title: A stochastic model dissects cell states in biological transition processes.
abstract: Many biological processes, including differentiation, reprogramming, and disease transformations, involve transitions of cells through distinct states. Direct, unbiased investigation of cell states and their transitions is challenging due to several factors, including limitations of single-cell assays. Here we present a stochastic model of cellular transitions that allows underlying single-cell information, including cell-state-specific parameters and rates governing transitions between states, to be estimated from genome-wide, population-averaged time-course data. The key novelty of our approach lies in specifying latent stochastic models at the single-cell level, and then aggregating these models to give a likelihood that links parameters at the single-cell level to observables at the population level. We apply our approach in the context of reprogramming to pluripotency. This yields new insights, including profiles of two intermediate cell states, that are supported by independent single-cell studies. Our model provides a general conceptual framework for the study of cell transitions, including epigenetic transformations.
authors.affiliation: Centre for Complexity Science, University of Warwick, Coventry, UK.
authors.forename: Jonathan W
authors.initials: JW
authors.lastname: Armond
authors.affiliation: Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA.
authors.forename: Krishanu
authors.initials: K
authors.lastname: Saha
authors.affiliation: 1] Centre for Complexity Science, University of Warwick, Coventry, UK [2] Division of Biochemistry, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
authors.forename: Anas A
authors.initials: AA
authors.lastname: Rana
authors.affiliation: 1] Centre for Complexity Science, University of Warwick, Coventry, UK [2] Division of Biochemistry, The Netherlands Cancer Institute, Amsterdam, The Netherlands [3] Department of Statistics, University of Warwick, Coventry, UK.
authors.forename: Chris J
authors.initials: CJ
authors.lastname: Oates
authors.affiliation: 1] The Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA, USA [2] Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
authors.forename: Rudolf
authors.initials: R
authors.lastname: Jaenisch
authors.affiliation: Dip.to di Scienze Fisiche, Univ. di Napoli "Federico II", INFN Napoli, Italy.
authors.forename: Mario
authors.initials: M
authors.lastname: Nicodemi
authors.affiliation: Division of Biochemistry, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
authors.forename: Sach
authors.initials: S
authors.lastname: Mukherjee
mesh_terms: D000465:Algorithms; D000818:Animals; D015415:Biomarkers; D002454:Cell Differentiation / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D059010:Single-Cell Analysis; D013269:Stochastic Processes; D059467:Transcriptome
chemical_list: D015415:Biomarkers