A central challenge in oncology is how to kill tumors containing heterogeneous cell populations defined by different combinations of mutated genes. Identifying these mutated genes and understanding how they cooperate requires single-cell analysis, but current single-cell analytic methods, such as PCR-based strategies or whole-exome sequencing, are biased, lack sequencing depth or are cost prohibitive. Transposon-based mutagenesis allows the identification of early cancer drivers, but current sequencing methods have limitations that prevent single-cell analysis. We report a liquid-phase, capture-based sequencing and bioinformatics pipeline, Sleeping Beauty (SB) capture hybridization sequencing (SBCapSeq), that facilitates sequencing of transposon insertion sites from single tumor cells in a SB mouse model of myeloid leukemia (ML). SBCapSeq analysis of just 26 cells from one tumor revealed the tumor's major clonal subpopulations, enabled detection of clonal insertion events not detected by other sequencing methods and led to the identification of dominant subclones, each containing a unique pair of interacting gene drivers along with three to six cooperating cancer genes with SB-driven expression changes.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.



  1. 1.

    , , & Sleeping Beauty mutagenesis: exploiting forward genetic screens for cancer gene discovery. Curr. Opin. Genet. Dev. 24, 16–22 (2014).

  2. 2.

    , , & Transposon insertional mutagenesis models of cancer. Cold Spring Harb. Protoc. 2014, 235–247 (2014).

  3. 3.

    et al. Transposon mutagenesis identifies genetic drivers of BrafV600E melanoma. Nat. Genet. 47, 486–495 (2015).

  4. 4.

    et al. Transposon mutagenesis identifies genes and evolutionary forces driving gastrointestinal tract tumor progression. Nat. Genet. 47, 142–150 (2015).

  5. 5.

    et al. Sleeping Beauty mutagenesis in a mouse medulloblastoma model defines networks that discriminate between human molecular subgroups. Proc. Natl. Acad. Sci. USA 110, E4325–E4334 (2013).

  6. 6.

    et al. The deubiquitinase USP9X suppresses pancreatic ductal adenocarcinoma. Nature 486, 266–270 (2012).

  7. 7.

    et al. A modified Sleeping Beauty transposon system that can be used to model a wide variety of human cancers in mice. Cancer Res. 69, 8150–8156 (2009).

  8. 8.

    , & Analysis of Fgf8 gene function in vertebrate development. Cold Spring Harb. Symp. Quant. Biol. 62, 159–168 (1997).

  9. 9.

    et al. Synergistic tumor suppressor activity of BRCA2 and p53 in a conditional mouse model for breast cancer. Nat. Genet. 29, 418–425 (2001).

  10. 10.

    et al. Mutant p53 gain of function in two mouse models of Li-Fraumeni syndrome. Cell 119, 847–860 (2004).

  11. 11.

    et al. p53 mutation and loss have different effects on tumourigenesis in a novel mouse model of pleomorphic rhabdomyosarcoma. J. Pathol. 222, 129–137 (2010).

  12. 12.

    et al. Two hot spot mutant p53 mouse models display differential gain of function in tumorigenesis. Cell Death Differ. 20, 898–909 (2013).

  13. 13.

    et al. Gain of function of a p53 hot spot mutation in a mouse model of Li-Fraumeni syndrome. Cell 119, 861–872 (2004).

  14. 14.

    et al. Mutant p53 drives metastasis and overcomes growth arrest/senescence in pancreatic cancer. Proc. Natl. Acad. Sci. USA 107, 246–251 (2010).

  15. 15.

    Normal structure, function, and histology of the spleen. Toxicol. Pathol. 34, 455–465 (2006).

  16. 16.

    et al. Ectopic expression of PAX5 promotes maintenance of biphenotypic myeloid progenitors coexpressing myeloid and B-cell lineage-associated genes. Blood 109, 3697–3705 (2007).

  17. 17.

    et al. WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues, 4th edn., Vol. 2 (The International Agency for Research on Cancer of the World Health Organization, 2008).

  18. 18.

    et al. A census of human cancer genes. Nat. Rev. Cancer 4, 177–183 (2004).

