We acknowledge Austin Cyphersmith and Mayandi Sivaguru (Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana&#8211;Champaign) for assistance with microscopy and for generously accommodating screen and video capture at the microscopy core.

The authors declare that they have no competing financial interests.

In our experiments, we have found that the most common failures are related to the quality of fabricated arrays and poorly characterized response in the biological system of interest. We refer the reader to Table 1 for common failure modes in cell microarray experiments and associated troubleshooting steps. Regarding quality of arrays in particular, we recommend the following. Confirm the technical quality and robustness of arraying programs, parameters, and buffers using fluorescently labeled molecules such as rhodamine-conjugated dextran. Thoroughly clean pins either before or after arraying per the manufacturer&#39;s instructions and further visually check that the pin channels are clear of debris using a light microscope. Confirm arrayed biomolecule retention using general protein stains or immunolabeling. Note that biomolecules with a molecular weight below 70 kDa are frequently not retained in the hydrogel23,31. Validate arrayed biomolecule cell-functionality using multiple cell types. Note that only adherent cells are compatible with arrays; additionally, adhesion to arrays is dependent on both cell-specific properties (e.g., integrin expression profile) and the selected ECM proteins.
Due to limited space, we have not provided an extensive treatment of array design, layout, and fabrication here and refer the reader to previous works23,25. We generally use 100 spot subarrays (150 &#181;m spot diameter, 450 &#181;m center-to-center distance) composed of 10&#8211;20 unique biomolecule conditions (i.e., 5&#8211;10 spots/condition). The number of subarrays in one array varies depending on the number of biomolecule conditions of interest, which can be comfortably scaled up to 1,280 on one 25 &#215; 75 mm microscope slide (~ 6,400 spots in 64 subarrays)25,31. The parameters above will further vary depending on the pattern size of interest; pins capable of generating patterns from 75 &#8211; 450 &#181;m are readily available.
Array experiments are best complemented by validation of high-scoring arrayed conditions of interest using other culture formats, assay readouts, and biological model systems. Specifically, we recommend further validating effects of select arrayed conditions using bulk cultures in conjunction with standard molecular biology techniques (e.g., qRT-PCR, immunoblotting) or standard TFM. Genetic manipulation (e.g., knockdown or overexpression) of the factor of interest in an appropriate biological model system can also serve to confirm effects observed in arrays. In vivo animal models represent another means of validation and were recently used, for example, to confirm the central role of galectin-3 and galectin-8 in the lung cancer metastatic niche, as initially identified via cell microarray31,49.
A number of other methods have been used to probe microenvironmental regulation of cellular functions, including a variety of two-dimensional microfabricated systems18,50,51,52,53,54,55 and three-dimensional engineered biomaterial systems56,57,58,59,60,61. In comparison with other methods, the particular advantages of the cell microarray platform described here consist of: (1) throughput up to hundreds or thousands of different combinations of factors, enabling analysis of interaction effects; (2) accessible, automated imaging and analysis; (3) integration of both biochemical and biophysical readouts with controlled presentation of arrayed factors; (4) ability to vary substrate material properties; and (5) high-content single-cell analysis of cell fate and function.
In summary, the combination of cell microarrays with TFM on substrates of tunable substrate stiffness enables thorough characterization of both biochemical and biophysical cues. As presented here, this platform is generalizable and can be readily applied to a variety of adherent cell types and tissue contexts towards an improved understanding of combinatorial microenvironmental regulation of cell differentiation and mechanotransduction.

