In:
eLife, eLife Sciences Publications, Ltd, Vol. 6 ( 2017-03-15)
Abstract:
The 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.
Type of Medium:
Online Resource
ISSN:
2050-084X
DOI:
10.7554/eLife.20487.001
DOI:
10.7554/eLife.20487.002
DOI:
10.7554/eLife.20487.003
DOI:
10.7554/eLife.20487.004
DOI:
10.7554/eLife.20487.005
DOI:
10.7554/eLife.20487.006
DOI:
10.7554/eLife.20487.007
DOI:
10.7554/eLife.20487.008
DOI:
10.7554/eLife.20487.009
DOI:
10.7554/eLife.20487.010
DOI:
10.7554/eLife.20487.011
DOI:
10.7554/eLife.20487.012
DOI:
10.7554/eLife.20487.013
DOI:
10.7554/eLife.20487.014
DOI:
10.7554/eLife.20487.015
DOI:
10.7554/eLife.20487.016
DOI:
10.7554/eLife.20487.017
DOI:
10.7554/eLife.20487.018
DOI:
10.7554/eLife.20487.019
DOI:
10.7554/eLife.20487.020
DOI:
10.7554/eLife.20487.021
DOI:
10.7554/eLife.20487.025
DOI:
10.7554/eLife.20487.026
DOI:
10.7554/eLife.20487.022
Language:
English
Publisher:
eLife Sciences Publications, Ltd
Publication Date:
2017
detail.hit.zdb_id:
2687154-3
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