This work was supported by the Canadian Institute of Health Research to CP (OR 142730 and OR 89931). We would like to thank Jelena Brkic for her initial idea of binary particle analysis in ImageJ.

1. Compound Microscope and Camera Setup (Cell Concentration Calculator)
<ol>
	<li>Increase bulb brightness to full with the light adjustment knob, switch to the 4X objective lens, and ensure phase contrast filters are selected. 
	NOTE: Any inverted phase contrast microscope for tissue culture with a dark background, e.g., PhP phase contrast, can be used following standard microscope and camera procedures.</li>
	<li>Within the microscope&#39;s software, set image capture settings to default values. 
	NOTE: Refer to the microscope&#39;s user manual to find the location of these settings.
	<ol>
		<li>Set &#39;brightness&#39;, &#39;contrast&#39;, and &#39;saturation&#39; to 100% and &#39;gamma&#39; and &#39;gain&#39; to 1.0. 
		NOTE: Depending on the software, &#39;brightness&#39; and &#39;contrast&#39; may default to a value of 0% instead of 100%.</li>
		<li>Set images to be captured in black and white using the highest resolution available (1,600 x 1,200 pixels (px) or greater). 
		NOTE: A &#39;saturation&#39; of 0% is sufficient if a black and white setting is unavailable.</li>
	</ol>
	</li>
	<li>Place a standard hemocytometer onto the microscope stage and capture an image as depicted in (Figure 1A); this is the &#39;Volume Calibration image&#39;. Adjust exposure timing as required.</li>
</ol>
2. Image Volume Calibration
<ol>
	<li>Open ImageJ and from the Plugins menu, start the Cell Concentration Calculator (CCC) plugin, e.g., Plugins &#62; Analyzer &#62; &#39;Cell Concentration Calculator&#39;.
	<ol>
		<li>If the right-side &#39;Image Volume Calibration&#39; panel is not visible, click on &#39;Calibrate&#39; to show it.</li>
	</ol>
	</li>
	<li>In ImageJ, open the &#39;Volume Calibration image&#39; from step 1.3 (&#39;File&#39; &#62; &#39;Open&#39;) and in CCC click on the &#39;Get Image Dimension&#39; button. 
	NOTE: This will fill in both Image Width and Height text boxes with the image resolution in pixels automatically.</li>
	<li>In ImageJ, select the &#39;Straight Line Tool&#39; next to the selection tools and draw a straight line across the entire length of the hemocytometer primary (P)-square demonstrated in (Figure 1B) by clicking and dragging the cursor.
	<ol>
		<li>Push the &#39;M&#39; key to display the Results window containing the straight line measurements. Type the value from the Length column into the &#39;P-square Length&#39; textbox in CCC (Figure 1B).</li>
		<li>Click on the &#39;Calculate Image Volume&#39; button to output the image volume into the Image Volume textbox. Alternately, if the volume of the image is already known, type the volume in nL into the Image Volume textbox.</li>
	</ol>
	</li>
	<li>Click the &#39;Save&#39; button. 
	NOTE: The plugin is now calibrated.</li>
</ol>
3. Camera Exposure Calibration
<ol>
	<li>Following cell harvesting for counting via hemocytometer, load 10 &#956;L of cells into a chamber of the hemocytometer and place it onto the microscope stage.</li>
	<li>Using the same settings from step 1.2, adjust the exposure time so that the background lines of the hemocytometer disappear.
	<ol>
		<li>Adjust the focus so that the interior of the cells is darker than the cell membrane, indicating focus within the central cross section of the cell and not the poles.</li>
		<li>Further adjust the exposure so that the cells are not overexposed and resemble those depicted in (Figure 1C). 
		NOTE: Slightly visible hemocytometer lines are acceptable. It is recommended to save or record these settings to maintain accuracy and reproducibility.</li>
	</ol>
	</li>
</ol>
4. Image Acquisition
<ol>
	<li>For each cell sample, load 10 &#956;L into both chambers of the hemocytometer to increase statistical inference power2. Place the hemocytometer on the microscope stage for imaging. 
	NOTE: The resolution and magnification of each image must be the same as the &#39;Volume Calibration image&#39;. The plugin counts all images of any selected folder; keep images to be counted together in the same folder.
	<ol>
		<li>If a filename auto increment function is available, turn it on and make sure each image is not shown after capturing to increase throughput. 
		NOTE: Manually saving and closing each image will drastically slow down the process. Refer to the microscope&#39;s user manual for information on the availability of an auto-increment function.</li>
		<li>Capture at least three non-overlapping images of the central region of the hemocytometer although more (5-10) is recommended to increase accuracy. 
		NOTE: Avoid both the top and bottom areas of the chambers as cells tend to increase in density at both locations. Take the same number of images for every chamber. This is required for proper functioning of the plugin during counting.</li>
	</ol>
	</li>
</ol>
5. Image Counting and Dilutions
<ol>
	<li>In CCC, click on &#39;Count Cells&#39; and observe the Choose Directory dialog box. Select a folder to be counted.</li>
	<li>Observe the sample number input box after selecting a folder. Enter the number of images taken per chamber, i.e., 4.1.2, and click &#39;Ok&#39;. The plugin will now count all the jpg, tiff, and png images in the selected folder in alphabetical order. 
	NOTE: Clicking the &#39;Sample Viewer&#39; button will bring up the Sample Viewer window displaying information about the counted samples. Sample concentration is the average concentration of all images taken per chamber. Samples with unitless concentrations can be added to this list, such as the addition of a drug or small molecule treatment.
	<ol>
		<li>Recount the cell samples if the concentrations vary significantly within counts of the same samples (section 4). 
		NOTE: To calculate dilutions for all the counted-samples, CCC automatically uses the formula C1V1 = C2V2.</li>
	</ol>
	</li>
	<li>Use the scenario of seeding 15,000 cells per 200 &#956;L into 30 wells of a 96-well plate + 1 extra:
	<ol>
		<li>Set the textbox to the right of the C2 label to 15,000 and the concentration volume textbox to 200, changing the adjacent combo box unit to &#39;&#956;L&#39;. 
		NOTE: The plugin will ultimately calculate the concentration in cells/mL.</li>
