Hi! I am new at the community and also a new user of CellProfiler
I am having some doubts and troubles with the analysis of RNAscope images. I would like to count dots within a population of neurons, so I select the expressing cells first and then I count specific dots only in these cells.
My starting images are in .nd2, but I am using a pipeline that starts with TIFF RGB images, so I first convert my images to this format using ImageJ software. When I establish the parameters to detect dots (IdentifyPrimaryObjects), I tell the program to detect objects with a typical diameter of 2-8 pixel units, to avoid large clusters of dots and background. I also choose “the method to distinguish clumped objetcs” as “Intensity”. When I run the pipeline in my images, it detects dots of different size within these cells that are all counted as dots. I mean, for example, for one cell, it detects 45 dots, but not all of them have the same size. This does not happen in all cells, because some of them have less expression than others; in that case, the “objects” are more separated and are counted as smaller dots.
At this point, I have several concerns:
Which is a pixel unit in an image? Every square is a pixel? Because when I established a 2-8 threshold for object size, I realize that some of the clusters that the program detects are bigger than this threshold (8). How could it be possible? Is “object” a synonym of “pixel” and “dot” is a group of these objects/pixels based on the method I choose: by intensity? If this is the case, what I set to the program is the size of pixel units, so it makes sense that some dots were bigger than 8, because they are formed by several pixels/objects.
Something similar also happen with nuclei, the program is not able to detect all the nuclei in my images. In this case, in IdentifyPrimaryObjects I am using the “Minimum cross entropy” method, but changing to another one remains the same or similar. The “typical diameter of objects” in this case was set to 10-80, and increasing the last threshold does not change the results. “The method to distinguish clumped object” is by “shape”. It seems not to be related to differences in DAPI expression o nuclei size, because several of the nuclei that are not detected are the biggest ones or even have the highest expression.
Maybe when converting images to TIFF, they lose resolution, so the separation into “single” dots is more dificult, although I think it would not be possible to separate them neither with the original .nd2 image, because there is a high expression in several cells. Any idea to improve clusters separation not to get so large dots?
I do not know if the differences in dot size are normal and assumable, or if people usually count these different dots and include all of them as “dots”, “foci” or “expression” when representing data. In papers using RNAscope, it seems that they include all dots when counting dots/expression, because I find in their images dots/foci with different sizes.
Any idea or help would be really appreciated. Thanks a lot!
I attach examples of dot detection with different sizes (and those large dots), and also nuclei detection.
Examples.zip (104.1 KB)