Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Three-dimensional single-cell imaging for the analysis of RNA and protein expression in intact tumour biopsies

Nature Biomedical Engineering volume 4, pages 875–888 (2020)Cite this article

Subjects

Abstract

Microscopy analysis of tumour samples is commonly performed on fixed, thinly sectioned and protein-labelled tissues. However, these examinations do not reveal the intricate three-dimensional structures of tumours, nor enable the detection of aberrant transcripts. Here, we report a method, which we name DIIFCO (for diagnosing in situ immunofluorescence-labelled cleared oncosamples), for the multimodal volumetric imaging of RNAs and proteins in intact tumour volumes and organoids. We used DIIFCO to spatially profile the expression of diverse coding RNAs and non-coding RNAs at the single-cell resolution in a variety of cancer tissues. Quantitative single-cell analysis revealed spatial niches of cancer stem-like cells, and showed that the niches were present at a higher density in triple-negative breast cancer tissue. The improved molecular phenotyping and histopathological diagnosis of cancers may lead to new insights into the biology of tumours of patients.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Development of the DIIFCO imaging method.
Fig. 2: RNA detection in intact fresh-frozen human tumours with DIIFCO.
Fig. 3: RNA detection in intact human FFPE tumours with DIIFCO.
Fig. 4: RNA and protein detection in intact human tumours with DIIFCO.
Fig. 5: RNA and protein detection in human-derived organoids.
Fig. 6: Singe-cell analysis of RNAs and proteins in intact human tumours.
Fig. 7: Quantitative cell-by-cell analysis in intact human breast tumours.

Similar content being viewed by others

Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. The raw and analysed datasets generated during the study are too large to be publicly shared, yet they are available for research purposes from the corresponding authors on reasonable request.

Code availability

The custom code used in this study is available at GitHub (https://github.com/uhlen-lab).

References

  1. Andor, N. et al. Pan-cancer analysis of the extent and consequences of intratumor heterogeneity. Nat. Med. 22, 105–113 (2016).

    Article  CAS  PubMed  Google Scholar 

  2. Wang, K. et al. Whole-genome sequencing and comprehensive molecular profiling identify new driver mutations in gastric cancer. Nat. Genet. 46, 573–582 (2014).

    Article  CAS  PubMed  Google Scholar 

  3. Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Sachs, N. et al. A living biobank of breast cancer organoids captures disease heterogeneity. Cell 172, 373–386 (2018).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  7. Uhlen, P. & Tanaka, N. Improved pathological examination of tumors with 3D light-sheet microscopy. Trends Cancer 4, 337–341 (2018).

    Article  PubMed  Google Scholar 

  8. Tanaka, N. et al. Whole-tissue biopsy phenotyping of three-dimensional tumours reveals patterns of cancer heterogeneity. Nat. Biomed. Eng. 1, 796–806 (2017).

    Article  CAS  PubMed  Google Scholar 

  9. Chen, F. et al. Nanoscale imaging of RNA with expansion microscopy. Nat. Methods 13, 679–684 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Choi, H. M. T. et al. Programmable in situ amplification for multiplexed imaging of mRNA expression. Nat. Biotechnol. 28, 1208–1212 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Lovett-Barron, M. et al. Ancestral circuits for the coordinated modulation of brain state. Cell 171, 1411–1423 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Choi, H. M. T. et al. Mapping a multiplexed zoo of mRNA expression. Development 143, 3632–3637 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Choi, H. M. T. et al. Third-generation in situ hybridization chain reaction: multiplexed, quantitative, sensitive, versatile, robust. Development 145, dev165753 (2018).

  14. Stefanits, H. et al. KINFix—A formalin-free non-commercial fixative optimized for histological, immunohistochemical and molecular analyses of neurosurgical tissue specimens. Clin. Neuropathol. 35, 3–12 (2016).

    Article  PubMed  Google Scholar 

  15. Kap, M. et al. Histological assessment of PAXgene tissue fixation and stabilization reagents. Plos ONE 6, e27704 (2011).