		<li>Make sure the volume combo box has V2 selected (final volume) and in the textbox to the right enter 6200 (200 &#956;L x (30 + 1)), selecting &#39;&#956;L&#39; in the volume unit combo box.</li>
	</ol>
	</li>
	<li>Click &#39;Calculate Dilution&#39; to add the currently entered dilution to the bottom left list box. 
	NOTE: For each dilution added, will be solved for each sample and displayed in the tree diagram to the right. Double click on each entry to expand.</li>
	<li>Clicking the &#39;Save&#39; button below the &#39;Sample Viewer&#39; button to write to file all sample data and dilutions.</li>
	<li>NOTE: These data can be recovered at any time by clicking &#39;Load&#39; and selecting the saved file.</li>
</ol>
6. Migration and Invasion (Counter)
<ol>
	<li>Perform the migration and invasion assays using the standard Boyden chamber method7-9.</li>
	<li>After cells have migrated/invaded, carefully remove the media within the insert by inverting and gently tapping. Wick away any excess media adhered to the bottom of the membrane by touching the edge to a paper towel. 
	NOTE: Do not touch the membrane itself to the towel, this may dislodge adhered cells.</li>
	<li>Fix and stain the cells as reported7-9 in a 24-well plate set up with each row containing ~500 &#181;l of a different solution, e.g., Fixative, Stain 1, Stain 2, and double distilled water (ddH2O). Fill a second plate with 1x PBS to place the inserts in before cutting.</li>
	<li>Wash the inserts by placing them into wells filled with ddH2O and fill the insert with ddH2O. Drain the water before swabbing away cells by inversion.</li>
	<li>Use a clean cotton applicator to remove un-migrated/un-invaded cells from the top of the membrane taking care not to damage the membrane. Be thorough around the edges of the membrane.</li>
	<li>Cut the membrane using a razor or a scalpel and carefully transfer the membrane (bottom-side up) onto a clean glass slide.</li>
	<li>Add a small drop of mounting solution beneath and on top of the membrane and cover with a thin cover slip. 
	NOTE: Avoid trapping bubbles within the mounting solution to preserve accuracy of the counts.</li>
</ol>
7. Dissecting Scope and Camera Setup
<ol>
	<li>Turn on the microscope&#39;s light source and camera. 
	NOTE: Refer to the microscope&#39;s user manual for detailed instructions.</li>
	<li>Within the microscope&#39;s software, set image capture settings to default values. 
	NOTE: If an averaging function exists, a value of four is recommended. This is a good compromise between the degree of averaging and image acquisition time. Likewise, a small degree of sharpening may increase image fidelity.
	<ol>
		<li>Set &#39;brightness&#39;, &#39;contrast&#39;, and &#39;saturation&#39; to 100% and &#39;gamma&#39; and &#39;gain&#39; to 1.0. 
		NOTE: Depending on the software, &#39;brightness&#39; and &#39;contrast&#39; may default to a value of 0% instead of 100%.</li>
		<li>Set display (real-time) and capture resolution to their maximum settings (1,600 x 1,200 px and 2,592 x 1,944 px, respectively). 
		NOTE: Display resolution can be adjusted as required if the refresh rate is too slow. Lower resolutions will make it more difficult to focus accurately but increase the refresh rate.</li>
	</ol>
	</li>
	<li>For the stage of the dissecting scope, use a solid white background; a black or glass background is insufficient.</li>
	<li>Use the above-stage light source, preferably from two flexible LED lights to the right and left of the stage. 
	NOTE: A below-stage light source will illuminate the pores within the migration assay membrane which may negatively influence the accuracy of subsequent counts.</li>
	<li>Place a completed migration assay membrane slide onto the stage. Looking at the real-time image displayed by the software, adjust the magnification with the zoom adjustment knob so that the edges of a single membrane are just within the camera&#39;s field of view. 
	NOTE: Placing the slide on a glass plate overtop the white background stage makes it easier to maneuver the slide for imaging.</li>
	<li>Adjust the light source positions (7.6.1) and exposure times to reproduce as closely as possible the ideal image depicted in Figure 2A. Depending on the brightness, exposure times of 5-60 ms should be sufficient. 
	NOTE: The goal is to produce an image with as little background color as possible and a uniformly illuminated membrane without reducing image fidelity, i.e., overexposure leading to a loss of visible cells.
	<ol>
		<li>Position the left and right light sources at a low angle relative to the slide. This will help remove background stain and chromatic aberrations. Try to keep each light source directly opposite to the other. 
		NOTE: While maneuvering each light source into position, turn the other off. This helps to center the area of illumination onto the membrane itself more easily and accurately.</li>
	</ol>
	</li>
	<li>Remove the slide and white balance the image using a single button click in the microscope software. 
	NOTE: The microscope is now calibrated. Save as many settings as possible for future use and take note of the light source positions and intensity.</li>
</ol>
8. Image Acquisition and Flatfield
<ol>
	<li>Within the software, set the capture folder location, and capture one image per membrane. Name the images following the general template of: Name - ###, e.g., Control - 001.tif, Control - 002.tif, Test drug - 001.tif, and so on. 
	NOTE: For best image results, save the image file type as tiff over other lossy formats such as jpeg. 
	NOTE: Images not following the general template will still be counted but will not be subject to automated grouping and flat fielding.
	<ol>
		<li>If flatfield correction is desired, for each slide find an empty area and take a Blank image following the naming convention: Name - Blank. 
		NOTE: A blank is an area on the slide that contains no membrane and represents the background illumination.</li>
		<li>Immediately before imaging, flatten each membrane by applying pressure over the coverslip and remove as many trapped bubbles as possible.</li>
	</ol>
	</li>
	<li>In ImageJ, go to Plugins and open the TC plugin; e.g., Plugins &#62; Analyze &#62; &#39;Transwell Counter&#39;.</li>