Interactions between cells and surrounding microenvironmental factors mediate a large variety of biological processes throughout development, homeostasis, and disease pathogenesis1,2,3,4. These microenvironmental interactions include delivery of soluble factors to cells, cell&#8211;matrix binding, and cell&#8211;cell interactions via ligand&#8211;receptor binding. In addition to the above biochemical considerations, biophysical parameters, such as substrate mechanical properties (e.g., Young&#39;s modulus, porosity) and cell shape, and associated downstream mechanotransduction have increasingly gained recognition as key mediators of cell differentiation5,6,7,8,9,10. Signals resulting from these microenvironmental interactions serve as inputs to gene networks and signaling pathways. Moreover, these cell-intrinsic components also provide feedback to the microenvironment via secreted factors and matrix-degrading enzymes, completing a complex co-regulatory loop between cell-intrinsic genetic programs and cell-extrinsic microenvironmental factors5,11,12.
The use of engineered systems for the controlled presentation of microenvironmental factors has proven useful in a range of different contexts13,14,15. Microfabricated systems in particular have facilitated precise spatial patterning of proteins and cells as well as highly parallelized analysis via miniaturization13,16,17,18,19,20,21,22. Cell microarrays represent one such microfabricated system in which combinations of biomolecules are contact-printed onto an elastic polyacrylamide hydrogel substrate23,24,25. The inclusion of cell-adhesive components (namely matrix proteins) enables sustained cell adhesion and culture on microarrays, which is frequently followed by downstream analysis via immunocytochemistry and fluorescent reporters. Cell microarrays have been productively directed towards attaining an improved understanding of liver cell phenotype23,26, neural precursor differentiation27, mammary progenitor fate decisions28, embryonic stem cell maintenance/differentiation23,29,30, lung cancer metastasis31, and therapeutic response in melanoma32. We have recently demonstrated the use of cell microarrays for defining the role of extracellular matrix (ECM) protein composition in endoderm specification33, liver progenitor differentiation34,35, and lung tumor cell drug response36. In these works, we have focused on extending the combinatorial capabilities of the array platform and exploring the intersections of cell-intrinsic signaling with extracellular matrix composition and biomechanics. In addition, we have implemented biophysical readouts in this array platform to provide the ability to quantitatively characterize the role of cell contractility in differentiation processes35. To do so, we integrated traction force microscopy (TFM) with cell microarrays to enable high-throughput assessment of cell-generated traction. TFM is a widely utilized method to measure cell-generated traction forces and has provided significant insights regarding the coordination of single-cell and tissue-level function with the composition and biomechanics of the local microenvironment37,38,39,40. Thus, combining TFM with cell microarrays provides a high-throughput system for measuring key, physiologically relevant biophysical parameters.
The cell microarray platform described here consists of four sections: fabrication of polyacrylamide substrates, fabrication of arrays, cell culture and assay readout, and analysis of data. See Figure 1 for a schematic summary of the first three experimental sections; see Figure 2 for a schematic summary of the final section with a focus on analysis of immunofluorescence data. In order to adapt the cell microarray platform to studies of biomechanical cell&#8211;substrate interactions, we used polyacrylamide substrates of tunable Young&#39;s modulus but similar porosity, per Wen et al.41. To enable TFM measurements of forces exerted by cells on their substrate, we implemented a glass-bottom Petri dish format in addition to the thick glass microscope slides frequently utilized by other groups. Thus, this cell microarray platform is capable of parallel measurements of cell differentiation via immunofluorescence on microscope slides and cell-generated forces via TFM on separate glass-bottom dishes. We have also applied several improvements to the analytical approach commonly used with cell microarrays. Specifically, instead of parametric Z-scoring of overall island intensity, we measure single-cell intensity and apply quantile normalization in order to account for non-normal distributions and more accurately describe cellular behavior. We believe these improvements provide particular utility towards investigations of biological processes in which both biochemical and biophysical cues play significant, intersecting roles. Further, our analytical improvements enable the application of cell microarrays to studies of a range of cellular functions for which single-cell and population-level behavior diverge.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>0.2 &#181;m syringe filter</td>
			<td>Pall Corporation</td>
			<td>4433</td>
			<td>Match with appropriately-sized Luer lock plastic syringes.</td>
		</tr>
		<tr>
			<td>100&#215; penicillin&#8211;streptomycin solution</td>
			<td>Fisher Scientific</td>
			<td>SV30010</td>
			<td></td>
		</tr>
		<tr>
			<td>22&#215;60 mm coverglasses</td>
			<td>Electron Microscope Sciences</td>
			<td>63765</td>
			<td></td>
		</tr>
		<tr>
			<td>3-(trimethoxysilyl)propyl methacrylate (3-TPM)</td>
			<td>Sigma-Aldrich</td>
			<td>440159</td>
			<td>Store under inert gas per manufacturer&#39;s instructions. Exposure of 3-TPM to air could compromise silanization of glass substrates. CAUTION: 3-TPM is a combustible liquid. Keep away from heat, sparks, open flames, and hot surfaces and use only in a chemical fume hood.</td>
