Recent advances in single-cell transcriptomics are ideally placed to unravel intratumoral heterogeneity and selective resistance of cancer stem cell (SC) subpopulations to molecularly targeted cancer therapies. However, current single-cell RNA-sequencing approaches lack the sensitivity required to reliably detect somatic mutations. We developed a method that combines high-sensitivity mutation detection with whole-transcriptome analysis of the same single cell. We applied this technique to analyze more than 2,000 SCs from patients with chronic myeloid leukemia (CML) throughout the disease course, revealing heterogeneity of CML-SCs, including the identification of a subgroup of CML-SCs with a distinct molecular signature that selectively persisted during prolonged therapy. Analysis of nonleukemic SCs from patients with CML also provided new insights into cell-extrinsic disruption of hematopoiesis in CML associated with clinical outcome. Furthermore, we used this single-cell approach to identify a blast-crisis-specific SC population, which was also present in a subclone of CML-SCs during the chronic phase in a patient who subsequently developed blast crisis. This approach, which might be broadly applied to any malignancy, illustrates how single-cell analysis can identify subpopulations of therapy-resistant SCs that are not apparent through cell-population analysis.

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Referenced accessions

Gene Expression Omnibus


  1. 1.

    & Biological and therapeutic impact of intratumor heterogeneity in cancer evolution. Cancer Cell 27, 15–26 (2015).

  2. 2.

    et al. Persistent malignant stem cells in del(5q) myelodysplasia in remission. N. Engl. J. Med. 363, 1025–1037 (2010).

  3. 3.

    , & Cancer stem cells: impact, heterogeneity, and uncertainty. Cancer Cell 21, 283–296 (2012).

  4. 4.

    et al. Myelodysplastic syndromes are propagated by rare and distinct human cancer stem cells in vivo. Cancer Cell 25, 794–808 (2014).

  5. 5.

    et al. Genotypic and functional diversity of phenotypically defined primitive hematopoietic cells in patients with chronic myeloid leukemia. Exp. Hematol. 41, 837–847 (2013).

  6. 6.

    et al. Impact of malignant stem cell burden on therapy outcome in newly diagnosed chronic myeloid leukemia patients. Leukemia 27, 1520–1526 (2013).

  7. 7.

    et al. Toward understanding and exploiting tumor heterogeneity. Nat. Med. 21, 846–853 (2015).

  8. 8.

    & Application of single-cell genomics in cancer: promise and challenges. Hum. Mol. Genet. 24, R74–R84 (2015).

  9. 9.

    & Advances and applications of single-cell sequencing technologies. Mol. Cell 58, 598–609 (2015).

  10. 10.

    et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).

  11. 11.

    et al. RNA-seq of single prostate CTCs implicates noncanonical Wnt signaling in antiandrogen resistance. Science 349, 1351–1356 (2015).

  12. 12.

    et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

  13. 13.

    et al. Effects of a selective inhibitor of the Abl tyrosine kinase on the growth of Bcr-Abl positive cells. Nat. Med. 2, 561–566 (1996).

  14. 14.

    & Targeting the BCR-ABL tyrosine kinase in chronic myeloid leukemia. N. Engl. J. Med. 344, 1084–1086 (2001).

  15. 15.

    Imatinib changed everything. N. Engl. J. Med. 376, 982–983 (2017).

  16. 16.

    , & Hurdles toward a cure for CML: the CML stem cell. Hematol./oncol. Clinics North Am. 25, 951–966 (2011).

  17. 17.

    et al. Persistence of leukemia stem cells in chronic myelogenous leukemia patients in prolonged remission with imatinib treatment. Blood 118, 5565–5572 (2011).

  18. 18.

    et al. Discontinuation of imatinib in patients with chronic myeloid leukaemia who have maintained complete molecular remission for at least 2 years: the prospective, ulticenter Stop Imatinib (STIM) trial. Lancet Oncol. 11, 1029–1035 (2010).

  19. 19.

    et al. Myeloproliferative neoplasia remodels the endosteal bone marrow niche into a self-reinforcing leukemic niche. Cell Stem Cell 13, 285–299 (2013).

