Stem cell differentiation as a non-Markov stochastic process
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Pluripotent stem cells can self-renew in culture and differentiate along all somatic lineages in vivo. While much is known about the molecular basis of pluripotency, the mechanisms of differentiation remain unclear. Here, we profile individual mouse embryonic stem cells as they progress along the neuronal lineage. We observe that cells pass from the pluripotent state to the neuronal state via an intermediate epiblast-like state. However, analysis of the rate at which cells enter and exit these observed cell states using a hidden Markov model indicates the presence of a chain of unobserved molecular states that each cell transits through stochastically in sequence. This chain of hidden states allows individual cells to record their position on the differentiation trajectory, thereby encoding a simple form of cellular memory. We suggest a statistical mechanics interpretation of these results that distinguishes between functionally distinct cellular “macrostates” and functionally similar molecular “microstates” and propose a model of stem cell differentiation as a non-Markov stochastic process.
Date Issued
2017-09-27
Date Acceptance
2017-08-07
Citation
Cell Systems, 2017, 5 (3), pp.268-282.e7
ISSN
2405-4712
Publisher
Elsevier
Start Page
268
End Page
282.e7
Journal / Book Title
Cell Systems
Volume
5
Issue
3
Copyright Statement
© 2017 The Authors. Published by Elsevier Inc.This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Sponsor
Biotechnology and Biological Sciences Research Council (BBSRC)
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000411874500015&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
BB/N011597/1
Subjects
Science & Technology
Life Sciences & Biomedicine
Biochemistry & Molecular Biology
Cell Biology
GROUND-STATE
STATISTICAL-MECHANICS
REGULATORY NETWORKS
GENE-EXPRESSION
DNA METHYLATION
FATE DECISIONS
PLURIPOTENT
TRANSITION
SYSTEMS
OTX2
Publication Status
Published
Date Publish Online
2017-09-27