		</tr>
		<tr>
			<td>3-[(3-Cholamidopropyl)dimethylammonio]-1-propanesulfonate hydrate (CHAPS)</td>
			<td>Sigma-Aldrich</td>
			<td>C3023</td>
			<td></td>
		</tr>
		<tr>
			<td>35 mm glass-bottom Petri dishes</td>
			<td>Cell E&#38;G</td>
			<td>GBD00002-200</td>
			<td>13 mm well consisting of #1.5 coverglass. Enables TFM and live-cell imaging.</td>
		</tr>
		<tr>
			<td>384-well polypropylene V-bottom microplate, non-sterile</td>
			<td>USA Scientific</td>
			<td>1823-8400</td>
			<td></td>
		</tr>
		<tr>
			<td>6-well polystyrene microplates</td>
			<td>Fisher Scientific</td>
			<td>08-772-1B</td>
			<td>35 mm glass-bottom Petri dishes fit into wells of microplate, easying array fabrication.</td>
		</tr>
		<tr>
			<td>Acetone</td>
			<td>Sigma-Aldrich</td>
			<td>179973</td>
			<td></td>
		</tr>
		<tr>
			<td>Acrylamide</td>
			<td>Sigma-Aldrich</td>
			<td>A3553</td>
			<td>CAUTION: Exposure to acrylamide can result in acute toxicity and irritation. Wear protective gloves, clothing, and eye protection.</td>
		</tr>
		<tr>
			<td>Collagen I, rat tail</td>
			<td>EMD Millipore</td>
			<td>08-115MI</td>
			<td></td>
		</tr>
		<tr>
			<td>Collagen III, human</td>
			<td>EMD Millipore</td>
			<td>CC054</td>
			<td></td>
		</tr>
		<tr>
			<td>Collagen IV, human</td>
			<td>EMD Millipore</td>
			<td>CC076</td>
			<td></td>
		</tr>
		<tr>
			<td>Crosslinker, 365 nm</td>
			<td>UVP</td>
			<td>CL-1000</td>
			<td></td>
		</tr>
		<tr>
			<td>Dextran, rhodamine B-conjugated, 70 kDa</td>
			<td>ThermoFisher Scientific</td>
			<td>D1841</td>
			<td>Used as a marker for array location.</td>
		</tr>
		<tr>
			<td>Dimethyl sulfoxide</td>
			<td>Fisher Scientific</td>
			<td>BP231</td>
			<td></td>
		</tr>
		<tr>
			<td>Dulbecco&#39;s phosphate-buffered saline (PBS)</td>
			<td>Fisher Scientific (HyClone)</td>
			<td>SH3001302</td>
			<td></td>
		</tr>
		<tr>
			<td>Ethyl alcohol</td>
			<td>Decon Labs</td>
			<td>2701</td>
			<td></td>
		</tr>
		<tr>
			<td>Ethylenediaminetetraacetic acid (EDTA)</td>
			<td>Sigma-Aldrich</td>
			<td>ED</td>
			<td></td>
		</tr>
		<tr>
			<td>Fc-recombinant DLL1, mouse</td>
			<td>R&#38;D Systems</td>
			<td>5026-DL-050</td>
			<td></td>
		</tr>
		<tr>
			<td>Fc-recombinant DLL4, mouse</td>
			<td>AdipoGen</td>
			<td>AG-40A-0145-C050</td>
			<td></td>
		</tr>
		<tr>
			<td>Fc-recombinant JAG1, rat</td>
			<td>R&#38;D Systems</td>
			<td>599-JG-100</td>
			<td></td>
		</tr>
		<tr>
			<td>Fibronectin, human</td>
			<td>Sigma-Aldrich</td>
			<td>F2006</td>
			<td></td>
		</tr>
		<tr>
			<td>Fluorescent microscope, inverted</td>
			<td>Zeiss</td>
			<td>Axiovert 200M</td>
			<td>Ensure microscope is equipped with a robotic stage for both automated fluorescent imaging and TFM. Environmental control (i.e., 37 &#176;C and 5% CO2) is highly advisable for TFM.</td>
		</tr>
		<tr>
			<td>Fluoromount G with DAPI</td>
			<td>SouthernBiotech</td>
			<td>0100-20</td>
			<td></td>
		</tr>
		<tr>
			<td>Glacial acetic acid</td>
			<td>Sigma-Aldrich</td>
			<td>695092</td>
			<td>CAUTION: Acetic acid is flammable and corrosive. Wear protective gloves, clothing, and eye protection.</td>
		</tr>
		<tr>
			<td>Glycerol</td>
			<td>Sigma-Aldrich</td>
			<td>M6145</td>
			<td></td>
		</tr>
		<tr>
			<td>Irgacure 2959</td>
			<td>BASF Corporation</td>
			<td>55047962</td>
			<td></td>
		</tr>
		<tr>
			<td>Laminin, mouse</td>
			<td>EMD Millipore</td>
			<td>CC095</td>
			<td></td>
		</tr>
		<tr>
			<td>Methanol</td>
			<td>Sigma-Aldrich</td>
			<td>179957</td>
			<td></td>
		</tr>
		<tr>
			<td>Microarray scanner</td>
			<td>GenePix</td>
			<td>4000B</td>
			<td>Fluorophores must be Cy3- or Cy5-compatible.</td>
		</tr>
		<tr>
			<td>Microarrayer</td>
			<td>Digilab</td>
			<td>OmniGrid Micro</td>
			<td>Other microarrayerse of similar or greater capability can readily be substituted.</td>
		</tr>
		<tr>
			<td>Microscope slides, 25&#215;75 mm</td>
			<td>Sigma-Aldrich</td>
			<td>CLS294775X25</td>
			<td>~0.9&#8211;1.1 mm thickness.</td>
		</tr>
		<tr>
			<td>N,N&#8242;-Methylenebisacrylamide (bisacrylamide)</td>
			<td>Sigma-Aldrich</td>
			<td>M7279</td>
			<td>CAUTION: Exposure to acrylamide can result in acute toxicity and irritation. Wear protective gloves, clothing, and eye protection.</td>
		</tr>
		<tr>
			<td>Paraformaldehyde (PFA), 16% v/v</td>
			<td>Electron Microscope Sciences</td>
			<td>RT15710</td>
			<td>Prepare PFA fresh (do not store) for optimal fixation. CAUTION: Exposure to PFA can result in acute toxicity and can also irritate or corrode skin on contact. Wear protective gloves, clothing, and eye protection and use only in a chemical fume hood.</td>
		</tr>
		<tr>
			<td>Protein A/G, recombinant</td>
			<td>ThermoFisher Scientific</td>
			<td>21186</td>
			<td></td>
		</tr>
		<tr>
			<td>Pyrex drying tray, 2000 ml</td>
			<td>Fisher Scientific</td>
			<td>15-242B&#160;</td>
			<td></td>
		</tr>
		<tr>
			<td>Rectangular 4-chambered culture dish</td>
			<td>Fisher Scientific (Nunc)</td>
			<td>12-565-495</td>
			<td>For cell culture on arrayed microscope slides.</td>
		</tr>
		<tr>
			<td>Sodium acetate</td>
			<td>Sigma-Aldrich</td>
			<td>S2889</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium hydroxide</td>
			<td>Sigma-Aldrich</td>
			<td>415413</td>
			<td>CAUTION: NaOH is highly caustic and can cause severe skin burns and eye damage. Wear protective gloves, clothing, and eye protection.</td>
		</tr>
		<tr>
			<td>Stealth pin for arraying</td>
			<td>ArrayIt</td>
			<td>SMP3</td>
			<td>Clean pins after each array run using the instructions of the manufacturer. Produces 150 micron domains; purchase other pin sizes (75&#8211;450 microns) as suited to your particular application.</td>
		</tr>
		<tr>
			<td>Triton X-100</td>
			<td>Sigma-Aldrich</td>
			<td>X100</td>
			<td></td>
		</tr>
	</tbody>
</table>

