Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells

Shalek, Alex K.; Satija, Rahul; Adiconis, Xian; Gertner, Rona S.; Gaublomme, Jellert T.; Raychowdhury, Raktima; Schwartz, Schraga; Yosef, Nir; Malboeuf, Christine M.; Lu, Diana; Trombetta, John J.; Gennert, Dave; Gnirke, Andreas; Goren, Alon; Hacohen, Nir; Levin, Joshua Z.; Park, Hongkun; Regev, Aviv

doi:10.1038/nature12172

Cited by 1,135 publications

(1,162 citation statements)

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“…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 .…”

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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“…Both the huge cell‐to‐cell variability and the amount of data these new technologies have brought to light constitute a great resource and at the same time a challenge for scientists. The parallel development of more sophisticated algorithms, faster computational approaches and new data visualization methods has already allowed scientists to gain new insights into immune system diversity 18, 23, 55, 56. Despite this incredible progress, there are still some issues that need to be solved and most of the works involving single‐cell sequencing are methodological.…”

Section: Resultsmentioning

confidence: 99%

“…Genes with higher transcriptional bursts and lower frequency are noisier than genes that are expressed in small frequent bursts 54. An scRNA‐seq based study of bone marrow‐derived dendritic cell activation revealed that certain genes had a bimodal pattern of gene expression 55. Subsequent studies showed that paracrine signalling of a subset of fast‐responding dendritic cells affects the whole population 56.…”

Section: Future Directionsmentioning

confidence: 99%

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Single‐cell technologies to study the immune system

Proserpio

Mahata

2015

Immunology

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SummaryThe immune system is composed of a variety of cells that act in a coordinated fashion to protect the organism against a multitude of different pathogens. The great variability of existing pathogens corresponds to a similar high heterogeneity of the immune cells. The study of individual immune cells, the fundamental unit of immunity, has recently transformed from a qualitative microscopic imaging to a nearly complete quantitative transcriptomic analysis. This shift has been driven by the rapid development of multiple single‐cell technologies. These new advances are expected to boost the detection of less frequent cell types and transient or intermediate cell states. They will highlight the individuality of each single cell and greatly expand the resolution of current available classifications and differentiation trajectories. In this review we discuss the recent advancement and application of single‐cell technologies, their limitations and future applications to study the immune system.

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“…1 In the field of systems immunology, single-cell transcriptomic profiling (RNA-seq) is an experimental technique that has proven effective for delineating molecular heterogeneity within immune cell subtypes. 2 Large-scale genomic techniques like single-cell RNA-seq are often described as hypothesis generating. One of the challenges of applying these experimental techniques is shortening the progression from hypothesis generation to hypothesis testing.…”

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confidence: 99%