  20. 20.

    et al. Leukemic cells create bone marrow niches that disrupt the behavior of normal hematopoietic progenitor cells. Science 322, 1861–1865 (2008).

  21. 21.

    , & Normal and leukemic stem cell niches: insights and therapeutic opportunities. Cell Stem Cell 16, 254–267 (2015).

  22. 22.

    et al. Treatment of chronic myelogenous leukemia by blocking cytokine alterations found in normal stem and progenitor cells. Cancer Cell 27, 671–681 (2015).

  23. 23.

    et al. IL-6 controls leukemic multipotent progenitor cell fate and contributes to chronic myelogenous leukemia development. Cancer Cell 20, 661–673 (2011).

  24. 24.

    et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

  25. 25.

    et al. Combined single-cell functional and gene expression analysis resolves heterogeneity within stem cell populations. Cell Stem Cell 16, 712–724 (2015).

  26. 26.

    et al. Extensive amplification of bcr/abl fusion genes clustered on three marker chromosomes in human leukemic cell line K-562. Leukemia 9, 858–862 (1995).

  27. 27.

    et al. Quantitative assessment of single-cell RNA-sequencing methods. Nat. Methods 11, 41–46 (2014).

  28. 28.

    et al. Highly multiplexed and strand-specific single-cell RNA 5′ end sequencing. Nat. Protoc. 7, 813–828 (2012).

  29. 29.

    et al. The hematopoietic stem cell in chronic phase CML is characterized by a transcriptional profile resembling normal myeloid progenitor cells and reflecting loss of quiescence. Leukemia 23, 892–899 (2009).

  30. 30.

    et al. The expansion of CML clones initiates at the CMP stage, and is associated with the down-regulation of IRF8 and GFI1. Blood 122, 1477 (2013).

  31. 31.

    & Regulation of hematopoietic stem cell activity by inflammation. Front. Immunol. 4, 204 (2013).

  32. 32.

    & Inflammatory modulation of HSCs: viewing the HSC as a foundation for the immune response. Nat. Rev. Immunol. 11, 685–692 (2011).

  33. 33.

    , , & Tumor necrosis factor restricts hematopoietic stem cell activity in mice: involvement of two distinct receptors. J. Exp. Med. 208, 1563–1570 (2011).

  34. 34.

    , , , & Mechanisms that regulate the cell cycle status of very primitive hematopoietic cells in long-term human marrow cultures. I. Stimulatory role of a variety of mesenchymal cell activators and inhibitory role of TGF-beta. Blood 75, 96–101 (1990).

  35. 35.

    et al. European LeukemiaNet recommendations for the management of chronic myeloid leukemia: 2013. Blood 122, 872–884 (2013).

  36. 36.

    et al. Autocrine activation of the MET receptor tyrosine kinase in acute myeloid leukemia. Nat. Med. 18, 1118–1122 (2012).

  37. 37.

    et al. Eaf1 and Eaf2 negatively regulate canonical Wnt/β-catenin signaling. Development 140, 1067–1078 (2013).

  38. 38.

    et al. Coexistence of LMPP-like and GMP-like leukemia stem cells in acute myeloid leukemia. Cancer Cell 19, 138–152 (2011).

  39. 39.

    Chronic myeloid leukemia stem cells. Hematology Am. Soc. Hematol. Educ. Program. 2008, 436–442 (2008).

  40. 40.

    et al. IL1RAP as a surface marker for leukemia stem cells is related to clinical phase of chronic myeloid leukemia patients. Int. J. Clin. Exp. Med. 7, 4787–4798 (2014).

  41. 41.

    et al. Dipeptidylpeptidase IV (CD26) defines leukemic stem cells (LSC) in chronic myeloid leukemia. Blood 123, 3951–3962 (2014).

  42. 42.

    et al. Genome-wide comparison of the transcriptomes of highly enriched normal and chronic myeloid leukemia stem and progenitor cell populations. Oncotarget 4, 715–728 (2013).

  43. 43.

    et al. TGF-α and IL-6 plasma levels selectively identify CML patients who fail to achieve an early molecular response or progress in the first year of therapy. Leukemia 30, 1263–1272 (2016).

  44. 44.

    , & Awakening dormant haematopoietic stem cells. Nat. Rev. Immunol. 10, 201–209 (2010).

