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

Sumin Jang, Sandeep Choubey, Leon Furchtgott, Ling Nan Zou, Adele Doyle, Vilas Menon, Ethan B. Loew, Anne Rachel Krostag, Refugio A. Martinez, Linda Madisen, Boaz P. Levi, Sharad Ramanathan

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

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.

Original languageEnglish (US)
Article numbere20487
JournaleLife
Volume6
DOIs
StatePublished - Mar 15 2017

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)
  • Immunology and Microbiology(all)
  • Biochemistry, Genetics and Molecular Biology(all)

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