I’m a new user of ImageJ, and I would like to discriminate activated neurons versus non-activated neurons after a stimulus based on the fluorescence intensity of those neurons.
I stained brain tissue sections (25µm thick) with antibodies that targeted the marker of neuronal activation c-Fos and revealed activated neurons in red thanks to a red fluorophore attached to the antibody.
However, after a long optimisation of the staining protocol, I have difficulties in determining which neurons are activated.
Indeed, I have different light intensities for activated neurons on a single image!
It may be caused by the image acquisition (but I can’t modify it), by the depth of the tissue ( but I can’t make thinner sections) or by a bad staining (but I already tried a lot of conditions).
(Another point is that my DAPI staining didn’t work properly, so I can’t detect the neurons via an independent channel: I’ll have to detect AND measure neurons with one staining only, so automated threshold will create a bias on neurons selection…)
So the critical point is to know which threshold to apply so I can be sure that above this threshold, cells are activated.
Pre-processing does not help to chose the right threshold ( I tried to subtract the background, to apply a Gaussian blur, …).
Do you think a statistical determination of the threshold by the plugin Statistical Region Merging could work? What about using hysteresis thresholding?
I attache a sample picture to show you my problem:
Thank you in advance for your help.