This work was supported by the Polish National Science Center under grant no. 2014/14/M/NZ1/00437.

1. Preparatory steps
<ol>
	<li>Cell seeding to obtain confluent monolayers
	<ol>
		<li>Before seeding, coat the wells of a 4-wells chamber slide with collagen I (or other ECM component of choice). For collagen I coating, follow a commercial protocol: https://www.sigmaaldrich.com/technical-documents/articles/biofiles/collagen-product-protocols.html at a concentration of 8 &#956;g/cm2.</li>
		<li>Seed 400,000 cells to one well of a 4-well chamber slide.</li>
		<li>Culture the cells for 24 h (or longer, depending on the experimental endpoints) before staining, in an incubator at 37 &#176;C, 5% CO2. This step allows for a maturation of cell-cell contacts and the formation of monolayers.</li>
		<li>Use an optical, inverted microscope to verify confluency (about 90% is required) and a general condition of monolayers. Do not proceed if cells are floating or look stressed.</li>
	</ol>
	</li>
	<li>Immunofluorescent staining 
	NOTE: Cells can be stained with a protocol of choice. In here, immunofluorescence was performed as previously described4.
	<ol>
		<li>For focal adhesion analysis, stain the cells with a focal adhesion protein of choice (in this protocol paxillin). For cell shape analysis stain with cell-cell junction protein of choice (in this protocol desmosomal protein plakoglobin).</li>
		<li>For the procedure, use 0.5 mL of specified solutions unless specified otherwise.</li>
		<li>Fix cells in 4% formaldehyde in PBS for 30 min on ice.</li>
		<li>Incubate with 0.1 M ammonium chloride (in PBS) for 10 min to quench auto-fluorescence.</li>
		<li>Add 0.5% Triton X-100 (in PBS) for 30 min (permeabilization).</li>
		<li>Block with 5% milk (or 1% BSA) in TBS-T for 1 h.</li>
		<li>Incubate with primary antibody at 4 &#176;C overnight (anti-paxillin: rabbit, 1:250, anti-plakoglobin: rabbit, 1:400)</li>
		<li>Incubate with secondary antibody for 30 min (Alexa Fluor 594 goat anti-rabbit, 1:500, 1:500)</li>
		<li>Stain with 300 nM DAPI for 1-5 min, protected from light. 
		NOTE: Optionally, additional staining of actin with fluorescent phalloidin conjugates (conc. 1:400) may be performed; phalloidin should be added at the same step as the secondary antibody.</li>
	</ol>
	</li>
	<li>Confocal imaging
	<ol>
		<li>Take images of single Z-slices using a confocal microscope (e.g., Zeiss LSM800). 
		NOTE: Optional actin staining may help assess the proper focal plane. Cortical actin staining is present in the apical region while actin stress fibers are present in the basal region, near the substrate, as illustrated on Figure 1.</li>
		<li>Focal adhesion imaging
		<ol>
			<li>Choose Z-slices for focal adhesion analysis from the basal region, close to the substrate.</li>
			<li>Use an objective with the highest numerical aperture available (preferable 63x N.A.: 1.4). 
			NOTE: The shape of FA is very specific and easily recognizable. Thus, it is recommended to start the scan from a focal plane below cells and then slowly scan towards them, until FAs are clearly visible. Image analysis will be more accurate from smaller fields of vision, which tend to be more uniform, but this implies that fewer cells will be calculated in a single field of view.</li>
		</ol>
		</li>
		<li>Cell-cell contacts imaging
		<ol>
			<li>Use a 40x or 63x objective.</li>
			<li>Select channels for nuclear staining and the preferred junction protein staining.</li>
			<li>Choose Z-slices for cell shape analysis from the apical region of the monolayers.</li>
			<li>Take pictures for at least 3 different fields of vision (200-400 cells).</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
2. Image analysis
NOTE: Provided macros work optimally on ImageJ version 1.50f or newer. Use for quantification only of images with a high signal-to-noise ratio and without under- or oversaturated pixels. The described methods include steps requiring manual parameter adjustment. Thus, a blind analysis/blinded experiment setup is recommended. For encrypting image file names, ImageJ plugins such as &#8220;Blind Analysis Tool&#8221; (available at: https://imagej.net/Blind_Analysis_Tools) can be used.
<ol>
	<li>Focal adhesion analysis 
	NOTE: The recommended input files for the following methods are: images of FAs represented in 8-bit grayscale saved in .tiff format.
	<ol>
		<li>Open image using ImageJ.</li>
		<li>Set the scale of an image to pixels (Analyze | Set Scale; Remove Scale and check Global option).</li>
		<li>Include the file name and the area of ROI in measurement options (Analyze | Set Measurements&#8230;); check&#160;Area&#160;and&#160;Display label&#160;options.</li>
		<li>Subtract background (Process | Subtract Background; set Rolling ball radius parameter to 50 pixels; check Sliding paraboloid option). In the case of pseudocolored RGB images: split RGB channels, leave the channel with FAs opened, close remaining channels (Image | Color | Split Channels).</li>
