Reconstructing cell cycle pseudo time-series via single-cell transcriptome data

Zehua Liu, Huazhe Lou, Kaikun Xie, Hao Wang, Ning Chen, Oscar M. Aparicio, Michael Q. Zhang, Rui Jiang () and Ting Chen ()
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Zehua Liu: Tsinghua University
Huazhe Lou: Tsinghua University
Kaikun Xie: Tsinghua University
Hao Wang: Tsinghua University
Ning Chen: Tsinghua University
Oscar M. Aparicio: University of Southern California
Michael Q. Zhang: Tsinghua University
Rui Jiang: Tsinghua University
Ting Chen: Tsinghua University

Nature Communications, 2017, vol. 8, issue 1, 1-9

Abstract: Abstract Single-cell mRNA sequencing, which permits whole transcriptional profiling of individual cells, has been widely applied to study growth and development of tissues and tumors. Resolving cell cycle for such groups of cells is significant, but may not be adequately achieved by commonly used approaches. Here we develop a traveling salesman problem and hidden Markov model-based computational method named reCAT, to recover cell cycle along time for unsynchronized single-cell transcriptome data. We independently test reCAT for accuracy and reliability using several data sets. We find that cell cycle genes cluster into two major waves of expression, which correspond to the two well-known checkpoints, G1 and G2. Moreover, we leverage reCAT to exhibit methylation variation along the recovered cell cycle. Thus, reCAT shows the potential to elucidate diverse profiles of cell cycle, as well as other cyclic or circadian processes (e.g., in liver), on single-cell resolution.

Date: 2017
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DOI: 10.1038/s41467-017-00039-z

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