	<li>Within TC, click on the &#39;Flatfield&#39; button and observe the Choose Directory dialog box. Select the folder where the membrane images were saved. 
	NOTE: Only images saved with the general template above will be automatically flatfield corrected and saved in a new folder called Flatfield within the chosen folder. See Figure 2B for an example of a flatfield corrected image.</li>
</ol>
9. Configuration Settings
<ol>
	<li>Open a migration assay membrane image in ImageJ (&#39;File&#39; &#62; &#39;Open&#39;) and select Image &#62; Adjust &#62; &#39;Color Threshold&#8230;&#39;. Observe the Color Threshold window to adjust what colors will be filtered out of the image.
	<ol>
		<li>At the bottom of the window, set Thresholding method to &#39;Shanbhag&#39;, Threshold color to &#39;White&#39;, and Color space to &#39;RGB&#39; (red green blue); uncheck Dark background if selected.</li>
		<li>Adjust the top sliders by clicking and dragging the markers to 0 and the bottom sliders to 255 (leave the bottom sliders at 255). Ensure that the image is fully white.</li>
	</ol>
	</li>
	<li>Adjust the green and red top sliders until only the nuclei are visible. 
	NOTE: The settings selected will vary entirely upon the cell stain used. See Figure 2C.</li>
	<li>In the TC plugin, input the values of the RGB top sliders into the associated Configuration Settings &#39;RGB Threshold&#39; textboxes. Click the &#39;Add/Modify&#39; button and overwrite the configuration.
	<ol>
		<li>Leave the Size Range Lower and Upper values at 1 - Infinity.</li>
	</ol>
	</li>
	<li>Within the Configuration Settings panel, click &#39;Save&#39; to write the settings to the hard drive.</li>
</ol>
10. Counting Images and Calibration
<ol>
	<li>Open the TC plugin (8.2) and click on &#39;Count Folder&#39; and observe the Choose Directory dialog box. Select the folder to be counted and wait for it to finish.</li>
	<li>Each counted-sample is automatically added to the main table. Key columns are &#39;Count&#39;, &#39;Quality&#39;, and &#39;Calibrate?&#39;. 
	NOTE: The un-calibrated Count is the number of particles per membrane within the area displayed in the column &#39;Area range&#39;. 
	NOTE: Quality ranges from approximately -0.8 to 1.7; Q &#8805; 0.5 is acceptable.
	<ol>
		<li>Observe a checkmark in the &#39;Calibrate?&#39; column if an image is flagged for calibration based on its resemblance to the ideal metrics. 
		NOTE: Both Quality and Calibration may suggest that the current settings are possibly insufficient. If after proper &#39;Color Thresholding&#39; and &#39;Size Ranges&#39; have been selected and calibration is still suggested, inspection of the original and counted images should be the final determinant of a valid count.</li>
	</ol>
	</li>
	<li>Select All the Samples in the Table Flagged for Calibration.
	<ol>
		<li>Right click in the table and select Recount &#62; &#39;Suggested size&#39;. This will recount the images with a suggested minimum particle area.</li>
		<li>Right click again and select &#39;Show Plot&#39;. Observe the frequency scatter plot of particle areas. An ideal image will have a graph resembling a long right-tailed normal distribution, i.e., the bell curve. See Figure 3B.</li>
		<li>Adjust the lower Size Range, if required, by selecting the sample, right clicking, and selecting Recount &#62; &#39;Manual settings&#39;. Observe the manual settings dialog. Enter the desired settings and click Count to recount the image with the new settings.</li>
	</ol>
	</li>
	<li>To adjust the counts manually, select the samples, right clicking the table, and select &#39;Open image with counts&#39;. Observe the original image with red markers representing each particle counted by the plugin. 
	NOTE: Recount &#62; &#39;Show counted binary image&#39; can be useful to check how well individual cells are being resolved by the color thresholding.
	<ol>
		<li>To add a count, hold &#39;Ctrl&#39; and left click. Observe a marker at the cursor location that will be added to the samples total count. To remove a marker, right click the image. The plugin will remove the marker closest to the cursor.</li>
		<li>To remove a group of markers, use a selection tool in ImageJ to select a region of interest (ROI). While holding &#39;Ctrl&#39;, right click the image and all markers inside the ROI will be removed. Observe the cell number in the &#39;Count&#39; column.</li>
	</ol>
	</li>
</ol>
11. Saving/Opening Results and Exporting to CSV
<ol>
	<li>In TC, go to the menu bar and click on &#39;File&#39; &#62; &#39;Save results&#39;. Observe the Save Results dialog box. Choose a name and destination and click &#39;Save&#39;.
	<ol>
		<li>Use &#39;File&#39; &#62; &#39;Open results&#39; to load a file containing all the data displayed in the main table, including the plot. 
		NOTE: The results file saves the directories the images are saved in. If the original images are moved after saving, the &#39;Open original image&#39; and &#39;Open image with counts&#39; functions will fail.</li>
		<li>To reset the image directory, select the samples, right click the table, and select &#39;Reset image directory&#39;. Observe the Choose Directory dialog box. Select the new folder and if the images exist, the directories will be reset. Re-save the results file.</li>
	</ol>
	</li>
	<li>To save a Comma Separated Values (CSV) file, in the menu bar go to &#39;File&#39; &#62; &#39;Export to .csv&#39;. This produces a file with samples organized into statistical groups with the mean count and standard error of the mean; its layout is designed for quick graphing in common graphing programs. 
	NOTE: The statistical groups are based on the groups created within TC.
	<ol>
		<li>If the samples follow the general naming template, select the samples to be grouped, right click and select &#39;Auto grouping&#39;. This adds samples with the same &#39;Name&#39; to the same group. Using the example Control - 1, Control - 2, Treatment - 1, Treatment - 2: both controls would be added to the group &#39;Control&#39; and treatments to &#39;Treatment&#39;.</li>
		<li>Add samples manually to groups by double clicking the sample&#39;s &#39;Group&#39; cell and typing in the group name. Select the samples to be added to this group, right click and select &#39;Add to group&#39;.</li>
	</ol>
	</li>
</ol>

