of confidence for each regulatory interaction).

5. Combining genetic and dynamic information in a resampling approach

To further improve the quality of our ranked list we applied a resampling approach to the pipeline

described above to generate an ensemble of putative regulatory networks. We denote the matrix of

response variables , by Y. Similarly we denote the matrix of predictor variables by X. We sample with

replacement from the indices of the columns of Y, generating a permutation of the indices, . We use this

permutation, , to permute the columns of Y and X, generating , and , respectively. Note that the columns

of Y match the columns of X in the sense that the time-lagged relationship between the response on the

predictors is preserved. We generated , and , and as described before, with the only difference being that

we use the response and explanatory vectors from and , respectively, instead of Y and X. We repeat this

procedure B times, with B=50. This generated an ensemble of B predicted regulatory networks. The final

weight we assign to each regulatory interaction is the median weight assigned to that interaction from

each of the B networks. Thus, the final weight can be considered an "ensemble vote" of the confidence the

ensemble of networks has in that edge .

6. Visualization and analysis

We use Cytoscape 1as our primary means of visualizing regulatory networks and overlaying associated

data (in this case, genes in proteomics and microarray experiments) since it displays attributes ( e.g.,

coloring based on gene expression) and conveys multiple channels of information in a single interactive

network representation.

The Inferelator output is a ranked list of regulatory interactions of the form "A regulates B". Each of these

interactions is assigned a confidence by the Inferelator, and the predicted interactions are sorted in order

of decreasing confidence. The usual process for visualizing networks generated by the Inferelator is to

convert the Inferelator output file to a Cytoscape .sif file, assign confidence and kinetic values to the

edges using edge attribute files, filter out low-confidence edges from the network, and display the filtered

network using edge coloring that reflects the kinetic parameters. However, here we wish to create a single

18

network visualization that will allow direct comparison of our two individual cell-line networks. We

approach this problem by defining two new confidence metrics for each edge in the final network: the

overall confidence that a regulatory interaction exists in either network and the likelihood that the edge

exists on one network but not the other. Since the confidence values generated by the Inferelator are

essentially Z-scores, we use Stouffer’s method to combine the scores for one edge in two networks into

an overall confidence metric for that edge. Likelihood for each edge uses the rank of that edge in the

ordered list of edges for each individual network, and is calculated as the log ratio of the two ranks.

Likelihoods of edges that exist only in one network or the other are set to the largest or smallest defined

values across all likelihood scores calculated as above. Undefined values are removed implicitly in the

filtering stage, which removes all edges with a combined confidence of zero. After defining overall

confidence and likelihood scores, processing for visualization proceeds as usual from the step of

generating a .sif file, using the overall confidence score instead of the single-network confidence scores.

Edge coloring for network comparisons usually reflects the edge likelihood score instead of the kinetic

parameter.

Sungear 24 is a visualization tool that enables rapid exploration of large data sets. Here the data are sets of

genes (e.g., genes that are differentially expressed in microarray experiments on one cell line as compared

to the other). The main Sungear window is a generalization of a Venn diagram to arbitrarily many sets.

By displaying all possible discrete intersections of input sets, the features that are unique to one set or

shared among combinations of sets are readily apparent. We prepared lists of genes from both microarray

and proteomics data for analysis using Sungear. We used Significance Analysis of Microarrays (SAM) 42

to compare the set of 103 MCF-10A microarray experiments to the 121 MDA-MB-231 microarray

experiments, selecting genes that were most differentially expressed in each cell line according to the

SAM t-statistic. This yielded one list of differentially expressed genes for each cell line. Control studies

have shown that changes greater than 1.5 are typically statistically significant. Consequently, we then

analyzed the SILAC proteomics data for differential expression based on L/H ratio, looking for change in

either direction (e.g., greater than 3/2 or less than 2/3), producing one further list for each cell line for

each SILAC experiment. Finally, we created a "present" set for each pair of SILAC experiments where

the criteria for list inclusion was simply identification of the protein as present in either experiment, with

or without a usable L/H ratio between experiments. These lists were then made available for exploration

within Sungear.

The Gaggle is a framework that enables communication between different analysis tools via a lightweight

server called the Gaggle Boss. Programs can be connected to the Gaggle via a simple "goose" API,

19

effectively allowing these programs to communicate directly with each other. We developed a new

Gaggled version of Sungear that worked with the Gaggled Cytoscape to enable both programs to send

lists of genes to the other, enabling rapid, iterative exploration of networks and genomic/proteomic data.

