A stochastic and dynamical view of pluripotency in mouse embryonic stem cells.

Yen Ting Lin,Esther J Lee,Peter G Hufton,Davit A Potoyan,Qing Nie

doi:10.1371/journal.pcbi.1006000

Abstract

Pluripotent embryonic stem cells are of paramount importance for biomedical sciences because of their innate ability for self-renewal and differentiation into all major cell lines. The fateful decision to exit or remain in the pluripotent state is regulated by complex genetic regulatory networks. The rapid growth of single-cell sequencing data has greatly stimulated applications of statistical and machine learning methods for inferring topologies of pluripotency regulating genetic networks. The inferred network topologies, however, often only encode Boolean information while remaining silent about the roles of dynamics and molecular stochasticity inherent in gene expression. Herein we develop a framework for systematically extending Boolean-level network topologies into higher resolution models of networks which explicitly account for the promoter architectures and gene state switching dynamics. We show the framework to be useful for disentangling the various contributions that gene switching, external signaling, and network topology make to the global heterogeneity and dynamics of transcription factor populations. We find the pluripotent state of the network to be a steady state which is robust to global variations of gene switching rates which we argue are a good proxy for epigenetic states of individual promoters. The temporal dynamics of exiting the pluripotent state, on the other hand, is significantly influenced by the rates of genetic switching which makes cells more responsive to changes in extracellular signals.

Highlights

Embryonic stem cells derived from mammalian blastocysts are pluripotent: they show an indefinite capacity for self-renewal and the ability to differentiate into every cell type constituting an adult organism [1,2,3]
The commitment to a cell type is controlled by pluripotency networks, the bio-molecular inventory which is unsurprisingly complex, spanning a myriad of transcription factors, genes and epigenetic factors
We outline the steps for constructing higher-resolution regulatory networks with explicit promoter architecture, gene states, and transcription factor copy numbers, starting from experimentally-inferred Boolean network topologies

Summary

Introduction

Embryonic stem cells derived from mammalian blastocysts are pluripotent: they show an indefinite capacity for self-renewal and the ability to differentiate into every cell type constituting an adult organism [1,2,3]. The development of healthy tissues hinges on the ability of these pluripotent stem cells to make critical decisions determining when and into which kind of cells to differentiate in response to the environment. While at the level of the organism development is often predictable, with a well-defined order of events, at the level of single cells fate determination is fundamentally stochastic [5, 6]. Many studies probing the transcription of pluripotency-regulating genes in single cells have found high variability in distributions of transcription factors and mRNA molecules [7,8,9]

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