  45. 45.

    The cancer stem cell: premises, promises and challenges. Nat. Med. 17, 313–319 (2011).

  46. 46.

    et al. Single-cell molecular analysis defines therapy response and immunophenotype of stem cell subpopulations in CML. Blood (2017).

  47. 47.

    et al. Oncogene regulation. An oncogenic super-enhancer formed through somatic mutation of a noncoding intergenic element. Science 346, 1373–1377 (2014).

  48. 48.

    , , & Present and future of molecular monitoring in chronic myeloid leukaemia. Br. J. Haematol. 173, 337–349 (2016).

  49. 49.

    et al. Platelet-biased stem cells reside at the apex of the haematopoietic stem-cell hierarchy. Nature 502, 232–236 (2013).

  50. 50.

    et al. Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst. Dev. Cell 18, 675–685 (2010).

  51. 51.

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

  52. 52.

    , , & An abundance of ubiquitously expressed genes revealed by tissue transcriptome sequence data. PLoS Comput. Biol. 5, e1000598 (2009).

  53. 53.

    et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat. Biotechnol. 32, 1053–1058 (2014).

  54. 54.

    & How deep is enough in single-cell RNA-seq? Nat. Biotechnol. 32, 1005–1006 (2014).

  55. 55.

    & How to detect and handle outliers (ASQC Quality Press, Milwaukee, Wis., 1993).

  56. 56.

    et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

  57. 57.

    , , & Characterizing heterogeneity in leukemic cells using single-cell gene expression analysis. Genome Biol. 15, 525 (2014).

  58. 58.

    et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

  59. 59.

    et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

  60. 60.

    , & Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

  61. 61.

    et al. Genome-wide analysis of transcriptional regulators in human HSPCs reveals a densely interconnected network of coding and noncoding genes. Blood 122, e12–e22 (2013).

  62. 62.

    & Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

  63. 63.

    et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

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This work was funded by a Medical Research Council Senior Clinical Fellowship (MR/L006340/1), MRC Confidence in Concept award (MC_PC_13073) and Rosetrees Trust award (A712: Rosetrees Trust Award (A712)) to A.J.M., the MRC Molecular Haematology Unit core award (A.J.M. and S.E.W.J.; MC_UU_12009/5), a MRC programme grant to S.E.W.J. (G0801073), an international-recruitment award from the Swedish Research Council (S.E.W.J.), and grants from the Tobias Foundation (S.E.W.J.) and the Center for Innovative Medicine (CIMED) at the Karolinska Institute (S.E.W.J.). This work was also supported by the MRC-funded Oxford Consortium for Single-cell Biology (MR/M00919X/1) and the Oxford NIHR Biomedical Centre based at Oxford University Hospitals NHS Trust and University of Oxford. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health or the NIH. The work was also supported by an educational grant from Novartis. The authors acknowledge the contributions of the WIMM Flow Cytometry Facility, supported by the MRC HIU; MRC MHU (MC_UU_12009); NIHR Oxford BRC and John Fell Fund (131/030 and 101/517), the EPA fund (CF182 and CF170) and by the WIMM Strategic Alliance awards G0902418 and MC_UU_12025. N.A. was supported by the Oxford–Wellcome Trust Institutional Strategic Support Fund. S.M. is supported by the Finnish Cancer Institute and the Finnish Cancer Organizations.

Author information

Author notes

    • Alice Giustacchini
    •  & Supat Thongjuea

    These authors contributed equally to this work.

    • Sten Eirik W Jacobsen
    •  & Adam J Mead

    These authors jointly directed this work.


  1. MRC Molecular Hematology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.

    • Alice Giustacchini
    • , Supat Thongjuea
    • , Nikolaos Barkas
    • , Benjamin J Povinelli
    • , Christopher A G Booth
    • , Paul Sopp
    • , Ruggiero Norfo
    • , Alba Rodriguez-Meira
    • , Neil Ashley
    • , Lauren Jamieson
    • , Paresh Vyas
    • , Sten Eirik W Jacobsen
    •  & Adam J Mead
  2. Haemopoietic Stem Cell Biology Laboratory, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.

