I have a pipeline that detects number of spots per nucleus. The spot is a telomere protein (TRF2) and I expect the number of spots to be similar in all cells (~138 for this hypertriploid cell line, U2OS). There could be some variability due to genomic instability in these cancer cells or due to multiple telomeres overlapping.
However, the number of spots per nucleus has a really wide spread in each sample. The best I can do is to set the threshold quite stringent (average of ~45 spots per nucleus) in order to avoid many cells with false positive spots (>150). My pipeline already filters out large cells so I assume G2/S cells are not counted. 2019_1210_Lois_SLX4_Tin2_TRF2_Colocalization_v2.cpproj (1.9 MB)
Any ideas on the following:
- how to get a more narrow spread of TRF2 per nucleus? I am using global thresholding, would adaptive help?
- can I compare the number of spots per nucleus across samples with automatic thresholding? or do I need to do manual?
- I am doing object based colocalization (number of red spots colocalized with green spots with a Pearson’s coefficient above a threshold, see pipeline). Can I do intensity based colocalization after setting a threshold? It seems like it is working but I worry that if the threshold is binary the coefficient is not legitimate.
Images are here.
I acquired them on a StellarVision SV20 microscope. This is synthetic aperture optics so there is some artifact.