Inferring accumulation times of mitochondrial DNA deletion mutants from cross-sectional single-cell data:...

Area of Science:

• Cell Biology
• Genetics
• Aging Research

Background:

• Mitochondrial DNA (mtDNA) deletions accumulate in aging mammalian cells, contributing to tissue decline in muscle and neurons.
• Current methods to measure mtDNA deletion accumulation times are destructive and experimentally inaccessible.

Purpose of the Study:

• To develop a non-destructive framework for inferring mtDNA deletion accumulation times from single-cell RNA sequencing (scRNAseq) data.
• To quantify mtDNA mutant dynamics and parameters like mutation probability and selection advantage.

Main Methods:

• Utilized cross-sectional scRNAseq data, linking mtDNA deletions to transcript levels.
• Employed the Moran process, a stochastic birth-death model, to analyze simulated and real data.
• Validated the framework using synthetic datasets from stochastic models of mitochondrial dynamics.

Main Results:

• The Moran model accurately reproduced accumulation time distributions from stochastic simulations.
• The framework successfully recovered key parameters including mutation probability and selection advantage.
• Demonstrated the feasibility of inferring mtDNA deletion dynamics from transcriptomic data.

Conclusions:

• Established a novel methodological framework for quantifying mtDNA mutant dynamics using scRNAseq.
• Provides a foundation for analyzing large datasets in aging research and understanding tissue decline.
• Enables non-destructive measurement of mtDNA deletion accumulation, overcoming experimental limitations.