    • Alice Giustacchini
    • , Supat Thongjuea
    • , Nikolaos Barkas
    • , Petter S Woll
    • , Benjamin J Povinelli
    • , Christopher A G Booth
    • , Ruggiero Norfo
    • , Alba Rodriguez-Meira
    • , Neil Ashley
    • , Lauren Jamieson
    • , Sten Eirik W Jacobsen
    •  & Adam J Mead
  3. Department of Cellular Therapy, Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway.

    • Kristina Anderson
  4. Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.

    • Åsa Segerstolpe
    • , Rickard Sandberg
    •  & Sten Eirik W Jacobsen
  5. Integrated Cardio Metabolic Center (ICMC), Karolinska Institutet, Huddinge, Sweden.

    • Åsa Segerstolpe
  6. Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden.

    • Hong Qian
    •  & Sten Eirik W Jacobsen
  7. Department of Medical Science and Division of Hematology, University Hospital, Uppsala, Sweden.

    • Ulla Olsson-Strömberg
  8. Hematology Research Unit Helsinki, Department of Clinical Chemistry and Hematology, University of Helsinki and Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland.

    • Satu Mustjoki
  9. Ludwig Institute for Cancer Research, Stockholm, Sweden.

    • Rickard Sandberg
  10. Karolinska University Hospital, Stockholm, Sweden.

    • Sten Eirik W Jacobsen
  11. NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK.

    • Adam J Mead


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A.G. designed, performed and analyzed experiments and contributed to writing the manuscript. S.T. designed and performed bioinformatic analyses and contributed to writing the manuscript. N.B. and B.J.P. performed analyses of RNA sequencing and qPCR results. P.S.W. and P.S. were involved in FACS analysis and sorting. R.N., A.R.-M., C.A.G.B. and L.J. performed experiments. N.A. maintained single-cell facility infrastructure. P.V., S.M. and H.Q. provided infrastructure for sample banking and provided input on experimental design and analysis. K.A. performed FISH experiments. Å.S. was involved in RNA-sequencing experiments. U.O.-S. collected clinical information. R.S. provided input on RNA-sequencing experiments. A.J.M. and S.E.W.J. conceived and supervised the project, designed and analyzed experiments and wrote the manuscript.

Competing interests

A.J.M. has received honoraria and research funding from Novartis.

Corresponding authors

Correspondence to Sten Eirik W Jacobsen or Adam J Mead.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–16 and Table 12–15.

Excel files

  1. 1.

    Supplementary Table 1

    Patient demographics and characteristics.

  2. 2.

    Supplementary Table 2

    Differentially expressed genes between normal HSCs, BCRABL+ and BCR-ABL- SCs from CP-CML patients at diagnosis.

  3. 3.

    Supplementary Table 3

    Gene-sets from previous studies on CML stem and progenitor cells.

  4. 4.

    Supplementary Table 4

    Results from GSEA comparing normal HSCs to BCRABL+ SCs and BCR-ABL- SCs from CP-CML patients at diagnosis and using gene-sets from previous studies on CML stem and progenitor cells.

  5. 5.

    Supplementary Table 5

    Results from GSEA comparing normal HSCs to BCRABL+ SCs and BCR-ABL- SCs from CP-CML patients at diagnosis and using HALLMARK gene sets

  6. 6.

    Supplementary Table 6

    Results from GSEA comparing diagnostic samples from good and poor responder CML patients.

  7. 7.

    Supplementary Table 7

    Top 500 informative genes for distinguishing normal-HSCs from BCR-ABL+ SCs at diagnosis and during remission.

  8. 8.

    Supplementary Table 8

    Results from GSEA on HALLMARK gene-sets comparing remission group-A BCR-ABL+ SCs to remission group-B BCRABL+ SCs.

  9. 9.

    Supplementary Table 9

    Differentially expressed genes between normal HSCs, BCRABL+ SCs from diagnosis, remission group-A and remission group-B.

  10. 10.

    Supplementary Table 10

    Results from GSEA comparing remission group-A BCRABL+ SCs to normal HSCs and remission BCR-ABL- SCs.

  11. 11.

    Supplementary Table 11

    Differentially expressed genes between single BCRABL+ SCs falling in CP-CML cluster and BCR-ABL+ SCs falling in BC-CML cluster.

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