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The authors declare that they have no competing financial interests.

Critical Steps, Troubleshooting, and Limitations
The very nature of automated computational methods, specifically those of particle analysis, necessitates the mathematical ability to define these particles. Consequently, the accuracy of both Cell Concentration Calculator and migration assay counter is majorly dependent on image fidelity, that is, how closely the captured image resembles the cell sample or migration assay membrane. It is therefore of the upmost importance to follow microscope and ...

In vitro cell counting is an important basic technique in a wide range of tissue culture experiments. Accurately determining the number of cells in a culture is essential for experimental reproducibility and standardization1,2. Cell counting can be performed manually using a hemocytometer as well as using a variety of automated methods, each with their own advantages and disadvantages3,4,5. Most of the automated methods for cell counting belong to one of two classes, those that use the Coulter principle or flow cytometry. Coulter counters take advantage of cells electrical resistance to determine cell number and size. They are fast, accurate and cheaper than flow cytometers. However, they are rarely used for only cell counting due to their considerable cost compared to manual counting3. Flow cytometers, on the other hand, are expensive but they have many applications such as cell counting, analysis of the cells shape, structure and measuring internal cell markers4,5. Machines that use either of these two principles are available from many manufacturers. Manual counting is affordable but time-consuming and subject to bias while the automated methods come with a fraction of the time required for the manual counting but using expensive machines6.
Other common cell culture procedures are in vitro cell motility assays, namely, cell migration and invasion7. Migration and invasion assays are commonly used to investigate cell motility and invasiveness in response to a chemotactic response. In addition, they are widely used to study embryonic development, differentiation, inflammatory response, and metastasis of multiple cell types7-11. Cells that have migrated or invaded through the porous membrane of a migration assay can be quantified in two different ways. Firstly, by staining the cells with a fluorescent dye, dissociation from the membrane, and quantification using a fluorescent reader12. A limitation of this method of quantification is that no record can be retained of the membranes and there is no possibility for further analysis13. The second quantification method is for migrated/invaded cells to be fixed and stained with fluorescent dye or more commonly, with cytological dyes such as crystal violet, toluidine blue dye or hematoxylin; then cells are quantified manually using inverted microscopic images of these membranes which is a very time-consuming task12,13.
To overcome the drawbacks of manual cell counting, two reliable and accurate automated cell counters for cell concentration and for the migration assay were developed. These automated cell counter algorithms were developed for ImageJ as a plugin using Oracle&#39;s Java computer language. ImageJ is a public and widely-used image processing tool developed by the National Institute of Health (NIH)14,15; thus, writing these plugins for ImageJ facilitates easy integration into the biological community.
Automation of cell counting ensures high throughput and reproducibility compared to manual counting. Although other available software and plugins can be used to calculate cell concentration through image analysis5,16,17, Cell Concentration Calculator plugin is fast and can also handle dilutions of cells and treatments. Moreover, all results and calculations from these two counters can be saved and exported. The two plugins described in this paper are optimized for the use of a phase contrast microscope for live cell imaging and large field of view (entire membrane capture) imaging for migration assay membranes through the use of a dissecting scope. The plugins are freely available for download with installation instructions from: http://peng.lab.yorku.ca/imagej-plugins.

