The work in this manuscript was supported by the NIH Common Fund Library of Network Cellular Signatures (LINCS) U54 grant HG008100 (J.W.G., L.M.H., and J.E.K) and NCI Cancer Systems Biology Consortium (CSBC) U54 CA209988grant (J.W.G., L.M.H., and J.E.K).

NOTE: An overview of the entire MEMA process, including estimated time, is outlined in the flow diagram shown in Figure 1. This protocol details the fabrication of MEMAs in 8-well plates. The protocol may be adapted for other plates or slides.
1. Preparation of Protein, Diluent, and Staining Buffers
<ol>
	<li>Equilibrate vials of ECMs, ligands, and cytokines to room temperature (RT) and briefly centrifuge. Add the appropriate volume of the appropriate RT buffer as indicated on the product data sheet. Follow manufacturer&#8217;s recommendation for stock concentrations. 
	NOTE: A full list of the ligands and ECMs with their stock and final concentrations are provided in Table 1 and Table 2. Both ligands and ECMs are typically used at the highest concentration of the range recommended by the manufacturer that elicits a biological effect in standard 2 day&#160;culture assays. Handle proteins gently and in biosafety cabinets under laminar flow to avoid contamination.</li>
	<li>Incubate vials with gentle rocking at RT for 1 h. Do not vortex proteins as this can cause them to denature.</li>
	<li>Aliquot proteins for long term storage so that all aliquots are single use only to avoid degradation with repeated freeze/thaw cycles. Store lyophilized proteins at -80 &#176;C (unless otherwise specified) until needed. Take care to collect all metadata for future reference, such as: (i) protein name, (ii) date prepared, (iii) lot/batch number, (iv) supplier, (v) catalog number, (vi) concentration, (vii) volume, and (viii) preparer.</li>
	<li>Prepare diluent buffer containing 20% (v/v) glycerol, 10 mM EDTA, 200 mM Tris-HCl, pH 7.2, and filter sterilize. Keep this buffer sterile and store at RT.</li>
	<li>Prepare staining buffer containing 2% (w/v) BSA, 1 mM MgCl2, and 0.02% NaN3 in phosphate-buffered saline (PBS). Filter and store at 4 &#176;C.</li>
</ol>
2. Preparation of an ECM Source Plate
<ol>
	<li>Remove aliquoted stocks of ECM proteins to be printed and thaw on ice. Record all lot numbers for metadata tracking.</li>
	<li>Flick tubes of thawed proteins gently to ensure proper resuspension and spin down in a centrifuge.</li>
	<li>Make ECM print mixtures (EPMs) and a fluorescent fiducial to be used by a liquid handling robot that will create the randomized 384-well source plates. 
	NOTE: The 384-well source plates will be used by a touch pin array printer to create the printed arrays in 8 well plates.
	<ol>
		<li>Label 1.5 mL microcentrifuge tubes for each EPM and the fiducial.</li>
		<li>Prepare each EPM by combining 125 &#181;L of diluent buffer (see step 1.4) with the appropriate volume of ECM stock and bring the mixture up to a total volume of 250 &#181;L with PBS. The final concentrations in each EPM tube will be 1x ECM protein, 5 mM EDTA, 10% glycerol, and 100 mM Tris.</li>
		<li>Prepare a fluorescent fiducial by dissolving it in the appropriate buffer specified by the manufacturer and transfer 250 &#181;L to a labelled fiducial tube.</li>
	</ol>
	</li>
</ol>
3. Creation of the Source Plate Using a Liquid Handler
<ol>
	<li>Design a 384-well plate layout that randomizes the positions of the ECMs and is optimized for the array printer pin head being used. Design the placement of the fiducial so that it will be printed in the row 1, column 1 position of each well to assist in array orientation. 
	NOTE: A total of 14&#8722;15 replicates of each ECM are used to ensure robust data. Include additional replicates of collagen or another ECM that yields robust attachment for assessment of uniformity of binding. The layout may need to utilize multiple 384-well plates depending on the number of ECMs of interest.</li>
	<li>Transfer EPM tubes to a liquid handler, keeping tubes at 4 &#176;C either with a cooled tube rack or by using a liquid handling robot located in a cold room.</li>
	<li>Using the liquid handler&#8217;s software, run a program to transfer 15 &#181;L of each EPM and the fiducial to the predesignated wells within the 384-well source plate(s).</li>
	<li>Pipet PBS into any unused wells to increase humidity and guard against desiccation during the printing process. 
	NOTE: See Figure 2 for an example of a 384-well source plate set that is optimized for a 4 x 7 pin head and includes a collagen I block and PBS.</li>
	<li>Seal plate(s) and keep at 4 &#176;C until ready to print.</li>
</ol>
4. Printing MEMAs Using an Array Printing Robot
NOTE: The following part of the protocol specifically describes the preparation and use of MEMA to investigate the impact of different microenvironment proteins on the growth and proliferation of MCF7 cells. However, the protocol can easily be adapted to use different ligands, ECMs, and cells to study other cell lines and endpoints of interest.
<ol>
	<li>Using a touch pin printer, print EPMs and fiducial spots into 8 well plates. Print multiple replicates of each ECM condition to ensure reproducibility. 
	NOTE: Other plate formats or slides can be used for printing, but buffer optimization may be required to achieve optimal spot formation.
	<ol>
		<li>Print the ECMs for the MEMA using 350 &#181;m diameter pins arranged in a 4 x 7 print head configuration. Print the arrays in the 8-well plates as 20 columns by 35 rows, for a total of ~700 spots. Larger arrays are possible in these plates but come with a trade-off of increased edge effects in both cell binding and staining.</li>
	</ol>
	</li>
	<li>After printing, store plates in a desiccator for a minimum of 3 days prior to use.</li>
</ol>
5. Creation of Ligand Treatment Plates
<ol>
	<li>Design a 96-well plate layout including ligands of interest. To facilitate treatment of many MEMA plates at once, design this plate with spacing that allows for the use of a multi-channel pipet with 4 spaced tips to transfer liquids between the wells of 8-well MEMAs and a 96-well plate. 
	NOTE: In this protocol, the full set of ligands listed in Table 2 are utilized.</li>
	<li>Thaw ligands on ice. Briefly flick and spin down each tube.</li>
	<li>Dilute ligands to a 200x working stock using the manufacturer&#8217;s recommended buffer (typically PBS).</li>