  19. 19.

    Cancer Genome Atlas Research Network. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 368, 2059–2074 (2013).

  20. 20.

    et al. Transposon mutagenesis reveals cooperation of ETS family transcription factors with signaling pathways in erythro-megakaryocytic leukemia. Proc. Natl. Acad. Sci. USA 110, 6091–6096 (2013).

  21. 21.

    et al. Mutant nucleophosmin and cooperating pathways drive leukemia initiation and progression in mice. Nat. Genet. 43, 470–475 (2011).

  22. 22.

    et al. Therapeutic targeting of the MEK/MAPK signal transduction module in acute myeloid leukemia. J. Clin. Invest. 108, 851–859 (2001).

  23. 23.

    et al. Quantitative single cell determination of ERK phosphorylation and regulation in relapsed and refractory primary acute myeloid leukemia. Leukemia 19, 1543–1549 (2005).

  24. 24.

    et al. Clonal expansion analysis of transposon insertions by high-throughput sequencing identifies candidate cancer genes in a PiggyBac mutagenesis screen. PLoS One 8, e72338 (2013).

  25. 25.

    et al. High-throughput semiquantitative analysis of insertional mutations in heterogeneous tumors. Genome Res. 21, 2181–2189 (2011).

  26. 26.

    et al. Novel molecular and computational methods improve the accuracy of insertion site analysis in Sleeping Beauty-induced tumors. PLoS One 6, e24668 (2011).

  27. 27.

    & Double indexing overcomes inaccuracies in multiplex sequencing on the Illumina platform. Nucleic Acids Res. 40, e3 (2012).

  28. 28.

    et al. High-throughput DNA sequencing errors are reduced by orders of magnitude using circle sequencing. Proc. Natl. Acad. Sci. USA 110, 19872–19877 (2013).

  29. 29.

    et al. Target-site preferences of Sleeping Beauty transposons. J. Mol. Biol. 346, 161–173 (2005).

  30. 30.

    et al. Cancer genome landscapes. Science 339, 1546–1558 (2013).

  31. 31.

    et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512, 155–160 (2014).

  32. 32.

    et al. The transcriptional programme controlled by Runx1 during early embryonic blood development. Dev. Biol. 366, 404–419 (2012).

  33. 33.

    et al. ERG dependence distinguishes developmental control of hematopoietic stem cell maintenance from hematopoietic specification. Genes Dev. 25, 251–262 (2011).

  34. 34.

    et al. ERG promotes T-acute lymphoblastic leukemia and is transcriptionally regulated in leukemic cells by a stem cell enhancer. Blood 117, 7079–7089 (2011).

  35. 35.

    et al. Cancer and Leukemia Group B Study. High expression levels of the ETS-related gene, ERG, predict adverse outcome and improve molecular risk-based classification of cytogenetically normal acute myeloid leukemia: a Cancer and Leukemia Group B Study. J. Clin. Oncol. 25, 3337–3343 (2007).

  36. 36.

    & The rapid activation of protein synthesis by growth hormone requires signaling through mTOR. Am. J. Physiol. Endocrinol. Metab. 292, E1647–E1655 (2007).

  37. 37.

    et al. Hyperexpression of NOTCH-1 is found in immature acute myeloid leukemia. Int. J. Clin. Exp. Pathol. 7, 882–889 (2014).

  38. 38.

    et al. Therapeutic antibody targeting of individual Notch receptors. Nature 464, 1052–1057 (2010).

  39. 39.

    et al. Role of Ets-1 in erythroid differentiation. Blood Cells Mol. Dis. 29, 553–561 (2002).

  40. 40.

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

  41. 41.

    et al. Fundamental properties of unperturbed haematopoiesis from stem cells in vivo. Nature 518, 542–546 (2015).

  42. 42.

    et al. Clonal dynamics of native haematopoiesis. Nature 514, 322–327 (2014).

  43. 43.

    , , , & Mammalian mutagenesis using a highly mobile somatic Sleeping Beauty transposon system. Nature 436, 221–226 (2005).

  44. 44.

    et al. A transposon-based genetic screen in mice identifies genes altered in colorectal cancer. Science 323, 1747–1750 (2009).

