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 H2O), 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>MCF7-basedHAX1KD cell line</td>
			<td>Cell line established in the National Institute of Oncology, Warsaw, described in Balcerak et al., 2019</td>
			<td></td>
			<td>MCF7 cell line withHAX1knockdown</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 &#8211; NaCl</td>
			<td>Sigma</td>
			<td>S9888</td>
			<td></td>
		</tr>
		<tr>
			<td>TBST &#8211; 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...

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

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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

Dissection of Organizer and Animal Pole Explants from Xenopus laevis Embryos and Assembly of a Cell Adhesion Assay

This video demonstrates the technique used for preparation of organizer and animal pole explants from Xenopus laevis embryos, including the use of the eyebrow knife - a specialized dissection tool made of one's eyebrow. The protocol for assembling an adhesion assay is also given, which probes for the presence of key adhesion molecules present on the surface organizer or animal pole cells that are critical for proper development.

Video Bioinformatics Analysis of Human Embryonic Stem Cell Colony Growth

Because video data are complex and are comprised of many images, mining information from video material is difficult to do without the aid of computer software.  Video bioinformatics is a powerful quantitative approach for extracting spatio-temporal data from video images using computer software to perform dating mining and analysis. In this article, we introduce a video bioinformatics method for quantifying the growth of human embryonic stem cells (hESC) by analyzing time-lapse videos collected in a Nikon BioStation CT incubator equipped with a camera for video imaging. In our experiments, hESC colonies that were attached to Matrigel were filmed for 48 hours in the BioStation CT.  To determine the rate of growth of these colonies, recipes were developed using CL-Quant software which enables users to extract various types of data from video images.  To accurately evaluate colony growth, three recipes were created. The first segmented the image into the colony and background, the second enhanced the image to define colonies throughout the video sequence accurately, and the third measured the number of pixels in the colony over time.  The three recipes were run in sequence on video data collected in a BioStation CT to analyze the rate of growth of individual hESC colonies over 48 hours. To verify the truthfulness of the CL-Quant recipes, the same data were analyzed manually using Adobe Photoshop software. When the data obtained using the CL-Quant recipes and Photoshop were compared, results were virtually identical, indicating the CL-Quant recipes were truthful. The method described here could be applied to any video data to measure growth rates of hESC or other cells that grow in colonies. In addition, other video bioinformatics recipes can be developed in the future for other cell processes such as migration, apoptosis, and cell adhesion.  

Video bioinformatics is the automated processing, analysis, understanding, and data mining of biological spatio-temporal data extracted from microscopic videos. The purpose of this article is to demonstrate a method for measuring human embryonic stem cell colony growth using a video bioinformatics method.

Because video data are complex and are comprised of many images, mining information from video material is difficult to do without the aid of computer ...

In situ Quantification of Pancreatic Beta-cell Mass in Mice

Tracing changes of specific cell populations in health and disease is an important goal of biomedical research. The process of monitoring pancreatic beta-cell proliferation and islet growth is particularly challenging. We have developed a method to capture the distribution of beta-cells in the intact pancreas of transgenic mice with fluorescence-tagged beta-cells with a macro written for ImageJ (rsb.info.nih.gov/ij/). Following pancreatic dissection and tissue clearing, the entire pancreas is captured as a virtual slice, after which the GFP-tagged beta-cells are examined. The analysis includes the quantification of total beta-cell area, islet number and size distribution with reference to specific parameters and locations for each islet and for small clusters of beta-cells. The entire distribution of islets can be plotted in three dimensions, and the information from the distribution on the size and shape of each islet allows a quantitative and qualitative comparison of changes in overall beta-cell area at a glance.

In situ Quantification of Pancreatic Beta-cell Mass in Mice

The following protocol outlines the process of pancreatic dissection for virtual slice imaging, and the subsequent quantification of all GFP-tagged beta-cells in the entire pancreas.

Tracing changes of specific cell populations in health and disease is an important goal of biomedical research. The process of monitoring pancreatic ...

