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

Abstract

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 https://github.com/friendsofstrandseq/mosaicatcher-pipeline, https://github.com/friendsofstrandseq/TranslocatoR and https://github.com/friendsofstrandseq/mosaicatcher). All code is available freely for academic research.

References

  1. 1.

    Ciriello, G. et al. Emerging landscape of oncogenic signatures across human cancers. Nat. Genet. 45, 1127–1133 (2013).

  2. 2.

    Mertens, F., Johansson, B., Fioretos, T. & Mitelman, F. The emerging complexity of gene fusions in cancer. Nat. Rev. Cancer 15, 371–381 (2015).

  3. 3.

    Northcott, P. A. et al. The whole-genome landscape of medulloblastoma subtypes. Nature 547, 311–317 (2017).

  4. 4.

    Beroukhim, R., Zhang, X. & Meyerson, M. Copy number alterations unmasked as enhancer hijackers. Nat. Genet. 49, 5–6 (2016).

  5. 5.

    Northcott, P. A. et al. Enhancer hijacking activates GFI1 family oncogenes in medulloblastoma. Nature 511, 428–434 (2014).

  6. 6.

    Kim, C. et al. Chemoresistance evolution in triple-negative breast cancer delineated by single-cell sequencing. Cell 173, 879–893 (2018).

  7. 7.

    Turajlic, S. et al. Tracking cancer evolution reveals constrained routes to metastases: TRACERx renal. Cell 173, 581–594 (2018).

  8. 8.

    Sottoriva, A. et al. A Big Bang model of human colorectal tumor growth. Nat. Genet. 47, 209–216 (2015).

  9. 9.

    Aparicio, S. & Caldas, C. The implications of clonal genome evolution for cancer medicine. N. Engl. J. Med. 368, 842–851 (2013).

  10. 10.

    Forsberg, L. A., Gisselsson, D. & Dumanski, J. P. Mosaicism in health and disease - clones picking up speed. Nat. Rev. Genet. 18, 128–142 (2017).

  11. 11.

    Stratton, M. R. Exploring the genomes of cancer cells: progress and promise. Science 331, 1553–1558 (2011).

  12. 12.

    Korbel, J. O. et al. Paired-end mapping reveals extensive structural variation in the human genome. Science 318, 420–426 (2007).

  13. 13.

    Layer, R. M., Chiang, C., Quinlan, A. R. & Hall, I. M. LUMPY: a probabilistic framework for structural variant discovery. Genome Biol. 15, R84 (2014).

  14. 14.

    Leibowitz, M. L., Zhang, C.-Z. & Pellman, D. Chromothripsis: a new mechanism for rapid karyotype evolution. Annu. Rev. Genet. 49, 183–211 (2015).

  15. 15.

    Navin, N. E. Cancer genomics: one cell at a time. Genome Biol. 15, 452 (2014).

  16. 16.

    Zahn, H. et al. Scalable whole-genome single-cell library preparation without preamplification. Nat. Methods 14, 167–173 (2017).

  17. 17.

    Gawad, C., Koh, W. & Quake, S. R. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17, 175–188 (2016).

  18. 18.

    Bakker, B. et al. Single-cell sequencing reveals karyotype heterogeneity in murine and human malignancies. Genome Biol. 17, 115 (2016).

  19. 19.

    Voet, T. et al. Single-cell paired-end genome sequencing reveals structural variation per cell cycle. Nucleic Acids Res. 41, 6119–6138 (2013).

  20. 20.

    Zhang, C. Z. et al. Chromothripsis from DNA damage in micronuclei. Nature 522, 179–184 (2015).

  21. 21.

    Falconer, E. et al. DNA template strand sequencing of single cells maps genomic rearrangements at high resolution. Nat. Methods 9, 1107–1112 (2012).

  22. 22.

    Porubsky, D. et al. Dense and accurate whole-chromosome haplotyping of individual genomes. Nat. Commun. 8, 1293 (2017).

  23. 23.

    Sanders, A. D. et al. Characterizing polymorphic inversions in human genomes by single-cell sequencing. Genome Res. 26, 1575–1587 (2016).

  24. 24.

    Chaisson, M. J. P. et al. Multi-platform discovery of haplotype-resolved structural variation in human genomes. Nat. Commun. 10, 1784 (2019).

  25. 25.

    Sanders, A. D., Falconer, E., Hills, M., Spierings, D. C. J. & Lansdorp, P. M. Single-cell template strand sequencing by Strand-seq enables the characterization of individual homologs. Nat. Protoc. 12, 1151–1176 (2017).

  26. 26.

    Yang, L. et al. Diverse mechanisms of somatic structural variations in human cancer genomes. Cell 153, 919–929 (2013).

  27. 27.

