# A hierarchical Bayesian framework for inferring mitochondrial clonal selection from single-cell data

## TL;DR

This paper introduces MitoBayes, a statistical tool that identifies how mitochondrial DNA mutations contribute to disease progression by analyzing single-cell data.

## Contribution

MitoBayes is a novel hierarchical Bayesian framework that jointly models mitochondrial clonal lineage and selection pressures in single-cell data.

## Key findings

- MitoBayes accurately recovers selection coefficients across various genetic heterogeneity and data sparsity scenarios.
- The tool reveals disease-specific mitochondrial clonal patterns in Alzheimer’s disease, lung cancer, and hepatocellular carcinoma.
- Elevated MT-ATP6 activity is linked to chemotherapy resistance and poor outcomes in small-cell lung cancer.

## Abstract

Mitochondrial genetic heterogeneity arises from the accumulation of somatic mitochondrial DNA (mtDNA) mutations within individual cells, generating intracellular clonal populations whose selective dynamics in disease remain poorly characterized. Here, we present MitoBayes, a hierarchical Bayesian framework that jointly models mitochondrial clonal lineage structure, allele frequency variation, and single-cell disease-relevant phenotypic burdens to infer clone-specific selection pressures. Extensive benchmarking demonstrates that MitoBayes accurately recovers ground-truth selection coefficients across a wide range of genetic heterogeneity, data sparsity, and lineage complexity scenarios. Application of MitoBayes to single-cell atlases of Alzheimer’s disease (AD) cortex, treatment-naïve non–small-cell lung cancer (NSCLC), and chemotherapy-resistant small-cell lung cancer (SCLC) revealed distinct, disease-specific patterns of mitochondrial clonal selection. These include selective expansion of high-risk mitochondrial clones associated with disruption of PVALB interneuron homeostasis in AD; disease-driven clonal remodeling in cycling T/NK cells from NSCLC tumors characterized by increased mitochondrial biogenesis and impaired immune regulatory programs; and preferential enrichment of a tumor-associated MT-ATP6 (m.8859A>G) clone linked to metabolic adaptation and platinum resistance in SCLC. Pan-cancer survival analyses further confirmed the clinical relevance of elevated MT-ATP6 activity, which was associated with inferior chemotherapy outcomes. Additionally, in hepatocellular carcinoma (HCC), a dominant m.2356C>G clone correlated with POLR2A activation and widespread transcriptional amplification, consistent with a mitochondria–nucleus signaling axis contributing to adverse prognosis in this cancer type. Collectively, these findings establish MitoBayes as a robust statistical framework linking mitochondrial genetic diversity to disease phenotypes and highlight mitochondrial clonal selection as a mechanistically and clinically actionable target for therapeutic and diagnostic development.