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

Summary

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.

Abstract

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.

Introduction

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 unit^1,2,3. 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.

Protocol

1. Preparatory steps

1. Cell seeding to obtain confluent monolayers
   1. 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 μg/cm².
   2. Seed 400,000 cells to one well of a 4-well chamber slide.
   3. Culture the cells for 24 h (or longer, depending on the experimental endpoints) before staining, in an incubator at 37 °C, 5% CO₂. This step allows for a maturation of cell-cell contacts and the formation of monolayers.
   4. 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.
2. Immunofluorescent staining
   NOTE: Cells can be stained with a protocol of choice. In here, immunofluorescence was performed as previously described⁴.
   1. 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).
   2. For the procedure, use 0.5 mL of specified solutions unless specified otherwise.
   3. Fix cells in 4% formaldehyde in PBS for 30 min on ice.
   4. Incubate with 0.1 M ammonium chloride (in PBS) for 10 min to quench auto-fluorescence.
   5. Add 0.5% Triton X-100 (in PBS) for 30 min (permeabilization).
   6. Block with 5% milk (or 1% BSA) in TBS-T for 1 h.
   7. Incubate with primary antibody at 4 °C overnight (anti-paxillin: rabbit, 1:250, anti-plakoglobin: rabbit, 1:400)
   8. Incubate with secondary antibody for 30 min (Alexa Fluor 594 goat anti-rabbit, 1:500, 1:500)
   9. 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.
3. Confocal imaging
   1. 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.
   2. Focal adhesion imaging
      1. Choose Z-slices for focal adhesion analysis from the basal region, close to the substrate.
      2. 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.
   3. Cell-cell contacts imaging
      1. Use a 40x or 63x objective.
      2. Select channels for nuclear staining and the preferred junction protein staining.
      3. Choose Z-slices for cell shape analysis from the apical region of the monolayers.
      4. Take pictures for at least 3 different fields of vision (200-400 cells).

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 “Blind Analysis Tool” (available at: https://imagej.net/Blind_Analysis_Tools) can be used.

1. 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.
   1. Open image using ImageJ.
   2. Set the scale of an image to pixels (Analyze | Set Scale; Remove Scale and check Global option).
   3. Include the file name and the area of ROI in measurement options (Analyze | Set Measurements…); check Area and Display label options.
   4. 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).
   5. 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).
   6. Convert image to binary by using one of the following methods:
      1. Set the global threshold (Image | Adjust | Threshold; check Default, B&W and Dark Background options, adjust threshold manually or set it automatically).
      2. Set the local threshold (Image | Adjust | Auto Local Threshold…; set Method to Phansalkar and check White objects on black background option. Next, invert the image (Edit | Invert).
   7. 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.
   8. 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.
   9. 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.
   10. Alternatively, to facilitate FAs counting, use the attached ImageJ macro (FAs.ijm).
       1. Move .ijm file with the macro to plugins or macros folder located in ImageJ source files folders.
       2. Determine an area of the smallest ROI as described in step 2.1.4.
       3. Open macro file (Plugins | Macros | Edit...).
       4. 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.
       5. Save changes (the macro should be ready to use).
       6. 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.
2. Cell shape analysis
   1. Manual
      1. 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.
      2. 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.
      3. 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.
   2. 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.
      1. 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.
      2. 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).
      3. 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.
      4. Start the macro with the standard Open dialog window. Select the image to be processed (grayscale).
      5. 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's clipboard.
         NOTE: Supplemental Figures S3 and S4 were included as a training dataset for the CSI.ijm macro.

3. Quantification

1. Quantification of FAs
   1. 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.
   2. 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–Wallis in conjunction with the appropriate post-hoc tests.
2. Cell shape analysis
   1. Calculation of shape descriptors
      1. 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:
         
         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 = 4π*area/(perimeter)²
         (2) AR = major axis/minor axis
         (3) Roundness = 4*area/π*(major axis)²
         (4) Solidity = area/convex area
   2. Histograms of cell shape distribution
      1. Plot cell shape distribution as a histogram of circularity (CSI). Classify cells according to their CSI value (calculated for the minimum of 200–400 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’ 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.
   3. Cumulative distribution plots
      1. 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.
      2. 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.
      3. For statistical analysis, use Kolgomorov-Smirnov nonparametric test.

Results

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...

Discussion

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 ...

Materials

Name	Company	Catalog Number	Comments
Alexa Fluor 594	ThermoFisher Scientific	A32740	goat anti-rabbit, 1:500
Ammonium chloride	Sigma	A9434
BSA	BioShop	ALB001.500
Collagen from calf skin	Sigma	C9791-10MG
DAPI	Sigma	D9542	1:10000 (stock 1 mg/mL in H2O), nucleic acid staining
DMEM + GlutaMAX, 1 g/L D-Glucose, Pyruvate	ThermoFisher Scientific	21885-025
FBS	ThermoFisher Scientific	10270-136
Junction plakoglobin	Cell Signaling	2309S	rabbit, 1:400
Laminar-flow cabinet class 2	Alpina		standard equipment
MCF7-basedHAX1KD cell line	Cell line established in the National Institute of Oncology, Warsaw, described in Balcerak et al., 2019		MCF7 cell line withHAX1knockdown
MCF7 cell line (CONTROL)	ATCC	ATCC HTB-22	epithelial, adherent breast cancer cell line
Olympus CK2 light microscope	Olympus
Paxillin	Abcam	ab32084	rabbit, 1:250, Y113
PBS	ThermoFisher Scientific	10010023
Phalloidin-TRITC conjugate	Sigma	P1951	1:400 (stock 5 mg/mL in DMSO), actin labeling
PTX	Sigma	T7402-1MG
TBST – NaCl	Sigma	S9888
TBST – Trizma base	Sigma	T1503
Triton X-100	Sigma	9002-93-11
Zeiss LSM800 Confocal microscope	Zeiss