		<li>Determine the area of the smallest region of interest (ROI). Using freehand or polygon selections outline the smallest single focal adhesion and measure its area (Analyze | Measure). Repeat this step for different ROIs (FAs) from a few randomly chosen images (for a total of 20 ROIs). Calculate and save the mean of obtained results. 
		NOTE: This step is required only when a given set of images is analyzed for a first time (specific cell line, coating slides with specific extracellular matrix components, different culture conditions).</li>
		<li>Convert image to binary by using one of the following methods:
		<ol>
			<li>Set the global threshold (Image | Adjust | Threshold; check Default, B&#38;W and Dark Background options, adjust threshold manually or set it automatically).</li>
			<li>Set the local threshold (Image | Adjust | Auto Local Threshold&#8230;; set Method to Phansalkar and check White objects on black background option. Next, invert the image (Edit | Invert).</li>
		</ol>
		</li>
		<li>Measure the number and the area of ROIs. Select Analyze | Analyze Particles; check Pixel units, Display results, Clear results and Summarize options, set Size parameter, as a lower boundary use the mean of the smallest ROIs from step 2.1.5. The upper boundary can be set to 25% of a typical cell area.</li>
		<li>Transfer data (that includes image name, number of FAs, total and mean area of FAs; respectively Slice, Count, Total Area, Average Size) from the Summary window to the data managing program of choice.</li>
		<li>Determine the number of cells per image by counting DAPI-stained nuclei. Counting can be done manually (Plugins | Analyze | Cell Counter) or as in available protocols such as: https://imagej.net/Nuclei_Watershed_Separation.</li>
		<li>Alternatively, to facilitate FAs counting, use the attached ImageJ macro (FAs.ijm).
		<ol>
			<li>Move .ijm file with the macro to plugins or macros folder located in ImageJ source files folders.</li>
			<li>Determine an area of the smallest ROI as described in step 2.1.4.</li>
			<li>Open macro file (Plugins | Macros | Edit...).</li>
			<li>Before running the macro set the value of three variables: fill value of area_of_the_smallest_region_of_interest with a number acquired during step 2.1.4. Set threshold_type value to manual or auto.</li>
			<li>Save changes (the macro should be ready to use).</li>
			<li>Call the macro from ImageJ panel or make a shortcut to it. The macro starts with the standard open dialog window. Select the image to be processed. 
			NOTE: In case of manual threshold adjustment, manual confirmation of threshold value will be required (avoid accepting changes using Apply button on Threshold dialog window, use Action Required custom dialog window instead). Results obtained by working with the macro are the same as those described in step 2.1.8 (included in Summary dialog window). Additionally, in the case of manual threshold adjustment Lower Threshold Level is displayed in a Log dialog window, as this value allow to reproduce obtained results in the future if needed. Supplemental Figures S1 and S2 were included as a training dataset for the FAs.ijm macro.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Cell shape analysis
	<ol>
		<li>Manual
		<ol>
			<li>Open an image in ImageJ or another image processing software with a similar set of functions (further instructions pertain to ImageJ). Choose the parameters to be measured by selecting from the menu Analysis | Set Measurements and ticking Shape descriptors in the appearing box.</li>
			<li>Manually delineate cell borders, marked by junction protein(s) of choice, using Freehand selections icon. The chosen parameters are automatically calculated for each cell. Store the results after outlining each cell by clicking Edit | Selection | Add to manager. Only complete, entirely visible cells, with uninterrupted borders should be counted.</li>
			<li>When all cells in the field of view are outlined, make the measurement by marking all of the numbers appearing in the left box of the ROI Manager (corresponding to cells) and clicking Measure The results appear in the Results box and can be transferred to the spreadsheet of choice.</li>
		</ol>
		</li>
		<li>Automated 
		NOTE: To facilitate the quantification of cell shape descriptors (CSI, aspect ratio, roundness, solidity) an ImageJ macro has been prepared and attached to this article (CSI.ijm). The macro is mainly based on ImageJ plugin called MorphoLibJ (https://imagej.net/MorphoLibJ)5. The macro executes the following steps: 1) Extension of each border of image by 10 black pixels [MorpholibJ]; 2) Rounds of dilations and erosions - morphological filter [MorpholibJ]; 3) Generation of binary image of cells boundaries by morphological segmentation [MorpholibJ]; 4) Dilation of cell boundaries; 5) Inversion of pixels value; 6) Generation of selections and measurement of area and perimeter of cells on the image; and 7) Saving image with outlined cells and ImageJ ROI selections to a new file.
		<ol>
			<li>Move the .ijm file with the macro to the plugins or macros folder located in the ImageJ source files folders. Call the macro from the ImageJ panel or make a shortcut to it.</li>
