scMOCHA framework resolves mitochondrial DNA heteroplasmy at single-cell resolution from standard RNA-seq data

Researchers have developed scMOCHA (Single-Cell Mitochondrial Omics for Cellular Heteroplasmy Analysis), a scalable computational framework that detects mitochondrial DNA (mtDNA) heteroplasmy — the coexistence of multiple mtDNA variants within individual cells — directly from standard single-cell RNA sequencing (scRNA-seq) data, without requiring dedicated mitochondrial sequencing protocols.

The preprint, posted to bioRxiv, describes application of scMOCHA to publicly available scRNA-seq datasets from human peripheral blood mononuclear cells. The analysis revealed that heteroplasmy distributions differ substantially across cell types, suggesting that selective pressures on mtDNA variants are cell-type-specific rather than uniform across tissues. The authors argue this has implications for understanding how pathogenic mtDNA variants manifest in disease and how variant load might be modulated by cellular context.

Mitochondrial heteroplasmy is relevant to a range of inherited mitochondrial disorders, ageing biology, and cancer metabolism. Because scMOCHA operates on datasets already generated for other purposes, it offers a route to retrospective analysis of large existing data repositories without additional experimental cost.

As this work is a preprint and has not yet undergone peer review, its conclusions should be treated as preliminary. Researchers working in single-cell genomics, mitochondrial disease, and functional genomics are the primary audience for this methodological advance.