	<li>Pipet 10 &#181;L of each 200x ligand stock into the corresponding well within the 96-well plate.</li>
	<li>Seal and store plates at -20 &#176;C. 
	NOTE: Make ligand treatment plates in batches, capturing all metadata for downstream analysis.</li>
</ol>
6. Culturing Cells on MEMAs
<ol>
	<li>Block MEMAs for 20 min with 2 mL per well of non-fouling blocking buffer containing 1% non-fouling blocking agent (Table of Materials) in double-distilled water (ddH2O).</li>
	<li>Aspirate blocking buffer and triple rinse wells with PBS. To prevent desiccation, leave final volume of PBS in wells until ready for cell plating. 
	NOTE: It is extremely helpful to have two bench workers for cell culture steps on MEMAs. One bench worker can perform aspiration steps, while the second performs addition steps. It is recommended to use a 1 mL multichannel pipet with tips spaced to match the 8-well plate for pipetting and a Y-splitter with two Pasteur pipettes to aspirate multiple wells at once.</li>
	<li>Seed 2 x 105 MCF7 cells per well in 2 mL of Dulbecco&#8217;s modified Eagle&#8217;s medium (DMEM) medium containing 10% fetal bovine serum (FBS). 
	NOTE: Prior to a full MEMA experiment, perform a cell titration experiment to optimize cell numbers such that MEMA spots have high cell numbers (but are not confluent) at the end of the desired experimental duration.</li>
	<li>After 2&#8722;18 h of adhesion, aspirate medium and replace with 2 mL of reduced-growth medium (DMEM with 0.1% FBS). 
	NOTE: Reduced serum (e.g., 0.1% FBS) or growth factor-depleted conditions can be used at this time to isolate the stimulatory impact of specific ligands.</li>
	<li>Thaw a ligand treatment plate on ice. Centrifuge thawed plate at 200 x g for 1 min.</li>
	<li>Transfer 200 &#181;L of medium from each well in the culture plate to the appropriate well in the treatment plate. Pipet up and down to mix ligand volume with medium and transfer this mixture back to the appropriate well in the MEMA plate.</li>
	<li>Lightly rock by hand and return MEMA plates to the incubator. Culture for the duration of the experiment in the presence of the ligand/ECM combination at 37 &#176;C and 5% CO2. 
	NOTE: A typical MEMA experiment runs for 72 h; longer duration experiments may require replacement of medium and re-treatment with ligand.</li>
	<li>Pulse MEMA wells at 71 h with 100x 5-ethynyl-2&#8217;-deoxyuridine (EdU) for a final concentration of 10 &#181;M. Incubate in experimental conditions with EdU for 1 h at 37 &#176;C and 5% CO2. 
	NOTE: Other live cell treatments may also be used at this time.</li>
</ol>
7. Fixing and Staining MEMAs
<ol>
	<li>After 72 h and any live cell treatments, aspirate wells. Fix MEMAs in 2 mL per well of 2% paraformaldehyde (PFA) for 15 min at RT.</li>
	<li>Aspirate PFA. Permeabilize with 2 mL per well of 0.1% nonionic surfactant&#160;for 15 min.</li>
	<li>Aspirate the nonionic surfactant and wash with 2 mL per well of PBS. Aspirate PBS. Wash with 2 mL of PBS with 0.05% polysorbate 20 (PBS-T). 
	NOTE: The MEMA surface is hydrophobic, and failure to wash with PBS-T before stain and antibody incubation will result in the formation of voids in wells during incubation steps and give rise to staining artifacts.</li>
	<li>Aspirate PBS-T. Add EdU detection reaction reagents. Incubate for 1 h at RT, rocking and protected from light. After 1 h incubation, quench reaction with the provided commercial quench buffer. 
	NOTE: EdU detection and staining/antibody steps may be performed in 1.5 mL per well to reduce cost.</li>
	<li>Aspirate the quench buffer and wash with PBS-T prior to incubating with stains or antibodies.</li>
	<li>Incubate MEMA wells with antibodies against histone H3K9me3 (1:1,000) and fibrillarin (1:400) in staining buffer containing 2% (w/v) bovine serum albumin (BSA), 1 mM MgCl2 and 0.02% NaN3 overnight at 4 &#176;C. 
	NOTE: Perform antibody titrations to determine optimal concentrations prior to using them on a full MEMA set.</li>
	<li>Following primary antibody or stain incubation, wash wells 2x with PBS and once with PBS-T.</li>
	<li>Add secondary antibodies (donkey anti-mouse IgG and donkey anti-rabbit IgG, both 1:300) and 0.5 &#181;g/mL 4&#8242; 6&#8208;diamidino&#8208;2&#8208;phenylindole (DAPI). Incubate for 1 h at RT in the dark.</li>
	<li>Wash wells 2x with 2 mL per well of PBS, leaving them in the final 2 mL PBS.</li>
	<li>Proceed to imaging or store stained MEMAs for later imaging in PBS at 4 &#176;C protected from light.</li>
</ol>
8. Imaging of MEMAs
<ol>
	<li>Image MEMA on an automated imaging system with appropriate fluorescent detection channels.</li>
	<li>Output resulting image data to an image management system. Segment cells and calculate intensity levels using CellProfiler8.</li>
</ol>
9. Data Analysis
NOTE: Data analysis consists of normalization, variation correction, and summarization of the raw CellProfiler derived data. In this instance, the R-environment with custom code is used to perform all the steps. However, any statistical environment or software program can be utilized to perform the equivalent actions. An example of the open source custom code for the R environment for analysis is available at: https://www.synapse.org/#!Synapse:syn2862345/wiki/72486.
<ol>
	<li>Preprocess and normalize the segmented image data.</li>
	<li>Determine spot cell count using the DAPI stained nuclei.</li>
	<li>Auto-gate EdU intensity to label cells as EdU+. Measure proliferation using the proportion of EdU+ cells in each spot.</li>
	<li>Median summarize cytoplasmic stains and nuclear morphology measurements on the spot level.</li>
	<li>Perform removal of unwanted variation (RUV) normalization on the data to improve data quality9. 
	NOTE: This approach is applied to each intensity and morphology signal independently as a matrix with arrays using the rows and spots as the columns as described previously9.</li>
	<li>Apply bivariate loess normalization to the RUV normalized residuals using the array row and array column as the independent variables to correct for spatial or intensity related effects.</li>
	<li>Once normalization is completed, median summarize the replicates for each microenvironment condition for reporting and further analysis.</li>
</ol>

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The authors have nothing to disclose.

