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Daphne Ezer; Victoria Moignard; Berthold Göttgens; Boris Adryan (2016)
Publisher: Public Library of Science (PLoS)
Journal: PLoS Computational Biology
Languages: English
Types: Article
Subjects: Applied Mathematics, Algorithms, Cell Differentiation, Research Article, Mathematics, Simulation and Modeling, Hematopoiesis, Genetics, Physical Sciences, Cellular Types, Stem Cells, Biology and life sciences, RNA, Developmental Biology, Research and Analysis Methods, Animal Cells, Nucleic acids, QH301-705.5, DNA transcription, Hematology, Cell Biology, Gene Regulation, Messenger RNA, Biochemistry, Medicine and Health Sciences, Hematopoietic Stem Cells, Gene expression, Biology (General)
Many genes are expressed in bursts, which can contribute to cell-to-cell heterogeneity. It is now possible to measure this heterogeneity with high throughput single cell gene expression assays (single cell qPCR and RNA-seq). These experimental approaches generate gene expression distributions which can be used to estimate the kinetic parameters of gene expression bursting, namely the rate that genes turn on, the rate that genes turn off, and the rate of transcription. We construct a complete pipeline for the analysis of single cell qPCR data that uses the mathematics behind bursty expression to develop more accurate and robust algorithms for analyzing the origin of heterogeneity in experimental samples, specifically an algorithm for clustering cells by their bursting behavior (Simulated Annealing for Bursty Expression Clustering, SABEC) and a statistical tool for comparing the kinetic parameters of bursty expression across populations of cells (Estimation of Parameter changes in Kinetics, EPiK). We applied these methods to hematopoiesis, including a new single cell dataset in which transcription factors (TFs) involved in the earliest branchpoint of blood differentiation were individually up- and down-regulated. We could identify two unique sub-populations within a seemingly homogenous group of hematopoietic stem cells. In addition, we could predict regulatory mechanisms controlling the expression levels of eighteen key hematopoietic transcription factors throughout differentiation. Detailed information about gene regulatory mechanisms can therefore be obtained simply from high throughput single cell gene expression data, which should be widely applicable given the rapid expansion of single cell genomics. This work was supported by: Royal Society Research Fellowship, Marshall Scholarship, Medical Research Council, the Leukemia and Lymphoma Society and core support grants from the Wellcome Trust to the Cambridge Institute for Medical Research and the Wellcome Trust and MRC Cambridge Stem Cell Institute. This is the final version of the article. It first appeared from the Public Library of Science via http://dx.doi.org/10.1371/journal.pcbi.1005072

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