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.

Using this platform, we investigated the role of both biochemical and biophysical cues in the fate specification of liver progenitors34,35. Protein A/G-conjugated Notch ligands showed improved retention and clustering in the polyacrylamide hydrogel (Figure 3A) and were furthermore capable of driving differentiation of liver progenitors towards a bile duct cell fate (Figure 3B). Using single-cell analysis, we quantified the response to the Notch ligands for the ECM proteins collagen I, collagen III, collagen IV, fibronectin, and laminin (Figure 3C), finding that the response of liver progenitors to the ligand depends also on the ECM context. Last, we utilized shRNA knockdown to generate liver progenitors without the ligands Dll1 and Jag1. The response to the arrayed Notch ligand varied depending on the presence of either ligand, confirming that the responsiveness to the cell-extrinsic ligand is also a function of the cell-intrinsic ligand expression (Figure 3D). Further, we observed a distinct subpopulation of double-positive (ALB+/OPN+) cells in the Dll1 knockdown (Figure 3D). Together, these representative results show: (1) the combinatorial capabilities of the array format, as exemplified by the pairing of multiple arrayed ECM proteins and Notch ligands with the knockdown of individual ligands; (2) the functionality of not only arrayed ECM proteins but also arrayed cell&#8211;cell ligand via Protein A/G-mediated conjugation; and (3) the implementation of our single-cell analysis and its ability to discern unique subpopulations.
We also observed that the differentiation of liver progenitors is dependent on both the substrate stiffness and the ECM composition (Figure 4A), specifically finding that collagen IV is supportive of differentiation on both soft and stiff substrates while fibronectin only supports differentiation on stiff substrates (Figure 4B). Representative heat maps of TFM measurements suggested that sustained traction stress at low substrate stiffness on collagen IV promoted differentiation into bile duct cells (Figure 4C), a finding confirmed by average root-mean-square values (Figure 4D). Together, these representative results show: (1) the successful integration of TFM with cell microarrays on substrates with a tunable stiffness to assess both the cell phenotype and the traction stress; (2) the coordination of the liver progenitor cell fate with both the matrix composition and the substrate stiffness; and (3) the implementation of our TFM analysis and typical traction stress profiles in cell microarrays.
<img alt="Figure 1" src="/files/ftp_upload/55362/55362fig1.jpg" /> 
Figure 1: Overview Schematic Showing the First Three Experimental Sections. In Section 1, glass substrates are cleaned and silanized to facilitate the fabrication of polyacrylamide hydrogels. In Section 2, the biomolecule combinations of interest are prepared in a 384-well source microplate. A robotic arrayer is then loaded with clean pins, the source microplate, and the polyacrylamide hydrogels and initialized, fabricating arrays on the hydrogels. In Section 3, cells are seeded onto the arrayed domains and allowed to adhere, after which the culture protocol of interest is performed. At the endpoint, cells are either fixed for immunocytochemistry/immunofluorescence or analyzed using TFM. Scale bars are 75 &#181;m. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55362/55362fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/55362/55362fig2.jpg" /> 
Figure 2: Processing and Analysis of Immunofluorescence Data from Arrays. (A) Tiled, composite 32-bit RGB images are first binned and then split into individual 8-bit channels. Using a combination of arrayed fluorescent markers and cell islands, three corners of the array are identified to allow for automated orientation and gridding of the arrays. (B) Single-cell data is generated for each channel of the input arrays. In order to account for experimental drift, quantile normalization is applied by biological replicate, producing a single shared distribution across all replicates. Quantile normalized data is subsequently plotted and interpreted via calculation of ensemble measurements (e.g., cells/island, mean intensity, percentage cells positive for a label) or direct analysis of single-cell distributions. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55362/55362fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/55362/55362fig3.jpg" /> 
Figure 3: Notch Ligand Presentation Mediates Liver Progenitor Differentiation. (A) Fc-recombinant Notch ligands Jagged-1 (JAG1) and Delta-like 1 (DLL1) exhibited improved retention and clustering when arrayed with Protein A/G. Scale bar is 50 &#181;m. (B) Liver progenitors differentiated into bile duct cells upon presentation with Notch ligand. 4&#39;,6-Diamidino-2-phenylindole (DAPI) is a nuclear label, albumin (ALB) is a hepatic cell marker, and osteopontin (OPN) is a bile duct cell marker. Scale bar is 150 &#181;m. (C) Quantification of percentage of cells positive for OPN for the Notch ligands JAG1, DLL1, and Delta-like 4 (DLL4) on the ECM proteins collagen I, collagen III, collagen IV, fibronectin, and laminin. Student&#39;s t-tests were performed against control IgG for each arrayed Notch ligand within each ECM protein with P-values indicated for P&#60;0.05 (*). (D) Imaging cytometry of ALB and OPN for cells on collagen III presented with the Notch ligands JAG1, DLL1, and DLL4. Liver progenitors without the Notch ligands Dll1 and Jag1 (i.e., shDll1 and shJag1) were generated using shRNA knockdown. Data in (C) presented as mean &#177; s.e.m. This figure has been modified from Kaylan et al.34. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55362/55362fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/55362/55362fig4.jpg" /> 
Figure 4: Matrix Composition and Substrate Stiffness Coordinate Liver Progenitor Differentiation. (A) Liver progenitor differentiation to bile duct cells is dependent on both ECM composition and substrate stiffness. DAPI is a nuclear label, ALB is a hepatic cell marker, and OPN is a bile duct cell marker. (B) Quantification of percentage of cells positive for OPN on substrates of Young&#39;s modulus 30 kPa, 13 kPa, and 4 kPa for collagen I (C1), collagen IV (C4), fibronectin (FN), and all two-way combinations of those ECM proteins. (C) Cell traction stress is dependent on both substrate stiffness and ECM composition. (D) Quantification of root-mean-square values of traction stress on substrates of Young&#39;s modulus 30 kPa and 4 kPa for collagen I (C1), collagen IV (C4), fibronectin (FN), and all two-way combinations of those ECM proteins. In (B) and (D), data were presented as mean &#177; s.e.m and Student&#39;s t-tests were performed against 30 kPa for each ECM combination with P-values indicated for P&#60; 0.05 (*), P&#60; 0.01 (**), and P&#60; 0.001 (***). Scale bars are 50 &#181;m. This figure has been modified from Kourouklis et al.35. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55362/55362fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td fo:width="1.2in">Section</td>
			<td fo:width="1.5in">Problem</td>
			<td fo:width="1.5in">Potential Causes</td>
			<td>Solution</td>
		</tr>
		<tr>
			<td rowspan="3">1. Fabrication of Polyacrylamide Substrate.</td>
			<td>Coverglass cannot be removed from hydrogel.</td>
			<td>Overpolymerization.</td>
			<td>Reduce polymerization time to &#60;10 minutes (4 W/m2). Check that UV crosslinker output is within expected range.</td>
		</tr>
		<tr>
			<td>Poor polyacrylamide hydrogel polymerization.</td>
			<td>Underpolymerization.</td>
			<td>Increase polymerization time to &#62;10 minutes (4 W/m2).&#160; Check that UV crosslinker output is within expected range.</td>
		</tr>
		<tr>
			<td>Polyacrylamide hydrogels are damaged after removal of coverglass.</td>
			<td>Soft polyacrylamide hydrogels are easy to damage.&#160;</td>
			<td>We observe decreasing hydrogel fabrication yield (~50%) for the softest (i.e., 4 kPa) hydrogels in particular. Handle hydrogels gently and increase starting numbers to attain desired yield.</td>
		</tr>
		<tr>
			<td rowspan="2">2. Fabrication of Arrays.</td>
			<td rowspan="2">Poor or inconsistent spot morphology.</td>
			<td>Inconsistent humidifier function.</td>
			<td>Check that humidifier and rheometer a functional throughout each print run and maintain 65% RH.</td>
		</tr>
		<tr>
			<td>Pins stuck in printhead or clogged.</td>
			<td>Clean the printhead to allow for free pin movement. Clean pins thoroughly before or after each print run to remove aggregates from pin channels.</td>
		</tr>
		<tr>
			<td rowspan="8">3. Cell Culture and Assay Execution.</td>
			<td rowspan="2">Cell detachment or death on arrays after initial attachment.</td>
			<td>Overseeding and excessive proliferation.</td>
			<td>Reduce initial seeding density and time. Use &#34;maintenance&#34; or &#34;differentiation&#34; media during array culture to reduce cell proliferation.</td>
		</tr>
		<tr>
			<td>Release of toxic acrylamide monomer from hydrogel.</td>
			<td>Soak hydrogels in dH2O for at least 3 d to allow for diffusion/release of acrylamide monomer and reduce cell toxicity.</td>
		</tr>
		<tr>
			<td rowspan="4">Cells don&#39;t attach to arrays.</td>
			<td>Underseeding.</td>
			<td>Increase initial seeding density and time. Use a more strongly adherent cell type.</td>
		</tr>
		<tr>
			<td>Poor deposition of matrix or biomolecule condition.</td>
			<td>Clean pins of particles and aggregates, confirm printing parameters, and evaluate spotting of fluorescent markers, e.g., rhodamine-conjugated dextran.</td>
		</tr>
		<tr>
			<td>Specificity of cell&#8211;matrix interactions.</td>
			<td>Different cell types adhere specifically to some but not other ECM proteins. Test multiple different ECM proteins with your cells.</td>
		</tr>
		<tr>
			<td>Suboptimal array storage after fabrication.</td>
			<td>We recommend storing fabricated arrays overnight at 65% RH and room temperature, in part to avoid phase changes during freezing. Cell adhesion is sensitive to both humidity, temperature, and storage time; make sure these parameters are consistent/optimized for your experiments.</td>
		</tr>
		<tr>
			<td rowspan="2">Detachment of hydrogel from glass substrate during cell culture.</td>
			<td>Poor slide cleaning and silanization.</td>
			<td>Replace working solutions for slide cleaning and silanization.</td>
		</tr>
		<tr>
			<td>Overdehydrated hydrogel.</td>
			<td>Don&#39;t leave hydrogels dehydrating on a hot plate for longer than 15&#8211;30 min.</td>
		</tr>
		<tr>
			<td>4. Analysis of Data.</td>
			<td>High variability between replicate spots and slides.</td>
			<td>Variability in array fabrication.</td>
			<td>Check that pins and printhead are clean. Confirm humidifier function. Visualize and quantify spot and array quality using fluorescent markers. Store arrays as recommended above.&#160;</td>
		</tr>
	</tbody>
</table>
Table 1: Troubleshooting.

