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 of cell-cell-contacts.
Here, we describe two simple methods of quantitative analysis of the area, number and shape of cellular structures stained by immunofluorescence, in this case focal adhesions and the whole cells in the cell layer. However, the proposed tools can be repurposed for the quantification of any chosen structure. The key issue for these analyses is the quality of immunofluorescent staining and confocal imaging. These methods can be implemented in any standard laboratory equipped with a cell-culture unit and a confocal microscope. They are designed to compare cell lines, especially when the differences (natural or induced by specific treatment) in the measured parameters are substantial. They are not recommended to measure minute differences or to establish absolute measurements, because they are sensitive to minimal changes in the initial arbitrary settings, especially in the case of focal adhesions. This method of FAs quantification is inferior to more advanced and specific methods like TIRF microscopy, but it has an advantage of not requiring sophisticated equipment.
Similar methods of focal adhesion measurements were described before7,8,9,10. Here, we specified the settings and created a free ImageJ macro, with several options, to facilitate the measurements.
Cell shape analysis was described many times, including very complex and detailed methods11,12. Here, we present a simple method to track the changes in epithelial cell monolayers, which could be very important for comparing cell morphology or developmental changes. The manual method of cell shape analysis in the monolayers presented in this protocol was described in a previous report6. The CSI formula as a way to describe and compare the shape of an object(s) is widely used in different disciplines13,14, including geology from which it originated. Presentation of the results in a form of a histogram and/or as a cumulative distribution function is commonly used for comparisons of distributions of any kind8,10,15.
Notably, we present here a tool for the automated cell shape analysis based on ImageJ plugin MorhpLibJ (https://imagej.net/MorphoLibJ). We provide a macro file, which can perform this analysis quickly and efficiently. This method does not always recognize the correct cell borders, but the percentage of faulty measurements (which are present in every automated analysis) is minimal and should not significantly affect the final result, especially if enough number of cells is analyzed. Cells without complete borders are eliminated. Automated cell shape analysis has undeniable advantages and we present this method so that it can be appreciated by the scientific community.

