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Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL

Nature Biotechnology volume 39, pages 207–214 (2021)Cite this article

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Abstract

Single-cell barcoding technologies enable genome sequencing of thousands of individual cells in parallel, but with extremely low sequencing coverage (<0.05×) per cell. While the total copy number of large multi-megabase segments can be derived from such data, important allele-specific mutations—such as copy-neutral loss of heterozygosity (LOH) in cancer—are missed. We introduce copy-number haplotype inference in single cells using evolutionary links (CHISEL), a method to infer allele- and haplotype-specific copy numbers in single cells and subpopulations of cells by aggregating sparse signal across hundreds or thousands of individual cells. We applied CHISEL to ten single-cell sequencing datasets of ~2,000 cells from two patients with breast cancer. We identified extensive allele-specific copy-number aberrations (CNAs) in these samples, including copy-neutral LOHs, whole-genome duplications (WGDs) and mirrored-subclonal CNAs. These allele-specific CNAs affect genomic regions containing well-known breast-cancer genes. We also refined the reconstruction of tumor evolution, timing allele-specific CNAs before and after WGDs, identifying low-frequency subpopulations distinguished by unique CNAs and uncovering evidence of convergent evolution.

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Fig. 1: The CHISEL algorithm.
Fig. 2: CHISEL reliably identifies allele-specific copy numbers.
Fig. 3: CHISEL reveals haplotype-specific CNAs and WGDs that shape tumor evolution.
Fig. 4: Reconstruction of tumor heterogeneity and evolution across multiple tumor sections.

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Data availability

The sequencing data from 10x Genomics Chromium Single Cell CNV Solution for patient S0 are available at https://support.10xgenomics.com/single-cell-dna/datasets. Raw read counts and phased SNP counts for patient S0 are available at https://doi.org/10.5281/zenodo.3817605 and for patient S1 at https://doi.org/10.5281/zenodo.3817536. The DOP-PCR sequencing data of 89 breast tumor cells are available from the NCBI Sequence Read Archive under accession SRA: SRP114962. All the processed data for all datasets of patients S0 and S1 and for the DOP-PCR data, as well as all the results of CHISEL, are available on GitHub at https://github.com/raphael-group/chisel-data.

Code availability

CHISEL is available on GitHub at https://github.com/raphael-group/chisel and on Code Ocean at https://doi.org/10.24433/CO.6796686.v1.

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Acknowledgements

We thank L. Hepler and K. Ganapathy from 10x Genomics for providing additional data for our study, for providing access to the published data of the total copy-number analysis, and for the useful feedback. This work is supported by a US National Institutes of Health (NIH) grants R01HG007069 and U24CA211000, US National Science Foundation (NSF) CAREER Award (CCF-1053753) and Chan Zuckerberg Initiative DAF grants 2018-182608 (B.J.R.). Additional support was provided by NIH grant (Rutgers) 2P30CA072720-20, the O’Brien Family Fund for Health Research and the Wilke Family Fund for Innovation (B.J.R.).

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  1. Department of Computer Science, Princeton University, Princeton, NJ, USA

    Simone Zaccaria & Benjamin J. Raphael

  2. Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA

    Benjamin J. Raphael

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  1. Simone Zaccaria
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S.Z. and B.J.R. conceived the project, developed the theory and algorithms and wrote the paper. S.Z. implemented the algorithms and performed the analyses.

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Correspondence to Benjamin J. Raphael.

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B.J.R. is a cofounder of, and consultant to, Medley Genomics.

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Zaccaria, S., Raphael, B.J. Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL. Nat Biotechnol 39, 207–214 (2021). https://doi.org/10.1038/s41587-020-0661-6

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