Imaging Cell Shape Change in Living Drosophila Embryos

The developing Drosophila melanogaster embryo undergoes a number of cell shape changes that are highly amenable to live confocal imaging. Cell shape changes in the fly are analogous to those in higher organisms, and they drive tissue morphogenesis. So, in many cases, their study has direct implications for understanding human disease (Table 1)1-5. On the sub-cellular scale, these cell shape changes are the product of activities ranging from gene expression to signal transduction, cell polarity, cytoskeletal remodeling and membrane trafficking. Thus, the Drosophila embryo provides not only the context to evaluate cell shape changes as they relate to tissue morphogenesis, but also offers a completely physiological environment to study the sub-cellular activities that shape cells.
The protocol described here is designed to image a specific cell shape change called cellularization. Cellularization is a process of dramatic plasma membrane growth, and it ultimately converts the syncytial embryo into the cellular blastoderm. That is, at interphase of mitotic cycle 14, the plasma membrane simultaneously invaginates around each of ~6000 cortically anchored nuclei to generate a sheet of primary epithelial cells. Counter to previous suggestions, cellularization is not driven by Myosin-2 contractility6, but is instead fueled largely by exocytosis of membrane from internal stores7. Thus, cellularization is an excellent system for studying membrane trafficking during cell shape changes that require plasma membrane invagination or expansion, such as cytokinesis or transverse-tubule (T-tubule) morphogenesis in muscle.
Note that this protocol is easily applied to the imaging of other cell shape changes in the fly embryo, and only requires slight adaptations such as changing the stage of embryo collection, or using "embryo glue" to mount the embryo in a specific orientation (Table 1)8-19. In all cases, the workflow is basically the same (Figure 1). Standard methods for cloning and Drosophila transgenesis are used to prepare stable fly stocks that express a protein of interest, fused to Green Fluorescent Protein (GFP) or its variants, and these flies provide a renewable source of embryos. Alternatively, fluorescent proteins/probes are directly introduced into fly embryos via straightforward micro-injection techniques9-10. Then, depending on the developmental event and cell shape change to be imaged, embryos are collected and staged by morphology on a dissecting microscope, and finally positioned and mounted for time-lapse imaging on a confocal microscope.

Imaging Cell Shape Change in Living Drosophila Embryos

Early development of the fruit fly, Drosophila melanogaster, is characterized by a number of cell shape changes that are well suited for imaging approaches. This article will describe basic tools and methods required for live confocal imaging of Drosophila embryos, and will focus on a cell shape change called cellularization.

The developing Drosophila melanogaster embryo undergoes a number of cell shape changes that are highly amenable to live confocal imaging.  Cell shape ...

Mechanical Stimulation-induced Calcium Wave Propagation in Cell Monolayers: The Example of Bovine Corneal Endothelial Cells

Intercellular communication is essential for the coordination of physiological processes between cells in a variety of organs and tissues, including the brain, liver, retina, cochlea and vasculature. In experimental settings, intercellular Ca2+-waves can be elicited by applying a mechanical stimulus to a single cell. This leads to the release of the intracellular signaling molecules IP3 and Ca2+ that initiate the propagation of the Ca2+-wave concentrically from the mechanically stimulated cell to the neighboring cells. The main molecular pathways that control intercellular Ca2+-wave propagation are provided by gap junction channels through the direct transfer of IP3 and by hemichannels through the release of ATP. Identification and characterization of the properties and regulation of different connexin and pannexin isoforms as gap junction channels and hemichannels are allowed by the quantification of the spread of the intercellular Ca2+-wave, siRNA, and the use of inhibitors of gap junction channels and hemichannels. Here, we describe a method to measure intercellular Ca2+-wave in monolayers of primary corneal endothelial cells loaded with Fluo4-AM in response to a controlled and localized mechanical stimulus provoked by an acute, short-lasting deformation of the cell as a result of touching the cell membrane with a micromanipulator-controlled glass micropipette with a tip diameter of less than 1 &mu;m. We also describe the isolation of primary bovine corneal endothelial cells and its use as model system to assess Cx43-hemichannel activity as the driven force for intercellular Ca2+-waves through the release of ATP. Finally, we discuss the use, advantages, limitations and alternatives of this method in the context of gap junction channel and hemichannel research.

Intercellular Ca2+-waves are driven by gap junction channels and hemichannels. Here, we describe a method to measure intercellular Ca2+-waves in cell monolayers in response to a local single-cell mechanical stimulus and its application to investigate the properties and regulation of gap junction channels and hemichannels.

Intercellular communication is essential for the coordination of physiological processes between cells in a variety of organs and tissues, including the ...

High Resolution Quantification of Crystalline Cellulose Accumulation in Arabidopsis Roots to Monitor Tissue-specific Cell Wall Modifications

Plant cells are surrounded by a cell wall, the composition of which determines their final size and shape. The cell wall is composed of a complex matrix containing polysaccharides that include cellulose microfibrils that form both crystalline structures and cellulose chains of amorphous organization. The orientation of the cellulose fibers and their concentrations dictate the mechanical properties of the cell. Several methods are used to determine the levels of crystalline cellulose, each bringing both advantages and limitations. Some can distinguish the proportion of crystalline regions within the total cellulose. However, they are limited to whole-organ analyses that are deficient in spatiotemporal information. Others relying on live imaging, are limited by the use of imprecise dyes. Here, we report a sensitive polarized light-based system for specific quantification of relative light retardance, representing crystalline cellulose accumulation in cross sections of Arabidopsis thaliana roots. In this method, the cellular resolution and anatomical data are maintained, enabling direct comparisons between the different tissues composing the growing root. This approach opens a new analytical dimension, shedding light on the link between cell wall composition, cellular behavior and whole-organ growth.