The importance of &#34;dimensionality&#34;&#160;and context has been a motivating factor in the development of in vitro culture systems as tools in the characterization of cancer cells through their interaction with the microenvironment11, and the ability of in vitro culture systems to mimic the in vivo environment is a driving force behind the quest to improve those culture systems. In vitro systems, however, remain significant tools of cancer research precisely because of their ability to distil...

Culturing of cancer cell lines on plastic in two-dimensional (2D) monolayers remains one of the major workhorses for cancer researchers. However, the microenvironment is increasingly being recognized for its ability to impact cellular phenotypes. In cancer, the tumor microenvironment is known to influence multiple cellular behaviors, including growth, survival, invasion, and response to therapy1,2. Traditional monolayer cell cultures typically lack microenvironment influences, which has led to the development of more complex three-dimensional (3D) assays to grow cells, including commercially available purified basement membrane extracts. However, these purified matrices are typically complicated to use and suffer from technical problems such as batch variability3 and complex compositions3. As a result, it can be difficult to assign function to specific proteins that may be impacting cellular phenotypes3.
To address these limitations, we have developed the microenvironment microarray (MEMA) technology, which reduces the microenvironment down to simple combinations of extracellular matrix (ECM) and soluble growth factor proteins4,5. The MEMA platform enables identification of dominant microenvironmental factors that impact the behavior of cells. By using an array format, thousands of combinations of microenvironment factors can be assayed in a single experiment. The MEMA described here interrogates ~2,500 different unique microenvironment conditions. ECM proteins printed into well plates form growth pads upon which cells can be cultured. Soluble ligands are added to individual wells, creating unique combinatorial microenvironments (ECM + ligand) on each different spot to which the cells are exposed. Cells are cultured for several days, then fixed, stained, and imaged to assess cellular phenotypes as a result of exposure to these specific microenvironment combinations. Since the microenvironments are simple combinations, it is straightforward to identify proteins that drive major phenotypic changes in cells. MEMAs have been used successfully to identify factors that influence multiple cellular phenotypes, including those that drive cell fate decisions and response to therapy4,5,6,7. These responses can be validated in simple 2D experiments and can then be assessed under conditions that more fully recapitulate the complexity of the tumor microenvironment. The MEMA platform is highly adaptable to a variety of cell types and endpoints, provided that good phenotypic biomarkers are available.

