The overall goal of this protocol is to use digital analysis tools to quickly make accurate counts of cells in a suspension or on a migration in an invasion assay membrane. This protocol can help researchers quantify cell number required for common techniques and in vitro cell motility assays. The main advantage of this method, is that it provides fast and accurate cell counts while including multiple filters and analysis tools.
To begin, set the microscope illumination to maximum. Switch to the 4x objective and ensure that the phase contrast filters are in use. Then, within the microscope software, set the image capture settings to their default values.
Now place a standard hemocytometer onto the microscope stage and capture an image for the image volume calibration step. Adjust the exposure time as needed. In ImageJ, under the cell concentration calculator, open the image and click on the Get Image Dimension button.
This action fills in the image width and height text boxes with the image resolution in pixels. Next, select the straight line tool and draw a straight line across the entire length of the hemocytometer's primary p-square by clicking and dragging the cursor. Then, push the M key to display the results window.
Now, type the value from the length column into the p-square length text box in the cell concentration calculator. Then, click on the Calculate Image Volume button to output the image volume into the image volume text box. Finally, click the Save button to complete the calibration of the plugin.
For sample analysis, load 10 microliters of cells into both chambers of a hemocytometer slide and adjust the focus so that the interior of the cells are darker than the cell membrane to provide focus within the central cross-section of the cell and not at the poles. Then, further adjust the exposure so that the cells are not overexposed. Slightly visible hemocytometer lines are acceptable.
Now, capture five to ten non overlapping images of the central region of the hemocytometer. The resolution and magnification of each image must be the same as was for the volume calibration image. Next, in the cell concentration calculator, click on Count Cells and in the popup window, select a folder to be counted.
Next, in the sample number input box, enter the number of images taken per chamber, and click OK.The plugin will now collect the data. See the text for more details. To begin, within the microscope software, set the image capture settings to their default values.
For the stage of the dissecting scope, use a solid white background and use an above stage light source, preferably two flexible LED lights. Now, place a completed migration assay membrane slide onto the stage. Looking at the real time image displayed by the software, manually adjust the magnification so that the edges of a single membrane are just within the camera's field of view.
Then, adjust the light source positions and exposure times to reproduce, as closely as possible, the ideal image. Which has minimal background color and a uniformly illuminated membrane. The accuracy of cell counting is dependent on high quality image acquisition.
It is therefore important to capture the best image achievable by the user's camera for the most efficient use of the plugins. Now, remove the slide and white balance the image in the microscope software to complete the calibration. Immediately before imaging, flatten each membrane by applying pressure to the cover slip to remove as many trapped bubbles as possible.
Now, within the software, set the capture folder location. Then, capture one image per membrane, saving the image files as TIFs using the naming convention of sample 001 with incremental increases to the number for each image. This naming convention is required for the plugin to find the files.
If flatfield correction is desired, for each slide find an empty area and take a blank image. Save the file as sample name blank. Next, in ImageJ, open the TC plugin.
Within TC, click on the Flatfield button, and from the Choose Directory dialogue box select the folder where the membrane images were saved. Now, open a migration assay membrane image and select Color Threshold. In the window, set the method to Shanbhag.
Set the threshold color to white. Set the color space to RGB and uncheck the dark background option. Next, adjust the top sliders to zero and the bottom sliders to 255 to make the image fully white.
Once white, adjust the green and red sliders until only the nuclei are visible. In the TC plugin, copy the values of the RGB sliders into the associated configuration settings RGB threshold text boxes. Then, click the Add/Modify button and overwrite the configuration.
Within the configuration settings panel, click Save to write the settings to the hard drive. Now, to count the images with the TC plugin, click on Count Folder and select the folder to be counted. The program will then process the images and automatically add the data to the main table.
Now, in the main table, there will be a check mark in the Calibrate? Column if in the image is flagged for calibration based on its resemblance to the ideal metrics. Select all the samples flagged for calibration.
Then, right click and select Recount and Suggested Size. This will recount the images with the suggested minimum particle area. Then, right click again and select Show Plot.
An ideal image will have a graph resembling a long right-tailed normal distribution. If required, adjust the lower size range or RGB threshold settings by selecting the sample, right clicking, and selecting Recount and Manual Settings. Then, enter the desired settings and click Count to recount the image.
To adjust the counts manually, select the samples, right click the table, and select Open image with counts. The original image will open with red marks that represent each particle counted by the plugin. Additionally, the Show counted binary image option can be used to check how well individual cells are being resolved by the color thresholding.
To manually add a count, hold the control key down and left click. This will add a marker at the cursor location which gets added to the total count. To manually remove a marker, right click the image and the plugin will remove the marker closest to the cursor.
To remove a group of markers, select a region of interest and right click the region while holding down the control key. The process of the cell concentration calculator depends on the p-square calibration image and calculation of p-square length and pixels. A hemocytometer's p-square has a volume of 100 nanoliters, and given this constant, the total image volume can be calculated after converting pixels to millimeters.
A comparison of manual and automated counts over 57 images from various experiments and cell types, shows that there is very tight correlation between both methods. Concentrations between five million and 2, 000 cells per milliliter were counted accurately using the automated process. The metric Q represents the overall images clarity based on the distribution of distinct particle sizes.
An adequate Q is greater than 0.5. Applying these qualifiers to a selection of modest to excellent images with regards to brightness and background noise, the calibrated image counts were significantly closer to manual counts than uncalibrated image counts. After watching this video, you should have a good understanding of how to accurately and efficiently count cells in suspension and from cell motility assays using CCC and TC ImageJ plugins.
Once the TC plugin is mastered, in which acquisition, quantification, and adjustment of cell counts can be made in under an hour. Both plugins rely on the particle counting feature from ImageJ and can be modified by editing the macros used by ImageJ. This offers greater flexibility for the user to expand the scope of the plugins to other particles.
