The 44 MT variants selected by Vijay.

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（Mitochondrial, MT）突变可作为天然遗传标记，用于基于单细胞测序数据推断细胞的克隆亲缘关系。然而，基于线粒体突变的谱系追踪所面临的核心挑战，在于如何自动识别具备信息价值的线粒体突变。本研究提出一款名为"MitoTracer"的开源计算算法，该算法可精准识别具有克隆信息价值的线粒体突变，并从scRNA-seq（单细胞RNA测序）或scATAC-seq（单细胞转座酶可及性测序）样本中推断细胞进化谱系。我们采用金标准实验谱系测序数据对MitoTracer进行基准测试，结果显示，凭借高灵敏度与高特异性，该算法的性能优于现有同类方法。MitoTracer可兼容多种单细胞测序平台。将其应用于癌症进化数据集后，我们从携带BRAF突变的癌细胞的scRNA-seq数据中，成功挖掘出与原发性BRAF抑制剂耐药相关的基因。综上，本研究为精准识别具备实际信息价值的线粒体突变以及追踪细胞间谱系关系提供了一款极具应用价值的工具。