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Inferring Accumulation Times of Mitochondrial DNA Deletion Mutants from Cross-Sectional Single-Cell Data: Application to Tabula Muris Senis

Kowald, A.; Kirkwood, T. B. L.

2026-02-01 molecular biology
10.64898/2026.01.30.702741 bioRxiv
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Mitochondrial DNA (mtDNA) deletion mutants accumulate clonally in post-mitotic cells during ageing and can reach high intracellular fractions that impair oxidative phosphorylation. Despite extensive experimental documentation, the mechanisms underlying this accumulation and the timescales over which deletion mutants expand within individual cells remain poorly constrained. In a companion study, we developed a stochastic modelling framework based on a Moran birth-death process and showed that key parameters of mtDNA dynamics, mutation probability, selection advantage, and the fraction of advantageous mutations, can be inferred from cross-sectional single-cell RNA sequencing (scRNAseq) data. Here, we apply this framework to experimental scRNAseq data from the Tabula Muris Senis project, which provides a lifespan-resolved atlas of mouse tissues. We identify and quantify mtDNA deletions in limb muscle cells across multiple ages. Gene-resolved analyses reveal pronounced deletion hotspots associated with elevated heteroplasmy at ND5 and ND6, consistent with predictions that disruption of transcriptional feedback regulation confers a replication advantage. Conversely, deletions affecting ATP6 are associated with reduced heteroplasmy, suggesting an unexpected role of this gene in supporting mtDNA propagation. The distribution of unique deletion species per cell follows a Poisson distribution with a low mean, indicating limited intracellular mutant diversity, further constraining plausible accumulation mechanisms. Parameter fits yield selection advantage estimates that imply mean accumulation times of approximately 3 months from first occurrence to near-complete dominance. Our results support a model in which mtDNA deletion accumulation is driven by rare mutation events followed by rapid clonal expansion, and they demonstrate the potential of single-cell omics data to quantify fundamental parameters of mitochondrial ageing dynamics.

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