A bioinformatics pipeline for identifying homoplasmic and heteroplasmic mitochondrial DNA SNVs in single-cell RNA-Seq...

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Mitochondrial DNA (mtDNA) single nucleotide variants (SNVs) are linked to pathologies, particularly in high-energy tissues like the brain and muscles.
• Accurate single-cell level characterization of mtDNA SNVs is essential for understanding disease mechanisms and clinical outcomes.
• Current single-cell RNA sequencing (scRNA-seq) analysis pipelines struggle with reliable detection of mtDNA SNVs.

Purpose of the Study:

• To develop and validate a novel bioinformatics pipeline for robust detection of mtDNA SNVs from scRNA-seq data.
• To address the limitations of existing methods in accurately identifying both homoplasmic and heteroplasmic mtDNA variants at the single-cell level.

Main Methods:

• A comprehensive bioinformatics pipeline was created, incorporating quality control, alignment to the mitochondrial genome, SNV calling, and annotation.
• The pipeline incorporates customizable, coverage-dependent thresholds for heteroplasmic SNV detection.
• Specific error types, including strand bias, RNA modification-induced errors, and overrepresented SNVs, were filtered out to enhance accuracy.

Main Results:

• The developed pipeline effectively detects both homoplasmic and heteroplasmic mtDNA SNVs within scRNA-seq datasets.
• The method demonstrates improved reliability in identifying variants compared to existing approaches.
• Filtering strategies successfully removed common sequencing and biological error sources.

Conclusions:

• The novel bioinformatics pipeline significantly enhances the capability to analyze mtDNA SNVs using scRNA-seq data.
• This advancement provides a valuable tool for researchers investigating the role of mtDNA variants in cellular function and disease.
• The pipeline offers a more accurate and reliable method for single-cell level mtDNA variant characterization.