<table><tbody><tr><td>Aushon 2470</td><td>Aushon BioSystems</td><td></td><td>Arrayer robot system used in the protocol</td></tr><tr><td>Nikon HCA</td><td>Nikon</td><td></td><td>High Content Imaging system designed around Nikon Eclipse Ti Inverted Microscope</td></tr><tr><td>BioTek Precision XS liquid Handler</td><td>BioTek</td><td></td><td>liquid handling robot used in the protocol</td></tr><tr><td>Trizma hydrochloride buffer solution</td><td>Sigma</td><td>T2069</td><td></td></tr><tr><td>EDTA</td><td>Invitrogen</td><td>15575-038</td><td></td></tr><tr><td>Glycerol</td><td>Sigma</td><td>G5516</td><td></td></tr><tr><td>Triton X100</td><td>Sigma</td><td>T9284</td><td></td></tr><tr><td>Tween 20</td><td>Sigma</td><td>P7949</td><td></td></tr><tr><td>Kolliphor P338</td><td>BASF</td><td>50424591</td><td></td></tr><tr><td>384-well microarray plate, cylindrical well</td><td>Thermo Fisher</td><td>ab1055</td><td></td></tr><tr><td>Nunc 8 well dish</td><td>Thermo Fisher</td><td>267062</td><td></td></tr><tr><td>Paraformaldehyde 16% solution</td><td>Electron Microscopy Science</td><td>15710</td><td></td></tr><tr><td>BSA</td><td>Fisher</td><td>BP-1600</td><td></td></tr><tr><td>Sodium Azide</td><td>Sigma</td><td>S2002</td><td></td></tr><tr><td>Cell Mask</td><td>Molecular Probes</td><td>H32713</td><td></td></tr><tr><td>Click-iTEdU Alexa Fluor</td><td>Molecular Probes</td><td>C10357</td><td></td></tr><tr><td>DAPI</td><td>Promo Kine</td><td>PK-CA70740043</td><td></td></tr><tr><td>ALCAM</td><td>R &amp;amp; D Systems</td><td>656-AL</td><td>ECM</td></tr><tr><td>Cadherin-20 (CDH20)</td><td>R &amp;amp; D Systems</td><td>5604-CA</td><td>ECM</td></tr><tr><td>Cadherin-6 (CDH6)</td><td>R &amp;amp; D Systems</td><td>2715-CA</td><td>ECM</td></tr><tr><td>Cadherin-8 (CDH8)</td><td>R &amp;amp; D Systems</td><td>188-C8</td><td>ECM</td></tr><tr><td>CD44</td><td>R &amp;amp; D Systems</td><td>3660-CD</td><td>ECM</td></tr><tr><td>CEACAM6</td><td>R &amp;amp; D Systems</td><td>3934-CM</td><td>ECM</td></tr><tr><td>Collagen I</td><td>Cultrex</td><td>3442-050-01</td><td>ECM</td></tr><tr><td>Collagen Type II</td><td>Millipore</td><td>CC052</td><td>ECM</td></tr><tr><td>Collagen Type III</td><td>Millipore</td><td>CC054</td><td>ECM</td></tr><tr><td>Collagen Type IV</td><td>Sigma</td><td>C5533</td><td>ECM</td></tr><tr><td>Collagen Type V</td><td>Millipore</td><td>CC077</td><td>ECM</td></tr><tr><td>COL23A1</td><td>R &amp;amp; D Systems</td><td>4165-CL</td><td>ECM</td></tr><tr><td>Desmoglein 2</td><td>R &amp;amp; D Systems</td><td>947-DM</td><td>ECM</td></tr><tr><td>E-cadherin (CDH1)</td><td>R &amp;amp; D Systems</td><td>648-EC</td><td>ECM</td></tr><tr><td>ECM1</td><td>R &amp;amp; D Systems</td><td>3937-EC</td><td>ECM</td></tr><tr><td>Fibronectin</td><td>R &amp;amp; D Systems</td><td>1918-FN</td><td>ECM</td></tr><tr><td>GAP43</td><td>Abcam</td><td>ab114188</td><td>ECM</td></tr><tr><td>HyA-500K</td><td>R &amp;amp; D Systems</td><td>GLR002</td><td>ECM</td></tr><tr><td>HyA-50K</td><td>R &amp;amp; D Systems</td><td>GLR001</td><td>ECM</td></tr><tr><td>ICAM-1</td><td>R &amp;amp; D Systems</td><td>720-IC</td><td>ECM</td></tr><tr><td>Laminin</td><td>Sigma</td><td>L6274</td><td>ECM</td></tr><tr><td>Laminin-5</td><td>Abcam</td><td>ab42326</td><td>ECM</td></tr><tr><td>Lumican</td><td>R &amp;amp; D Systems</td><td>2846-LU</td><td>ECM</td></tr><tr><td>M-Cad (CDH15)</td><td>R &amp;amp; D Systems</td><td>4096-MC</td><td>ECM</td></tr><tr><td>Nidogen-1</td><td>R &amp;amp; D Systems</td><td>2570-ND</td><td>ECM</td></tr><tr><td>Osteoadherin/OSAD</td><td>R &amp;amp; D Systems</td><td>2884-AD</td><td>ECM</td></tr><tr><td>Osteopontin (SPP)</td><td>R &amp;amp; D Systems</td><td>1433-OP</td><td>ECM</td></tr><tr><td>P-Cadherin (CDH3)</td><td>R &amp;amp; D Systems</td><td>861-PC</td><td>ECM</td></tr><tr><td>PECAM1</td><td>R &amp;amp; D Systems</td><td>ADP6</td><td>ECM</td></tr><tr><td>Tenascin C</td><td>R &amp;amp; D Systems</td><td>3358-TC</td><td>ECM</td></tr><tr><td>VCAM1</td><td>R &amp;amp; D Systems</td><td>ADP5</td><td>ECM</td></tr><tr><td>Vitronectin</td><td>R &amp;amp; D Systems</td><td>2308-VN</td><td>ECM</td></tr><tr><td>Biglycan</td><td>R &amp;amp; D Systems</td><td>2667-CM</td><td>ECM</td></tr><tr><td>Decorin</td><td>R &amp;amp; D Systems</td><td>143-DE</td><td>ECM</td></tr><tr><td>Periostin</td><td>R &amp;amp; D Systems</td><td>3548-F2</td><td>ECM</td></tr><tr><td>SPARC/osteonectin</td><td>R &amp;amp; D Systems</td><td>941-SP</td><td>ECM</td></tr><tr><td>Thrombospondin-1/2</td><td>R &amp;amp; D Systems</td><td>3074-TH</td><td>ECM</td></tr><tr><td>Brevican</td><td>R &amp;amp; D Systems</td><td>4009-BC</td><td>ECM</td></tr><tr><td>Elastin</td><td>BioMatrix</td><td>5052</td><td>ECM</td></tr><tr><td>Fibrillin</td><td>Lynn Sakai Lab OHSU</td><td>N/A</td><td>ECM</td></tr><tr><td>ANGPT2</td><td>RnD_Systems_Own</td><td>623-AN-025</td><td>Ligand</td></tr><tr><td>IL1B</td><td>RnD_Systems_Own</td><td>201-LB-005</td><td>Ligand</td></tr><tr><td>CXCL8</td><td>RnD_Systems_Own</td><td>208-IL-010</td><td>Ligand</td></tr><tr><td>IGF1</td><td>RnD_Systems_Own</td><td>291-G1-200</td><td>Ligand</td></tr><tr><td>TNFRSF11B</td><td>RnD_Systems_Own</td><td>185-OS</td><td>Ligand</td></tr><tr><td>BMP6</td><td>RnD_Systems_Own</td><td>507-BP-020</td><td>Ligand</td></tr><tr><td>FLT3LG</td><td>RnD_Systems_Own</td><td>308-FK-005</td><td>Ligand</td></tr><tr><td>CXCL1</td><td>RnD_Systems_Own</td><td>275-GR-010</td><td>Ligand</td></tr><tr><td>DLL4</td><td>RnD_Systems_Own</td><td>1506-D4-050</td><td>Ligand</td></tr><tr><td>HGF</td><td>RnD_Systems_Own</td><td>294-HGN-005</td><td>Ligand</td></tr><tr><td>Wnt5a</td><td>RnD_Systems_Own</td><td>645-WN-010</td><td>Ligand</td></tr><tr><td>CTGF</td><td>Life_Technologies_Own</td><td>PHG0286</td><td>Ligand</td></tr><tr><td>LEP</td><td>RnD_Systems_Own</td><td>398-LP-01M</td><td>Ligand</td></tr><tr><td>FGF2</td><td>Sigma_Aldrich_Own</td><td>SRP4037-50UG</td><td>Ligand</td></tr><tr><td>FGF6</td><td>RnD_Systems_Own</td><td>238-F6</td><td>Ligand</td></tr><tr><td>IL7</td><td>RnD_Systems_Own</td><td>207-IL-005</td><td>Ligand</td></tr><tr><td>TGFB1</td><td>RnD_Systems_Own</td><td>246-LP-025</td><td>Ligand</td></tr><tr><td>PDGFB</td><td>RnD_Systems_Own</td><td>220-BB-010</td><td>Ligand</td></tr><tr><td>WNT10A</td><td>Genemed_Own</td><td>90009</td><td>Ligand</td></tr><tr><td>PTN</td><td>RnD_Systems_Own</td><td>252-PL-050</td><td>Ligand</td></tr><tr><td>BMP3</td><td>RnD_Systems_Own</td><td>113-BP-100</td><td>Ligand</td></tr><tr><td>BMP4</td><td>RnD_Systems_Own</td><td>314-BP-010</td><td>Ligand</td></tr><tr><td>TNFSF11</td><td>RnD_Systems_Own</td><td>390-TN-010</td><td>Ligand</td></tr><tr><td>CSF2</td><td>RnD_Systems_Own</td><td>215-GM-010</td><td>Ligand</td></tr><tr><td>BMP5</td><td>RnD_Systems_Own</td><td>615-BMC-020</td><td>Ligand</td></tr><tr><td>DLL1</td><td>RnD_Systems_Own</td><td>1818-DL-050</td><td>Ligand</td></tr><tr><td>NRG1</td><td>RnD_Systems_Own</td><td>296-HR-050</td><td>Ligand</td></tr><tr><td>KNG1</td><td>RnD_Systems_Own</td><td>1569-PI-010</td><td>Ligand</td></tr><tr><td>GPNMB</td><td>RnD_Systems_Own</td><td>2550-AC-050</td><td>Ligand</td></tr><tr><td>CXCL12</td><td>RnD_Systems_Own</td><td>350-NS-010</td><td>Ligand</td></tr><tr><td>IL15</td><td>RnD_Systems_Own</td><td>247-ILB-005</td><td>Ligand</td></tr><tr><td>TNF</td><td>RnD_Systems_Own</td><td>210-TA-020</td><td>Ligand</td></tr><tr><td>IGFBP3</td><td>RnD_Systems_Own</td><td>675-B3-025</td><td>Ligand</td></tr><tr><td>WNT3A</td><td>RnD_Systems_Own</td><td>5036-WNP-010</td><td>Ligand</td></tr><tr><td>PDGFAB</td><td>RnD_Systems_Own</td><td>222-AB</td><td>Ligand</td></tr><tr><td>AREG</td><td>RnD_Systems_Own</td><td>262-AR-100</td><td>Ligand</td></tr><tr><td>JAG1</td><td>RnD_Systems_Own</td><td>1277-JG-050</td><td>Ligand</td></tr><tr><td>BMP7</td><td>RnD_Systems_Own</td><td>354-BP-010</td><td>Ligand</td></tr><tr><td>TGFB2</td><td>RnD_Systems_Own</td><td>302-B2-010</td><td>Ligand</td></tr><tr><td>VEGFA</td><td>RnD_Systems_Own</td><td>293-VE-010</td><td>Ligand</td></tr><tr><td>IL6</td><td>RnD_Systems_Own</td><td>206-IL-010</td><td>Ligand</td></tr><tr><td>CXCL12</td><td>RnD_Systems_Own</td><td>351-FS-010</td><td>Ligand</td></tr><tr><td>NRG1</td><td>RnD_Systems_Own</td><td>378-SM</td><td>Ligand</td></tr><tr><td>IGFBP2</td><td>RnD_Systems_Own</td><td>674-B2-025</td><td>Ligand</td></tr><tr><td>SHH</td><td>RnD_Systems_Own</td><td>1314-SH-025</td><td>Ligand</td></tr><tr><td>FASLG</td><td>RnD_Systems_Own</td><td>126-FL-010</td><td>Ligand</td></tr></tbody></table>

