Department of Statistics, University of Oxford, Oxford, UK.
Nat Biotechnol. 2013 Aug;31(8):748-52. doi: 10.1038/nbt.2642. Epub 2013 Jul 21.
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.
组织或培养样本中的多个单个细胞的基因表达根据细胞周期、遗传、表观遗传和细胞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视了。在这里，我们测量了 15 个人的 1440 个单个细胞中受 Wnt 信号影响的 92 个基因的表达，以将单核苷酸多态性 (SNP) 与基因表达表型相关联，同时考虑到细胞间的随机和细胞周期差异。我们提供的证据表明，许多遗传功能的变异——例如爆发大小、爆发频率、细胞周期特异性表达以及细胞间的表达相关性/噪声——在对许多细胞进行平均表达时会被掩盖。我们的结果表明，单细胞分析如何深入了解遗传变异性的机制和网络效应，以及如何提高对这些效应在基因表达上的建模的统计能力。