<table><tbody><tr><td>HyClone Classical Liquid Media: RPMI 1640 - With L-Glutamine</td><td>Fisher Scientific</td><td>SH3002702</td><td>Cell culturing media&amp;nbsp;</td></tr><tr><td>Fetal bovian serum (FBS)</td><td>GIBCO BRL</td><td>P00015</td><td>Media suppliment</td></tr><tr><td>HTR8/SVneo trophoblast cell line</td><td>Cells were obtained from Dr. Charles Graham (Queen&amp;rsquo;s University, Kingston, Canada)</td><td></td><td>Software is designed to work with any cell line.</td></tr><tr><td>Trypsin</td><td>GIBCO BRL</td><td>27250-018</td><td>Prepared as 0.20% (w/v) in 10 &amp;micro;M EDTA 1x PBS</td></tr><tr><td>Accutase</td><td>Innovative Cell Technologies</td><td>AT104</td><td></td></tr><tr><td>10 cm cell culture plates</td><td>SARSTEDT</td><td>833902</td><td>Any tissue culture treated plates will be suitable</td></tr><tr><td>Transwell Polyester Membrane Inserts - 8.0&amp;nbsp;&amp;micro;m Pore size</td><td>Costar 3422 ordered from Fisher Scientific</td><td>7200150</td><td>For 24-well plates; Pore size: 8.0 &amp;micro;m; 6.5 mm diameter; 0.33 cm&lt;sup&gt;2&lt;/sup&gt; growth area</td></tr><tr><td>HARLECO Hematology Stains and Reagents, EMD Millipore - Soluntions 1, 2 &amp;amp; 3</td><td>EMD Millipore and ordered from VWR</td><td>65044A, B, &amp;amp; C</td><td>Hemacolor stain set consists of three 500 ml (16.9 oz.) poly bottles &amp;amp; includes a methanol fixative (Solution 1), an eosin or acid stain (Solution 2), and a methylene blue or basic stain (Solution 3)</td></tr><tr><td>Cotton Tipped Applicator</td><td>Puritan Medical</td><td>806-WC</td><td></td></tr><tr><td>Single-edge industrial razor blades</td><td>VWR</td><td>55411 - 055</td><td>Thickness: 0.30 mm (0.012&quot;)</td></tr><tr><td>Microscope Slides - Precleaned/Plain</td><td>Fisher Scientific</td><td>12550A3</td><td>Dimentions: 25 mm x 75 mm x 1.0 mm</td></tr><tr><td>Fisherbrand Cover Glasses - Rectangles no. 1</td><td>Fisher Scientific</td><td>12-545E</td><td>Thickness: 0.13 to 0.17 mm; Size: 50 mm x 22 mm</td></tr><tr><td>Fisher Chemical Permount Mounting Medium</td><td>Fisher Scientific</td><td>SP15-500</td><td></td></tr><tr><td>Leica Stereo dissecting microscope</td><td>Leica Microsystems</td><td></td><td>The microsope is equipped with Leica microscope camera Model MC170 HD &amp;amp; camera software is Leica App. Suite (LAS E2) Version 3.1.1 [Build: 490]. Microscope parts:&amp;nbsp; LED3000 Spot Light Illumination Model: MEB126, Leica M80 Optic Carrier Model M80, Objective achromat 1.0X, WD=90&amp;nbsp;mm Model: MOB306 &amp;amp; Objective achromat 0.32X, WD=303&amp;nbsp;mm Model: MOB315, Video Objective 0.5X Model: MTU-293</td></tr><tr><td>Hemacytometer</td><td>Assistant Germany&amp;nbsp;</td><td></td><td>0.100 mm Depth - 0.0025 mm&lt;sup&gt;2&lt;/sup&gt;</td></tr><tr><td>Olympus inverted light microscope</td><td>Olympus Corporation</td><td>CKX41SF</td><td>The microsope is equipped with Lumenera Infinity 1-2&lt;br /&gt;2.0 Megapixel CMOS Color Camera &amp;amp; camera software is Infinity analyze Version 6.5.2</td></tr><tr><td>Laminar flow cabinet 1300 Series A2</td><td>Thermo Scientific&amp;nbsp;</td><td>Model: 1375</td><td>Any laminar flow cabinet for cell culture work will be suitable&amp;nbsp;</td></tr><tr><td>Cell culture incubator</td><td>Thermo Scientific&amp;nbsp;</td><td>Model: 370</td><td>Any cell culture incubator will be suitable - Cells were cultured under humidefied environment, 5% CO&lt;sub&gt;2&lt;/sub&gt;, 37 &amp;deg;C&amp;nbsp;</td></tr><tr><td>ImageJ</td><td>NIH</td><td>Version 1.50e</td><td>Minimum version required</td></tr><tr><td>Java Runtime Environment</td><td>Oracle</td><td>Version 1.8.0_66</td><td>Minimum version required</td></tr></tbody></table>

