title: Determining Physical Mechanisms of Gene Expression Regulation from Single Cell Gene Expression Data.
abstract: 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.
authors.affiliation: Department of Genetics, Systems Biology Centre, University of Cambridge, Cambridge, United Kingdom.|Sainsbury Laboratory, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Daphne
authors.identifier: 0000-0002-1685-6909
authors.initials: D
authors.lastname: Ezer
authors.affiliation: Department of Haematology, Wellcome Trust and MRC Cambridge Stem Cell Institute and Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Victoria
authors.initials: V
authors.lastname: Moignard
authors.affiliation: Department of Haematology, Wellcome Trust and MRC Cambridge Stem Cell Institute and Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Berthold
authors.initials: B
authors.lastname: Göttgens
authors.affiliation: Department of Genetics, Systems Biology Centre, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Boris
authors.initials: B
authors.lastname: Adryan
mesh_terms: D000465:Algorithms; D000818:Animals; D002460:Cell Line; D019295:Computational Biology; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000235:genetics* / Q000502:physiology*; D006410:Hematopoiesis / Q000235:genetics / Q000502:physiology; D006801:Humans; D007700:Kinetics; D051379:Mice; D059010:Single-Cell Analysis / Q000379:methods*; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism
chemical_list: D014157:Transcription Factors