Dynamics of embryonic stem cell differentiation inferred from single-cell transcriptomics show a series of transitions through discrete cell states

eLife

  ^{1    1    1    1    1    2    3    4    4    4    4    5} 

[1] Allen Institute for Brain Science, Seattle, United States;FAS Center for Systems Biology, Harvard University, Cambridge, United States;FAS Center for Systems Biology, Harvard University, Cambridge, United States;Biophysics Program, Harvard University, Cambridge, United States;FAS Center for Systems Biology, Harvard University, Cambridge, United States;Department of Molecular and Cellular Biology, Harvard University, Cambridge, United States;FAS Center for Systems Biology, Harvard University, Cambridge, United States;Department of Molecular and Cellular Biology, Harvard University, Cambridge, United States;Allen Institute for Brain Science, Seattle, United States;School of Engineering and Applied Sciences, Harvard University, Cambridge, United States;Harvard Stem Cell Institute, Harvard University, Cambridge, United States;

关键词: germ layer differentiation;    context dependence;    Single-cell RNA-seq;    Mouse;   

DOI  :  10.7554/eLife.20487

来源: publisher

【 摘 要 】

10.7554/eLife.20487.001The complexity of gene regulatory networks that lead multipotent cells to acquire different cell fates makes a quantitative understanding of differentiation challenging. Using a statistical framework to analyze single-cell transcriptomics data, we infer the gene expression dynamics of early mouse embryonic stem (mES) cell differentiation, uncovering discrete transitions across nine cell states. We validate the predicted transitions across discrete states using flow cytometry. Moreover, using live-cell microscopy, we show that individual cells undergo abrupt transitions from a naïve to primed pluripotent state. Using the inferred discrete cell states to build a probabilistic model for the underlying gene regulatory network, we further predict and experimentally verify that these states have unique response to perturbations, thus defining them functionally. Our study provides a framework to infer the dynamics of differentiation from single cell transcriptomics data and to build predictive models of the gene regulatory networks that drive the sequence of cell fate decisions during development.DOI: http://dx.doi.org/10.7554/eLife.20487.001

【 授权许可】

CC BY