# Single-cell multi-omic analysis of mitochondrial mutational mosaicism and dynamics

## TL;DR

This paper introduces new metrics to study mitochondrial DNA mutations in individual cells, revealing hidden patterns and how they differ in health and disease.

## Contribution

The study introduces two novel metrics, scmtMPM and scwMSS, for analyzing mitochondrial DNA mutations and mosaicism at the single-cell level.

## Key findings

- Individual POLGD274A cells show complex mutational landscapes with subthreshold pathogenic mutations.
- Constrained mutations in complex I are identified in healthy donors and mitochondriopathy patients.
- The new metrics reveal previously unrecognized mtDNA mutational heterogeneity at the single-cell level.

## Abstract

Mitochondrial DNA (mtDNA) mutations occur more frequently than nuclear mutations and are associated with various diseases. While single-cell sequencing enables mtDNA variant heteroplasmy analysis, a holistic view of mtDNA mutational landscapes in individual cells has remained limited. Here, we leverage mitochondrial single-cell ATAC-seq and mtDNA-hypermutated POLGD274A knock-in HEK293 cell lines to introduce two metrics—single-cell mtDNA mutations per million base pairs (scmtMPM) and heteroplasmy-weighted mitochondrial local constraint scores (scwMSS)—to capture cellular mutational loads and somatic mosaicism. We demonstrate that individual POLGD274A cells exhibit complex mutational landscapes, with pathogenic mutations and truncating variants only present at subthreshold levels, indicative of their negative selection. In human healthy donors and mitochondriopathy patients, we identify constrained mutations in complex I, highlighting previously unrecognized mtDNA mutational landscape heterogeneity present on the single-cell level. Overall, scmtMPM and scwMSS provide a framework to investigate fundamental properties of mitochondrial genetics, disease, and somatic mosaicism.

Mitochondrial DNA mutations shape human phenotypes, yet mapping mutational landscapes in single cells remains challenging. Here, Hsieh et al. quantify mutational load and mosaicism, revealing constrained, selected mtDNA mutations and hidden heterogeneity in health and disease.