Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells

Yan, Liying; Yang, Mingyu; Guo, Hongshan; Yang, Lu; Wu, Jun; Li, Rong; Liu, Ping; Lian, Ying; Zheng, Xiaoying; Yan, Jie; Huang, Jin; Li, Ming; Wu, Xinglong; Wen, Lu; Lao, Kaiqin; Li, Ruiqiang; Qiao, Jie; Tang, Fuchou

doi:10.1038/nsmb.2660

Cited by 1,499 publications

(1,700 citation statements)

References 60 publications

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“…To validate the predicted MXE candidates, we made use of over 15 billion publically available RNA‐Seq reads, selecting 515 samples comprising 31 tissues and organs, 12 cell lines and seven developmental stages (Barbosa‐Morais et al , 2012; Djebali et al , 2012; Tilgner et al , 2012; Xue et al , 2013; Yan et al , 2013; Fagerberg et al , 2014; Dataset EV1). The data were chosen to encompass common and rare potential splice events in a broad range of tissues, cell types and embryonic stages.…”

Section: Resultsmentioning

confidence: 99%

“…To understand the expression patterns of MXEs, we conducted a differential inclusion analysis using the Human Protein Atlas (Fagerberg et al , 2014), Embryonic Development (Yan et al , 2013) and ENCODE datasets (Djebali et al , 2012). Of the 1,399 MXEs, 608 MXEs (345 unique genes), 573 MXEs (389 unique genes) and 552 MXEs (330 unique genes) are differentially expressed, respectively (adjusted P ‐value < 0.05; Fig 3A, Appendix Figs S23–S26, Dataset EV5 and EV6).…”

Section: Resultsmentioning

confidence: 99%

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The landscape of human mutually exclusive splicing

Hatje

Rahman

Vidal

et al. 2017

Molecular Systems Biology

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Mutually exclusive splicing of exons is a mechanism of functional gene and protein diversification with pivotal roles in organismal development and diseases such as Timothy syndrome, cardiomyopathy and cancer in humans. In order to obtain a first genomewide estimate of the extent and biological role of mutually exclusive splicing in humans, we predicted and subsequently validated mutually exclusive exons (MXEs) using 515 publically available RNA‐Seq datasets. Here, we provide evidence for the expression of over 855 MXEs, 42% of which represent novel exons, increasing the annotated human mutually exclusive exome more than fivefold. The data provide strong evidence for the existence of large and multi‐cluster MXEs in higher vertebrates and offer new insights into MXE evolution. More than 82% of the MXE clusters are conserved in mammals, and five clusters have homologous clusters in Drosophila. Finally, MXEs are significantly enriched in pathogenic mutations and their spatio‐temporal expression might predict human disease pathology.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

The landscape of human mutually exclusive splicing

Hatje

Rahman

Vidal

et al. 2017

Molecular Systems Biology

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“…Single-cell RNA-seq is now commonly used to study cell differentiation 15,24 . Here, we reanalyzed data from a single-cell RNA-seq experiment that was originally designed to study the differentiation of naive T cells into T H 2 cells 25 .…”

Section: Cell Cycle Variation Affects Global Gene Expressionmentioning

confidence: 99%

“…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 .…”

mentioning

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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“…Similar results have been obtained in other systems, such as the study of murine lung development, in which single cell transcriptomics data revealed cell‐type specific transcriptional regulators that discriminate between different populations that define the cellular hierarchy of the distal mouse lung epithelium 38. Single‐cell studies of the sub‐regions of the embryo, have also provided key insights of the initial phases of multicellular organisms development, allowing the identification of regulators triggering segregation between cell populations in early mouse embryos 39, and the delineation of gene regulatory mechanisms underlying progressive development of early mammalian embryos 40, 41. Profiling of the epigenetics landscape of individual cells has shown that lineage‐specific master regulators are associated with single‐cell epigenomic variability across several cell types, suggesting that control of single‐cell variance is a fundamental characteristic of different biological states 32.…”

Section: Single‐cell Profiling Is Key For Studying Pluripotent Statementioning

confidence: 99%

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

Angarica

Sol

2016

BioEssays

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Pluripotency can be considered a functional characteristic of pluripotent stem cells (PSCs) populations and their niches, rather than a property of individual cells. In this view, individual cells within the population independently adopt a variety of different expression states, maintained by different signaling, transcriptional, and epigenetics regulatory networks. In this review, we propose that generation of integrative network models from single cell data will be essential for getting a better understanding of the regulation of self‐renewal and differentiation. In particular, we suggest that the identification of network stability determinants in these integrative models will provide important insights into the mechanisms mediating the transduction of signals from the niche, and how these signals can trigger differentiation. In this regard, the differential use of these stability determinants in subpopulation‐specific regulatory networks would mediate differentiation into different cell fates. We suggest that this approach could offer a promising avenue for the development of novel strategies for increasing the efficiency and fidelity of differentiation, which could have a strong impact on regenerative medicine.

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Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells

Cited by 1,499 publications

References 60 publications

The landscape of human mutually exclusive splicing

The landscape of human mutually exclusive splicing

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

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

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