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

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

doi:10.7554/elife.20487

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.

Highlights

During differentiation, cells repeatedly choose between alternative fates in order to give rise to a multitude of distinct cell types
By using learned sparse patterns of gene expression from established experimental systems (Furchtgott et al, 2016), we can analyze single-cell transcriptomics data to uncover the gene expression dynamics of differentiation. This method naturally identifies a small set of transcription factors whose expression profiles are multimodal across neighboring cell states
While cell states can be characterized by the gene expression patterns of key sets of genes, these states can only be fully validated by demonstrating distinct physiological properties

Summary

Introduction

Cells repeatedly choose between alternative fates in order to give rise to a multitude of distinct cell types. A major challenge in developmental biology is to uncover the dynamics of gene expression and the underlying gene regulatory networks that lead cells to their different fates. Given the complexity of gene regulatory networks, with their large number of components and even larger number of potential interactions between those components, building detailed predictive mathematical models is challenging. The lack of sufficient data requires a large number of assumptions to be made in order to constrain all the parameters in such models (Karr et al, 2012). Our accompanying work on extracting cell states and the sequence of cell state transitions from gene expression data (Furchtgott et al, 2016) suggested that following the dynamics of a key set of genes was sufficient to trace these transitions, and in several instances the set of key genes that we discovered were functionally important for lineage decisions. We asked whether we could determine the suitable parameters to quantitatively describe cell state transitions during

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