title: A regression model approach to enable cell morphology correction in high-throughput flow cytometry.
abstract: Cells exposed to stimuli exhibit a wide range of responses ensuring phenotypic variability across the population. Such single cell behavior is often examined by flow cytometry; however, gating procedures typically employed to select a small subpopulation of cells with similar morphological characteristics make it difficult, even impossible, to quantitatively compare cells across a large variety of experimental conditions because these conditions can lead to profound morphological variations. To overcome these limitations, we developed a regression approach to correct for variability in fluorescence intensity due to differences in cell size and granularity without discarding any of the cells, which gating ipso facto does. This approach enables quantitative studies of cellular heterogeneity and transcriptional noise in high-throughput experiments involving thousands of samples. We used this approach to analyze a library of yeast knockout strains and reveal genes required for the population to establish a bimodal response to oleic acid induction. We identify a group of epigenetic regulators and nucleoporins that, by maintaining an 'unresponsive population,' may provide the population with the advantage of diversified bet hedging.
authors.affiliation: Institute for Systems Biology, 401 Terry Avenue North, 1441 North 34th Street, Seattle, WA 98109-5234, USA.
authors.forename: Theo A
authors.initials: TA
authors.lastname: Knijnenburg
authors.forename: Oriol
authors.initials: O
authors.lastname: Roda
authors.forename: Yakun
authors.initials: Y
authors.lastname: Wan
authors.forename: Garry P
authors.initials: GP
authors.lastname: Nolan
authors.forename: John D
authors.initials: JD
authors.lastname: Aitchison
authors.forename: Ilya
authors.initials: I
authors.lastname: Shmulevich
mesh_terms: D048429:Cell Size; D057890:Epigenomics; D005434:Flow Cytometry / Q000379:methods / Q000706:statistics & numerical data; D005453:Fluorescence; D014644:Genetic Variation; D005947:Glucose / Q000378:metabolism / Q000494:pharmacology; D049452:Green Fluorescent Proteins / Q000032:analysis; D057166:High-Throughput Screening Assays; D015233:Models, Statistical; D009154:Mutation; D028861:Nuclear Pore Complex Proteins / Q000235:genetics / Q000378:metabolism; D019301:Oleic Acid / Q000378:metabolism / Q000494:pharmacology; D030781:Organisms, Genetically Modified / Q000235:genetics / Q000378:metabolism; D012441:Saccharomyces cerevisiae / Q000166:cytology* / Q000187:drug effects / Q000235:genetics* / Q000378:metabolism; D034501:Telomere-Binding Proteins / Q000235:genetics / Q000378:metabolism; D014158:Transcription, Genetic / Q000187:drug effects*
chemical_list: D028861:Nuclear Pore Complex Proteins; D034501:Telomere-Binding Proteins; D049452:Green Fluorescent Proteins; D019301:Oleic Acid; D005947:Glucose