Watch this Scientific Journal Video about A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces at JoVE.com

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

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 ...

a-high-throughput-cell-microarray-platform-for-correlative-analysis

Nanyang Technological University

Research

JoVE Journal

Bioengineering

9.5K Views.  University of Illinois at Urbana–Champaign. 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–substrate interactions as a function of microenvironmental context.

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

High Throughput MicroRNA Profiling: Optimized Multiplex qRT-PCR at Nanoliter Scale on the Fluidigm Dynamic ArrayTM IFCs

The broad involvement of miRNAs in critical processes underlying development, tissue homoeostasis and disease has led to a surging interest among the research and pharmaceutical communities. To study miRNAs, it is essential that the quantification of microRNA levels is accurate and robust. By comparing wild-type to small RNA deficient mouse embryonic stem cells (mESC), we revealed a lack of accuracy and robustness in previous published multiplex qRT-PCR techniques. Here, we describe an optimized method, including purifying away excessive primers from previous multiplex steps before singleplex real time detection, which dramatically increases the accuracy and robustness of the technique. Furthermore, we explain how performing the technique on a microfluidic chip at nanoliter volumes significantly reduces reagent costs and permits time effective high throughput miRNA expression profiling.

Here we describe an optimized multiplex reverse transcriptase quantitative PCR (qRT-PCR) protocol in combination with a microfluidic platform as a cost and time effective high-throughput screening tool for microRNA (miRNA) expression levels, especially when working with limited amounts of sample.

The broad involvement of miRNAs in critical processes underlying development, tissue homoeostasis and disease has led to a surging interest among the ...

Alginate Microcapsule as a 3D Platform for Propagation and Differentiation of Human Embryonic Stem Cells (hESC) to Different Lineages

Human embryonic stem cells (hESC) are emerging as an attractive alternative source for cell replacement therapy since they can be expanded in culture indefinitely and differentiated to any cell types in the body. Various types of biomaterials have also been used in stem cell cultures to provide a microenvironment mimicking the stem cell niche1-3. The latter is important for promoting cell-to-cell interaction, cell proliferation, and differentiation into specific lineages as well as tissue organization by providing a three-dimensional (3D) environment4 such as encapsulation. The principle of cell encapsulation involves entrapment of living cells within the confines of semi-permeable membranes in 3D cultures2. These membranes allow for the exchange of nutrients, oxygen and stimuli across the membranes, whereas antibodies and immune cells from the host that are larger than the capsule pore size are excluded5. Here, we present an approach to culture and differentiate hESC DA neurons in a 3D microenvironment using alginate microcapsules. We have modified the culture conditions2 to enhance the viability of encapsulated hESC. We have previously shown that the addition of p160-Rho-associated coiled-coil kinase (ROCK) inhibitor, Y-27632 and human fetal fibroblast-conditioned serum replacement medium (hFF-CM) to the 3D platform significantly enhanced the viability of encapsulated hESC in which the cells expressed definitive endoderm marker genes1. We have now used this 3D platform for the propagation of hESC and efficient differentiation to DA neurons. Protein and gene expression analyses after the final stage of DA neuronal differentiation showed an increased expression of tyrosine hydroxylase (TH), a marker for DA neurons, &gt;100 folds after 2 weeks. We hypothesized that our 3D platform using alginate microcapsules may be useful to study the proliferation and directed differentiation of hESC to various lineages. This 3D system also allows the separation of feeder cells from hESC during the process of differentiation and also has potential for immune-isolation during transplantation in the future.

Alginate Microcapsule as a 3D Platform for Propagation and Differentiation of Human Embryonic Stem Cells hESC to Different Lineages

We have optimized a microencapsulation technique as an effective 3D platform for propagation and differentiation of embryonic stem cells to endoderm and dopaminergic (DA) neurons. It also provides an opportunity for immune-isolation of cells from the host during transplantation. This platform can be adapted for other cell types.

Human embryonic stem cells (hESC) are emerging as an attractive alternative source for cell replacement therapy since they can be expanded in culture ...

