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Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq.

ABSTRACT: Our understanding of the development and maintenance of tissues has been greatly aided by large-scale gene expression analysis. However, tissues are invariably complex, and expression analysis of a tissue confounds the true expression patterns of its constituent cell types. Here we describe a novel strategy to access such complex samples. Single-cell RNA-seq expression profiles were generated, and clustered to form a two-dimensional cell map onto which expression data were projected. The resulting cell map integrates three levels of organization: the whole population of cells, the functionally distinct subpopulations it contains, and the single cells themselves-all without need for known markers to classify cell types. The feasibility of the strategy was demonstrated by analyzing the transcriptomes of 85 single cells of two distinct types. We believe this strategy will enable the unbiased discovery and analysis of naturally occurring cell types during development, adult physiology, and disease.

SUBMITTER: Islam S

PROVIDER: S-EPMC3129258 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature

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Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq.

Islam Saiful S Kjällquist Una U Moliner Annalena A Zajac Pawel P Fan Jian-Bing JB Lönnerberg Peter P Linnarsson Sten S

Genome research 20110504 7

Our understanding of the development and maintenance of tissues has been greatly aided by large-scale gene expression analysis. However, tissues are invariably complex, and expression analysis of a tissue confounds the true expression patterns of its constituent cell types. Here we describe a novel strategy to access such complex samples. Single-cell RNA-seq expression profiles were generated, and clustered to form a two-dimensional cell map onto which expression data were projected. The resulti ...[more]

PMID: 21543516

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Project description:Rationale: V-domain immunoglobulin suppressor of T cell activation (VISTA) is a novel inhibitory immune checkpoint molecule. Vsir-/- mice have exacerbated psoriasis-like skin inflammation. The immune cell subsets involved in inflammation in Vsir-/- psoriatic mice are largely unknown. We have used scRNA-seq as an unbiased profiling strategy to study the heterogeneity of immune cells at a single cell level in the skin of Vsir-/- psoriatic mice. Methods: In the present study, the right ear and shaved back skin of wild type and Vsir-/- mice were treated with IMQ for 5 consecutive days to induce psoriasis-like dermatitis. Then, the single-cell RNA sequencing analysis of mouse back skin lesions was performed using 10 × Genomics technique. Results: We identified 12 major cell subtypes among 23,258 cells. The major populations of the skin cells included macrophages, dendritic cells and fibroblasts. Macrophages constituted the main immune cell population in the WT (61.29%) and Vsir-/- groups (77.7%). It should be noted that DCs and fibroblasts were expanded in the Vsir-/- psoriatic mice. Furthermore, the gene expression signatures were assessed. We observed that Hspb1 and Cebpb were significantly upregulated in the Vsir-/- psoriatic mice. Differential gene expression and gene ontology enrichment analyses revealed specific gene expression patterns distinguishing these subsets and uncovered putative functions of each cell type. Date analysis resulted in the discovery of a number of novel psoriasis-associated genes in Vsir-/- mice. Conclusion: We present a comprehensive single-cell landscape of the skin immune cells in Vsir-/- psoriatic mice. These unprecedented data uncovered the transcriptional landscape and phenotypic heterogeneity of skin macrophages in psoriasis and identified their gene expression signature suggesting specialized functions in Vsir-/- mice. Our findings will open novel opportunities to investigate the role of VISTA in driving psoriasis.

Dataset Information

Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq.

Publications

Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq.

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