using microarrays to interrogate microenvironmental impact on cellular phenotypes in cancer

Understanding the impact of the microenvironment on the phenotype of cells is a difficult problem due to the complex mixture of both soluble growth factors and matrix-associated proteins in the microenvironment in vivo. Furthermore, readily available reagents for the modeling of microenvironments in vitro typically utilize complex mixtures of proteins that are incompletely defined and suffer from batch to batch variability. The microenvironment microarray (MEMA) platform allows for the assessment of thousands of simple combinations of microenvironment proteins for their impact on cellular phenotypes in a single assay. The MEMAs are prepared in well plates, which allows the addition of individual ligands to separate wells containing arrayed extracellular matrix (ECM) proteins. The combination of the soluble ligand with each printed ECM forms a unique combination. A typical MEMA assay contains greater than 2,500 unique combinatorial microenvironments that cells are exposed to in a single assay. As a test case, the breast cancer cell line MCF7 was plated on the MEMA platform. Analysis of this assay identified factors that both enhance and inhibit the growth and proliferation of these cells. The MEMA platform is highly flexible and can be extended for use with other biological questions beyond cancer research.

To simplify microenvironmental impacts on cell growth and proliferation and to identify conditions that promoted or inhibited cell growth and proliferation, the breast cancer cell line MCF7 was seeded on a set of eight 8-well MEMAs as described in the protocol. This assay exposed the cells to 48 different ECMs and 57 different ligands, for a total of 2736 combinatorial microenvironmental conditions. After 71 h in culture, cells were pulsed with EdU, fixed, permeablized, and stained with DAPI, the reaction for EdU detecti...

Watch this Scientific Journal Video about Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer at JoVE.com

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

The purpose of the method presented here is to show how microenvironment microarrays (MEMA) can be fabricated and used to interrogate the impact of thousands of simple combinatorial microenvironments on the phenotype of cultured cells.