High Resolution Quantification of Crystalline Cellulose Accumulation in Arabidopsis Roots to Monitor Tissue-specific Cell Wall Modifications

Crystalline cellulose is an important constituent of the plant cell wall. However, its quantification at a cellular resolution is technically challenging. Here, we report the use of polarized light technology and root cross sections to obtain information of cell wall composition at a spatiotemporal resolution.

Plant cells are surrounded by a cell wall, the composition of which determines their final size and shape. The cell wall is composed of a complex matrix ...

Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins

The National Institute of Health&#39;s ImageJ is a powerful, freely available image processing software suite. ImageJ has comprehensive particle analysis algorithms which can be used effectively to count various biological particles. When counting large numbers of cell samples, the hemocytometer presents a bottleneck with regards to time. Likewise, counting membranes from migration/invasion assays with the ImageJ plugin Cell Counter, although accurate, is exceptionally labor intensive, subjective, and infamous for causing wrist pain. To address this need, we developed two plugins within ImageJ for the sole task of automated hemocytometer (or known volume) and migration/invasion cell counting. Both plugins rely on the ability to acquire high quality micrographs with minimal background. They are easy to use and optimized for quick counting and analysis of large sample sizes with built-in analysis tools to help calibration of counts. By combining the core principles of Cell Counter with an automated counting algorithm and post-counting analysis, this greatly increases the ease with which migration assays can be processed without any loss of accuracy.

This paper describes the quantification of hemocytometer and migration/invasion micrographs through two new open-source ImageJ plugins Cell Concentration Calculator and migration assay Counter. Furthermore, it describes image acquisition and calibration protocols as well as discusses in detail the input requirements of the plugins.

The National Institute of Health&#39;s ImageJ is a powerful, freely available image processing software suite. ImageJ has comprehensive particle analysis ...

Quantification of Interbacterial Competition using Single-Cell Fluorescence Imaging

Interbacterial competition can directly impact the structure and function of microbiomes. This work describes a fluorescence microscopy approach that can be used to visualize and quantify competitive interactions between different bacterial strains at the single-cell level. The protocol described here provides methods for advanced approaches in slide preparation on both upright and inverted epifluorescence microscopes, live-cell and time-lapse imaging techniques, and quantitative image analysis using the open-source software FIJI. The approach in this manuscript outlines the quantification of competitive interactions between symbiotic Vibrio fischeri populations by measuring the change in area over time for two coincubated strains that are expressing different fluorescent proteins from stable plasmids. Alternative methods are described for optimizing this protocol in bacterial model systems that require different growth conditions. Although the assay described here uses conditions optimized for V. fischeri, this approach is highly reproducible and can easily be adapted to study competition among culturable isolates from diverse microbiomes.

This manuscript describes a method for using single-cell fluorescence microscopy to visualize and quantify bacterial competition in coculture.

Interbacterial competition can directly impact the structure and function of microbiomes. This work describes a fluorescence microscopy approach that can ...

Automating Aggregate Quantification in Caenorhabditis elegans

A rise in the prevalence of neurodegenerative protein conformational diseases (PCDs) has fostered a great interest in this subject over the years. This increased attention has called for the diversification and improvement of animal models capable of reproducing disease phenotypes observed in humans with PCDs. Though murine models have proven invaluable, they are expensive and are associated with laborious, low-throughput methods. Use of the Caenorhabditis elegans nematode model to study PCDs has been justified by its relative ease of maintenance, low cost, and rapid generation time, which allow for high-throughput applications. Additionally, high conservation between the C. elegans and human genomes makes this model an invaluable discovery tool. Nematodes that express fluorescently tagged tissue-specific polyglutamine (polyQ) tracts exhibit age- and polyQ length-dependent aggregation characterized by fluorescent foci. Such reporters are often employed as proxies to monitor changes in proteostasis across tissues. Manual aggregate quantification is time-consuming, limiting experimental throughput. Furthermore, manual foci quantification can introduce bias, as aggregate identification can be highly subjective. Herein, a protocol consisting of worm culturing, image acquisition, and data processing was standardized to support high-throughput aggregate quantification using C. elegans that express intestine-specific polyQ. By implementing a C. elegans-based image processing pipeline using CellProfiler, an image analysis software, this method has been optimized to separate and identify individual worms and enumerate their respective aggregates. Though the concept of automation is not entirely unique, the need to standardize such procedures for reproducibility, elimination of bias from manual counting, and increase throughput is high. It is anticipated that these methods can drastically simplify the screening process of large bacterial, genomic, or drug libraries using the C. elegans model.

Automating Aggregate Quantification in Caenorhabditis elegans

The following protocol describes the development and optimization of a high-throughput workflow for worm culturing, fluorescence imaging, and automated image processing to quantify polyglutamine aggregates as an assessment of changes in proteostasis.

A rise in the prevalence of neurodegenerative protein conformational diseases (PCDs) has fostered a great interest in this subject over the years. This ...

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.