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.

Cell Concentration Calculator
Figure 1 presents the overall process of CCC calibration and countable image acquisition. Figure 1A and 1B depict the P-square calibration image and calculation of P-square length in pixels. CCC determines cell concentration in a given volume using the formula:
<img alt="Equation 1" src="/files/ftp_upload/54719/54719eq1.jpg"...

Watch this Scientific Journal Video about Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins at JoVE.com

Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins

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 ...

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Research

JoVE Journal

Biology

46.7K Views.  York University. 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.

Video: Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins

A Method of Targeted Cell Isolation via Glass Surface Functionalization

One of the limiting factors to the adoption and advancement of personalized medicine is the inability to develop diagnostic tools to probe individual nuances in expression from patient to patient. Current methodologies that try to separate cells to fill this niche result in disruption of physiological expression, making the separation technique useless as a diagnostic tool. In this protocol, we describe the functionalization and optimization of a surface for the cellular capture and release. This functionalized surface integrates biotinylated antibodies with a glass surface functionalized with an aminosilane (APTES), desthiobiotin and streptavidin. Cell release is facilitated through the introduction of biotin, allowing the recollection and purification of cells captured by the surface. This release is done through the targeting of the secondary moiety desthiobiotin, which results in a much more gentle release paradigm. This reduction in harsh reagents and shear forces reduces changes in cellular expression. The functionalized surface captures up to 80% of cells in a single cell mixture and has demonstrated 50% capture in a dual-cell mixture. Applications of this technology to xenografts and cancer separation studies are investigated. Quantification techniques for surface verification such as plate reader and ImageJ analyses are described as well.

This protocol describes customizable surface functionalization of the desthiobiotin, streptavidin, and APTES system in order to isolate specific cell types of interest. In addition, this manuscript covers the applications, optimization, and verification of this process.

One of the limiting factors to the adoption and advancement of personalized medicine is the inability to develop diagnostic tools to probe individual ...

Cancer Research

Improved Visualization and Quantitative Analysis of Drug Effects Using Micropatterned Cells

To date, most HCA (High Content Analysis) studies are carried out with adherent cell lines grown on a homogenous substrate in tissue-culture treated micro-plates. Under these conditions, cells spread and divide in all directions resulting in an inherent variability in cell shape, morphology and behavior. The high cell-to-cell variance of the overall population impedes the success of HCA, especially for drug development. The ability of micropatterns to normalize the shape and internal polarity of every individual cell provides a tremendous opportunity for solving this critical bottleneck 1-2.
To facilitate access and use of the micropatterning technology, CYTOO has developed a range of ready to use micropatterns, available in coverslip and microwell formats. In this video article, we provide detailed protocols of all the procedures from cell seeding on CYTOOchip micropatterns, drug treatment, fixation and staining to automated acquisition, automated image processing and final data analysis. With this example, we illustrate how micropatterns can facilitate cell-based assays. Alterations of the cell cytoskeleton are difficult to quantify in cells cultured on homogenous substrates, but culturing cells on micropatterns results in a reproducible organization of the actin meshwork due to systematic positioning of the cell adhesion contacts in every cell. Such normalization of the intracellular architecture allows quantification of even small effects on the actin cytoskeleton as demonstrated in these set of protocols using blebbistatin, an inhibitor of the actin-myosin interaction.

Adhesive micropatterns that normalize cellular architecture can be used to increase sensitivity in the detection of drug effects, improve reproducibility and simplify automated image acquisition and analysis. Such technology will benefit drug/siRNA screening assays, performed on conventional cell culture supports and consequently suffering from excessive cell-to-cell variability.

To date, most HCA (High Content Analysis) studies are carried out with adherent cell lines grown on a homogenous substrate in tissue-culture treated ...

