Minh, thanks so much. I actually didn’t see you responded until now(!), so thanks for your prompt help.
The initial segmenting based on the membrane works quite well. I’m still optimizing parameters and images a bit, but this will be the strategy moving forward.
As you can see in the sample image I gave, there are GFP-positive nuclei, as well as GFP-negative in my data set. I would like to analyze individual cell features in the context of their GFP+ clonal properties (and versus wild-type GFP-). Specifically, I want to measure individual cell shapes while classifying them with respect to 1) if they are part of a clone (i.e. GFP+ or GFP-), and 2) which clone they are part of (and perhaps size/shape of each clone; although just distinguishing and IDing individual cells between multiple clones within one field of view is most important).
I’m trying to figure out the best way to do this. It seems like options are: 1) use ‘ClassifyObjects’, which could be done with individual cells as the input objects and two classification measurements (one for clone number, one for GFP+ vs. GFP-), 2) use ‘RelateObjects’ to relate individual cells (child) with clones (parent) objects, or 3) use PrimaryObjectIdentification (clone) and SecondaryObjectIdentification (individual cell). Do you have thoughts on the best approach? Other ideas? Is their a way to connect previously classified neighbors?
Also I’m having trouble segmenting/identifying ‘whole-clones’ as objects (all GFP+ cells that are touching). Since the nuclei are well-separated, ObjectIdentification wants to grab individual cells, even if the input size estimates are drastically altered.
All help appreciated,