<p>The accumulation of mitochondrial DNA (mtDNA) deletion mutants in post-mitotic cells is a hallmark of mammalian ageing and a key contributor to tissue decline in skeletal muscle and neurons. A transcription-coupled replication model predicts that deletions affecting a negative feedback mechanism gain a selective replication advantage, leading to relatively short accumulation times for mutant takeover. However, these accumulation times are experimentally inaccessible since single-cell measurements are destructive. Here, we present a framework to infer such accumulation times from cross-sectional single-cell RNA sequencing (scRNAseq) data, exploiting the fact that mtDNA deletions are also reflected at the transcript level. To establish feasibility, we generated synthetic datasets using two stochastic models of the mitochondrial life cycle and used these as a gold standard. We then applied the Moran process, a stochastic birth-death model, to calculate distributions of accumulation times and to extract key parameters. The Moran model reproduced the distributions obtained from stochastic simulations with high fidelity across different assumptions about mitochondrial regulation. Fitting the model to synthetic data, successfully recovered mutation probability, selection advantage, and the fraction of advantageous mutants. These results establish a methodological framework for quantifying mtDNA mutant dynamics from single-cell transcriptomic data and provide a foundation for analysing large experimental datasets in ageing research.</p>