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Single-cell analysis of structural variations and complex rearrangements with tri-channel processing


Structural variation (SV), involving deletions, duplications, inversions and translocations of DNA segments, is a major source of genetic variability in somatic cells and can dysregulate cancer-related pathways. However, discovering somatic SVs in single cells has been challenging, with copy-number-neutral and complex variants typically escaping detection. Here we describe single-cell tri-channel processing (scTRIP), a computational framework that integrates read depth, template strand and haplotype phase to comprehensively discover SVs in individual cells. We surveyed SV landscapes of 565 single cells, including transformed epithelial cells and patient-derived leukemic samples, to discover abundant SV classes, including inversions, translocations and complex DNA rearrangements. Analysis of the leukemic samples revealed four times more somatic SVs than cytogenetic karyotyping, submicroscopic copy-number alterations, oncogenic copy-neutral rearrangements and a subclonal chromothripsis event. Advancing current methods, single-cell tri-channel processing can directly measure SV mutational processes in individual cells, such as breakage–fusion–bridge cycles, facilitating studies of clonal evolution, genetic mosaicism and SV formation mechanisms, which could improve disease classification for precision medicine.

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Fig. 1: Haplotype-aware discovery of SVs in single cells by scTRIP.
Fig. 2: Analysis pipeline for predicting somatic SVs in individual cells.
Fig. 3: Unbiased translocation discovery based on correlated segregation.
Fig. 4: Analysis of complex and ongoing DNA rearrangement processes.
Fig. 5: Haplotype-resolved karyotypes and subclonal heterogeneity of T-ALL relapses.
Fig. 6: Locating previously unrecognized SVs in a T-ALL relapse sample.

Data availability

Sequencing data from this study can be retrieved from the European Genome-phenome Archive (EGA) and the European Nucleotide Archive (accession codes: PRJEB30027, PRJEB30059, PRJEB8037, PRJEB33731, EGAS00001003248, EGAS00001003365). Access to human patient data is governed by the EGA Data Access Committee.

Code availability

The computational code of our analytical framework is hosted on GitHub (see, and All code is available freely for academic research.


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We thank W. Huber, O. Stegle, F. Marass and P. Lansdorp for discussions and T. Christiansen for software documentation. We thank M. Paulsen (Flow Cytometry Core Facility) for assistance in sorting and C. Eckert for primary T-ALL samples for engraftment and N. Habermann for project support. J.O.K. acknowledges funding from European Research Council Starting (grant no. 336045) and Consolidator (grant no. 773026) grants and the National Institutes of Health (grant no. 3U41HG007497-04S1). Funding also came from the German Research Foundation (grant nos. 391137747 and 395192176) to T.M., the José Carreras Foundation (grant no. DJCLS 06R/2016) to J.O.K., A.E.K. and J.B.K., the Baden-Württemberg Stiftung (grant no. ID16) to A.E.K. and the Iten-Kohaut Stiftung to J.P.B. A.D.S. and H.Y. received postdoctoral fellowships through the Alexander von Humboldt Foundation.

Author information




A.D.S., T.M. and J.O.K. conceived the study. A.D.S., S.M., M.G., D.P., T.M. and J.O.K. described the SV footprints. The Strand-seq library preparation workflow was created by A.D.S., B.R., G.M.C.L., J.Z. and V.B. BM510 was generated by B.R.M. and J.O.K. T-ALL samples were processed by A.D.S., S.J., B.R., B.B. and J.-P.B. The MosaiCatcher tool for scTRIP data analysis was developed by S.M., M.G., D.P., A.D.S., T.R., T.M. and J.O.K. The Bayesian framework was created by M.G., S.M., D.P., A.D.S., T.R., J.O.K. and T.M. Cell mixing and simulations experiments were performed by S.M., T.R., D.B. and T.M. SCE detection was developed by S.M., V.K. T.M. and A.D.S. Translocations were discovered and validated by A.v.V., A.D.S., D.P. and J.O.K. Clustered rearrangement analyses were done by A.D.S., D.P., T.R., T.M. and J.O.K. CNN-LOHs were detected by D.P., A.D.S. and T.M. Haplotagging was done by M.G., D.P., A.D.S. and T.M. The bulk DNA sequencing was done by T.R. and B.R. T-ALL clinical/cytogenetic data were collected by P.R.-P., J.B.K., M.S., A.K., B.B. and J.-P.B. T-ALL expression was analysed by H.J., P.R.-P., J.B.K., S.J., B.B., B.R., J.-P.B. and A.K. The manuscript was written by A.D.S., T.M. and J.O.K., which was edited and approved by all authors.

Corresponding authors

Correspondence to Tobias Marschall or Jan O. Korbel.

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Competing interests

The following authors have disclosed a patent application (no. EP19169090): A.D.S., J.O.K., T.M., D.P., S.M. and M.G.

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Supplementary information

Supplementary information

Supplementary Figs. 1–24, Tables 1,2,6,8 and 9 and Notes

Reporting Summary

Supplementary Table 3

Overview of Strand-seq libraries included in the study. Metrics of the single cell sequencing data for RPE-1, C7, BM510, P33 and P1 samples, with total number of high-quality mapped fragments per library listed.

Supplementary Table 4

SV calls generated with our framework and using external methodologies. Overview of the single cell SV calls generated for RPE-1, C7, BM510, P33 and P1 samples, with variant allele frequencies and orthogonal validation notes included.

Supplementary Table 5

Presumed clonal CNA events in RPE cells detected by genomic sequencing. Data are shown for RPE-1, C7 and BM510. WGS, whole-genome sequencing; MP, mate-pair sequencing. Only regions of 200 kb and longer are reported.

Supplementary Table 7

Copy-number of BFB segments in single C7 cells. Description of the 10p BFB locus for every C7 cell with the CN estimate provided for each stepwise segment used to predict the BFB cycle number for that cell.

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Sanders, A.D., Meiers, S., Ghareghani, M. et al. Single-cell analysis of structural variations and complex rearrangements with tri-channel processing. Nat Biotechnol 38, 343–354 (2020).

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