Deep sequencing reveals cell-type-specific patterns of single-cell transcriptome variation.
Deep sequencing reveals cell-type-specific patterns of single-cell transcriptome variation.
复制标题
深度测序揭示了单细胞转录组变化的细胞类型特异性模式。
DOI：
10.1186/s13059-015-0683-4
复制
发表时间：
2015-06-09
期刊：
影响因子：
12.3
通讯作者：
Kim J
中科院分区：
文献类型：
--
作者：
Dueck H;Khaladkar M;Kim TK;Spaethling JM;Francis C;Suresh S;Fisher SA;Seale P;Beck SG;Bartfai T;Kuhn B;Eberwine J;Kim J
后生动物细胞的分化需要执行不同的基因表达程序，但最近的单细胞转录组图谱显示，似乎相同表型的细胞内存在相当大的差异。这给转录组状态和细胞表型之间的关系带来了疑问。此外，单细胞转录学提出了独特的分析挑战，需要解决才能回答这个问题。我们提供了来自五个小鼠组织的91个细胞和来自两个大鼠组织的18个细胞的高质量深读深度单细胞RNA测序，以及30个稀释到单细胞水平的散装RNA的对照样本。我们发现，不同组织的转录本在表达的基因数量、平均表达模式和细胞内类型变异模式方面存在全球差异。我们开发了筛选基因以进行可靠的量化和校准生物变异的方法。所有细胞类型都包括以组织特有的方式在表达上具有高度变异性的基因。我们还发现证据表明，小鼠神经元基因的单细胞可变性与大鼠的相关，符合变异水平可能是保守的假设。单细胞RNA测序数据提供了转录组功能的独特视角；然而，为了将单细胞RNA测序测量用于这一目的，需要进行仔细的分析。在表达变异的单细胞RNA测序研究中必须考虑技术变异。对于基因的一个子集，每种细胞类型内的生物可变性似乎受到调节，以执行动态功能，而不仅仅是分子噪声。本文的在线版本(doi：10.1186/s13059-0150683-4)包含补充材料，授权用户可以使用。
Differentiation of metazoan cells requires execution of different gene expression programs but recent single-cell transcriptome profiling has revealed considerable variation within cells of seeming identical phenotype. This brings into question the relationship between transcriptome states and cell phenotypes. Additionally, single-cell transcriptomics presents unique analysis challenges that need to be addressed to answer this question. We present high quality deep read-depth single-cell RNA sequencing for 91 cells from five mouse tissues and 18 cells from two rat tissues, along with 30 control samples of bulk RNA diluted to single-cell levels. We find that transcriptomes differ globally across tissues with regard to the number of genes expressed, the average expression patterns, and within-cell-type variation patterns. We develop methods to filter genes for reliable quantification and to calibrate biological variation. All cell types include genes with high variability in expression, in a tissue-specific manner. We also find evidence that single-cell variability of neuronal genes in mice is correlated with that in rats consistent with the hypothesis that levels of variation may be conserved. Single-cell RNA-sequencing data provide a unique view of transcriptome function; however, careful analysis is required in order to use single-cell RNA-sequencing measurements for this purpose. Technical variation must be considered in single-cell RNA-sequencing studies of expression variation. For a subset of genes, biological variability within each cell type appears to be regulated in order to perform dynamic functions, rather than solely molecular noise. The online version of this article (doi:10.1186/s13059-015-0683-4) contains supplementary material, which is available to authorized users.
登录
查看更多内容
DOI：
10.1093/bioinformatics/btu638
发表时间：
2015-01-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Anders S;Pyl PT;Huber W
通讯作者：
Huber W
影响因子：
29
作者：
Eguchi, Jun;Yan, Qing-Wu;Rosen, Evan D.
通讯作者：
Rosen, Evan D.
影响因子：
11.8
作者：
Guo, Guoji;Huss, Mikael;Robson, Paul
通讯作者：
Robson, Paul
影响因子：
2.7
作者：
Cornelison, DDW;Wold, BJ
通讯作者：
Wold, BJ
DOI：
10.1093/bioinformatics/btn615
发表时间：
2009-01-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Carbon S;Ireland A;Mungall CJ;Shu S;Marshall B;Lewis S;AmiGO Hub;Web Presence Working Group
通讯作者：
Web Presence Working Group