			<li>Before the quantification of a new dataset, determine values of the smallest and the largest regions of interest. Outline (freehand or polygon selection) a few (3-10) examples of the smallest and the largest cells on the image and then measure their area (Analyze | Measure).</li>
			<li>Alternatively, run the macro with default settings (lower size limit is set to 0 and upper limit is set to infinity), wait for the macro to finish and select Set cell size boundaries option. Measure the area of the smallest and the largest cells by clicking on their label and then press Measure in the ImageJ ROI Manager. Set the value of the_smallest_cell and the_biggest_cell variables. Save changes, close all macro dialog windows and run the macro again. 
			NOTE: The macro can be used without setting ROI size boundaries but it is not recommended because it significantly increases a chance of measuring inappropriate cell fragments or cell clusters.</li>
			<li>Start the macro with the standard Open dialog window. Select the image to be processed (grayscale).</li>
			<li>Analyze the results. The output provided by the macro consists of: table of the results (cell label, image label, cell area [pixels2], cell perimeter [pixels2], circularity [CSI], aspect ratio, roundness, solidity), image with outlined cells and ROI selections list (which will be also saved in new file in the Results subfolder). The results table will be automatically copied to the user&#39;s clipboard. 
			NOTE: Supplemental Figures S3 and S4 were included as a training dataset for the CSI.ijm macro.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
3. Quantification
<ol>
	<li>Quantification of FAs
	<ol>
		<li>Calculate the mean FAs number and average FA size per cell/nuclei. 
		NOTE: For some cell lines it is possible to count FAs separately in distinct cells. For cell lines that have strong cell-cell contacts and grow as monolayers such as MCF7, number and size of FAs per cell can be calculated by dividing the values obtained from FAs counting by the number of nuclei in the whole image.</li>
		<li>Assess statistical significance of potential differences between populations (experimental groups). Depending on the distribution and variance of the data, for the comparison of the two different groups use Student t-test (normal distribution) or non-parametric U-test (Mann-Whitney). For comparison of multiple groups use one-way ANOVA or Kruskal&#8211;Wallis in conjunction with the appropriate post-hoc tests.</li>
	</ol>
	</li>
	<li>Cell shape analysis
	<ol>
		<li>Calculation of shape descriptors
		<ol>
			<li>Manual analysis: Calculate cell shape index (CSI, also called circularity or cell shape) in the spreadsheet of choice for each measured cell from the appropriate area and perimeter using the formula: 
			<img alt="Equation 1" src="/files/ftp_upload/61461/61461equ01.jpg" /> 
			NOTE: CSI assumes values between 1 (circular) and 0 (elongated). The examples of various cell shapes (with the same area) and their respective CSIs are presented in Figure 2. In automated analysis the values of shape descriptors (enlisted and defined below) are calculated automatically and appear in the result box: 
			(1) CSI = 4&#960;*area/(perimeter)2 
			(2) AR = major axis/minor axis 
			(3) Roundness = 4*area/&#960;*(major axis)2 
			(4) Solidity = area/convex area</li>
		</ol>
		</li>
		<li>Histograms of cell shape distribution
		<ol>
			<li>Plot cell shape distribution as a histogram of circularity (CSI). Classify cells according to their CSI value (calculated for the minimum of 200&#8211;400 cells), to one of the ten uniform intervals (range: 0-1, bin width: 0.1). The histogram displays the number of cells in every category. 
			NOTE: The histogram showing shape distribution of the typical MCF7 cell layer shows a peak of around 0.7-0.8 CSI. If the cells&#8217; shape is distorted by some factor (for example paclitaxel treatment, which causes G2/M phase arrest and in the consequence more cells are round) it should be reflected on the histogram.</li>
		</ol>
		</li>
		<li>Cumulative distribution plots
		<ol>
			<li>Compare cumulative CSI distributions for each cell line, because it is the best way to assess statistically important differences in cell shape changes (or any other changes in distribution. For example, it can be applied to track the changing distribution of FAs.</li>
			<li>Calculate the Cumulative Distribution Function (CDF) to compare distributions. CDF assigns for a given CSI value (plotted on X axis) the percentage (or relative count) for which all values are less or equal to this CSI value (plotted on Y axis). Thus, as the CSI value gets higher, the percentage of the set of values that are less or equal to this value also gets higher. CDF can be calculated by the statistical software of choice, or manually.</li>
			<li>For statistical analysis, use Kolgomorov-Smirnov nonparametric test.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