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 from each cell line were quantified using the described protocol. The mean value for the smallest focal adhesion was set to 50 (pixel2). Representative images of FAs count with ImageJ, including the final, numbered outlines and the overlay of the FAs outlines with the original image, are shown for both cell lines on Figure 3A. Differences in number and size of FAs in both cell lines are presented on Figure 3B.
Cell shape analysis 
Manual assessment: MCF7 cells were cultured for 24 h, the medium was exchanged for the same fresh medium (untreated) or the medium with 0.1 &#956;M paclitaxel (PTX) &#8211; to induce cell rounding - and cultured for another 24 h. Images of the confluent MCF7 monolayers stained with anti-plakoglobin antibody and DAPI were obtained using a confocal microscope (single Z-slice from apical region). About 200-400 cells (2-3 fields of view) from each experiment (untreated/treated) were documented (40x objective) and the images were assessed using ImageJ image processing software (Figure 4A). Representative regions of each cell layer with outlined cells are shown in Figure 4A (untreated and PTX-treated cells). All cells were categorized according to their CSI values (10 intervals, bin width 0.1) and presented in the respective histograms (Figure 4B), which show an increase in the last bin (0.9-1) and the flattening of the main peaks (0.6-0.9) for the PTX-treated cells, comparing to the untreated control. Differences in the cumulative distribution of CSI were calculated for statistical significance using Kolgomorov-Smirnov test (K-S), a nonparametric test of the equality of one-dimensional probability distributions. Frequency distribution histograms and cumulative distribution plots (Figure 4C) were generated with software.
Automated assessment: MCF7 cells were cultured for 24 h, fixed and stained with fluorophore-conjugated phalloidin to visualize actin. Images were taken using a confocal microscope (single Z-slice from apical region). The grayscale images of the monolayers were analyzed using the attached macro file, according to the included protocol (Figure 5A-C). Overall, 512 cells from 12 fields of view were quantified. Results were plotted as a histogram presenting distribution of circularity (Figure 5D).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61461/61461fig01v2.jpg" /> 
Figure 1: Actin staining with fluorophore-conjugated phalloidin, two different Z-slices from the same field of view.&#160;(A) The apical region with cortical actin. (B) The basal region with actin stress fibers. Bar: 10 &#956;m. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/61461/61461fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61461/61461fig02v2.jpg" /> 
Figure 2: Examples of different Cell Shape Index (CSI) values for the shapes with distinct perimeters, but the same area.&#160;The very elongated shape on the left has CSI close to 0, while the ideal circle on the right has CSI of 1. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/61461/61461fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/61461/61461fig03v2.jpg" /> 
Figure 3: Focal adhesion quantification.&#160;(A) Representative images of MCF7-based cell lines: CONTROL and HAX1 KD stained with paxillin; from left to right: (1) paxillin and nuclei (2) outlined and numbered FAs (3) overlay of FAs outlines and the original image. Bar: 20 &#956;m. Insets (below each picture) show zoomed boxed areas. (B) Plots showing the difference in FAs size and number between the two cell lines. Statistical significance was assessed using Student t-test. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/61461/61461fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/61461/61461fig04v2.jpg" /> 
Figure 4: Changes in cell shape induced by paclitaxel (PTX) in MCF7 cell layer; manual count.&#160;(A) Representative regions of MCF7 monolayers, cells untreated and treated with PTX, stained with junction protein plakoglobin and DAPI, bar: 20 &#956;m. The panel on the right shows the image processed in ImageJ; cell edges are outlined and all counted cells are numbered. (B) Histograms showing CSI distribution in untreated and PTX-treated cells, 200-400 cells in each experiment (untreated/treated), bin width: 0.1. Plots were generated using frequency distribution analysis with relative frequencies as a fraction. (C) The plot of cumulative distribution for the untreated and PTX-treated monolayers. Statistical difference calculated using Kolgomorov-Smirnov nonparametric test (KS). <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/61461/61461fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/61461/61461fig05v2.jpg" /> 
Figure 5: Cell shape in MCF7 monolayers assessed using the automated method.&#160;(A-C) An example of the automated image analysis illustrating subsequent steps executed by the attached macro. (A) Input image; cortical actin; grayscale, scale bar 10 &#956;m (for informative purposes; scale bars should not be embedded into analyzed images). (B) Cell layer after segmentation; cell borders outlined; cells without complete borders eliminated. (C) Overlay of the cell outlines on the original image. (D) A histogram showing the distribution of CSI in the analyzed dataset, 512 cells. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/61461/61461fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplemental Figure S1, S2: MCF7 cells, paxillin and DAPI-stained to visualize FAs and nuclei. Provided as a training dataset for the FAs.ijm macro. 
Figure S1: <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/61461/61461supfig01.jpg" target="_blank">Please click here to download this figure.</a> 
Figure S2: <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/61461/61461supfig02.jpg" target="_blank">Please click here to download this figure.</a>
Supplemental Figure S3, S4: MCF7 cells, plakoglobin and DAPI-stained to visualize cell-cell junctions and nuclei. Provided as a training dataset for the CSI.ijm macro. 
Figure S3: <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/61461/61461supfig03.jpg" target="_blank">Please click here to download this figure.</a> 
Figure S4: <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/61461/61461supfig04.jpg" target="_blank">Please click here to download this figure.</a>
FAs.txt: <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/61461/FAs.txt" target="_blank">Please click here to download this file.</a>
CSI.txt: <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/61461/CSI.txt" target="_blank">Please click here to download this file.</a>

Watch this Scientific Journal Video about Quantification of Cell-Substrate Adhesion Area and Cell Shape Distributions in MCF7 Cell Monolayers at JoVE.com

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

Universidad San Sebastian

Research

JoVE Journal

Biology

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

Analysis of Cancer Cell Invasion and Anti-metastatic Drug Screening Using Hydrogel Micro-chamber Array (HMCA)-based Plates

