Now, type the value from the length column into the p-square length text box in the cell concentration calculator. Then, push the M key to display the results window. 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. This action fills in the image width and height text boxes with the image resolution in pixels. In ImageJ, under the cell concentration calculator, open the image and click on the Get Image Dimension button. Now place a standard hemocytometer onto the microscope stage and capture an image for the image volume calibration step. Then, within the microscope software, set the image capture settings to their default values. Switch to the 4x objective and ensure that the phase contrast filters are in use. To begin, set the microscope illumination to maximum. The main advantage of this method, is that it provides fast and accurate cell counts while including multiple filters and analysis tools. This protocol can help researchers quantify cell number required for common techniques and in vitro cell motility assays. However I am not fully satisfied with this approach.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. the nuclei of the cells which lack expression are not counted and most of the nuclei of the cells which have expression are counted. Comparison of the outcomes of such analysis with examination “by eye” reveals that it quite OK i.e. I am aware that this is biased by the fact that all cells in which the expression is located a bit farther from nucleus will be counted as negative. So I simply pick nuclei which have at least some contact with my protein of interest and consider number of such nuclei as a count of the positive cells. I set a binary picture of nuclei channel as “Object image” and a binary picture of my protein channel as “Selector image” and “Object overlap” feature I set at 1%. What I am using now is Binary Feature Extractor function in BioVoxxel plugin. Thus, some another solution must be applied.
Unfortunately this “supporting” staining was not supportive enough i.e. Of course I wouldn’t like to draw the cells’ borders manually in hundreds of pictures and I hoped that some edge detection tool would do it for me.
My first idea was that I could use some edge detection tool to obtain a map of the cells’ borders based on this staining and then somehow overlap it with a map of my protein of interest expression and use this to count positive and negative cells. There is also staining of yet another protein which locates in cell membrane which I hoped would be useful to delineate cells’ borders for the analysis. I have wide-field images of immunostained cells with nuclei stained with DAPI and my protein of interest stained with another fluorophore. Expression of the protein is scattered all over the cell with no regular pattern with some tendency to be in a proximity of nucleus. Maybe someone more familiar with image analysis can help me with this. I am using ImageJ to solve this problem but I believe that there is better solution than the one that I came up with. Of course I would like the computer to do it for me. I would like to quantify the fraction of both depending on certain culture conditions. Some of them do express it, some others don’t. My protein of interest is not equally expressed among the cells in culture.
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