MitoTracer facilitates the identification of informative mitochondrial mutations for precise lineage reconstruction

TL;DR

MitoTracer is a new tool that accurately identifies mitochondrial mutations to trace cell lineages in single-cell sequencing data.

Contribution

MitoTracer introduces an automated, end-to-end pipeline for identifying informative mitochondrial mutations across multiple sequencing platforms.

Findings

MitoTracer outperforms existing methods in sensitivity and specificity for identifying clonally informative mitochondrial mutations.

The tool is compatible with multiple single-cell sequencing platforms and was validated using ground-truth lineage sequencing data.

Application to cancer data revealed genes related to BRAF-inhibitor resistance in BRAF-mutated cancer cells.

Abstract

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.

Uncovering heterogeneous cell populations in single-cell sequencing datasets has provided valuable insights into the tumor microenvironment and developmental processes. Traditional lineage tracing methods have relied on gene expression and nuclear genome mutations. Recently, researchers have recognized the mitochondrial genome as an ideal natural cell barcode due to its small size and high copy number. Several lineage reconstruction strategies leveraging mitochondrial mutations have been developed for single-cell RNA and/or DNA sequencing data. However, these methods still face limitations, including lower accuracy, lack of an automated pipeline, and incompatibility with all single-cell sequencing platforms. To overcome these challenges, we developed MitoTracer, a fully automated end-to-end computational method for identifying informative mitochondrial mutations across single-cell RNA and DNA sequencing data. This pipeline performs all essential analysis steps, including read mapping, generating a mitochondrial variant allele frequency matrix, selecting informative mitochondrial mutations, and inferring clonal structures with higher accuracy compared to existing methods.

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