  45. 45.

    , & The utility of immunohistochemistry for the identification of hematopoietic and lymphoid cells in normal tissues and interpretation of proliferative and inflammatory lesions of mice and rats. Toxicol. Pathol. 40, 345–374 (2012).

  46. 46.

    , , & SNES: single nucleus exome sequencing. Genome Biol. 16, 55 (2015).

  47. 47.

    et al. Australian Pancreatic Cancer Genome Initiative. Sleeping Beauty mutagenesis reveals cooperating mutations and pathways in pancreatic adenocarcinoma. Proc. Natl. Acad. Sci. USA 109, 5934–5941 (2012).

  48. 48.

    et al. Insertional mutagenesis identifies multiple networks of cooperating genes driving intestinal tumorigenesis. Nat. Genet. 43, 1202–1209 (2011).

  49. 49.

    , , , & Detecting statistically significant common insertion sites in retroviral insertional mutagenesis screens. PLoS Comput. Biol. 2, e166 (2006).

  50. 50.

    , , & Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

  51. 51.

    , & TopHat: discovering splice junctions with RNA-seq. Bioinformatics 25, 1105–1111 (2009).

  52. 52.

    R Core Team. R: a language and environment for statistical computing. (R Foundation for Statistical Computing, 2013).

  53. 53.

    , , & affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20, 307–315 (2004).

  54. 54.

    et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).

  55. 55.

    et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

  56. 56.

    , & ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

  57. 57.

    & A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics 23, 657–663 (2007).

  58. 58.

    et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

  59. 59.

    et al. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat. Biotechnol. 31, 46–53 (2013).

  60. 60.

    , , , & circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).

  61. 61.

    ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).

  62. 62.

    et al. Dissection of stromal and cancer cell-derived signals in melanoma xenografts before and after treatment with DMXAA. Br. J. Cancer 106, 1134–1147 (2012).

Download references


The authors thank D. Adams, T. Whipp, R. Rance and the Wellcome Trust Sanger Institute sequencing and informatics teams for 454 sequencing; the Institute for Molecular and Cell Biology Histopathology Core; P. Cheok, N. Lim, D. Chen and C. Wee for assistance with tumor monitoring and animal husbandry at IMCB (Singapore), H. Lee and E. Freiter for assistance with animal husbandry at HMRI (Houston), R. Zahr (Integrated DNA Technologies, Inc.) for assistance with SBCapture probe design and D. Adams and C. Print for valuable discussions and critical reading of the manuscript. Histology work was performed by the Advanced Molecular Pathology Laboratory, IMCB, A*STAR. This work was supported by the Cancer Prevention Research Institute of Texas (N.G.C. and N.A.J.), the Biomedical Research Council, Agency for Science, Technology, and Research, Singapore (N.G.C. and N.A.J.), Cancer Research UK (A.G.R.), the Medical Research Council, UK (A.G.R.) and the Wellcome Trust (A.G.R.).

Author information

Author notes

    • Jerrold M Ward
    • , Alistair G Rust
    • , Christopher Chin Kuan Yew
    • , Jill L Waters
    •  & Luxmanan Selvanesan

    Present addresses: Global VetPathology, Montgomery Village, Maryland, USA (J.M.W.), Tumour Profiling Unit, the Institute of Cancer Research, Chester Beatty Laboratories, London, UK (A.G.R.), National Heart Research Institute Singapore, Republic of Singapore (C.C.K.Y.), Illumina, Inc., San Diego, California, USA (J.L.W.) and Pacific Edge Limited, Dunedin, Otago, New Zealand (L.S.).

    • Karen M Mann
    •  & Michael B Mann

    These authors contributed equally to this work.


  1. Cancer Research Program, Houston Methodist Research Institute, Houston, Texas, USA.

    • Karen M Mann
    • , Justin Y Newberg
    • , Devin J Jones
    • , Felipe Amaya-Manzanares
    • , Liliana Guzman-Rojas
    • , Takahiro Kodama
    • , Nancy A Jenkins
    • , Neal G Copeland
    •  & Michael B Mann
  2. Institute of Molecular and Cell Biology, Singapore, Republic of Singapore.

