Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments
Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments
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DOI：
10.1038/nbt.2642
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发表时间：
2013-08-01
影响因子：
46.9
通讯作者：
Holmes, Chris
中科院分区：
文献类型：
--
作者：
Wills, Quin F.;Livak, Kenneth J.;Holmes, Chris
来自组织或培养样品的多个单个细胞中的基因表达根据细胞周期、遗传、表观遗传和细胞之间的随机差异而变化。然而，在分析遗传变异的功能后果时，单细胞差异在很大程度上被忽略了。在这里，我们测量了来自 15 个个体的 1,440 个单细胞中受 Wnt 信号传导影响的 92 个基因的表达，将单核苷酸多态性 (SNP) 与基因表达表型相关联，同时考虑了细胞之间的随机和细胞周期差异。我们提供的证据表明，当对许多细胞的表达进行平均时，基因功能的许多遗传变异（例如突发大小、突发频率、细胞周期特异性表达和细胞之间的表达相关性/噪声）被掩盖。我们的结果证明了单细胞分析如何提供对遗传变异的机制和网络效应的见解，并通过改进的统计能力来模拟这些对基因表达的影响。
Gene expression in multiple individual cells from a tissue or culture sample varies according to cell-cycle, genetic, epigenetic and stochastic differences between the cells. However, single-cell differences have been largely neglected in the analysis of the functional consequences of genetic variation. Here we measure the expression of 92 genes affected by Wnt signaling in 1,440 single cells from 15 individuals to associate single-nucleotide polymorphisms (SNPs) with gene-expression phenotypes, while accounting for stochastic and cell-cycle differences between cells. We provide evidence that many heritable variations in gene function-such as burst size, burst frequency, cell cycle-specific expression and expression correlation/noise between cells-are masked when expression is averaged over many cells. Our results demonstrate how single-cell analyses provide insights into the mechanistic and network effects of genetic variability, with improved statistical power to model these effects on gene expression.