Resolution of Cell Fate Decisions Revealed by Single-Cell Gene Expression Analysis from Zygote to Blastocyst

Guo, Guoji; Huss, Mikael; Tong, Guo Qing; Wang, Chaoyang; Sun, Li; Clarke, Neil D.; Robson, Paul

doi:10.1016/j.devcel.2010.02.012

Cited by 780 publications

(1,065 citation statements)

References 42 publications

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“…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.…”

Section: A N a Ly S I Smentioning

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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Section: A N a Ly S I Smentioning

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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“…Tgfbr1 and Tgfb2 belong to the transforming growth factor b (TGF-b) signaling pathway important for the mesenchymal to epithelial transition, a process recently shown to play a key role in the initiation of MEF reprogramming [37,38]. Nr6a1, Zfp371, and Zfp459 are transcription factors possibly involved in the pluripotency and self-renewal of ESCs [39]. To confirm the microarray findings, we applied qRT-PCR assays for these five genes and for Nanog and Dnmt3b genes, which are both implicated in the maintenance of pluripotency in ESCs.…”

Section: Analysis Of Sox2 Downstream Gene Expressionmentioning

confidence: 99%

Two-Phase Analysis of Molecular Pathways Underlying Induced Pluripotent Stem Cell Induction

Lin

Perez

Lei³

et al. 2011

Stem Cells

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Induced pluripotent stem cells (iPSCs) can be reprogrammed from adult somatic cells by transduction with Oct4, Sox2, Klf4, and c-Myc, but the molecular cascades initiated by these factors remain poorly understood. Impeding their elucidation is the stochastic nature of the iPS induction process, which results in heterogeneous cell populations. Here we have synchronized the reprogramming process by a two-phase induction: an initial stable intermediate phase following transduction with Oct4, Klf4, and c-Myc, and a final iPS phase following overexpression of Sox2. This approach has enabled us to examine temporal gene expression profiles, permitting the identification of Sox2 downstream genes critical for induction. Furthermore, we have validated the feasibility of our new approach by using it to confirm that downregulation of transforming growth factor b signaling by Sox2 proves essential to the reprogramming process. Thus, we present a novel means for dissecting the details underlying the induction of iPSCs, an approach with significant utility in this arena and the potential for wide-ranging implications in the study of other reprogramming mechanisms. STEM

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“…So far, the focus of single-cell studies in the mouse embryo has been on gene expression patterns that characterize particular developmental stages or lineages within the blastocyst or mono versus bi-allelic gene expression (Biase et al, 2014;Deng et al, 2014;Guo et al, 2010;Shi et al, 2015;Tang et al, 2011;Xue et al, 2013), rather than on investigating the functional consequences of heterogeneity within the same embryo for cellfate specification. Here, using single-cell transcriptomics, we determined the extent of transcriptional heterogeneities between individual cells in pre-implantation embryos and identified that target genes of the pluripotency master regulators Oct4 and Sox2 are highly heterogeneous at the 4-cell stage.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneity in Oct4 and Sox2 Targets Biases Cell Fate in 4-Cell Mouse Embryos

Goolam

Scialdone

Graham

et al. 2016

Obstetrical &Amp; Gynecological Survey

125

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SUMMARYThe major and essential objective of pre-implantation development is to establish embryonic and extra-embryonic cell fates. To address when and how this fundamental process is initiated in mammals, we characterize transcriptomes of all individual cells throughout mouse pre-implantation development. This identifies targets of master pluripotency regulators Oct4 and Sox2 as being highly heterogeneously expressed between blastomeres of the 4-cell embryo, with Sox21 showing one of the most heterogeneous expression profiles. Live-cell tracking demonstrates that cells with decreased Sox21 yield more extra-embryonic than pluripotent progeny. Consistently, decreasing Sox21 results in premature upregulation of the differentiation regulator Cdx2, suggesting that Sox21 helps safeguard pluripotency. Furthermore, Sox21 is elevated following increased expression of the histone H3R26-methylase CARM1 and is lowered following CARM1 inhibition, indicating the importance of epigenetic regulation. Therefore, our results indicate that heterogeneous gene expression, as early as the 4-cell stage, initiates cell-fate decisions by modulating the balance of pluripotency and differentiation.

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Resolution of Cell Fate Decisions Revealed by Single-Cell Gene Expression Analysis from Zygote to Blastocyst

Cited by 780 publications

References 42 publications

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

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

Two-Phase Analysis of Molecular Pathways Underlying Induced Pluripotent Stem Cell Induction

Heterogeneity in Oct4 and Sox2 Targets Biases Cell Fate in 4-Cell Mouse Embryos

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