Counting DAPI stained nuclei in epidermis

I would like to count the number of nuclei in a specific region of my tissue samples; how can I specify the program to do this? I understand you can crop images, but I would like to crop them to only include the epidermis. Is there a way to do this using Cell Profiler? (Like maybe by tracing the area I want to crop beforehand?)

Thank you!

I think you could use IdentifyObjectsManually and select that region as an object and then detect the nuclei objects in that region, but in this way you have to trace by hand for every single image.
It’s hard to say without seeing an image, but if the region of interest could be distinguished from the rest of the image (based on intensity) you could try detecting that region and then masking your large image with that detected region.
We could provide better help if you could upload an annotated image.


Thank you so much for your help and quick response!

I tried IdentifyObjectsManually but I realized that, as you said, I would have to trace each nuclei individually. Is there a modified version of it where I can trace the desired epidermal region, and then I can apply “IdentifyPrimaryObjects” to this specified region? Or is there a way to crop the image so that it only includes the epidermis?

I have included a sample picture of DAPI staining where the epidermal region which I want to quantify is traced in red. There is greater intensity here, since the cells are more squished together. Is there a way to use that maybe to detect if it is the epidermis or not? (Like checking if the distance between cells exceeds a certain threshold?)

Thank you again for your help! I am really new to this and I appreciate the support.

(I cannot use pictures from my own lab so I used an image from the paper " TRPV3 Channel in Keratinocytes in Scars with Post-Burn Pruritus", I hope that’s okay!)

Based on this image, you could use “Smooth” module to basically smooth the image and then use “IdentifyPrimaryObject” (adjust the size to only detect large objects) to detected these squished cells as a region. Then use “MaskImage” to mask the original (not the smoothed) image, then you’d have the region of interest. Then you could use the “IdentifyPrimaryObject” to detect the nuclei in this region.

You could also try “Threshold” module (instead of the the first IPO above) if the intensity of this region is higher (adjust it to detect only the higher intensities).

Please take a look at our workshops and tutorials for more help on some of these modules.

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Thank you so much for your help! I really appreciate it!!

I tried the “Smooth” module and looked at the tutorials, but I am still a little confused as to how use it. I understand the principle that the squished cells would be blurred into one large blue mass, and this could be identified as one object. However, whenever I try the “Smooth” module, I get a weird output. I have attached the image below. Also, when I convert my original image to grayscale (called “OrigBlue”) in NamesAndTypes, the image essentially turns black. Is there a way to change the threshold?

Thank you!!

For the smooth module, you need to choose the right smoothing method based on your image and what you would want to do. In this case, I think you can start with the Gaussian filter and check.

For the images, if you hover over the image, you could see the pixel intensity in the bottom of the window, check to see if they show 0 or a different number? If they show more than 0, then the image contains information, and you can R-click on the image, Image contrast—> log normalized and increase the normalization factor to have a better visual of the image content.
Hope this helps!

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Yes, thank you so much!!! :slight_smile:

You are welcome! :slight_smile:

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