Inference of differentiation time for single cell transcriptomes using cell population reference data

Na Sun, Xiaoming Yu, Fang Li, Denghui Liu, Shengbao Suo, Weiyang Chen, Shirui Chen, Lu Song, Christopher D. Green, Joseph McDermott, Qin Shen, Naihe Jing and Jing-Dong J. Han ()
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Na Sun: Chinese Academy of Sciences
Xiaoming Yu: Chinese Academy of Sciences
Fang Li: Chinese Academy of Sciences
Denghui Liu: Chinese Academy of Sciences
Shengbao Suo: Chinese Academy of Sciences
Weiyang Chen: Chinese Academy of Sciences
Shirui Chen: Chinese Academy of Sciences
Lu Song: Chinese Academy of Sciences
Christopher D. Green: Chinese Academy of Sciences
Joseph McDermott: Chinese Academy of Sciences
Qin Shen: Tsinghua University
Naihe Jing: Chinese Academy of Sciences
Jing-Dong J. Han: Chinese Academy of Sciences

Nature Communications, 2017, vol. 8, issue 1, 1-12

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.

Date: 2017
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DOI: 10.1038/s41467-017-01860-2

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