scMitoMut for calling mitochondrial lineage-related mutations in single cells

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Cell lineage tracing is crucial for understanding biological processes.
• Mitochondrial DNA (mtDNA) is a valuable tool for human data, especially when combined with single-cell chromatin accessibility data.
• Existing mutation calling tools struggle with mtDNA's polyploid nature and lack robust statistical frameworks.

Purpose of the Study:

• Introduce scMitoMut, an R package for accurate single-cell mitochondrial DNA mutation calling.
• Leverage statistical methodologies, including beta-binomial distribution, for precise lineage-related mutation identification.
• Enhance sensitivity and precision compared to current mutation calling methods.

Main Methods:

• Developed scMitoMut, an R package utilizing statistical methodologies for mtDNA mutation calling.
• Assigned mutation quality q-values based on beta-binomial distribution for each mutation locus.
• Validated scMitoMut on single-cell DNA sequencing, scATAC sequencing, and 10× Genomics multiome datasets.

Main Results:

• scMitoMut demonstrated superior sensitivity in detecting low proportions of cancer cell lines in mixed populations.
• Identified more mutations than state-of-the-art methods in a human colorectal cancer scATAC dataset.
• Effectively measured lineage distance in blood and brain tissues using multiome datasets.

Conclusions:

• scMitoMut is a computationally efficient and memory-efficient R package for mtDNA mutation calling.
• Significantly advances single-cell sequencing applications for precise mitochondrial mutation delineation.
• Facilitates lineage tracing in development, tumor, and stem cell biology.