Bioengineering

Automated Quantification of Hematopoietic Cell &#8211; Stromal Cell Interactions in Histological Images of Undecalcified Bone

Confocal microscopy is the method of choice for the analysis of localization of multiple cell types within complex tissues such as the bone marrow. However, the analysis and quantification of cellular localization is difficult, as in many cases it relies on manual counting, thus bearing the risk of introducing a rater-dependent bias and reducing interrater reliability. Moreover, it is often difficult to judge whether the co-localization between two cells results from random positioning, especially when cell types differ strongly in the frequency of their occurrence. Here, a method for unbiased quantification of cellular co-localization in the bone marrow is introduced. The protocol describes the sample preparation used to obtain histological sections of whole murine long bones including the bone marrow, as well as the staining protocol and the acquisition of high-resolution images. An analysis workflow spanning from the recognition of hematopoietic and non-hematopoietic cell types in 2-dimensional (2D) bone marrow images to the quantification of the direct contacts between those cells is presented. This also includes a neighborhood analysis, to obtain information about the cellular microenvironment surrounding a certain cell type. In order to evaluate whether co-localization of two cell types is the mere result of random cell positioning or reflects preferential associations between the cells, a simulation tool which is suitable for testing this hypothesis in the case of hematopoietic as well as stromal cells, is used. This approach is not limited to the bone marrow, and can be extended to other tissues to permit reproducible, quantitative analysis of histological data.

A strategy to quantitatively analyze histological data in the bone marrow is presented. Confocal microscopy of fluorescently labeled cells in tissue sections results in 2-dimensional images, which are automatically analyzed. Co-localization analyses of different cell types are compared to data from simulated images, giving quantitative information about cellular interactions.

Confocal microscopy is the method of choice for the analysis of localization of multiple cell types within complex tissues such as the bone marrow. ...

Developmental Biology

Quantitative Immunofluorescence to Measure Global Localized Translation

The mechanisms regulating mRNA translation are involved in various biological processes, such as germ line development, cell differentiation, and organogenesis, as well as in multiple diseases. Numerous publications have convincingly shown that specific mechanisms tightly regulate mRNA translation. Increased interest in the translation-induced regulation of protein expression has led to the development of novel methods to study and follow de novo protein synthesis in cellulo. However, most of these methods are complex, making them costly and often limiting the number of mRNA targets that can be studied. This manuscript proposes a method that requires only basic reagents and a confocal fluorescence imaging system to measure and visualize the changes in mRNA translation that occur in any cell line under various conditions. This method was recently used to show localized translation in the subcellular structures of adherent cells over a short period of time, thus offering the possibility of visualizing de novo translation for a short period during a variety of biological processes or of validating changes in translational activity in response to specific stimuli.

This manuscript describes a method to visualize and quantify localized translation events in subcellular compartments. The approach proposed in this manuscript requires a basic confocal imaging system and reagents and is rapid and cost-effective.

The mechanisms regulating mRNA translation are involved in various biological processes, such as germ line development, cell differentiation, and ...

Biochemistry

Monitoring Protein Aggregation Kinetics In Vivo using Automated Inclusion Counting in Caenorhabditis elegans

Protein aggregation into insoluble inclusions is a hallmark of a variety of human diseases, many of which are age-related. The nematode Caenorhabditis elegans is a well-established model organism that has been widely used in the field to study protein aggregation and toxicity. Its optical transparency enables the direct visualization of protein aggregation by fluorescence microscopy. Moreover, the fast reproductive cycle and short lifespan make the nematode a suitable model to screen for genes and molecules that modulate this process.
However, the quantification of aggregate load in living animals is poorly standardized, typically performed by manual inclusion counting under a fluorescence dissection microscope at a single time point. This approach can result in high variability between observers and limits the understanding of the aggregation process. In contrast, amyloid-like protein aggregation in vitro is routinely monitored by thioflavin T fluorescence in a highly quantitative and time-resolved fashion.
Here, an analogous method is presented for the unbiased analysis of aggregation kinetics in living C. elegans, using a high-throughput confocal microscope combined with custom-made image analysis and data fitting. The applicability of this method is demonstrated by monitoring inclusion formation of a fluorescently labeled polyglutamine (polyQ) protein in the body wall muscle cells. The image analysis workflow allows the determination of the numbers of inclusions at different timepoints, which are fitted to a mathematical model based on independent nucleation events in individual muscle cells. The method described here may prove useful to assess the effects of proteostasis factors and potential therapeutics for protein aggregation diseases in a living animal in a robust and quantitative manner.

Monitoring Protein Aggregation Kinetics In Vivo using Automated Inclusion Counting in Caenorhabditis elegans

Here, a method is presented for the analysis of protein aggregation kinetics in the nematode Caenorhabditis elegans. Animals from an age-synchronized population are imaged at different time points, followed by semiautomated inclusion counting in CellProfiler and fitting to a mathematical model in AmyloFit.

Protein aggregation into insoluble inclusions is a hallmark of a variety of human diseases, many of which are age-related. The nematode Caenorhabditis ...