Cell-cell and cell-substrate adhesion constitute inherent attributes of the epithelial cells and play the critical role in tissue morphogenesis and embriogenesis. In adult tissues the proper regulation of mechanical properties of the cell layer is crucial in maintaining homeostasis and preventing pathological responses like tumor progression and metastasis. The size and number of focal adhesions depend on the strength of cell-substrate adhesion, while cell shape depends on contractile forces and is related to the status ...

Epithelial cell monolayers act as a collective in which cell-cell and cell-substrate adhesion as well as contractile forces and tensions represent important parameters and their proper balance contributes to the overall integrity of the unit1,2,3. Thus, assessing these parameters represents a way to establish the current status of the cell layer.
The two methods described here represent a two-dimensional analysis of the confluent monolayers of adherent, epithelial cells (in this case MCF7 breast cancer cell line). The analysis is performed using confocal images (single Z-slices) from different regions on the Z-axis; basal region near a substrate for focal adhesion (FA) measurements and apical region for cell shape measurements. The presented methods are relatively simple and require standard laboratory techniques and open-source software. Confocal microscopy is sufficient for this protocol, so it can be performed without employing more specialized TIRF (Total Internal Reflection Fluorescence) microscopy. Thus, the protocol could be implemented in a relatively standard laboratory setting. Although the accuracy of the methods is limited, they can distinguish basic differences in focal adhesion and cell shape.
Both methods described here consist of the standard experimental procedures such as cell culturing, immunostaining, confocal imaging and image analysis performed using ImageJ. However, any image processing software with the appropriate functions can be used. The presented methods can track and compare changes inflicted by pharmacological treatment or minimal genetic modification. Obtaining definite values is not recommended, due to the limited precision of these methods. Two automated macros were included, to facilitate the measurements of many images.

<table><tbody><tr><td>Alexa Fluor 594</td><td>ThermoFisher Scientific</td><td>A32740</td><td>goat anti-rabbit, 1:500</td></tr><tr><td>Ammonium chloride</td><td>Sigma</td><td>A9434</td><td></td></tr><tr><td>BSA</td><td>BioShop</td><td>ALB001.500</td><td></td></tr><tr><td>Collagen from calf skin</td><td>Sigma</td><td>C9791-10MG</td><td></td></tr><tr><td>DAPI</td><td>Sigma</td><td>D9542</td><td>1:10000 (stock 1 mg/mL in H&lt;sub&gt;2&lt;/sub&gt;O), nucleic acid staining</td></tr><tr><td>DMEM + GlutaMAX, 1 g/L D-Glucose, Pyruvate</td><td>ThermoFisher Scientific</td><td>21885-025</td><td></td></tr><tr><td>FBS</td><td>ThermoFisher Scientific</td><td>10270-136</td><td></td></tr><tr><td>Junction plakoglobin</td><td>Cell Signaling</td><td>2309S</td><td>rabbit, 1:400</td></tr><tr><td>Laminar-flow cabinet class 2</td><td>Alpina</td><td></td><td>standard equipment</td></tr><tr><td>&lt;em&gt;MCF7-based&lt;/em&gt;HAX1KD cell line</td><td>Cell line established in the National Institute of Oncology, Warsaw, described in Balcerak et al., 2019</td><td></td><td>&lt;em&gt;MCF7 cell line with&lt;/em&gt;HAX1knockdown</td></tr><tr><td>MCF7 cell line (CONTROL)</td><td>ATCC</td><td>ATCC HTB-22</td><td>epithelial, adherent breast cancer cell line</td></tr><tr><td>Olympus CK2 light microscope</td><td>Olympus</td><td></td><td></td></tr><tr><td>Paxillin</td><td>Abcam</td><td>ab32084</td><td>rabbit, 1:250, Y113</td></tr><tr><td>PBS</td><td>ThermoFisher Scientific</td><td>10010023</td><td></td></tr><tr><td>Phalloidin-TRITC conjugate</td><td>Sigma</td><td>P1951</td><td>1:400 (stock 5 mg/mL in DMSO), actin labeling</td></tr><tr><td>PTX</td><td>Sigma</td><td>T7402-1MG</td><td></td></tr><tr><td>TBST &amp;ndash; NaCl</td><td>Sigma</td><td>&lt;strong&gt;S9888&lt;/strong&gt;</td><td></td></tr><tr><td>TBST &amp;ndash; Trizma base</td><td>Sigma</td><td>T1503</td><td></td></tr><tr><td>Triton X-100</td><td>Sigma</td><td>9002-93-11</td><td></td></tr><tr><td>Zeiss LSM800 Confocal microscope</td><td>Zeiss</td><td></td><td></td></tr></tbody></table>

quantification of cell-substrate adhesion area and cell shape distributions in mcf7 cell monolayers