Understanding the impact of the microenvironment on the phenotype of cells is a difficult problem due to the complex mixture of both soluble growth ...

using-microarrays-to-interrogate-microenvironmental-impact-on

Universidad San Sebastian

Research

JoVE Journal

Cancer Research

5.5K Views.  Oregon Health and Science University. The purpose of the method presented here is to show how microenvironment microarrays (MEMA) can be fabricated and used to interrogate the impact of thousands of simple combinatorial microenvironments on the phenotype of cultured cells.

Video: Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Analysis of Combinatorial miRNA Treatments to Regulate Cell Cycle and Angiogenesis

Lung cancer (LC) is the leading cause of cancer-related deaths worldwide. Similar to other cancer cells, a fundamental characteristic of LC cells is unregulated proliferation and cell division. Inhibition of proliferation by halting cell cycle progression has been shown to be a promising approach for cancer treatment, including LC. 
miRNA therapeutics have emerged as important post-transcriptional gene regulators and are increasingly being studied for use in cancer treatment. In recent work, we utilized two miRNAs, miR-143 and miR-506, to regulate cell cycle progression. A549 non-small cell lung cancer (NSCLC) cells were transfected, gene expression alterations were analyzed, and apoptotic activity due to the treatment was finally analyzed. Downregulation of cyclin-dependent kinases (CDKs) were detected (i.e., CDK1, CDK4 and CDK6), and cell cycle halted at the G1/S and G2/M phase transitions. Pathway analysis indicated potential antiangiogenic activity of the treatment, which endows the approach with multifaceted activity. Here, described are the methodologies used to identify miRNA activity regarding cell cycle inhibition, induction of apoptosis, and effects of treatment on endothelial cells by inhibition of angiogenesis. It is hoped that the methods presented here will support future research on miRNA therapeutics and corresponding activity and that the representative data will guide other researchers during experimental analyses.

miRNA therapeutics have significant potential in regulating cancer progression. Demonstrated here are analytical approaches used for identification of the activity of a combinatorial miRNA treatment in halting cell cycle and angiogenesis.

Lung cancer (LC) is the leading cause of cancer-related deaths worldwide. Similar to other cancer cells, a fundamental characteristic of LC cells is ...

A Multiplexed Luciferase-based Screening Platform for Interrogating Cancer-associated Signal Transduction in Cultured Cells

Genome-scale interrogation of gene function using RNA interference (RNAi) holds tremendous promise for the rapid identification of chemically tractable cancer cell vulnerabilities. Limiting the potential of this technology is the inability to rapidly delineate the mechanistic basis of phenotypic outcomes and thus inform the development of molecularly targeted therapeutic strategies. We outline here methods to deconstruct cellular phenotypes induced by RNAi-mediated gene targeting using multiplexed reporter systems that allow monitoring of key cancer cell-associated processes. This high-content screening methodology is versatile and can be readily adapted for the screening of other types of large molecular libraries.

Achieving a systems level understanding of cellular processes is a goal of modern-day cell biology. We describe here strategies for multiplexing luciferase reporters of various cellular function end-points to interrogate gene function using genome-scale RNAi libraries.

Genome-scale interrogation of gene function using RNA interference (RNAi)  holds tremendous promise for the rapid identification of chemically tractable  ...

Medicine

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Brain metastases are the most lethal cancer lesions; 10-30% of all cancers metastasize to the brain, with a median survival of only ~5-20 months, depending on the cancer type. To reduce the brain metastatic tumor burden, gaps in basic and translational knowledge need to be addressed. Major challenges include a paucity of reproducible preclinical models and associated tools. Three-dimensional models of brain metastasis can yield the relevant molecular and phenotypic data used to address these needs when combined with dedicated analysis tools. Moreover, compared to murine models, organ-on-a-chip models of patient tumor cells traversing the blood brain barrier into the brain microenvironment generate results rapidly and are more interpretable with quantitative methods, thus amenable to high throughput testing. Here we describe and demonstrate the use of a novel 3D microfluidic blood brain niche (&#181;mBBN) platform where multiple elements of the niche can be cultured for an extended period (several days), fluorescently imaged by confocal microscopy, and the images reconstructed using an innovative confocal tomography technique; all aimed to understand the development of micro-metastasis and changes to the tumor micro-environment (TME) in a repeatable and quantitative manner. We demonstrate how to fabricate, seed, image, and analyze the cancer cells and TME cellular and humoral components, using this platform. Moreover, we show how artificial intelligence (AI) is used to identify the intrinsic phenotypic differences of cancer cells that are capable of transit through a model &#181;mBBN and to assign them an objective index of brain metastatic potential. The data sets generated by this method can be used to answer basic and translational questions about metastasis, the efficacy of therapeutic strategies, and the role of the TME in both.

Here, we present a protocol for preparing and culturing a blood brain barrier metastatic tumor micro-environment and then quantifying its state using confocal imaging and artificial intelligence (machine learning).

Brain metastases are the most lethal cancer lesions; 10-30% of all cancers metastasize to the brain, with a median survival of only ~5-20 months, ...

Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells

Estrogen plays vital roles in mammary gland development and breast cancer progression. It mediates its function by binding to and activating the estrogen receptors (ERs), ER&#945;, and ER&#946;. ER&#945; is frequently upregulated in breast cancer and drives the proliferation of breast cancer cells. The ERs function as transcription factors and regulate gene expression. Whereas ER&#945;&#39;s regulation of protein-coding genes is well established, its regulation of noncoding microRNA (miRNA) is less explored. miRNAs play a major role in the post-transcriptional regulation of genes, inhibiting their translation or degrading their mRNA. miRNAs can function as oncogenes or tumor suppressors&#160;and are also promising biomarkers. Among the miRNA assays available, microarray and quantitative real-time polymerase chain reaction (qPCR) have been extensively used to detect and quantify miRNA levels. To identify miRNAs regulated by estrogen signaling in breast cancer, their expression in ER&#945;-positive breast cancer cell lines were compared before and after estrogen-activation using both the &#181;Paraflo-microfluidic microarrays and Dual Labeled Probes-low density arrays. Results were validated using specific qPCR assays, applying both Cyanine dye-based and Dual Labeled Probes-based chemistry. Furthermore, a time-point assay was used to identify regulations over time. Advantages of the miRNA assay approach used in this study is that it enables a fast screening of mature miRNA regulations in numerous samples, even with limited sample amounts. The layout, including the specific conditions for cell culture and estrogen treatment, biological and technical replicates, and large-scale screening followed by in-depth confirmations using separate techniques, ensures a robust detection of miRNA regulations,&#160;and eliminates false positives and other artifacts. However, mutated or unknown miRNAs, or regulations at the primary and precursor transcript level, will not be detected. The method presented here represents a thorough investigation of estrogen-mediated miRNA regulation.

