High-resolution detection of copy number alterations in single cells with HiScanner

Area of Science:

• Genomics
• Computational Biology
• Cancer Research

Background:

• Single-cell whole-genome sequencing (scWGS) advances enable somatic copy number alteration (CNA) characterization.
• Existing computational methods struggle with high-resolution CNA detection, especially in low-coverage cancer samples.

Purpose of the Study:

• Introduce HiScanner, a novel computational tool for high-resolution CNA detection in scWGS data.
• Evaluate HiScanner's performance against state-of-the-art methods using simulated and real-world data.

Main Methods:

• HiScanner integrates read depth, B-allele frequency, and haplotype phasing for precise CNA identification.
• Performance was assessed using simulated datasets with diverse CNA types and sizes.
• The tool was applied to high-coverage scWGS data from human brain cells and longitudinal meningioma samples.

Main Results:

• HiScanner consistently outperformed existing methods in detecting various CNA types and sizes in simulations.
• It demonstrated superior ability in identifying smaller CNAs in human brain cells, distinguishing neuronal and oligodendrocyte patterns.
• Integration of CNAs with mutations in meningioma cells elucidated tumor cell evolutionary trajectories.

Conclusions:

• HiScanner provides accurate, high-resolution characterization of CNA frequency, clonality, and distribution at the single-cell level.
• The tool is effective for both non-neoplastic and neoplastic cells, advancing genomic analysis.
• HiScanner enhances the understanding of cellular heterogeneity and tumor evolution through detailed CNA profiling.