Abstract

Microscopy often reveals the existence of phenotypically distinct cellular subpopulations. However, additional characterization of observed subpopulations can be limited by the number of biomolecular markers that can be simultaneously monitored. Here we present a computational approach for extensibly profiling cellular subpopulations by freeing one or more imaging channels to monitor additional probes. In our approach, we trained classifiers to re-identify subpopulations accurately based on an enhanced collection of phenotypic features extracted from only a subset of the original markers. Then we constructed subpopulation profiles step-wise from replicate experiments, in which cells were labeled with different but overlapping marker sets. We applied our approach to identify molecular differences among subpopulations and to identify functional groupings of markers, in populations of differentiating mouse preadipocytes, polarizing human neutrophil-like cells and dividing human cancer cells.