AI Article Synopsis

  • Improvements in single-cell whole-genome sequencing (scWGS) have allowed for better analysis of somatic copy number alterations (CNAs) on a single-cell basis, making it possible to observe genetic variations within individual cells.
  • The newly developed tool HiScanner combines various data metrics to identify CNAs with greater precision, outperforming existing methods in simulated tests and real high-coverage scWGS data from human brain cells.
  • HiScanner's application revealed detailed differences in CNA patterns between neuron types and tracked evolutionary changes in tumor cells by integrating CNAs with point mutations across meningioma samples.

Article Abstract

Improvements in single-cell whole-genome sequencing (scWGS) assays have enabled detailed characterization of somatic copy number alterations (CNAs) at the single-cell level. Yet, current computational methods are mostly designed for detecting chromosome-scale changes in cancer samples with low sequencing coverage. Here, we introduce HiScanner (High-resolution Single-Cell Allelic copy Number callER), which combines read depth, B-allele frequency, and haplotype phasing to identify CNAs with high resolution. In simulated data, HiScanner consistently outperforms state-of-the-art methods across various CNA types and sizes. When applied to high-coverage scWGS data from human brain cells, HiScanner shows a superior ability to detect smaller CNAs, uncovering distinct CNA patterns between neurons and oligodendrocytes. For 179 cells we sequenced from longitudinal meningioma samples, integration of CNAs with point mutations revealed evolutionary trajectories of tumor cells. These findings show that HiScanner enables accurate characterization of frequency, clonality, and distribution of CNAs at the single-cell level in both non-neoplastic and neoplastic cells.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11092458PMC
http://dx.doi.org/10.1101/2024.04.26.587806DOI Listing

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