Molecular signaling through both estrogen and microRNAs are critical in breast cancer development and growth. Estrogen activates the estrogen receptors, which are transcription factors. Many transcription factors can regulate the expression of microRNAs, and estrogen-regulated microRNAs can be profiled using different large-scale techniques.

Estrogen plays vital roles in mammary gland development and breast cancer progression. It mediates its function by binding to and activating the estrogen ...

Molecular Profiling of the Invasive Tumor Microenvironment in a 3-Dimensional Model of Colorectal Cancer Cells and Ex vivo Fibroblasts

Invading colorectal cancer (CRC) cells have acquired the capacity to break free from their sister cells, infiltrate the stroma, and remodel the extracellular matrix (ECM). Characterizing the biology of this phenotypically distinct group of cells could substantially improve our understanding of early events during the metastatic cascade.

Tumor invasion is a dynamic process facilitated by bidirectional interactions between malignant epithelium and the cancer associated stroma. In order to examine cell-specific responses at the tumor stroma-interface we have combined organotypic co-culture and laser micro-dissection techniques.
Organotypic models, in which key stromal constituents such as fibroblasts are 3-dimentioanally co-cultured with cancer epithelial cells, are highly manipulatable experimental tools which enable invasion and cancer-stroma interactions to be studied in near-physiological conditions.

Laser microdissection (LMD) is a technique which entails the surgical dissection and extraction of the various strata within tumor tissue, with micron level precision.
By combining these techniques with genomic, transcriptomic and epigenetic profiling we aim to develop a deeper understanding of the molecular characteristics of invading tumor cells and surrounding stromal tissue, and in doing so potentially reveal novel biomarkers and opportunities for drug development in CRC.&#160;&#160;&#160;

Molecular Profiling of the Invasive Tumor Microenvironment in a 3-Dimensional Model of Colorectal Cancer Cells and Ex vivo Fibroblasts

Molecular profiling of laser microdissected cells and stroma from synthetic, tissue-engineered colorectal cancer models represents a novel and manipulatable approach to characterize and dissect the distinctive biology at the interface between tumor cells at the invasive front and cancer associated stromal cells. &#160;

Invading colorectal cancer (CRC) cells have acquired the capacity to break free from their sister cells, infiltrate the stroma, and remodel the ...

A Next-generation Tissue Microarray (ngTMA) Protocol for Biomarker Studies

Biomarker research relies on tissue microarrays (TMA). TMAs are produced by repeated transfer of small tissue cores from a &#8216;donor&#8217; block into a &#8216;recipient&#8217; block and then used for a variety of biomarker applications. The construction of conventional TMAs is labor intensive, imprecise, and time-consuming. Here, a protocol using next-generation Tissue Microarrays (ngTMA) is outlined. ngTMA is based on TMA planning and design, digital pathology, and automated tissue microarraying. The protocol is illustrated using an example of 134 metastatic colorectal cancer patients. Histological, statistical and logistical aspects are considered, such as the tissue type, specific histological regions, and cell types for inclusion in the TMA, the number of tissue spots, sample size, statistical analysis, and number of TMA copies. Histological slides for each patient are scanned and uploaded onto a web-based digital platform. There, they are viewed and annotated (marked) using a 0.6-2.0 mm diameter tool, multiple times using various colors to distinguish tissue areas. Donor blocks and 12 &#8216;recipient&#8217; blocks are loaded into the instrument. Digital slides are retrieved and matched to donor block images. Repeated arraying of annotated regions is automatically performed resulting in an ngTMA. In this example, six ngTMAs are planned containing six different tissue types/histological zones. Two copies of the ngTMAs are desired. Three to four slides for each patient are scanned; 3 scan runs are necessary and performed overnight. All slides are annotated; different colors are used to represent the different tissues/zones, namely tumor center, invasion front, tumor/stroma, lymph node metastases, liver metastases, and normal tissue. 17 annotations/case are made; time for annotation is 2-3 min/case. 12 ngTMAs are produced containing 4,556 spots. Arraying time is 15-20 hr. Due to its precision, flexibility and speed, ngTMA is a powerful tool to further improve the quality of TMAs used in clinical and translational research.

A Next-generation Tissue Microarray ngTMA Protocol for Biomarker Studies

The protocol aims at optimizing the construction and quality of tissue microarrays for biomarker research. It includes aspects of planning and design, digital pathology, virtual slide annotation, and automated tissue arraying.

Biomarker research relies on tissue microarrays (TMA). TMAs are produced by repeated transfer of small tissue cores from a &#8216;donor&#8217; block into ...

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Microfabricated cellular microarrays, which consist of contact-printed combinations of biomolecules on an elastic hydrogel surface, provide a tightly controlled, high-throughput engineered system for measuring the impact of arrayed biochemical signals on cell differentiation. Recent efforts using cell microarrays have demonstrated their utility for combinatorial studies in which many microenvironmental factors are presented in parallel. However, these efforts have focused primarily on investigating the effects of biochemical cues on cell responses. Here, we present a cell microarray platform with tunable material properties for evaluating both cell differentiation by immunofluorescence and biomechanical cell&#8211;substrate interactions by traction force microscopy. To do so, we have developed two different formats utilizing polyacrylamide hydrogels of varying Young&#39;s modulus fabricated on either microscope slides or glass-bottom Petri dishes. We provide best practices and troubleshooting for the fabrication of microarrays on these hydrogel substrates, the subsequent cell culture on microarrays, and the acquisition of data. This platform is well-suited for use in investigations of biological processes for which both biochemical (e.g., extracellular matrix composition) and biophysical (e.g., substrate stiffness) cues may play significant, intersecting roles.

