scMitoMut for calling mitochondrial lineage-related mutations in single cells.

Tracing cell lineages has become a valuable tool for studying biological processes. Among the available tools for human data, mitochondrial DNA (mtDNA) has a high potential due to its ability to be used in conjunction with single-cell chromatin accessibility data, giving access to the cell phenotype. Nonetheless, the existing mutation calling tools are ill-equipped to deal with the polyploid nature of the mtDNA and lack a robust statistical framework. Here we introduce scMitoMut, an innovative R package that leverages statistical methodologies to accurately identify mitochondrial lineage-related mutations at the single-cell level. scMitoMut assigns a mutation quality q-value based on beta-binomial distribution to each mutation at each locus within individual cells, ensuring higher sensitivity and precision of lineage-related mutation calling in comparison to current methodologies. We tested scMitoMut using single-cell DNA sequencing, single-cell transposase-accessible chromatin (scATAC) sequencing, and 10× Genomics single-cell multiome datasets. Using a single-cell DNA sequencing dataset from a mixed population of cell lines, scMitoMut demonstrated superior sensitivity in identifying a small proportion of cancer cell line compared to existing methods. In a human colorectal cancer scATAC dataset, scMitoMut identified more mutations than state-of-the-art methods. Applied to 10× Genomics multiome datasets, scMitoMut effectively measured the lineage distance in cells from blood or brain tissues. Thus, the scMitoMut is a freely available, and well-engineered toolkit (https://www.bioconductor.org/packages/devel/bioc/html/scMitoMut.html) for mtDNA mutation calling with high memory and computational efficiency. Consequently, it will significantly advance the application of single-cell sequencing, facilitating the precise delineation of mitochondrial mutations for lineage-tracing purposes in development, tumour, and stem cell biology.