Cancer metastasis is known to cause 90% of cancer lethality. Metastasis is a multistage process which initiates with the penetration/invasion of tumor cells into neighboring tissue. Thus, invasion is a crucial step in metastasis, making the invasion process research and development of anti-metastatic drugs, highly significant. To address this demand, there is a need to develop 3D in vitro models which imitate the architecture of solid tumors and their microenvironment most closely to in vivo state on one hand, but at the same time be reproducible, robust and suitable for high yield and high content measurements. Currently, most invasion assays lean on sophisticated microfluidic technologies which are adequate for research but not for high volume drug screening. Other assays using plate-based devices with isolated individual spheroids in each well are material consuming and have low sample size per condition. The goal of the current protocol is to provide a simple and reproducible biomimetic 3D cell-based system for the analysis of invasion capacity in large populations of tumor spheroids. We developed a 3D model for invasion assay based on HMCA imaging plate for the research of tumor invasion and anti-metastatic drug discovery. This device enables the production of numerous uniform spheroids per well (high sample size per condition) surrounded by ECM components, while continuously and simultaneously observing and measuring the spheroids at single-element resolution for medium throughput screening of anti-metastatic drugs. This platform is presented here by the production of HeLa and MCF7 spheroids for exemplifying single cell and collective invasion. We compare the influence of the ECM component hyaluronic acid (HA) on the invasive capacity of collagen surrounding HeLa spheroids. Finally, we introduce Fisetin (invasion inhibitor) to HeLa spheroids and nitric oxide (NO) (invasion activator) to MCF7 spheroids. The results are analyzed by in-house software which enables semi-automatic, simple and fast analysis which facilitates multi-parameter examination.

Analysis of Cancer Cell Invasion and Anti-metastatic Drug Screening Using Hydrogel Micro-chamber Array HMCA-based Plates

A HMCA-based imaging plate is presented for invasion assay performance. This plate facilitates the formation of three-dimensional (3D) tumor spheroids and the measurement of cancer cell invasion into the extracellular matrix (ECM). The invasion assay quantification is achieved by semi-automatic analysis.

Cancer metastasis is known to cause 90% of cancer lethality. Metastasis is a multistage process which initiates with the penetration/invasion of tumor ...

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

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

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

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

Using 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

Quantification of Breast Cancer Cell Invasiveness Using a Three-dimensional (3D) Model

It is now well known that the cellular and tissue microenvironment are critical regulators influencing tumor initiation and progression. Moreover, the extracellular matrix (ECM) has been demonstrated to be a critical regulator of cell behavior in culture and homeostasis in vivo. The current approach of culturing cells on two-dimensional (2D), plastic surfaces results in the disturbance and loss of complex interactions between cells and their microenvironment. Through the use of three-dimensional (3D) culture assays, the conditions for cell-microenvironment interaction are established resembling the in vivo microenvironment. This article provides a detailed methodology to grow breast cancer cells in a 3D basement membrane protein matrix, exemplifying the potential of 3D culture in the assessment of cell invasion into the surrounding environment. In addition, we discuss how these 3D assays have the potential to examine the loss of signaling molecules that regulate epithelial morphology by immunostaining procedures. These studies aid to identify important mechanistic details into the processes regulating invasion, required for the spread of breast cancer.

Quantification of Breast Cancer Cell Invasiveness Using a Three-dimensional 3D Model

This article provides detailed methodologies for the use of three-dimensional (3D) assays to quantify breast cancer cell invasion. Specifically, we discuss the procedures required to set up such assays, quantification, and data analysis, as well as methods to examine the loss of membrane integrity that occurs when cells invade.

It is now well known that the cellular and tissue microenvironment are critical regulators influencing tumor initiation and progression. Moreover, the ...

Medicine

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 ...

