title: Characterizing heterogeneous cellular responses to perturbations.
abstract: Cellular populations have been widely observed to respond heterogeneously to perturbation. However, interpreting the observed heterogeneity is an extremely challenging problem because of the complexity of possible cellular phenotypes, the large dimension of potential perturbations, and the lack of methods for separating meaningful biological information from noise. Here, we develop an image-based approach to characterize cellular phenotypes based on patterns of signaling marker colocalization. Heterogeneous cellular populations are characterized as mixtures of phenotypically distinct subpopulations, and responses to perturbations are summarized succinctly as probabilistic redistributions of these mixtures. We apply our method to characterize the heterogeneous responses of cancer cells to a panel of drugs. We find that cells treated with drugs of (dis-)similar mechanism exhibit (dis-)similar patterns of heterogeneity. Despite the observed phenotypic diversity of cells observed within our data, low-complexity models of heterogeneity were sufficient to distinguish most classes of drug mechanism. Our approach offers a computational framework for assessing the complexity of cellular heterogeneity, investigating the degree to which perturbations induce redistributions of a limited, but nontrivial, repertoire of underlying states and revealing functional significance contained within distinct patterns of heterogeneous responses.
authors.affiliation: Department of Pharmacology, Simmons Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
authors.forename: Michael D
authors.initials: MD
authors.lastname: Slack
authors.forename: Elisabeth D
authors.initials: ED
authors.lastname: Martinez
authors.forename: Lani F
authors.initials: LF
authors.lastname: Wu
authors.forename: Steven J
authors.initials: SJ
authors.lastname: Altschuler
mesh_terms: D000903:Antibiotics, Antineoplastic / Q000494:pharmacology; D000964:Antimetabolites, Antineoplastic / Q000494:pharmacology; D016477:Artifacts; D014408:Biomarkers, Tumor / Q000235:genetics / Q000378:metabolism*; D004261:DNA Replication / Q000187:drug effects*; D003907:Dexamethasone / Q000494:pharmacology; D004305:Dose-Response Relationship, Drug; D004317:Doxorubicin / Q000494:pharmacology; D018740:Genetic Heterogeneity; D005938:Glucocorticoids / Q000494:pharmacology; D006367:HeLa Cells; D006801:Humans; D006877:Hydroxamic Acids / Q000494:pharmacology; D008727:Methotrexate / Q000494:pharmacology; D008856:Microscopy, Fluorescence / Q000379:methods*; D008954:Models, Biological; D009369:Neoplasms / Q000235:genetics / Q000473:pathology*; D017239:Paclitaxel / Q000494:pharmacology; D010641:Phenotype; D011500:Protein Synthesis Inhibitors / Q000494:pharmacology; D050257:Tubulin Modulators / Q000494:pharmacology
chemical_list: D000903:Antibiotics, Antineoplastic; D000964:Antimetabolites, Antineoplastic; D014408:Biomarkers, Tumor; D005938:Glucocorticoids; D006877:Hydroxamic Acids; D011500:Protein Synthesis Inhibitors; D050257:Tubulin Modulators; C012589:trichostatin A; D003907:Dexamethasone; D004317:Doxorubicin; D017239:Paclitaxel; D008727:Methotrexate