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

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

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

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

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

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Quantification of cells is necessary for a wide range of biological and biochemical studies. Conventional image analysis of cells typically employs either fluorescence detection approaches, such as immunofluorescent staining or transfection with fluorescent proteins or edge detection techniques, which are often error-prone due to noise and other non-idealities in the image background. 
We designed a new algorithm that could accurately count and distinguish macrophages and fibroblasts, cells of different phenotypes that often colocalize during tissue regeneration. MATLAB was used to implement the algorithm, which differentiated distinct cell types based on differences in height from the background. A primary algorithm was developed using an area-based method to account for variations in cell size/structure and high-density seeding conditions. 
Non-idealities in cell structures were accounted for with a secondary, iterative algorithm utilizing internal parameters such as cell coverage computed using experimental data for a given cell type. Finally, an analysis of coculture environments was carried out using an isolation algorithm in which various cell types were selectively excluded based on the evaluation of relative height differences within the image. This approach was found to accurately count cells within a 5% error margin for monocultured cells and within a 10% error margin for cocultured cells.

We present a method, which utilizes a generalizable area-based image analysis approach to identify cell counts. Analysis of different cell populations exploited the significant cell height and structure differences between distinct cell types within an adaptive algorithm.

Quantification of cells is necessary for a wide range of biological and biochemical studies. Conventional image analysis of cells typically employs either ...

Studying the Protein Quality Control System of D. discoideum Using Temperature-controlled Live Cell Imaging

The complex lifestyle of the social amoebae Dictyostelium discoideum makes it a valuable model for the study of various biological processes. Recently, we showed that D. discoideum is remarkably resilient to protein aggregation and can be used to gain insights into the cellular protein quality control system. However, the use of D. discoideum as a model system poses several challenges to microscopy-based experimental approaches, such as the high motility of the cells and their susceptibility to photo-toxicity. The latter proves to be especially challenging when studying protein homeostasis, as the phototoxic effects can induce a cellular stress response and thus alter to behavior of the protein quality control system. 
Temperature increase is a commonly used way to induce cellular stress. Here, we describe a temperature-controllable imaging protocol, which allows observing temperature-induced perturbations in D. discoideum. Moreover, when applied at normal growth temperature, this imaging protocol can also noticeably reduce photo-toxicity, thus allowing imaging with higher intensities. This can be particularly useful when imaging proteins with very low expression levels. Moreover, the high mobility of the cells often requires the acquisition of multiple fields of view to follow individual cells, and the number of fields needs to be balanced against the desired time interval and exposure time.

Studying the Protein Quality Control System of D. discoideum Using Temperature-controlled Live Cell Imaging

The social amoebae Dictyostelium discoideum has recently been established as a system to study protein misfolding and proteostasis. Here, we describe a new imaging-based methodology to study temperature-induced protein aggregation and the cellular stress response in D. discoideum.

The complex lifestyle of the social amoebae Dictyostelium discoideum makes it a valuable model for the study of various biological processes. Recently, we ...

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.

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 ...

Automated Imaging and Analysis for the Quantification of Fluorescently Labeled Macropinosomes

Macropinocytosis is a non-specific fluid-phase uptake pathway that allows cells to internalize large extracellular cargo, such as proteins, pathogens, and cell debris, through bulk endocytosis. This pathway plays an essential role in a variety of cellular processes, including the regulation of immune responses and cancer cell metabolism. Given this importance in biological function, examining cell culture conditions can provide valuable information by identifying regulators of this pathway and optimizing conditions to be employed in the discovery of novel therapeutic approaches. The study describes an automated imaging and analysis technique using standard laboratory equipment and a cell imaging multi-mode plate reader for the rapid quantification of the macropinocytic index in adherent cells. The automated method is based on the uptake of high molecular weight fluorescent dextran and can be applied to 96-well microplates to facilitate assessments of multiple conditions in one experiment or fixed samples mounted onto glass coverslips. This approach is aimed at maximizing reproducibility and reducing experimental variation while being both time-saving and cost-effective.

Automated assays using multi-well microplates are advantageous approaches for identifying pathway regulators by allowing the assessment of a multitude of conditions in a single experiment. Here, we have adapted the well-established macropinosome imaging and quantification protocol to a 96-well microplate format and provide a comprehensive outline for automation using a multi-mode plate reader.

Macropinocytosis is a non-specific fluid-phase uptake pathway that allows cells to internalize large extracellular cargo, such as proteins, pathogens, and ...

Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins

Summary

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Chapters in this video

A Method of Targeted Cell Isolation via Glass Surface Functionalization

Improved Visualization and Quantitative Analysis of Drug Effects Using Micropatterned Cells

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Quantitative Immunofluorescence to Measure Global Localized Translation

Monitoring Protein Aggregation Kinetics In Vivo using Automated Inclusion Counting in Caenorhabditis elegans

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

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Studying the Protein Quality Control System of D. discoideum Using Temperature-controlled Live Cell Imaging

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

Automated Imaging and Analysis for the Quantification of Fluorescently Labeled Macropinosomes