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      Dynamic Heterogeneity and DNA Methylation in Embryonic Stem Cells

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          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.

          Graphical Abstract


          • smFISH in ESCs reveals two transcriptional states and highly stochastic expression
          • Live-cell expression dynamics reveal the in situ transition rates between states
          • DNA methylation regulates state-switching dynamics
          • “2i” signaling inhibitors impact both gene expression noise and state transitions


          Using single-molecule RNA-FISH and quantitative time-lapse movies, Singer et al. discriminate two primary sources of gene expression heterogeneity in embryonic stem cells: random switching between correlated gene expression states and burst-like transcriptional noise. Perturbing specific signaling pathways modulates both processes, and DNA methylation is key to maintaining these states.

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          Most cited references 49

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          Stability and flexibility of epigenetic gene regulation in mammalian development.

           Wolf Reik (2007)
          During development, cells start in a pluripotent state, from which they can differentiate into many cell types, and progressively develop a narrower potential. Their gene-expression programmes become more defined, restricted and, potentially, 'locked in'. Pluripotent stem cells express genes that encode a set of core transcription factors, while genes that are required later in development are repressed by histone marks, which confer short-term, and therefore flexible, epigenetic silencing. By contrast, the methylation of DNA confers long-term epigenetic silencing of particular sequences--transposons, imprinted genes and pluripotency-associated genes--in somatic cells. Long-term silencing can be reprogrammed by demethylation of DNA, and this process might involve DNA repair. It is not known whether any of the epigenetic marks has a primary role in determining cell and lineage commitment during development.
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            Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells

            Recent molecular studies have revealed that, even when derived from a seemingly homogenous population, individual cells can exhibit substantial differences in gene expression, protein levels, and phenotypic output 1–5 , with important functional consequences 4,5 . Existing studies of cellular heterogeneity, however, have typically measured only a few pre-selected RNAs 1,2 or proteins 5,6 simultaneously because genomic profiling methods 3 could not be applied to single cells until very recently 7–10 . Here, we use single-cell RNA-Seq to investigate heterogeneity in the response of bone marrow derived dendritic cells (BMDCs) to lipopolysaccharide (LPS). We find extensive, and previously unobserved, bimodal variation in mRNA abundance and splicing patterns, which we validate by RNA-fluorescence in situ hybridization (RNA-FISH) for select transcripts. In particular, hundreds of key immune genes are bimodally expressed across cells, surprisingly even for genes that are very highly expressed at the population average. Moreover, splicing patterns demonstrate previously unobserved levels of heterogeneity between cells. Some of the observed bimodality can be attributed to closely related, yet distinct, known maturity states of BMDCs; other portions reflect differences in the usage of key regulatory circuits. For example, we identify a module of 137 highly variable, yet co-regulated, antiviral response genes. Using cells from knockout mice, we show that variability in this module may be propagated through an interferon feedback circuit involving the transcriptional regulators Stat2 and Irf7. Our study demonstrates the power and promise of single-cell genomics in uncovering functional diversity between cells and in deciphering cell states and circuits.
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              Transcriptome-wide noise controls lineage choice in mammalian progenitor cells.

              Phenotypic cell-to-cell variability within clonal populations may be a manifestation of 'gene expression noise', or it may reflect stable phenotypic variants. Such 'non-genetic cell individuality' can arise from the slow fluctuations of protein levels in mammalian cells. These fluctuations produce persistent cell individuality, thereby rendering a clonal population heterogeneous. However, it remains unknown whether this heterogeneity may account for the stochasticity of cell fate decisions in stem cells. Here we show that in clonal populations of mouse haematopoietic progenitor cells, spontaneous 'outlier' cells with either extremely high or low expression levels of the stem cell marker Sca-1 (also known as Ly6a; ref. 9) reconstitute the parental distribution of Sca-1 but do so only after more than one week. This slow relaxation is described by a gaussian mixture model that incorporates noise-driven transitions between discrete subpopulations, suggesting hidden multi-stability within one cell type. Despite clonality, the Sca-1 outliers had distinct transcriptomes. Although their unique gene expression profiles eventually reverted to that of the median cells, revealing an attractor state, they lasted long enough to confer a greatly different proclivity for choosing either the erythroid or the myeloid lineage. Preference in lineage choice was associated with increased expression of lineage-specific transcription factors, such as a >200-fold increase in Gata1 (ref. 10) among the erythroid-prone cells, or a >15-fold increased PU.1 (Sfpi1) (ref. 11) expression among myeloid-prone cells. Thus, clonal heterogeneity of gene expression level is not due to independent noise in the expression of individual genes, but reflects metastable states of a slowly fluctuating transcriptome that is distinct in individual cells and may govern the reversible, stochastic priming of multipotent progenitor cells in cell fate decision.

                Author and article information

                Mol Cell
                Mol. Cell
                Molecular Cell
                Cell Press
                17 July 2014
                17 July 2014
                : 55
                : 2
                : 319-331
                [1 ]Computation and Neural Systems, California Institute of Technology, Pasadena, CA 91125, USA
                [2 ]Division of Biology, California Institute of Technology, Pasadena, CA 91125, USA
                [3 ]Biological Network Modeling Center, California Institute of Technology, Pasadena, CA 91125, USA
                [4 ]Program in Biochemistry and Molecular Biophysics and Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
                [5 ]Howard Hughes Medical Institute and Division of Biology and Department of Applied Physics, California Institute of Technology, Pasadena, CA 91125, USA
                [6 ]The Wellcome Trust/Cancer Research UK Gurdon Institute, The Henry Wellcome Building of Cancer and Developmental Biology, University of Cambridge, Tennis Court Road, Cambridge CB2 1QN, UK
                Author notes
                []Corresponding author melowitz@

                Co-first author

                © 2014 The Authors

                This is an open access article under the CC BY license (


                Molecular biology


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