Polymer Microarrays for High Throughput Discovery of Biomaterials

The discovery of novel biomaterials that are optimized for a specific biological application is readily achieved using polymer microarrays, which allows a combinatorial library of materials to be screened in a parallel, high throughput format1. Herein is described the formation and characterization of a polymer microarray using an on-chip photopolymerization technique 2. This involves mixing monomers at varied ratios to produce a library of monomer solutions, transferring the solution to a glass slide format using a robotic printing device and curing with UV irradiation. This format is readily amenable to many biological assays, including stem cell attachment and proliferation, cell sorting and low bacterial adhesion, allowing the ready identification of 'hit' materials that fulfill a specific biological criterion3-5. Furthermore, the use of high throughput surface characterization (HTSC) allows the biological performance to be correlated with physio-chemical properties, hence elucidating the biological-material interaction6. HTSC makes use of water contact angle (WCA) measurements, atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). In particular, ToF-SIMS provides a chemically rich analysis of the sample that can be used to correlate the cell response with a molecular moiety. In some cases, the biological performance can be predicted from the ToF-SIMS spectra, demonstrating the chemical dependence of a biological-material interaction, and informing the development of hit materials5,3.

A description of the formation of a polymer microarray using an on-chip photopolymerization technique. The high throughput surface characterization using atomic force microscopy, water contact angle measurements, X-ray photoelectron spectroscopy and time of flight secondary ion mass spectrometry and a cell attachment assay is also described.

The discovery of novel biomaterials that are optimized for a specific biological application is readily achieved using polymer microarrays, which allows a ...

High-throughput Protein Expression Generator Using a Microfluidic Platform

Rapidly increasing fields, such as systems biology, require the development and implementation of new technologies, enabling high-throughput and high-fidelity measurements of large systems. Microfluidics promises to fulfill many of these requirements, such as performing high-throughput screening experiments on-chip, encompassing biochemical, biophysical, and cell-based assays1. Since the early days of microfluidics devices, this field has drastically evolved, leading to the development of microfluidic large-scale integration2,3. This technology allows for the integration of thousands of micromechanical valves on a single device with a postage-sized footprint (Figure 1). We have developed a high-throughput microfluidic platform for generating in vitro expression of protein arrays (Figure 2) named PING (Protein Interaction Network Generator). These arrays can serve as a template for many experiments such as protein-protein 4, protein-RNA5 or protein-DNA6 interactions.
The device consist of thousands of reaction chambers, which are individually programmed using a microarrayer. Aligning of these printed microarrays to microfluidics devices programs each chamber with a single spot eliminating potential contamination or cross-reactivity Moreover, generating microarrays using standard microarray spotting techniques is also very modular, allowing for the arraying of proteins7, DNA8, small molecules, and even colloidal suspensions. The potential impact of microfluidics on biological sciences is significant. A number of microfluidics based assays have already provided novel insights into the structure and function of biological systems, and the field of microfluidics will continue to impact biology.

We present a microfluidic approach for the expression of protein arrays. The device consists of thousands of reaction chambers controlled by micro-mechanical valves. The microfluidic device is mated to a microarray-printed gene library. These genes are then transcribed and translated on-chip, resulting in a protein array ready for experimental use.

Rapidly increasing fields, such as systems biology, require the development and implementation of new technologies, enabling high-throughput and ...

