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Detection of Copy Number Alterations Using Single Cell Sequencing
Published on: February 17, 2017
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High-resolution detection of copy number alterations in single cells with HiScanner
Yifan Zhao1,2, Lovelace J Luquette1, Alexander D Veit1
1Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Nature Communications
|July 2, 2025
Summary
HiScanner accurately detects copy number alterations (CNAs) in single cells using advanced algorithms. This new tool enhances understanding of genomic changes in both healthy and cancerous cells.
Area of Science:
- Genomics
- Computational Biology
- Single-cell analysis
Background:
- Single-cell whole-genome sequencing (scWGS) enables detailed characterization of somatic copy number alterations (CNAs).
- Existing computational methods often struggle with low-coverage data and chromosome-scale changes.
Purpose of the Study:
- Introduce HiScanner, a novel computational tool for high-resolution CNA detection at the single-cell level.
- Evaluate HiScanner's performance against state-of-the-art methods using simulated and real-world scWGS data.
Main Methods:
- HiScanner integrates read depth, B-allele frequency, and haplotype phasing for CNA identification.
- The method was validated on simulated datasets and scWGS data from human brain cells and a meningioma patient.
Main Results:
- HiScanner outperforms existing methods in detecting various CNA types and sizes, especially smaller alterations.
- Distinct CNA patterns were identified between neurons and oligodendrocytes in neurotypical brains.
- Analysis of tumor cells revealed evolutionary trajectories by integrating CNAs with point mutations.
Conclusions:
- HiScanner provides accurate characterization of CNA frequency, clonality, and distribution in single cells.
- The tool is effective for both non-neoplastic and neoplastic cells, advancing single-cell genomic analysis.
