Yu Xuexin, Hu Jing, Tan Yuhao, Pan Mingyao, Zhang Hongyi, Li Bo
Sichuan Medical Laboratory Clinical Medical Research Center, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America.
PLoS Comput Biol. 2025 Jun 23;21(6):e1013090. doi: 10.1371/journal.pcbi.1013090. eCollection 2025 Jun.
Mitochondrial (MT) mutations serve as natural genetic markers for inferring clonal relationships using single cell sequencing data. However, the fundamental challenge of MT mutation-based lineage tracing is automated identification of informative MT mutations. Here, we introduced an open-source computational algorithm called "MitoTracer", which accurately identified clonally informative MT mutations and inferred evolutionary lineage from scRNA-seq or scATAC-seq samples. We benchmarked MitoTracer using the ground-truth experimental lineage sequencing data and demonstrated its superior performance over the existing methods measured by high sensitivity and specificity. MitoTracer is compatible with multiple single cell sequencing platforms. Its application to a cancer evolution dataset revealed the genes related to primary BRAF-inhibitor resistance from scRNA-seq data of BRAF-mutated cancer cells. Overall, our work provided a valuable tool for capturing real informative MT mutations and tracing the lineages among cells.
线粒体(MT)突变可作为利用单细胞测序数据推断克隆关系的天然遗传标记。然而,基于MT突变的谱系追踪的根本挑战在于自动识别信息丰富的MT突变。在此,我们引入了一种名为“MitoTracer”的开源计算算法,它能准确识别具有克隆信息的MT突变,并从单细胞RNA测序(scRNA-seq)或单细胞转座酶可及染色质测序(scATAC-seq)样本中推断进化谱系。我们使用真实的实验谱系测序数据对MitoTracer进行了基准测试,并证明其在高灵敏度和特异性方面优于现有方法。MitoTracer与多个单细胞测序平台兼容。它在一个癌症进化数据集上的应用从BRAF突变癌细胞的scRNA-seq数据中揭示了与原发性BRAF抑制剂耐药相关的基因。总体而言,我们的工作为捕获真正信息丰富的MT突变和追踪细胞间谱系提供了一个有价值的工具。