Robustness and reliability of single-cell regulatory multi-omics with deep mitochondrial mutation profiling

Area of Science:

• Genomics and Molecular Biology
• Cellular and Developmental Biology

Background:

• Single-cell mitochondrial DNA (mtDNA) mutation detection is crucial for understanding cellular relationships and states.
• Previous methods for lineage tracing using mtDNA mutations were limited by focusing on high heteroplasmy mutations, which are subject to selection bias.
• Intermediate to low heteroplasmy mtDNA mutations offer greater diversity and abundance for more robust lineage tracing but are harder to detect.

Purpose of the Study:

• To introduce the single-cell Regulatory multi-omics with Deep Mitochondrial mutation profiling (ReDeeM) approach for enhanced mtDNA mutation detection and fine-scale lineage tracing.
• To address concerns regarding potential artifacts in the ReDeeM analytical workflow, specifically edge mutations in mtDNA molecules.
• To refine the ReDeeM framework with improved error correction and filtering strategies to ensure the accuracy and utility of detected mutations.

Main Methods:

• Development of the integrated experimental and computational ReDeeM framework for deep mitochondrial mutation profiling in single cells.
• Detailed analysis and error correction of detected mtDNA mutations, distinguishing true biological variants from potential sequencing artifacts.
• Implementation of an additional filtering option (ReDeeM-R) to address positional biases and remove excess low molecule high connectedness mutations.

Main Results:

• The ReDeeM approach significantly enhances mtDNA mutation detection, offering over a 10-fold increase in variant detection compared to prior methods.
• Edge mutations identified by ReDeeM are distinct from known sequencing artifacts and represent a small fraction of total mutation calls.
• The ReDeeM-R filtering option effectively removes positional biases and artifactual mutations while preserving the majority of bona fide mutations and maintaining cellular connectivity.

Conclusions:

• The ReDeeM framework, with its refined error correction and filtering, provides a powerful and accurate tool for single-cell lineage tracing using low heteroplasmy mtDNA mutations.
• The developed methods ensure the reliability of detected mutations, enabling deeper biological insights into cellular dynamics.
• The ReDeeM approach represents a significant advancement in the field, facilitating further research and development in single-cell genomics.