The methods presented here quantify some parameters of confluent adherent cell monolayers from multiple appropriately stained confocal images: adhesion to the substrate as a function of the number and size of focal adhesions, and cell shape, characterized by the cell shape index and other shape descriptors. Focal adhesions were visualized by paxillin staining and cell-cell borders were marked by junction plakoglobin and actin. The methods for cell culture and staining were standard; images represent single focal planes; image analysis was performed using publicly available image processing software. The presented protocols are used to quantify the number and size of focal adhesions and the differences in cell shape distribution in the monolayers, but they can be repurposed for the quantification of the size and shape of any other distinct cellular structure that can be stained (e.g., mitochondria or nuclei). Assessing these parameters is important in the characterization of the dynamic forces in adherent cell layer, including cell adhesion and actomyosin contractility that affects cell shape.

Focal adhesion analysis 
The knockdown of HAX1 gene was previously shown to affect focal adhesions6. Cells were cultured on collagen I-coated surface for 48 h. Images of the MCF7 control cells and MCF7 cells with a HAX1 knockdown (HAX1 KD) from three independent experiments stained with focal adhesion protein paxillin were obtained using a confocal microscope (image from single focal plane/Z-slice from basal region). FAs from about 2,000-2,500 cells...

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Quantification of Cell-Substrate Adhesion Area and Cell Shape Distributions in MCF7 Cell Monolayers

The article describes quantification of 1) the size and number of focal adhesions and 2) cell shape index and its distribution from confocal images of the confluent monolayers of MCF7 cells.

The methods presented here quantify some parameters of confluent adherent cell monolayers from multiple appropriately stained confocal images: adhesion to ...

quantification-cell-substrate-adhesion-area-cell-shape-distributions

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8.3K Views.  Maria Sklodowska-Curie National Research Institute of Oncology. The article describes quantification of 1) the size and number of focal adhesions and 2) cell shape index and its distribution from confocal images of the confluent monolayers of MCF7 cells.

Video: Quantification of Cell-Substrate Adhesion Area and Cell Shape Distributions in MCF7 Cell Monolayers

Characterization of Cell Membrane Extensions and Studying Their Roles in Cancer Cell Adhesion Dynamics

The cell membrane&#39;s extension repertoire modulates various malignant behaviors of cancer cells, including their adhesive and migratory potentials. The ability to accurately classify and quantify cell extensions and measure the effect on a cell&#39;s adhesive capacity is critical to determining how cell-signaling events impact cancer cell behavior and aggressiveness. Here, we describe the in vitro design and use of a cell extension quantification method in conjunction with an adhesion capacity assay in an established in vitro model for adrenocortical carcinoma (ACC). Specifically, we test the effects of DKK3, a putative tumor suppressor and a pro-differentiation factor, on the membrane extension phenotype of the ACC cell line, SW-13. We propose these assays to provide relatively simple, reliable, and easily interpretable metrics to measures these characteristics under various experimental conditions.

This study demonstrates the utility and ease of quantitative cell membrane extension measurement and its correlation to adhesive capacity of cells. As a representative example, we show here that Dickkopf-related protein 3 (DKK3) promotes increased lobopodia formation and cell adhesiveness in adrenocortical carcinoma cells in vitro.

The cell membrane&#39;s extension repertoire modulates various malignant behaviors of cancer cells, including their adhesive and migratory potentials. The ...

Cancer Research

Examining the Dynamics of Cellular Adhesion and Spreading of Epithelial Cells on Fibronectin During Oxidative Stress

The adhesion and spreading of cells onto the extracellular matrix (ECM) are essential cellular processes during organismal development and for the homeostasis of adult tissues. Interestingly, oxidative stress can alter these processes, thus contributing to the pathophysiology of diseases such as metastatic cancer. Therefore, understanding the mechanism(s) of how cells attach and spread on the ECM during perturbations in redox status can provide insight into normal and disease states. Described below is a step-wise protocol that utilizes an immunofluorescence-based assay to specifically quantify cell adhesion and spreading of immortalized fibroblast cells on fibronectin (FN) in vitro. Briefly, anchorage-dependent cells are held in suspension and exposed to the ATM kinase inhibitor Ku55933 to induce oxidative stress. Cells are then plated on FN-coated surface and allowed to attach for predetermined periods of time. Cells that remain attached are fixed and labeled with fluorescence-based antibody markers of adhesion (e.g., paxillin) and spreading (e.g., F-actin). Data acquisition and analysis are performed using commonly available laboratory equipment, including an epifluorescence microscope and freely available Fiji software. This procedure is highly versatile and can be modified for a variety of cell lines, ECM proteins, or inhibitors in order to examine a broad range of biological questions.

This method is useful for quantifying the early dynamics of cellular adhesion and spreading of anchorage-dependent cells onto the fibronectin. Furthermore, this assay can be used to investigate the effects of altered redox homeostasis on cell spreading and/or cell adhesion-related intracellular signaling pathways.