For example, Sungear can quickly isolate the sets of genes that are present or differentially expressed in

the cell line comparison SILAC experiments. These genes can then be broadcast to Cytoscape, assigned

attributes, and quickly highlighted by any of Cytoscape’s visual attributes (e.g., different colors on the

network visualization). Differentially-expressed genes that appear in key places in the network (e.g.,

transcription factors hypothesized to regulate many other genes or TFs) can then be selected and sent

back to Sungear, where they can be examined for membership in other sets (e.g., differential regulation

under taxol treatment) (see Supplement Fig. 4).

20

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SUPPLEMENTARY FIGURESSupplementary Figure 1.

24

Supplementary Figure 2.

25

Supplementary Figure 3.

26

Supplementary Figure 4.

27

Supplementary Figure 5.

28

SUPPLEMENTAL FIGURE LEGENDS

Supplemental Figure 1: In-depth characterization of MCF-10A and MDA-MB-231 cell lines

(A) Distribution of cell lines to PSOC Network. (B) 10× Phase micrographs of cells in culture. Scale bars: 100 µm. (C) Epifluorescence micrographs of cells stained with Hoechst 33258 (nuclear DNA) and Alexa Fluor 488 phalloidin (cytoskeletal actin). Top: MCF-10A. Bottom: MDA-MB-231. Left: Untreated. Right: 2 h after treatment with 40 µM Y-27632 ROCK inhibitor. Scale bars: 20 µm. (D) MCF-10A cells at various time points after initial seeding and at two magnifications. Cell density shown at the 72 h time point is optimal for passaging the cells. Cells shown at 120 h are overgrown. (E) MDA-MB-231 cells at various time points after initial seeding and at two magnifications. Cell density shown at the 96 h time point is optimal for passaging the cells. Cells shown at 144 h are overgrown. (F) MCF-10A cell cycle analysis profile. (G) MDA-MB-231 cell cycle analysis profile. (H) MCF-10A karyotype. (I) MDA-MB-231 karyotype.

Supplemental Figure 2: Stress Response

(A) Fluorescence micrographs from viability assay of cells grown in 3D culture (alginate discs). (B) Oxygen drawdown over time of MCF-10A and MDA-MB-231 cells grown in normoxic and hypoxic conditions. (C) Raw fluorescence histograms of CEA expression of CEA in MCF-10A and MDA-MB-231 cells in normoxic and hypoxic conditions. (D) Mean fluorescence intensity minus isotype (MFI) of CD44 in MCF-10A and MDA-MB-231 cells in normoxic and hypoxic conditions. (E) Schematic of 3D alginate gel fabrication and experiment.

29

Supplemental Figure 3: Overall integrated regulatory network

The overall network with the top 5000 edges ranked by combined confidence from the two cell line inference runs. As in Figures 1A-1D, edge color denotes differential inferred regulation in MCF-10A (yellow) or MDA-MB-231 (blue). Nodes are rendered semi-transparent so that the distribution of cell-line-specific regulatory edges (connecting lines) can be clearly seen. The inset histogram also shows the distribution of cell-line-specific edges: edges specific to MDA-MB-231 are more prevalent, particularly at the extreme. Proteomics data from MCF-10A/MDA-MB-231 comparison are also shown using node colors: differential expression in MCF-10A is shown in yellow, and MDA-MB-231 in blue. Genes present but not differentially expressed are shown in darker gray.

Supplemental Figure 4: Sungear Diagram

Sungear diagram showing differential expression of ROCK and Taxol-treated MDA-MB-231 and MCF-10A cells. Shown is the main Sungear plot, which displays data set labels around the outside of the plot and circles representing intersections between these sets in the interior. The data here are eight sets of proteins up- or down-regulated in different conditions: from the top, these are proteins up-regulated in MCF-10A with ROCK treatment (Rock_10A+), down-regulated in MCF-10A/ROCK (Rock_10A-), up- and down-regulated in MDA-MB-231 with ROCK treatment (Rock_231+, Rock_231-), and up- and down-regulated proteins in both cell lines with taxol treatment (Taxol_10A+, Taxol_10A-, Taxol_231+, Taxol_231-). Circle size represents the number of proteins in a given intersection, and location is based on the sets in the intersection: the large circle closest to the Rock_10A- label represents the 57 proteins that are unique to that set, while the highlighted red circle between Rock_10A- and Taxol_10A+ represents the six proteins common to both sets. Using the Gaggle, proteins up- and down-regulated in different treatments were sent to Cytoscape. We then used the Gaggle again to send the list of differentially expressed proteins in the ITGB4 1-hop network (Fig. 1d) back to Sungear: of these six genes, three were in the intersection highlighted in pink.

Supplemental Figure 5: Wound healing assay

Wound healing assay. Top: Micrographs taken at the initial time point and at 12 hr intervals after scratching. Bottom: Quantitation of scratched area filled over time.

30