High Throughput Single-cell and Multiple-cell Micro-encapsulation

Microfluidic encapsulation methods have been previously utilized to capture cells in picoliter-scale aqueous, monodisperse drops, providing confinement from a bulk fluid environment with applications in high throughput screening, cytometry, and mass spectrometry. We describe a method to not only encapsulate single cells, but to repeatedly capture a set number of cells (here we demonstrate one- and two-cell encapsulation) to study both isolation and the interactions between cells in groups of controlled sizes. By combining drop generation techniques with cell and particle ordering, we demonstrate controlled encapsulation of cell-sized particles for efficient, continuous encapsulation. Using an aqueous particle suspension and immiscible fluorocarbon oil, we generate aqueous drops in oil with a flow focusing nozzle. The aqueous flow rate is sufficiently high to create ordering of particles which reach the nozzle at integer multiple frequencies of the drop generation frequency, encapsulating a controlled number of cells in each drop. For representative results, 9.9 &mu;m polystyrene particles are used as cell surrogates. This study shows a single-particle encapsulation efficiency Pk=1 of 83.7% and a double-particle encapsulation efficiency Pk=2 of 79.5% as compared to their respective Poisson efficiencies of 39.3% and 33.3%, respectively. The effect of consistent cell and particle concentration is demonstrated to be of major importance for efficient encapsulation, and dripping to jetting transitions are also addressed. 
Introduction 
Continuous media aqueous cell suspensions share a common fluid environment which allows cells to interact in parallel and also homogenizes the effects of specific cells in measurements from the media. High-throughput encapsulation of cells into picoliter-scale drops confines the samples to protect drops from cross-contamination, enable a measure of cellular diversity within samples, prevent dilution of reagents and expressed biomarkers, and amplify signals from bioreactor products. Drops also provide the ability to re-merge drops into larger aqueous samples or with other drops for intercellular signaling studies.1,2 The reduction in dilution implies stronger detection signals for higher accuracy measurements as well as the ability to reduce potentially costly sample and reagent volumes.3 Encapsulation of cells in drops has been utilized to improve detection of protein expression,4 antibodies,5,6 enzymes,7 and metabolic activity8 for high throughput screening, and could be used to improve high throughput cytometry.9 Additional studies present applications in bio-electrospraying of cell containing drops for mass spectrometry10 and targeted surface cell coatings.11 Some applications, however, have been limited by the lack of ability to control the number of cells encapsulated in drops. Here we present a method of ordered encapsulation12 which increases the demonstrated encapsulation efficiencies for one and two cells and may be extrapolated for encapsulation of a larger number of cells.
To achieve monodisperse drop generation, microfluidic "flow focusing" enables the creation of controllable-size drops of one fluid (an aqueous cell mixture) within another (a continuous oil phase) by using a nozzle at which the streams converge.13 For a given nozzle geometry, the drop generation frequency f and drop size can be altered by adjusting oil and aqueous flow rates Qoil and Qaq. As the flow rates increase, the flows may transition from drop generation to unstable jetting of aqueous fluid from the nozzle.14 
When the aqueous solution contains suspended particles, particles become encapsulated and isolated from one another at the nozzle. For drop generation using a randomly distributed aqueous cell suspension, the average fraction of drops Dk containing k cells is dictated by Poisson statistics, where Dk = &lambda;k exp(-&lambda;)/(k!) and &lambda; is the average number of cells per drop. The fraction of cells which end up in the "correctly" encapsulated drops is calculated using Pk = (k x Dk)/&Sigma;(k' x Dk'). The subtle difference between the two metrics is that Dk relates to the utilization of aqueous fluid and the amount of drop sorting that must be completed following encapsulation, and Pk relates to the utilization of the cell sample. As an example, one could use a dilute cell suspension (low &lambda;) to encapsulate drops where most drops containing cells would contain just one cell. While the efficiency metric Pk would be high, the majority of drops would be empty (low Dk), thus requiring a sorting mechanism to remove empty drops, also reducing throughput.15
Combining drop generation with inertial ordering provides the ability to encapsulate drops with more predictable numbers of cells per drop and higher throughputs than random encapsulation. Inertial focusing was first discovered by Segre and Silberberg16 and refers to the tendency of finite-sized particles to migrate to lateral equilibrium positions in channel flow. Inertial ordering refers to the tendency of the particles and cells to passively organize into equally spaced, staggered, constant velocity trains. Both focusing and ordering require sufficiently high flow rates (high Reynolds number) and particle sizes (high Particle Reynolds number).17,18 Here, the Reynolds number Re =uDh/&nu; and particle Reynolds number Rep =Re(a/Dh)2, where u is a characteristic flow velocity, Dh [=2wh/(w+h)] is the hydraulic diameter, &nu; is the kinematic viscosity, a is the particle diameter, w is the channel width, and h is the channel height. Empirically, the length required to achieve fully ordered trains decreases as Re and Rep increase. Note that the high Re and Rep requirements (for this study on the order of 5 and 0.5, respectively) may conflict with the need to keep aqueous flow rates low to avoid jetting at the drop generation nozzle. Additionally, high flow rates lead to higher shear stresses on cells, which are not addressed in this protocol. The previous ordered encapsulation study demonstrated that over 90% of singly encapsulated HL60 cells under similar flow conditions to those in this study maintained cell membrane integrity.12 However, the effect of the magnitude and time scales of shear stresses will need to be carefully considered when extrapolating to different cell types and flow parameters. The overlapping of the cell ordering, drop generation, and cell viability aqueous flow rate constraints provides an ideal operational regime for controlled encapsulation of single and multiple cells.
Because very few studies address inter-particle train spacing,19,20 determining the spacing is most easily done empirically and will depend on channel geometry, flow rate, particle size, and particle concentration. Nonetheless, the equal lateral spacing between trains implies that cells arrive at predictable, consistent time intervals. When drop generation occurs at the same rate at which ordered cells arrive at the nozzle, the cells become encapsulated within the drop in a controlled manner. This technique has been utilized to encapsulate single cells with throughputs on the order of 15 kHz,12 a significant improvement over previous studies reporting encapsulation rates on the order of 60-160 Hz.4,15 In the controlled encapsulation work, over 80% of drops contained one and only one cell, a significant efficiency improvement over Poisson (random) statistics, which predicts less than 40% efficiency on average.12
In previous controlled encapsulation work,12 the average number of particles per drop &lambda; was tuned to provide single-cell encapsulation. We hypothesize that through tuning of flow rates, we can efficiently encapsulate any number of cells per drop when &lambda; is equal or close to the number of desired cells per drop. While single-cell encapsulation is valuable in determining individual cell responses from stimuli, multiple-cell encapsulation provides information relating to the interaction of controlled numbers and types of cells. Here we present a protocol, representative results using polystyrene microspheres, and discussion for controlled encapsulation of multiple cells using a passive inertial ordering channel and drop generation nozzle.

Combining monodisperse drop generation with inertial ordering of cells and particles, we describe a method to encapsulate a desired number of cells or particles in a single drop at kHz rates. We demonstrate efficiencies twice exceeding those of unordered encapsulation for single- and double-particle drops.

Microfluidic encapsulation methods have been previously utilized to capture cells in picoliter-scale aqueous, monodisperse drops, providing confinement ...

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

Cryo-electron tomography (cryoET) allows 3D visualization of cellular structures at molecular resolution in a close-to-physiological state1. However, direct visualization of individual viral complexes in their host cellular environment with cryoET is challenging2, due to the infrequent and dynamic nature of viral entry, particularly in the case of HIV-1. While time-lapse live-cell imaging has yielded a great deal of information about many aspects of the life cycle of HIV-13-7, the resolution afforded by live-cell microscopy is limited (~ 200 nm). Our work was aimed at developing a correlation method that permits direct visualization of early events of HIV-1 infection by combining live-cell fluorescent light microscopy, cryo-fluorescent microscopy, and cryoET. In this manner, live-cell and cryo-fluorescent signals can be used to accurately guide the sampling in cryoET. Furthermore, structural information obtained from cryoET can be complemented with the dynamic functional data gained through live-cell imaging of fluorescent labeled target.
In this video article, we provide detailed methods and protocols for structural investigation of HIV-1 and host-cell interactions using 3D correlative high-speed live-cell imaging and high-resolution cryoET structural analysis. HeLa cells infected with HIV-1 particles were characterized first by confocal live-cell microscopy, and the region containing the same viral particle was then analyzed by cryo-electron tomography for 3D structural details. The correlation between two sets of imaging data, optical imaging and electron imaging, was achieved using a home-built cryo-fluorescence light microscopy stage. The approach detailed here will be valuable, not only for study of virus-host cell interactions, but also for broader applications in cell biology, such as cell signaling, membrane receptor trafficking, and many other dynamic cellular processes.

We describe a correlative microscopy method that combines high-speed 3D live-cell fluorescent light microscopy and high-resolution cryo-electron tomography. We demonstrate the capability of the correlative method by imaging dynamic, small HIV-1 particles interacting with host HeLa cells.

Cryo-electron tomography (cryoET) allows 3D visualization of cellular structures at molecular resolution in a close-to-physiological state1. However, ...

