Cell-Cycle Control of Developmentally Regulated Transcription Factors Accounts for Heterogeneity in Human Pluripotent Cells

Singh, Arashdeep; Chappell, James; Trost, Robert; Lin, Li; Wang, Tao; Tang, Jie; Matlock, Brittany K.; Weller, Kevin P.; Wu, Hao; Zhao, Shaying; Jin, Peng; Dalton, Stephen

doi:10.1016/j.stemcr.2013.10.009

Cited by 136 publications

(196 citation statements)

References 41 publications

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“…PP1 has proven to be a potent tool to regulate the pluripotent G1 phase, as it enhances the hypophosphorylated state of Rb and enriches cells in the early G1 phase before directed differentiation. Our findings also support recent work suggesting that the G1 phase of the cell cycle may play an important role in enhancing the differentiation of hPSCs (Sela et al, 2012;Pauklin and Vallier, 2013;Singh et al, 2013). Given its role in regulating Rb and the cell cycle, PP1 could also be a useful tool in controlling the tumorigenicity of pluripotent stem cells and their differentiated progeny to enhance their therapeutic utility (Ben-David and Benvenisty, 2011).…”

Section: Discussionsupporting

confidence: 88%

A Src inhibitor regulates the cell cycle of human pluripotent stem cells and improves directed differentiation

Chetty¹,

Engquist²,

Mehanna³

et al. 2015

J Exp Med

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Section: Discussionsupporting

confidence: 88%

A Src inhibitor regulates the cell cycle of human pluripotent stem cells and improves directed differentiation

Chetty¹,

Engquist²,

Mehanna³

et al. 2015

J Exp Med

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“…This is consistent with earlier reports showing that developmental genes are primed for transcription in G1 phase (Singh et al, 2013(Singh et al, , 2015. Increased transcription as cells transition into G1 phase is thought to localize at genomic regions pre-marked with H3K27ac.…”

Section: Entry To and Exit From G1 Phasesupporting

confidence: 93%

“…This is an intriguing observation because it establishes a level of dynamic epigenetic regulation at developmental genes that was not previously appreciated. Consistent with changes in H3K4me3 in G1 phase, developmental genes become transcriptionally competent (Singh et al, 2013). The study suggests that developmental genes are primed for activation each time they go through G1 phase but are not activated unless the appropriate signaling networks are also active.…”

Section: Cell Fate Decisions Are Linked To G1-phase Progression In Pscsmentioning

confidence: 56%

Cycling through developmental decisions: how cell cycle dynamics control pluripotency, differentiation and reprogramming

2016

Self Cite

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A strong connection exists between the cell cycle and mechanisms required for executing cell fate decisions in a wide-range of developmental contexts. Terminal differentiation is often associated with cell cycle exit, whereas cell fate switches are frequently linked to cell cycle transitions in dividing cells. These phenomena have been investigated in the context of reprogramming, differentiation and trans-differentiation but the underpinning molecular mechanisms remain unclear. Most progress to address the connection between cell fate and the cell cycle has been made in pluripotent stem cells, in which the transition through mitosis and G1 phase is crucial for establishing a window of opportunity for pluripotency exit and the initiation of differentiation. This Review will summarize recent developments in this area and place them in a broader context that has implications for a wide range of developmental scenarios.

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“…The cell cycle is known to have wide-ranging effects on cellular physiology 26,27 and can modulate both differentiation and gene expression profiles 28 (Fig. 1a).…”

Section: Cell Cycle Variation Affects Global Gene Expressionmentioning

confidence: 99%

“…1a). Cells that are analyzed during development are likely to be in different stages of the cell cycle 28 . When we examined sets of genes whose expression is known to be associated with different cell-cycle stages, we observed that their expression levels varied considerably among single cells (Supplementary Fig.…”

Section: Cell Cycle Variation Affects Global Gene Expressionmentioning

confidence: 99%

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

et al. 2015

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Recent technical developments have enabled the transcriptomes of hundreds of cells to be assayed in an unbiased manner, opening up the possibility that new subpopulations of cells can be found. However, the effects of potential confounding factors, such as the cell cycle, on the heterogeneity of gene expression and therefore on the ability to robustly identify subpopulations remain unclear. We present and validate a computational approach that uses latent variable models to account for such hidden factors. We show that our single-cell latent variable model (scLVM) allows the identification of otherwise undetectable subpopulations of cells that correspond to different stages during the differentiation of naive T cells into T helper 2 cells. Our approach can be used not only to identify cellular subpopulations but also to tease apart different sources of gene expression heterogeneity in single-cell transcriptomes.Single-cell measurements of gene expression, using imaging techniques such as RNA-FiSH (fluorescence in situ hybridization), have provided important insights into the kinetics of transcription and cell-to-cell variation in gene expression [1][2][3] . However, such approaches can examine the expression of only a small number of genes in each experiment, thus restricting our ability to examine co-expression patterns and to robustly identify subpopulations of cells. Protocols have been developed to overcome these limitations by amplifying small quantities of mRNA 4,5 , which, in combination with microfluidics approaches for isolating individual cells 6,7 , have been used to analyze the co-expression of tens to hundreds of genes in single cells 8,9 . These protocols also allow the entire transcriptome of large numbers of single cells to be assayed in an unbiased way. This was initially done using microarrays 10,11 but is more often now done using next-generation sequencing [12][13][14][15] . Such approaches have been used to model early embryogenesis in the mouse 16 and to investigate bimodality in gene expression patterns of differentiating immune cell types 17 .After the generation of single-cell RNA-sequencing (RNA-seq) profiles from hundreds of cells, one goal to identify subpopulations that share a common gene-expression profile. Some of these subpopulations may represent previously unidentified cell types. Additionally, by studying patterns of gene expression in different single cells, insights into the regulatory landscape of each cell population can be obtained.However, methods for identifying subpopulations of cells and modeling their gene regulatory landscapes are only now beginning to emerge 18,19 . To fully exploit single-cell RNA-seq data, we have to account for the random noise inherent to such data sets 20 and, equally important, to account for different hidden factors that might result in gene expression heterogeneity. Although the importance of accounting for unobserved factors is well established in bulk RNA-seq studies [21][22][23] , robust approaches to detect and account for confounding f...

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Cell-Cycle Control of Developmentally Regulated Transcription Factors Accounts for Heterogeneity in Human Pluripotent Cells

Cited by 136 publications

References 41 publications

A Src inhibitor regulates the cell cycle of human pluripotent stem cells and improves directed differentiation

A Src inhibitor regulates the cell cycle of human pluripotent stem cells and improves directed differentiation

Cycling through developmental decisions: how cell cycle dynamics control pluripotency, differentiation and reprogramming

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

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