RNA-Seq analysis to capture the transcriptome landscape of a single cell

Tang, Fuchou; Bárbácioru, Cátálin; Nordman, Ellen; Li, Bin; Xu, Nanlan; Bashkirov, Vladimir I.; Lao, Kaiqin; Surani, M. Azim

doi:10.1038/nprot.2009.236

Cited by 460 publications

(395 citation statements)

References 47 publications

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“…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 .…”

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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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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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“…In the past 6 years, five main methods have been developed and optimized to reverse transcribe the mRNA and amplify the cDNA from one single cell to achieve a better coverage and a lower cost per cell6, 7, 8, 9, 10, 11, 12, 13, 14 (Table 2). A parallel development of multiple algorithms has taken place in order to deal with the huge amount of data these new experiments have produced 15.…”

Section: Recent Development Of Single‐cell Techniquesmentioning

confidence: 99%

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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“…The single-cell RNA-seq strategy [13] was employed to capture the transcriptomes of 25 hematopoietic cells. An RNAseq library was prepared using the protocol of NEBNext Singleplex (#E7350) oligos from Illumina.…”

Section: Rna Sequencing and Expression Analysismentioning

confidence: 99%

Cell Type-Specific Expression Profile and Signaling Requirements in Early Hematopoietic Reprogramming

Kang

et al. 2015

Stem Cells and Development

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RNA-Seq analysis to capture the transcriptome landscape of a single cell

Cited by 460 publications

References 47 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

Single‐cell technologies to study the immune system

Cell Type-Specific Expression Profile and Signaling Requirements in Early Hematopoietic Reprogramming

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