    • Karen M Mann
    • , Jerrold M Ward
    • , Christopher Chin Kuan Yew
    • , Keith Rogers
    • , Susan M Rogers
    • , Nancy A Jenkins
    • , Neal G Copeland
    •  & Michael B Mann
  3. Department of Biochemistry, University of Otago, Dunedin, New Zealand.

    • Michael A Black
    • , Leslie A McNoe
    •  & Luxmanan Selvanesan
  4. Experimental Cancer Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.

    • Alistair G Rust
    •  & Louise van der Weyden
  5. Department of Genetics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

    • Jill L Waters
    • , Marco L Leung
    •  & Nicholas Navin


  1. Search for Karen M Mann in:

  2. Search for Justin Y Newberg in:

  3. Search for Michael A Black in:

  4. Search for Devin J Jones in:

  5. Search for Felipe Amaya-Manzanares in:

  6. Search for Liliana Guzman-Rojas in:

  7. Search for Takahiro Kodama in:

  8. Search for Jerrold M Ward in:

  9. Search for Alistair G Rust in:

  10. Search for Louise van der Weyden in:

  11. Search for Christopher Chin Kuan Yew in:

  12. Search for Jill L Waters in:

  13. Search for Marco L Leung in:

  14. Search for Keith Rogers in:

  15. Search for Susan M Rogers in:

  16. Search for Leslie A McNoe in:

  17. Search for Luxmanan Selvanesan in:

  18. Search for Nicholas Navin in:

  19. Search for Nancy A Jenkins in:

  20. Search for Neal G Copeland in:

  21. Search for Michael B Mann in:


K.M.M., M.B.M., N.G.C. and N.A.J. designed the study, directed the research, interpreted the data and wrote the manuscript. K.M.M. and M.B.M. performed experimental work, designed the SBCapSeq oligos, coordinated sequencing efforts and analyzed the data. M.A.B., J.M.W., A.G.R. and N.N. contributed to the experimental design. J.Y.N., M.A.B. and A.G.R. provided essential statistical and bioinformatics resources. J.Y.N. wrote the Python code for SBCapSeq and splink HiSeq workflows and gCIS analysis; performed statistical and bioinformatic analysis for SBCapSeq, splink HiSeq, and RNA-seq data; and managed compute resources and data archiving of SBCapSeq and splink HiSeq data. M.A.B. wrote the R code and performed statistical and bioinformatic analysis for microarray, RNA-seq and WGS data analysis. A.G.R. wrote the R and Perl code for splink 454 workflow and managed resources and data archiving of splink 454 data. K.R. and S.M.R. performed and directed necropsy and histopathological analysis. J.M.W. performed and directed veterinary pathology analysis, including tumor grading and diagnosis. L.v.d.W. optimized library preparation for splink 454 sequencing. C.C.K.Y. performed bioinformatic analysis for splink 454 data. L.A.M. and L.S. isolated RNA and performed microarray hybridizations. K.M.M. developed and optimized staining protocols for FACS analysis. J.L.W., M.L.L. and N.N. isolated single cells by FACS and performed WGA of single-cell genomes. D.J.J. performed and optimized library preparation for SBCapSeq method and performed library preparation for splink HiSeq sequencing. L.G.-R. and F.A.-M. performed and optimized capture hybridizations for the SBCapSeq method and performed Ion Torrent sequencing for SBCapSeq, RNA-seq and WGS experiments. T.K. optimized library preparation splink HiSeq sequencing. All coauthors contributed to editing the manuscript before submission. N.G.C. and N.A.J. provided laboratory resources and personnel for animal husbandry, specimen archiving, sequencing and computer management.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Neal G Copeland or Michael B Mann.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–9

  2. 2.

    Supplementary Note

    Supplementary Table legends and Supplementary Note

Excel files

  1. 1.

    Supplementary Tables

    Supplementary Tables 1–6

Zip files

  1. 1.

    Supplementary Code

    Supplementary source code and scripts

About this article

Publication history






Further reading

Newsletter Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing