Institute for Artificial Intelligence, State Key Lab of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China; Tsinghua-Fuzhou Institute of Digital Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China.
Institute for Artificial Intelligence, State Key Lab of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China; Tsinghua-Fuzhou Institute of Digital Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China; Center for Computational and Integrative Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA.
Genomics Proteomics Bioinformatics. 2021 Apr;19(2):292-305. doi: 10.1016/j.gpb.2020.06.014. Epub 2021 Feb 17.
The recent advancement of single-cell RNA sequencing (scRNA-seq) technologies facilitates the study of cell lineages in developmental processes and cancer. In this study, we developed a computational method, called redPATH, to reconstruct the pseudo developmental time of cell lineages using a consensus asymmetric Hamiltonian path algorithm. Besides, we developed a novel approach to visualize the trajectory development and implemented visualization methods to provide biological insights. We validated the performance of redPATH by segmenting different stages of cell development on multiple neural stem cell and cancer datasets, as well as other single-cell transcriptome data. In particular, we identified a stem cell-like subpopulation in malignant glioma cells. These cells express known proliferative markers, such as GFAP, ATP1A2, IGFBPL1, and ALDOC, and remain silenced for quiescent markers such as ID3. Furthermore, we identified MCL1 as a significant gene that regulates cell apoptosis and CSF1R for reprogramming macrophages to control tumor growth. In conclusion, redPATH is a comprehensive tool for analyzing scRNA-seq datasets along the pseudo developmental time. redPATH is available at https://github.com/tinglabs/redPATH.
单细胞 RNA 测序 (scRNA-seq) 技术的最新进展促进了发育过程和癌症中细胞谱系的研究。在这项研究中，我们开发了一种名为 redPATH 的计算方法，该方法使用一致的不对称哈密顿路径算法来重建细胞谱系的伪发育时间。此外，我们开发了一种新的方法来可视化轨迹发展，并实现了可视化方法以提供生物学见解。我们通过在多个神经干细胞和癌症数据集以及其他单细胞转录组数据上分割细胞发育的不同阶段来验证 redPATH 的性能。特别是，我们在恶性神经胶质瘤细胞中鉴定出了一个类似于干细胞的亚群。这些细胞表达已知的增殖标志物，如 GFAP、ATP1A2、IGFBPL1 和 ALDOC，并且对静止标志物如 ID3 保持沉默。此外，我们还鉴定出 MCL1 是调节细胞凋亡的重要基因，CSF1R 是将巨噬细胞重编程以控制肿瘤生长的关键基因。总之，redPATH 是分析沿着伪发育时间的 scRNA-seq 数据集的综合工具。redPATH 可在 https://github.com/tinglabs/redPATH 上获得。
