title: An approach for extensibly profiling the molecular states of cellular subpopulations.
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.
authors.affiliation: Green Center for Systems Biology and Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
authors.forename: Lit-Hsin
authors.initials: LH
authors.lastname: Loo
authors.forename: Hai-Jui
authors.initials: HJ
authors.lastname: Lin
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Steininger
authors.forename: Yanqin
authors.initials: Y
authors.lastname: Wang
authors.forename: Lani F
authors.initials: LF
authors.lastname: Wu
authors.forename: Steven J
authors.initials: SJ
authors.lastname: Altschuler
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D015415:Biomarkers / Q000378:metabolism*; D002478:Cells, Cultured / Q000166:cytology* / Q000378:metabolism*; D020869:Gene Expression Profiling / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*
chemical_list: D015415:Biomarkers