The adhesion and spreading of cells onto the extracellular matrix (ECM) are essential cellular processes during organismal development and for the ...

Biochemistry

Using Cell-substrate Impedance and Live Cell Imaging to Measure Real-time Changes in Cellular Adhesion and De-adhesion Induced by Matrix Modification

Cell-matrix adhesion plays a key role in controlling cell morphology and signaling. Stimuli that disrupt cell-matrix adhesion (e.g., myeloperoxidase and other matrix-modifying oxidants/enzymes released during inflammation) are implicated in triggering pathological changes in cellular function, phenotype and viability in a number of diseases. Here, we describe how cell-substrate impedance and live cell imaging approaches can be readily employed to accurately quantify real-time changes in cell adhesion and de-adhesion induced by matrix modification (using endothelial cells and myeloperoxidase as a pathophysiological matrix-modifying stimulus) with high temporal resolution and in a non-invasive manner. The xCELLigence cell-substrate impedance system continuously quantifies the area of cell-matrix adhesion by measuring the electrical impedance at the cell-substrate interface in cells grown on gold microelectrode arrays. Image analysis of time-lapse differential interference contrast movies quantifies changes in the projected area of individual cells over time, representing changes in the area of cell-matrix contact. Both techniques accurately quantify rapid changes to cellular adhesion and de-adhesion processes. Cell-substrate impedance on microelectrode biosensor arrays provides a platform for robust, high-throughput measurements. Live cell imaging analyses provide additional detail regarding the nature and dynamics of the morphological changes quantified by cell-substrate impedance measurements. These complementary approaches provide valuable new insights into how myeloperoxidase-catalyzed oxidative modification of subcellular extracellular matrix components triggers rapid changes in cell adhesion, morphology and signaling in endothelial cells. These approaches are also applicable for studying cellular adhesion dynamics in response to other matrix-modifying stimuli and in related adherent cells (e.g., epithelial cells).

Here, we present a protocol to continuously quantify cell adhesion and de-adhesion processes with high temporal resolution in a non-invasive manner by cell-substrate impedance and live cell imaging analyses. These approaches reveal the dynamics of cell adhesion/de-adhesion processes triggered by matrix modification and their temporal relationship to adhesion-dependent signaling events.

Cell-matrix adhesion plays a key role in controlling cell morphology and signaling. Stimuli that disrupt cell-matrix adhesion (e.g., myeloperoxidase and ...

Bioengineering

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 ...

Assay of Adhesion Under Shear Stress for the Study of T Lymphocyte-Adhesion Molecule Interactions

Overall, T cell adhesion is a critical component of function, contributing to the distinct processes of cellular recruitment to sites of inflammation and interaction with antigen presenting cells (APC) in the formation of immunological synapses. These two contexts of T cell adhesion differ in that T cell-APC interactions can be considered static, while T cell-blood vessel interactions are challenged by the shear stress generated by circulation itself. T cell-APC interactions are classified as static in that the two cellular partners are static relative to each other. Usually, this interaction occurs within the lymph nodes. As a T cell interacts with the blood vessel wall, the cells arrest and must resist the generated shear stress.1,2 These differences highlight the need to better understand static adhesion and adhesion under flow conditions as two distinct regulatory processes. The regulation of T cell adhesion can be most succinctly described as controlling the affinity state of integrin molecules expressed on the cell surface, and thereby regulating the interaction of integrins with the adhesion molecule ligands expressed on the surface of the interacting cell. Our current understanding of the regulation of integrin affinity states comes from often simplistic in vitro model systems. The assay of adhesion using flow conditions described here allows for the visualization and accurate quantification of T cell-epithelial cell interactions in real time following a stimulus. An adhesion under flow assay can be applied to studies of adhesion signaling within T cells following treatment with inhibitory or stimulatory substances. Additionally, this assay can be expanded beyond T cell signaling to any adhesive leukocyte population and any integrin-adhesion molecule pair.

This flow adhesion assay provides a simple, high impact model of T cell-epithelial cell interactions. A syringe pump is used to generate shear stress, and confocal microscopy captures images for quantification. The goal of these studies is to effectively quantify T cell adhesion using flow conditions.

Overall, T cell adhesion is a critical component of function, contributing to the distinct processes of cellular recruitment to sites of inflammation and ...