Mammosphere Formation Assay from Human Breast Cancer Tissues and Cell Lines

Similar to healthy tissues, many blood and solid malignancies are now thought to be organised hierarchically, with a subset of stem-like cancer cells that self-renew while giving rise to more differentiated progeny. Understanding and targeting these cancer stem cells in breast cancer, which may possess enhanced chemo- and radio-resistance compared to the non-stem tumor bulk, has become an important research area. Markers including CD44, CD24, and ALDH activity can be assessed using fluorescence activated cell sorting (FACS) to prospectively isolate cells that display enhanced tumorigenicity when implanted into immunocompromised mice: the mammosphere assay has also become widely used for its ability to retrospectively identify sphere-forming cells that develop from single stem cell-like clones. Here we outline approaches for the appropriate culturing of mammospheres from cell lines or primary patient samples, their passaging, and calculations to estimate sphere forming efficiency (SFE). First we discuss key considerations and pitfalls in the appropriate planning and interpretation of mammosphere experiments.

Floating mammosphere assays can investigate the subset of stem-like breast cancer cells that survive in suspension conditions and show enhanced tumorigenesis when implanted into mice. This protocol provides a convenient in vitro measure of sphere-forming ability, a proxy for in vivo tumorigenesis, while facilitating analysis of the stem-associated transcriptional landscape.

Similar to healthy tissues, many blood and solid malignancies are now thought to be organised hierarchically, with a subset of stem-like cancer cells that ...

Heteromulticellular Stromal Cells in Scaffold-free 3D Cultures of Epithelial Cancer Cells to Drive Invasion

Breast cancer is the second leading cause of cancer-related death among women in the U.S. Organoid models of solid tumors have been shown to faithfully recapitulate aspects of cancer progression such as proliferation and invasion. Although patient-derived organoids and patient-derived xenograft organoids are pathophysiologically relevant, they are costly to propagate, difficult to manipulate, and comprised primarily of the most proliferative cell types within the tumor microenvironment (TME). These limitations prevent their use for elucidating cellular mechanisms of disease progression that depend upon tumor-associated stromal cells which are found within the TME and known to contribute to metastasis and therapy resistance. 
Here, we report on methods for cultivating epithelial-stromal multicellular 3D cultures. The advantages of these methods include a cost-effective system for rapidly generating organoid-like 3D cultures within scaffold-free environments that can be used to track invasion at single-cell resolution within hydrogel scaffolds. Specifically, we demonstrate how to generate these heteromulticellular 3D cultures using BT-474 breast cancer cells in combination with fibroblasts (BJ-5ta), monocyte-like cells (THP-1), and/or endothelial cells (EA.hy926). Additionally, differential fluorescent labeling of cell populations enables time-lapse microscopy to define 3D culture assembly and invasion dynamics. 
Notably, the addition of any two stromal cell combinations to 3D cultures of BT-474 cells significantly reduces circularity of the 3D cultures, consistent with the presence of organoid-like or secondary spheroid structures. In tracker dye experiments, fibroblasts and endothelial cells co-localize in the peripheral organoid-like protrusions and are spatially segregated from the primary BT-474 spheroid. Finally, heteromulticellular 3D cultures of BT-474 cells have increased hydrogel invasion capacity. Since we observed these protrusive structures in heteromulticellular 3D cultures of both non-tumorigenic and tumorigenic breast epithelial cells, this work provides an efficient and reproducible method for generating organoid-like 3D cultures in a scaffold-free environment for subsequent analyses of phenotypes associated with solid tumor progression.

There is a critical need for 3D cancer models that capture heterocellular crosstalk to study cancer metastasis. Our study presents the generation of heteromulticellular stromal-epithelial in a scaffold and scaffold-free environment that can be used to study invasion and cellular spatial distributions.

Breast cancer is the second leading cause of cancer-related death among women in the U.S. Organoid models of solid tumors have been shown to faithfully ...

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.

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

Summary

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Chapters in this video

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

Analysis of Cancer Cell Invasion and Anti-metastatic Drug Screening Using Hydrogel Micro-chamber Array (HMCA)-based Plates

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

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

Quantification of Breast Cancer Cell Invasiveness Using a Three-dimensional (3D) Model

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

Mammosphere Formation Assay from Human Breast Cancer Tissues and Cell Lines

Heteromulticellular Stromal Cells in Scaffold-free 3D Cultures of Epithelial Cancer Cells to Drive Invasion

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