Inference of differentiation time for single cell transcriptomes using cell population reference data. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Sun, Na; Yu, Xiaoming; Li, Fang; Liu, Denghui; Suo, Shengbao; Chen, Weiyang; Chen, Shirui; Song, Lu; Green, Christopher; McDermott, Joseph; Shen, Qin; Jing, Naihe; Han, Jing-Dong
• Is Part Of: Nature communications. Volume 8:Issue 1(2017)
• Page Start: 1
• Page End: 12
• Publication Date: 2017-12
• Subjects: Biology -- Periodicals; Physical sciences -- Periodicals; 505
• DOI: 10.1038/s41467-017-01860-2
• Languages: English
• ISSNs: 2041-1723

Abstract

Abstract Single-cell RNA sequencing (scRNA-seq) is a powerful method for dissecting intercellular heterogeneity during development. Conventional trajectory analysis provides only a pseudotime of development, and often discards cell-cycle events as confounding factors. Here using matched cell population RNA-seq (cpRNA-seq) as a reference, we developed an "iCpSc" package for integrative analysis of cpRNA-seq and scRNA-seq data. By generating a computational model for reference "biological differentiation time" using cell population data and applying it to single-cell data, we unbiasedly associated cell-cycle checkpoints to the internal molecular timer of single cells. Through inferring a network flow from cpRNA-seq to scRNA-seq data, we predicted a role of M phase in controlling the speed of neural differentiation of mouse embryonic stem cells, and validated it through gene knockout (KO) experiments. By linking temporally matched cpRNA-seq and scRNA-seq data, our approach provides an effective and unbiased approach for identifying developmental trajectory and timing-related regulatory events. Single cell transcriptome data can be used to determine developmental lineage trajectories. Here the authors map single cell transcriptomes onto a differentiation trajectory defined by cell population transcriptomes and show that cell cycle regulators have a role in differentiation timing.