Immunology and Infection

Dendrimer-based Uneven Nanopatterns to Locally Control Surface Adhesiveness: A Method to Direct Chondrogenic Differentiation

Cellular adhesion and differentiation is conditioned by the nanoscale disposition of the extracellular matrix (ECM) components, with local concentrations having a major effect. Here we present a method to obtain large-scale uneven nanopatterns of arginine-glycine-aspartic acid (RGD)-functionalized dendrimers that permit the nanoscale control of local RGD surface density. Nanopatterns are formed by surface adsorption of dendrimers from solutions at different initial concentrations and are characterized by water contact angle (CA), X-ray photoelectron spectroscopy (XPS), and scanning probe microscopy techniques such as scanning tunneling microscopy (STM) and atomic force microscopy (AFM). The local surface density of RGD is measured using AFM images by means of probability contour maps of minimum interparticle distances and then correlated with cell adhesion response and differentiation. The nanopatterning method presented here is a simple procedure that can be scaled up in a straightforward manner to large surface areas. It is thus fully compatible with cell culture protocols and can be applied to other ligands that exert concentration-dependent effects on cells.

A method to obtain dendrimer-based uneven nanopatterns that permit the nanoscale control of local arginine-glycine-aspartic acid (RGD) surface density is described and applied for the study of cell adhesion and chondrogenic differentiation.

Cellular adhesion and differentiation is conditioned by the nanoscale disposition of the extracellular matrix (ECM) components, with local concentrations ...

Quantitative Measurement of Invadopodia-mediated Extracellular Matrix Proteolysis in Single and Multicellular Contexts 

Cellular invasion into local tissues is a process important in development and homeostasis. Malregulated invasion and subsequent cell movement is characteristic of multiple pathological processes, including inflammation, cardiovascular disease and tumor cell metastasis1. Focalized proteolytic degradation of extracellular matrix (ECM) components in the epithelial or endothelial basement membrane is a critical step in initiating cellular invasion. In tumor cells, extensive in vitro analysis has determined that ECM degradation is accomplished by ventral actin-rich membrane protrusive structures termed invadopodia2,3. Invadopodia form in close apposition to the ECM, where they moderate ECM breakdown through the action of matrix metalloproteinases (MMPs). The ability of tumor cells to form invadopodia directly correlates with the ability to invade into local stroma and associated vascular components3. 
Visualization of invadopodia-mediated ECM degradation of cells by fluorescent microscopy using dye-labeled matrix proteins coated onto glass coverslips has emerged as the most prevalent technique for evaluating the degree of matrix proteolysis and cellular invasive potential4,5. Here we describe a version of the standard method for generating fluorescently-labeled glass coverslips utilizing a commercially available Oregon Green-488 gelatin conjugate. This method is easily scaled to rapidly produce large numbers of coated coverslips. We show some of the common microscopic artifacts that are often encountered during this procedure and how these can be avoided. Finally, we describe standardized methods using readily available computer software to allow quantification of labeled gelatin matrix degradation mediated by individual cells and by entire cellular populations. The described procedures provide the ability to accurately and reproducibly monitor invadopodia activity, and can also serve as a platform for evaluating the efficacy of modulating protein expression or testing of anti-invasive compounds on extracellular matrix degradation in single and multicellular settings.

Quantitative Measurement of Invadopodia-mediated Extracellular Matrix Proteolysis in Single and Multicellular Contexts

We describe the prototypical method for producing microscope coverslips coated with fluorescent gelatin for visualizing invadopodia-mediated matrix degradation. Computational techniques using available software are presented for quantifying the resultant levels of matrix proteolysis by single cells within a mixed population and for multicellular groups encompassing entire microscopic fields.

Cellular invasion into local tissues is a process important in development and homeostasis.  Malregulated invasion and subsequent cell movement is ...

Quantitation of Endothelial Cell Adhesiveness In Vitro

One of the cardinal processes of inflammation is the infiltration of immune cells from the lumen of the blood vessel to the surrounding tissue. This occurs when endothelial cells, which line blood vessels, become adhesive to circulating immune cells such as monocytes. In vitro measurement of this adhesiveness has until now been done by quantifying the total number of monocytes that adhere to an endothelial layer either as a direct count or by indirect measurement of the fluorescence of adherent monocytes. While such measurements do indicate the average adhesiveness of the endothelial cell population, they are confounded by a number of factors, such as cell number, and do not reveal the proportion of endothelial cells that are actually adhesive. Here we describe and demonstrate a method which allows the enumeration of adhesive cells within a tested population of endothelial monolayer. Endothelial cells are grown on glass coverslips and following desired treatment are challenged with monocytes (that may be fluorescently labeled). After incubation, a rinsing procedure, involving multiple rounds of immersion and draining, the cells are fixed. Adhesive endothelial cells, which are surrounded by monocytes are readily identified and enumerated, giving an adhesion index that reveals the actual proportion of endothelial cells within the population that are adhesive.

Quantitation of Endothelial Cell Adhesiveness In Vitro

We report an in vitro method that allows the quantitation of the actual number of adhesive cells within an endothelial cell monolayer.

