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单细胞转录组分析揭示了髓质胸腺上皮细胞中协调的异位基因表达模式。

Single-cell transcriptome analysis reveals coordinated ectopic gene-expression patterns in medullary thymic epithelial cells.

作者信息

Brennecke Philip, Reyes Alejandro, Pinto Sheena, Rattay Kristin, Nguyen Michelle, Küchler Rita, Huber Wolfgang, Kyewski Bruno, Steinmetz Lars M

机构信息

1] Department of Genetics, Stanford University, School of Medicine, California, USA. [2] Stanford Genome Technology Center, Stanford University, California, USA.

European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.

出版信息

Nat Immunol. 2015 Sep;16(9):933-41. doi: 10.1038/ni.3246. Epub 2015 Aug 3.

DOI:10.1038/ni.3246
PMID:26237553
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4675844/
Abstract

Expression of tissue-restricted self antigens (TRAs) in medullary thymic epithelial cells (mTECs) is essential for the induction of self-tolerance and prevents autoimmunity, with each TRA being expressed in only a few mTECs. How this process is regulated in single mTECs and is coordinated at the population level, such that the varied single-cell patterns add up to faithfully represent TRAs, is poorly understood. Here we used single-cell RNA sequencing and obtained evidence of numerous recurring TRA-co-expression patterns, each present in only a subset of mTECs. Co-expressed genes clustered in the genome and showed enhanced chromatin accessibility. Our findings characterize TRA expression in mTECs as a coordinated process that might involve local remodeling of chromatin and thus ensures a comprehensive representation of the immunological self.

摘要

组织限制性自身抗原(TRA)在髓质胸腺上皮细胞(mTEC)中的表达对于诱导自身耐受和预防自身免疫至关重要,每种TRA仅在少数mTEC中表达。目前对于这一过程在单个mTEC中如何调控以及在群体水平如何协调,使得多样的单细胞模式加起来能如实地呈现TRA,仍知之甚少。在此,我们利用单细胞RNA测序获得了众多反复出现的TRA共表达模式的证据,每种模式仅存在于一部分mTEC中。共表达基因在基因组中聚集,并显示出增强的染色质可及性。我们的研究结果表明,mTEC中TRA的表达是一个协调过程,可能涉及染色质的局部重塑,从而确保免疫自身的全面呈现。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54f9/4675844/b4a73ca12644/nihms706814f6.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54f9/4675844/b4a73ca12644/nihms706814f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54f9/4675844/c0cfe585e089/nihms706814f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54f9/4675844/c66d13789df4/nihms706814f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54f9/4675844/7ca1b71795d3/nihms706814f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54f9/4675844/d8831607edd3/nihms706814f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54f9/4675844/21ad2d28bf07/nihms706814f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54f9/4675844/b4a73ca12644/nihms706814f6.jpg

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Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells.单细胞 RNA 测序数据中细胞间异质性的计算分析揭示了细胞的隐藏亚群。
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