Cell differentiation is regulated by a host of microenvironmental factors, including both matrix composition and substrate material properties. We describe here a technique utilizing cell microarrays in conjunction with traction force microscopy to evaluate both cell differentiation and biomechanical cell&#8211;substrate interactions as a function of microenvironmental context.

Microfabricated cellular microarrays, which consist of contact-printed combinations of biomolecules on an elastic hydrogel surface, provide a tightly ...

Bioengineering

Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array

Cellular microenvironments consist of a variety of cues, such as growth factors, extracellular matrices, and intercellular interactions. These cues are well orchestrated and are crucial in regulating cell functions in a living system. Although a number of researchers have attempted to investigate the correlation between environmental factors and desired cellular functions, much remains unknown. This is largely due to the lack of a proper methodology to mimic such environmental cues in vitro, and simultaneously test different environmental cues on cells. Here, we report an integrated platform of microfluidic channels and a nanofiber array, followed by high-content single-cell analysis, to examine stem cell phenotypes altered by distinct environmental factors. To demonstrate the application of this platform, this study focuses on the phenotypes of self-renewing human pluripotent stem cells (hPSCs). Here, we present the preparation procedures for a nanofiber array and the microfluidic structure in the fabrication of a Multiplexed Artificial Cellular MicroEnvironment (MACME) array. Moreover, overall steps of the single-cell profiling, cell staining with multiple fluorescent markers, multiple fluorescence imaging, and statistical analyses, are described.

This article describes the detailed methodology to prepare a Multiplexed Artificial Cellular MicroEnvironment (MACME) array for high-throughput manipulation of physical and chemical cues mimicking in vivo cellular microenvironments and to identify the optimal cellular environment for human pluripotent stem cells (hPSCs) with single-cell profiling.

Cellular microenvironments consist of a variety of cues, such as growth factors, extracellular matrices, and intercellular interactions. These cues are ...

Fabrication and Use of MicroEnvironment microArrays (MEArrays)

The interactions between cells and their surrounding microenvironment have functional consequences for cellular behaviour. On the single cell level, distinct microenvironments can impose differentiation, migration, and proliferation phenotypes, and on the tissue level the microenvironment processes as complex as morphogenesis and tumorigenesis1. Not only do the cell and molecular contents of microenvironments impact the cells within, but so do the elasticity2 and geometry3 of the tissue. Defined as the sum total of cell-cell, -ECM, and -soluble factor interactions, in addition to physical characteristics, the microenvironment is complex. The phenotypes of cells within a tissue are partially due to their genomic content and partially due to the combinatorial interactions with the microenviroment. A major challenge is to link specific combinations of microenvironmental components with distinctive behaviours.
Here, we present the microenvironment microarray (MEArray) platform for cell-based functional screening of interactions with combinatorial microenvironments4. The method allows for simultaneous control of the molecular composition and the elastic modulus, and combines the use of widely available microarray and micropatterning technologies. MEArray screens require as few as 10,000 cells per array, which facilitates functional studies of rare cell types such as adult progenitor cells. A limitation of the technology is that entire tissue microenvironments cannot be completely recapitulated on MEArrays. However, comparison of responses in the same cell type to numerous related microenvironments, for instance pairwise combinations of ECM proteins that characterize a given tissue, will provide insights into how microenvironmental components elicit tissue-specific functional phenotypes.
MEArrays can be printed using a wide variety of recombinant growth factors, cytokines, and purified ECM proteins, and combinations thereof. The platform is limited only by the availability of specific reagents. MEArrays are amenable to time-lapsed analysis, but most often are used for end point analyses of cellular functions that are measureable with fluorescent probes. For instance, DNA synthesis, apoptosis, acquisition of differentiated states, or production of specific gene products are commonly measured. Briefly, the basic flow of an MEArray experiment is to prepare slides coated with printing substrata and to prepare the master plate of proteins that are to be printed. Then the arrays are printed with a microarray robot, cells are allowed to attach, grow in culture, and then are chemically fixed upon reaching the experimental endpoint. Fluorescent or colorimetric assays, imaged with traditional microscopes or microarray scanners, are used to reveal relevant molecular and cellular phenotypes (Figure 1).

Fabrication and Use of MicroEnvironment microArrays MEArrays

A combinatorial functional screening method for gaining insights into the impacts of the molecular composition of microenvironments on cellular functions is described. The method takes advantage of existing microarray-based technologies to generate arrays of defined combinatorial microenvironments that support cell adhesion and functional analysis.

The interactions between cells and their surrounding microenvironment have functional consequences for cellular behaviour. On the single cell level, ...

Biology

Expression Profiling with Microarrays

Microarrays are important tools for profiling gene expression, and are based on complementary binding between probes that are attached to glass chips and nucleic acids derived from samples. Using these arrays, scientists can simultaneously evaluate the expression of thousands of genes. In addition, the expression profiles of different cells or tissue types can be compared, allowing researchers to deduce how the expression of different genes change during biological processes, and thus gain insight into how the genes may function in pathways or networks.Here, JoVE explains the principles behind microarrays. This is followed by a general protocol for performing a microarray experiment, and a brief introduction to analyzing microarray data. We end on a discussion of how scientists are currently using microarrays, for example to compare gene expression between different cell types derived from cancerous and non-cancerous tissues, to study important biological problems.

Microarrays are important tools for profiling gene expression, and are based on complementary binding between probes that are attached to glass chips and ...

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Summary

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Chapters in this video

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Molecular Profiling of the Invasive Tumor Microenvironment in a 3-Dimensional Model of Colorectal Cancer Cells and Ex vivo Fibroblasts

A Next-generation Tissue Microarray (ngTMA) Protocol for Biomarker Studies

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Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array

Fabrication and Use of MicroEnvironment microArrays (MEArrays)

Expression Profiling with Microarrays