One of the cardinal processes of inflammation is the infiltration of immune cells from the lumen of the blood vessel to the surrounding tissue. This ...

Quantitative Analysis of Cell Edge Dynamics during Cell Spreading

Cell spreading is a dynamic process in which a cell suspended in media attaches to a substrate and flattens itself from a rounded to a thin and spread-out shape. Following the cell-substrate attachment, the cell forms a thin sheet of lamellipodia emanating from the cell body. In the lamellipodia, globular actin (G-actin) monomers polymerize into a dense filamentous actin (F-actin) meshwork that pushes against the plasma membrane, thereby providing the mechanical forces required for the cell to spread. Notably, the molecular players that control the actin polymerization in lamellipodia are essential for many other cellular processes, such as cell migration and endocytosis. 
Since spreading cells form continuous lamellipodia that span the entire cell periphery and persistently expand outward, cell spreading assays have become an efficient tool to assess the kinetics of lamellipodial protrusions. Although several technical implementations of the cell spreading assay have been developed, a detailed description of the workflow, which would include both a step-by-step protocol and computational tools for data analysis, is currently lacking. Here, we describe the experimental procedures of the cell spreading assay and present an open-source tool for quantitative and unbiased analysis of cell edge dynamics during spreading. When combined with pharmacological manipulations and/or gene-silencing techniques, this protocol is amenable to a large-scale screen of molecular players regulating lamellipodial protrusions.

In this protocol, we present the experimental procedures of a cell spreading assay that is based on live-cell microscopy. We provide an open-source computational tool for the unbiased segmentation of fluorescently labeled cells and quantitative analysis of lamellipodia dynamics during cell spreading.

Cell spreading is a dynamic process in which a cell suspended in media attaches to a substrate and flattens itself from a rounded to a thin and spread-out ...

Imaging Molecular Adhesion in Cell Rolling by Adhesion Footprint Assay

Rolling adhesion, facilitated by selectin-mediated interactions, is a highly dynamic, passive motility in recruiting leukocytes to the site of inflammation. This phenomenon occurs in postcapillary venules, where blood flow pushes leukocytes in a rolling motion on the endothelial cells. Stable rolling requires a delicate balance between adhesion bond formation and their mechanically-driven dissociation, allowing the cell to remain attached to the surface while rolling in the direction of flow. Unlike other adhesion processes occurring in relatively static environments, rolling adhesion is highly dynamic as the rolling cells travel over thousands of microns at tens of microns per second. Consequently, conventional mechanobiology methods such as traction force microscopy are unsuitable for measuring the individual adhesion events and the associated molecular forces due to the short timescale and high sensitivity required. Here, we describe our latest implementation of the adhesion footprint assay to image the P-selectin: PSGL-1 interactions in rolling adhesion at the molecular level. This method utilizes irreversible DNA-based tension gauge tethers to produce a permanent history of molecular adhesion events in the form of fluorescence tracks. These tracks can be imaged in two ways: (1) stitching together thousands of diffraction-limited images to produce a large field of view, enabling the extraction of adhesion footprint of each rolling cell over thousands of microns in length, (2) performing DNA-PAINT to reconstruct super-resolution images of the fluorescence tracks within a small field of view. In this study, the adhesion footprint assay was used to study HL-60 cells rolling at different shear stresses. In doing so, we were able to image the spatial distribution of the P-selectin: PSGL-1 interaction and gain insight into their molecular forces through fluorescence intensity. Thus, this method provides the groundwork for the quantitative investigation of the various cell-surface interactions involved in rolling adhesion at the molecular level.

This protocol presents the experimental procedures to perform the adhesion footprint assay to image the adhesion events during fast cell rolling adhesion.

Quantification of Cell-Substrate Adhesion Area and Cell Shape Distributions in MCF7 Cell Monolayers

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Characterization of Cell Membrane Extensions and Studying Their Roles in Cancer Cell Adhesion Dynamics

Examining the Dynamics of Cellular Adhesion and Spreading of Epithelial Cells on Fibronectin During Oxidative Stress

Using Cell-substrate Impedance and Live Cell Imaging to Measure Real-time Changes in Cellular Adhesion and De-adhesion Induced by Matrix Modification

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

Assay of Adhesion Under Shear Stress for the Study of T Lymphocyte-Adhesion Molecule Interactions

Dendrimer-based Uneven Nanopatterns to Locally Control Surface Adhesiveness: A Method to Direct Chondrogenic Differentiation

Quantitative Measurement of Invadopodia-mediated Extracellular Matrix Proteolysis in Single and Multicellular Contexts

Quantitation of Endothelial Cell Adhesiveness In Vitro

Quantitative Analysis of Cell Edge Dynamics during Cell Spreading

Imaging Molecular Adhesion in Cell Rolling by Adhesion Footprint Assay