Trapnell Cole, Cacchiarelli Davide, Grimsby Jonna, Pokharel Prapti, Li Shuqiang, Morse Michael, Lennon Niall J, Livak Kenneth J, Mikkelsen Tarjei S, Rinn John L
Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA.
The Broad Institute of MIT and Harvard, Cambridge, Massachussetts, USA.
Nat Biotechnol. 2014 Apr;32(4):381-386. doi: 10.1038/nbt.2859. Epub 2014 Mar 23.
Defining the transcriptional dynamics of a temporal process such as cell differentiation is challenging owing to the high variability in gene expression between individual cells. Time-series gene expression analyses of bulk cells have difficulty distinguishing early and late phases of a transcriptional cascade or identifying rare subpopulations of cells, and single-cell proteomic methods rely on a priori knowledge of key distinguishing markers. Here we describe Monocle, an unsupervised algorithm that increases the temporal resolution of transcriptome dynamics using single-cell RNA-Seq data collected at multiple time points. Applied to the differentiation of primary human myoblasts, Monocle revealed switch-like changes in expression of key regulatory factors, sequential waves of gene regulation, and expression of regulators that were not known to act in differentiation. We validated some of these predicted regulators in a loss-of function screen. Monocle can in principle be used to recover single-cell gene expression kinetics from a wide array of cellular processes, including differentiation, proliferation and oncogenic transformation.
由于个体细胞之间基因表达的高度可变性，定义诸如细胞分化等时间过程的转录动态具有挑战性。对大量细胞进行的时间序列基因表达分析难以区分转录级联反应的早期和晚期阶段，也难以识别罕见的细胞亚群，而单细胞蛋白质组学方法则依赖于关键区分标记的先验知识。在此，我们描述了Monocle，这是一种无监督算法，它使用在多个时间点收集的单细胞RNA测序数据来提高转录组动态的时间分辨率。应用于原代人成肌细胞的分化，Monocle揭示了关键调控因子表达的类似开关的变化、基因调控的连续波以及未知在分化中起作用的调节因子的表达。我们在功能丧失筛选中验证了其中一些预测的调节因子。原则上，Monocle可用于从广泛的细胞过程中恢复单细胞基因表达动力学，包括分化、增殖和致癌转化。