    Li, Y. et al. Patterns of structural variation in human cancer. Preprint at bioRxiv https://doi.org/10.1101/181339 (2017).

  28. 28.

    Janssen, A., van der Burg, M., Szuhai, K., Kops, G. J. & Medema, R. H. Chromosome segregation errors as a cause of DNA damage and structural chromosome aberrations. Science 333, 1895–1898 (2011).

  29. 29.

    Mardin, B. R. et al. A cell-based model system links chromothripsis with hyperploidy. Mol. Syst. Biol. 11, 828 (2015).

  30. 30.

    Maciejowski, J., Li, Y., Bosco, N., Campbell, P. J. & de Lange, T. Chromothripsis and kataegis induced by telomere crisis. Cell 163, 1641–1654 (2015).

  31. 31.

    Riches, A. et al. Neoplastic transformation and cytogenetic changes after Gamma irradiation of human epithelial cells expressing telomerase. Radiat. Res. 155, 222–229 (2001).

  32. 32.

    Rausch, T. et al. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 28, i333–i339 (2012).

  33. 33.

    Hills, M., O’Neill, K., Falconer, E., Brinkman, R. & Lansdorp, P. M. BAIT: Organizing genomes and mapping rearrangements in single cells. Genome Med. 5, 82 (2013).

  34. 34.

    Amatu, A., Sartore-Bianchi, A. & Siena, S. NTRK gene fusions as novel targets of cancer therapy across multiple tumour types. ESMO Open 1, e000023 (2016).

  35. 35.

    Zhang, C.-Z., Leibowitz, M. L. & Pellman, D. Chromothripsis and beyond: rapid genome evolution from complex chromosomal rearrangements. Genes Dev. 27, 2513–2530 (2013).

  36. 36.

    Campbell, P. J. et al. The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature 467, 1109–1113 (2010).

  37. 37.

    Rode, A., Maass, K. K., Willmund, K. V., Lichter, P. & Ernst, A. Chromothripsis in cancer cells: An update. Int. J. Cancer 138, 2322–2333 (2016).

  38. 38.

    Selvarajah, S. et al. The breakage–fusion–bridge (BFB) cycle as a mechanism for generating genetic heterogeneity in osteosarcoma. Chromosoma 115, 459–467 (2006).

  39. 39.

    Li, Y. et al. Constitutional and somatic rearrangement of chromosome 21 in acute lymphoblastic leukaemia. Nature 508, 98–102 (2014).

  40. 40.

    McClintock, B. The stability of broken ends of chromosomes in Zea mays. Genetics 26, 234–282 (1941).

  41. 41.

    Gisselsson, D. et al. Chromosomal breakage-fusion-bridge events cause genetic intratumor heterogeneity. Proc. Natl Acad. Sci. USA 97, 5357–5362 (2000).

  42. 42.

    Thompson, S. L., Bakhoum, S. F. & Compton, D. A. Mechanisms of chromosomal instability. Curr. Biol. 20, R285–R295 (2010).

  43. 43.

    Stephens, P. J. et al. Massive genomic rearrangement acquired in a single catastrophic event during cancer development. Cell 144, 27–40 (2011).

  44. 44.

    Korbel, J. O. & Campbell, P. J. Criteria for inference of chromothripsis in cancer genomes. Cell 152, 1226–1236 (2013).

  45. 45.

    Girardi, T., Vicente, C., Cools, J. & De Keersmaecker, K. The genetics and molecular biology of T-ALL. Blood 129, 1113–1123 (2017).

  46. 46.

    Richter-Pechańska, P. et al. PDX models recapitulate the genetic and epigenetic landscape of pediatric T cell leukemia. EMBO Mol. Med. 10, e9443 (2018).

  47. 47.

    Liu, Y. et al. The genomic landscape of pediatric and young adult T-lineage acute lymphoblastic leukemia. Nat. Genet. 49, 1211–1218 (2017).

  48. 48.

    Wang, Q. et al. Mutations of PHF6 are associated with mutations of NOTCH1, JAK1 and rearrangement of SET-NUP214 in T cell acute lymphoblastic leukemia. Haematologica 96, 1808–1814 (2011).

  49. 49.

    Rao, S. et al. Inactivation of ribosomal protein L22 promotes transformation by induction of the stemness factor, Lin28B. Blood 120, 3764–3773 (2012).

  50. 50.

    Nagel, S. et al. Activation of TLX3 and NKX2-5 in t(5;14)(q35;q32) T cell acute lymphoblastic leukemia by remote 3′-BCL11B enhancers and coregulation by PU.1 and HMGA1. Cancer Res. 67, 1461–1471 (2007).

  51. 51.

    Bernard, O. A. et al. A new recurrent and specific cryptic translocation, t(5;14)(q35;q32), is associated with expression of the Hox11L2 gene in T acute lymphoblastic leukemia. Leukemia 15, 1495–1504 (2001).

