Joint inference of cell lineage and mitochondrial evolution from single-cell sequencing data

Area of Science:

• Genomics
• Computational Biology
• Cell Biology

Background:

• Eukaryotic cells possess mitochondria with their own genomes, prone to higher mutation rates than nuclear DNA.
• Mitochondrial mutations are valuable for tracking cellular lineage, especially with single-cell sequencing.
• Existing lineage inference methods fail to account for mitochondrial heteroplasmy, the presence of multiple mitochondrial clones within a cell.

Purpose of the Study:

• To develop a method for inferring concordant cell lineage and mitochondrial clone trees from single-cell sequencing data, explicitly modeling heteroplasmy.
• To address the limitations of current approaches in reconstructing cellular evolutionary histories.

Main Methods:

• Formalized the problem as the Nested Perfect Phylogeny Mixture (NPPM) problem.
• Developed the MERLIN algorithm, utilizing a mixed integer linear program for exact solutions.
• Evaluated MERLIN's performance on simulated data and real single-cell whole-genome sequencing data from a gastric cancer cell line.

Main Results:

• MERLIN accurately infers both mitochondrial clone trees and cell lineage trees, outperforming methods that do not model heteroplasmy or tree concordance.
• Analysis of gastric cancer cell line data revealed MERLIN infers more biologically plausible evolutionary relationships compared to existing methods.
• The study provides a combinatorial characterization for solutions to the NPPM problem.

Conclusions:

• MERLIN offers a significant advancement in reconstructing cellular lineage by accurately modeling mitochondrial heteroplasmy and its concordance with cell lineage.
• This approach enhances the biological plausibility and accuracy of inferred cell lineage trees from single-cell sequencing data.
• MERLIN provides a robust computational tool for analyzing mitochondrial genomics in cellular evolution and disease.