Dynamic heterogeneity and DNA methylation in embryonic stem cells.

Zakary S Singer, John Yong, Julia Tischler, Jamie A Hackett, Alphan Altinok, M Azim Surani, Long Cai, Michael B Elowitz

Research output: Contribution to journalArticlepeer-review

218 Scopus citations


Cell populations can be strikingly heterogeneous, composed of multiple cellular states, each exhibiting stochastic noise in its gene expression. A major challenge is to disentangle these two types of variability and to understand the dynamic processes and mechanisms that control them. Embryonic stem cells (ESCs) provide an ideal model system to address this issue because they exhibit heterogeneous and dynamic expression of functionally important regulatory factors. We analyzed gene expression in individual ESCs using single-molecule RNA-FISH and quantitative time-lapse movies. These data discriminated stochastic switching between two coherent (correlated) gene expression states and burst-like transcriptional noise. We further showed that the "2i" signaling pathway inhibitors modulate both types of variation. Finally, we found that DNA methylation plays a key role in maintaining these metastable states. Together, these results show how ESC gene expression states and dynamics arise from a combination of intrinsic noise, coherent cellular states, and epigenetic regulation.
Original languageEnglish (US)
Pages (from-to)319-331
Number of pages13
JournalMolecular Cell
Issue number2
StatePublished - Jul 18 2014
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We thank Jordi Garcia-Ojalvo, Xiling Shen, Georg Seelig, Arjun Raj, and David Sprinzak for helpful comments on the manuscript; the Kathrin Plath Lab, the Austin Smith Lab, and RIKEN for kindly providing reporter and knockout cell lines; and the Caltech FACS Facility for assistance with cell sorting. This work was supported by the National Institutes of Health grants R01HD075605A, R01GM086793A, and P50GM068763; the Weston Havens Foundation; Human Frontiers Science Program; the Packard Foundation; a Wellcome Trust Investigators Grant to M. A. S.; and a KAUST, APART, and CIRM Fellowship to J.T. This work is funded by the Gordon and Betty Moore Foundation through Grant GBMF2809 to the Caltech Programmable Molecular Technology Initiative.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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