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

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.DOI: http://dx.doi.org/10.7554/eLife.20487.001

Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis

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Discovering sparse transcription factor codes for cell states and state transitions during development

“…We studied the differentiation of stem cells both into germ layer progenitors (Jang et al, 2017) and into cortical neurons. To study the latter, we analyzed single-cell gene expression data from 2217 cells from an in vitro differentiation protocol for early human neuronal development (Yao et al, 2017).…”

Synthetic gene circuits and cellular decision-making in human pluripotent stem cells

“…Recent efforts in the field have combined predictive computational models with single cell data to elaborate the core GRN network during cell state transitions [34,35]. Recently, this approach has been expanded to connect the core GRN with the essential cues of the extra-cellular environment to predict if, and under what conditions, a cell state transition can be encouraged [36].…”