A Versatile Automated Platform for Micro-scale Cell Stimulation Experiments

Study of cells in culture (in vitro analysis) has provided important insight into complex biological systems. Conventional methods and equipment for in vitro analysis are well suited to study of large numbers of cells (&ge;105) in milliliter-scale volumes (&ge;0.1 ml). However, there are many instances in which it is necessary or desirable to scale down culture size to reduce consumption of the cells of interest and/or reagents required for their culture, stimulation, or processing. Unfortunately, conventional approaches do not support precise and reproducible manipulation of micro-scale cultures, and the microfluidics-based automated systems currently available are too complex and specialized for routine use by most laboratories. To address this problem, we have developed a simple and versatile technology platform for automated culture, stimulation, and recovery of small populations of cells (100 - 2,000 cells) in micro-scale volumes (1 - 20 &mu;l). The platform consists of a set of fibronectin-coated microcapillaries ("cell perfusion chambers"), within which micro-scale cultures are established, maintained, and stimulated; a digital microfluidics (DMF) device outfitted with "transfer" microcapillaries ("central hub"), which routes cells and reagents to and from the perfusion chambers; a high-precision syringe pump, which powers transport of materials between the perfusion chambers and the central hub; and an electronic interface that provides control over transport of materials, which is coordinated and automated via pre-determined scripts. As an example, we used the platform to facilitate study of transcriptional responses elicited in immune cells upon challenge with bacteria. Use of the platform enabled us to reduce consumption of cells and reagents, minimize experiment-to-experiment variability, and re-direct hands-on labor. Given the advantages that it confers, as well as its accessibility and versatility, our platform should find use in a wide variety of laboratories and applications, and prove especially useful in facilitating analysis of cells and stimuli that are available in only limited quantities.

We have developed an automated cell culture and interrogation platform for micro-scale cell stimulation experiments. The platform offers simple, versatile, and precise control in cultivating and stimulating small populations of cells, and recovering lysates for molecular analyses. The platform is well suited to studies that use precious cells and/or reagents.

Study of cells in culture (in vitro analysis) has provided important insight into complex biological systems. Conventional methods and equipment for in ...

A Novel Stretching Platform for Applications in Cell and Tissue Mechanobiology

Tools that allow the application of mechanical forces to cells and tissues or that can quantify the mechanical properties of biological tissues have contributed dramatically to the understanding of basic mechanobiology. These techniques have been extensively used to demonstrate how the onset and progression of various diseases are heavily influenced by mechanical cues. This article presents a multi-functional biaxial stretching (BAXS) platform that can either mechanically stimulate single cells or quantify the mechanical stiffness of tissues. The BAXS platform consists of four voice coil motors that can be controlled independently. Single cells can be cultured on a flexible substrate that can be attached to the motors allowing one to expose the cells to complex, dynamic, and spatially varying strain fields. Conversely, by incorporating a force load cell, one can also quantify the mechanical properties of primary tissues as they are exposed to deformation cycles. In both cases, a proper set of clamps must be designed and mounted to the BAXS platform motors in order to firmly hold the flexible substrate or the tissue of interest. The BAXS platform can be mounted on an inverted microscope to perform simultaneous transmitted light and/or fluorescence imaging to examine the structural or biochemical response of the sample during stretching experiments. This article provides experimental details of the design and usage of the BAXS platform and presents results for single cell and whole tissue studies. The BAXS platform was used to measure the deformation of nuclei in single mouse myoblast cells in response to substrate strain and to measure the stiffness of isolated mouse aortas. The BAXS platform is a versatile tool that can be combined with various optical microscopies in order to provide novel mechanobiological insights at the sub-cellular, cellular and whole tissue levels.

We present in this article a novel stretching platform that can be used to investigate single cell responses to complex anisotropic biaxial mechanical deformation and quantify the mechanical properties of biological tissues.

Tools that allow the application of mechanical forces to cells and tissues or that can quantify the mechanical properties of biological tissues have ...

A Novel Method for Localizing Reporter Fluorescent Beads Near the Cell Culture Surface for Traction Force Microscopy

PA gels have long been used as a platform to study cell traction forces due to ease of fabrication and the ability to tune their elastic properties. When the substrate is coated with an extracellular matrix protein, cells adhere to the gel and apply forces, causing the gel to deform. The deformation depends on the cell traction and the elastic properties of the gel. If the deformation field of the surface is known, surface traction can be calculated using elasticity theory. Gel deformation is commonly measured by embedding fluorescent marker beads uniformly into the gel. The probes displace as the gel deforms. The probes near the surface of the gel are tracked. The displacements reported by these probes are considered as surface displacements. Their depths from the surface are ignored. This assumption introduces error in traction force evaluations. For precise measurement of cell forces, it is critical for the location of the beads to be known. We have developed a technique that utilizes simple chemistry to confine fluorescent marker beads, 0.1 and 1 &#181;m in diameter, in PA gels, within 1.6 &#956;m of the surface. We coat a coverslip with poly-D-lysine (PDL) and fluorescent beads. PA gel solution is then sandwiched between the coverslip and an adherent surface. The fluorescent beads transfer to the gel solution during curing. After polymerization, the PA gel contains fluorescent beads on a plane close to the gel surface.

Traditional techniques for fabricating polyacrylamide (PA) gels containing fluorescent probes involve sandwiching a gel between an adherent surface and a glass slide. Here, we show that coating this slide with poly-D-lysine (PDL) and fluorescent probes localizes the probes to within 1.6 &#181;m from the gel surface.

PA gels have long been used as a platform to study cell traction forces due to ease of fabrication and the ability to tune their elastic properties. When ...

A Microfluidic Platform for High-throughput Single-cell Isolation and Culture

Studying the heterogeneity of single cells is crucial for many biological questions, but is technically difficult. Thus, there is a need for a simple, yet high-throughput, method to perform single-cell culture experiments. Here, we report a microfluidic chip-based strategy for high-efficiency single-cell isolation (~77%) and demonstrate its capability of performing long-term single-cell culture (up to 7 d) and cellular heterogeneity analysis using clonogenic assay. These applications were demonstrated with KT98 mouse neural stem cells, and A549 and MDA-MB-435 human cancer cells. High single-cell isolation efficiency and long-term culture capability are achieved by using different sizes of microwells on the top and bottom of the microfluidic channel. The small microwell array is designed for precisely isolating single-cells, and the large microwell array is used for single-cell clonal culture in the microfluidic chip. This microfluidic platform constitutes an attractive approach for single-cell culture applications, due to its flexibility of adjustable cell culture spaces for different culture strategies, without decreasing isolation efficiency.

Here, we present a protocol for isolating and culturing single cells with a microfluidic platform, which utilizes a new microwell design concept to allow for high-efficiency single cell isolation and long-term clonal culture.

Studying the heterogeneity of single cells is crucial for many biological questions, but is technically difficult. Thus, there is a need for a simple, yet ...