Liu Zehua, Lou Huazhe, Xie Kaikun, Wang Hao, Chen Ning, Aparicio Oscar M, Zhang Michael Q, Jiang Rui, Chen Ting
MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, TNLIST, Department of Automation, Tsinghua University, Beijing, 100084, China.
MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, TNLIST, Department of Computer Sciences, State Key Lab of Intelligent Technology and Systems, Tsinghua University, Beijing, 100084, China.
Nat Commun. 2017 Jun 19;8(1):22. doi: 10.1038/s41467-017-00039-z.
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.In single-cell RNA sequencing data of heterogeneous cell populations, cell cycle stage of individual cells would often be informative. Here, the authors introduce a computational model to reconstruct a pseudo-time series from single cell transcriptome data, identify the cell cycle stages, identify candidate cell cycle-regulated genes and recover the methylome changes during the cell cycle.
单细胞mRNA测序能够对单个细胞进行全转录组分析,已被广泛应用于研究组织和肿瘤的生长与发育。解析此类细胞群体的细胞周期具有重要意义,但常用方法可能无法充分实现这一目标。在此,我们开发了一种基于旅行商问题和隐马尔可夫模型的计算方法reCAT,用于从未同步的单细胞转录组数据中恢复细胞周期随时间的变化。我们使用多个数据集独立测试了reCAT的准确性和可靠性。我们发现细胞周期基因聚集成两个主要的表达波,分别对应于两个著名的检查点G1和G2。此外,我们利用reCAT展示了在恢复的细胞周期中甲基化的变化。因此,reCAT显示出在单细胞分辨率下阐明细胞周期以及其他周期性或昼夜节律过程(如在肝脏中)的多种特征的潜力。在异质细胞群体的单细胞RNA测序数据中,单个细胞的细胞周期阶段往往具有重要信息。在此,作者引入了一种计算模型,用于从单细胞转录组数据重建伪时间序列、识别细胞周期阶段、识别候选细胞周期调控基因以及恢复细胞周期中的甲基化组变化。