MitoDelta: identifying mitochondrial DNA deletions at cell-type resolution from single-cell RNA sequencing data

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Mitochondrial DNA (mtDNA) deletions are linked to diseases like neurodegeneration.
• Bulk sequencing masks cell-type-specific mtDNA mutation patterns.
• Single-cell resolution is crucial for understanding mtDNA deletion impact.

Purpose of the Study:

• Develop a method to detect cell-type-specific mtDNA deletions from single-cell RNA sequencing (scRNA-seq) data.
• Leverage the high abundance of mtDNA reads in scRNA-seq data.
• Provide a tool for analyzing mitochondrial genome alterations at single-cell resolution.

Main Methods:

• MitoDelta pipeline uses sensitive alignment and beta-binomial statistical filtering.
• Analyzes reads pooled by annotated cell types for deletion burden quantification.
• Benchmarked against existing tools, showing superior performance.

Main Results:

• MitoDelta accurately identifies mtDNA deletions from noisy scRNA-seq data.
• Quantifies deletion burden across distinct cellular populations.
• Applied to Parkinson's disease data, revealing cell-type-specific deletion patterns in neuronal subtypes.

Conclusions:

• MitoDelta enables transcriptome-integrated, cell-type-specific detection of mtDNA deletions from scRNA-seq data.
• Offers a framework for reanalyzing public datasets and studying mitochondrial alterations.
• Facilitates investigation into mtDNA deletions in cell-type-specific disease mechanisms.