  52. 52.

    Kunz, J. B. et al. Pediatric T cell lymphoblastic leukemia evolves into relapse by clonal selection, acquisition of mutations and promoter hypomethylation. Haematologica 100, 1442–1450 (2015).

  53. 53.

    Li, L. et al. A far downstream enhancer for murine Bcl11b controls its T cell-specific expression. Blood 122, 902–911 (2013).

  54. 54.

    Sugimoto, K.-J. et al. T cell lymphoblastic leukemia/lymphoma with t(7;14)(p15;q32) [TCRγ-TCL1A translocation]: a case report and a review of the literature. Int. J. Clin. Exp. Pathol. 7, 2615–2623 (2014).

  55. 55.

    Virgilio, L. et al. Deregulated expression of TCL1 causes T cell leukemia in mice. Proc. Natl Acad. Sci. USA 95, 3885–3889 (1998).

  56. 56.

    Alkan, C., Coe, B. P. & Eichler, E. E. Genome structural variation discovery and genotyping. Nat. Rev. Genet. 12, 363–376 (2011).

  57. 57.

    Campbell, I. M., Shaw, C. A., Stankiewicz, P. & Lupski, J. R. Somatic mosaicism: implications for disease and transmission genetics. Trends Genet. 31, 382–392 (2015).

  58. 58.

    Dou, Y., Gold, H. D., Luquette, L. J. & Park, P. J. Detecting somatic mutations in normal cells. Trends Genet. 34, 545–557 (2018).

  59. 59.

    Voet, T. et al. Breakage-fusion-bridge cycles leading to inv dup del occur in human cleavage stage embryos. Hum. Mutat. 32, 783–793 (2011).

  60. 60.

    Bakhoum, S. F. et al. The mitotic origin of chromosomal instability. Curr. Biol. 24, R148–R149 (2014).

  61. 61.

    Wang, Y. K. et al. Genomic consequences of aberrant DNA repair mechanisms stratify ovarian cancer histotypes. Nat. Genet. 49, 856–865 (2017).

  62. 62.

    Rücker, F. G. et al. Chromothripsis is linked to TP53 alteration, cell cycle impairment, and dismal outcome in acute myeloid leukemia with complex karyotype. Haematologica 103, e17–e20 (2018).

  63. 63.

    Navin, N. E. & Hicks, J. Tracing the tumor lineage. Mol. Oncol. 4, 267–283 (2010).

  64. 64.

    Lee, H. & Kim, J.-S. Unexpected CRISPR on-target effects. Nat. Biotechnol. 36, 703–704 (2018).

  65. 65.

    Yoshihara, M., Hayashizaki, Y. & Murakawa, Y. Genomic instability of iPSCs: challenges towards their clinical applications. Stem Cell Rev. 13, 7–16 (2017).

  66. 66.

    Rausch, T. et al. Genome sequencing of pediatric medulloblastoma links catastrophic DNA rearrangements with TP53 mutations. Cell 148, 59–71 (2012).

  67. 67.

    Fan, J. et al. Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq data. Genome Res. 28, 1217–1227 (2018).

  68. 68.

    Lapunzina, P. & Monk, D. The consequences of uniparental disomy and copy number neutral loss-of-heterozygosity during human development and cancer. Biol. Cell 103, 303–317 (2011).

  69. 69.

    Frismantas, V. et al. Ex vivo drug response profiling detects recurrent sensitivity patterns in drug-resistant acute lymphoblastic leukemia. Blood 129, e26–e37 (2017).

  70. 70.

    van Wietmarschen, N. & Lansdorp, P. M. Bromodeoxyuridine does not contribute to sister chromatid exchange events in normal or Bloom syndrome cells. Nucleic Acids Res. 44, 6787–6793 (2016).

  71. 71.

    1000 Genomes Project Consortiumet al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

  72. 72.

    Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. Preprint at https://arxiv.org/abs/1207.3907 (2012).

  73. 73.

    Huber, W., Toedling, J. & Steinmetz, L. M. Transcript mapping with high-density oligonucleotide tiling arrays. Bioinformatics 22, 1963–1970 (2006).

  74. 74.

    Claussin, C. et al. Genome-wide mapping of sister chromatid exchange events in single yeast cells using Strand-seq. eLife 6, e30560 (2017).

  75. 75.

    Porubsky, D. et al. Direct chromosome-length haplotyping by single-cell sequencing. Genome Res. 26, 1565–1574 (2016).

  76. 76.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

  77. 77.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol. 57, 289–300 (1995).

  78. 78.

    Klambauer, G. et al. cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate. Nucleic Acids Res. 40, e69 (2012).

  79. 79.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

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Acknowledgements

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.

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 (2019). https://doi.org/10.1038/s41587-019-0366-x

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