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- Title
Inference of differentiation time for single cell transcriptomes using cell population reference data.
- Authors
Na Sun; Xiaoming Yu; Fang Li; Denghui Liu; Shengbao Suo; Weiyang Chen; Shirui Chen; Lu Song; Green, Christopher D.; McDermott, Joseph; Qin Shen; Naihe Jing; Han, Jing-Dong J.
- 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.
- Subjects
RNA sequencing; TRANSCRIPTOMES; CELL differentiation; CELL populations; GENE knockout
- Publication
Nature Communications, 2017, Vol 8, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-017-01860-2