  16. Lykidis, D. et al. Novel zinc-based fixative for high quality DNA, RNA and protein analysis. Nucleic Acids Res. 35, e85 (2007).

  17. Wester, K. et al. Zinc-based fixative improves preservation of genomic DNA and proteins in histoprocessing of human tissues. Lab. Invest. 83, 889–899 (2003).

    Article  CAS  PubMed  Google Scholar 

  18. Sylwestrak, E. L., Rajasethupathy, P., Wright, M. A., Jaffe, A. & Deisseroth, K. Multiplexed intact-tissue transcriptional analysis at cellular resolution. Cell 164, 792–804 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Renier, N. et al. Mapping of brain activity by automated volume analysis of immediate early genes. Cell 165, 1789–1802 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hatzis, C. et al. Effects of tissue handling on RNA integrity and microarray measurements from resected breast cancers. J. Natl Cancer Inst. 103, 1871–1883 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Snippert, H. J. et al. Prominin-1/CD133 marks stem cells and early progenitors in mouse small intestine. Gastroenterology 136, 2187–2194 (2009).

    Article  CAS  PubMed  Google Scholar 

  22. Medema, J. P. Cancer stem cells: the challenges ahead. Nat. Cell Biol. 15, 338–344 (2013).

    Article  CAS  PubMed  Google Scholar 

  23. Brugnoli, F., Grassilli, S., Al-Qassab, Y., Capitani, S. & Bertagnolo, V. CD133 in breast cancer cells: more than a stem cell marker. J. Oncol. 2019, 7512632 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Joseph, C. et al. Overexpression of the cancer stem cell marker CD133 confers a poor prognosis in invasive breast cancer. Breast Cancer Res. Treat. 174, 387–399 (2019).

    Article  CAS  PubMed  Google Scholar 

  25. Liu, T. J. et al. CD133+ cells with cancer stem cell characteristics associates with vasculogenic mimicry in triple-negative breast cancer. Oncogene 32, 544–553 (2013).

    Article  CAS  PubMed  Google Scholar 

  26. Panaccione, A., Guo, Y., Yarbrough, W. G. & Ivanov, S. V. Expression profiling of clinical specimens supports the existence of neural progenitor-like stem cells in basal breast cancers. Clin. Breast Cancer 17, 298–306 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Iwai, Y., Hamanishi, J., Chamoto, K. & Honjo, T. Cancer immunotherapies targeting the PD-1 signaling pathway. J. Biomed. Sci. 24, 26 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Anastasiadou, E., Jacob, L. S. & Slack, F. J. Non-coding RNA networks in cancer. Nat. Rev. Cancer 18, 5–18 (2018).

    Article  CAS  PubMed  Google Scholar 

  29. Schmitt, A. M. & Chang, H. Y. Long noncoding RNAs in cancer pathways. Cancer Cell 29, 452–463 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Arun, G. et al. Differentiation of mammary tumors and reduction in metastasis upon Malat1 lncRNA loss. Genes Dev. 30, 34–51 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Xue, M., Chen, W. & Li, X. Urothelial cancer associated 1: a long noncoding RNA with a crucial role in cancer. J. Cancer Res. Clin. Oncol. 142, 1407–1419 (2016).

    Article  PubMed  Google Scholar 

  32. Weber, J. et al. Adjuvant nivolumab versus ipilimumab in resected stage III or IV melanoma. N. Engl. J. Med. 377, 1824–1835 (2017).

    Article  CAS  PubMed  Google Scholar 

  33. Uhlen, M. et al. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

    Article  PubMed  CAS  Google Scholar 

  34. Clevers, H. Modeling development and disease with organoids. Cell 165, 1586–1597 (2016).

    Article  CAS  PubMed  Google Scholar 

  35. Choi, H. M., Beck, V. A. & Pierce, N. A. Next-generation in situ hybridization chain reaction: higher gain, lower cost, greater durability. ACS Nano 8, 4284–4294 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Han, R., Chen, S., Wang, J., Zhao, Y. & Li, G. LncRNA UCA1 affects epithelial–mesenchymal transition, invasion, migration and apoptosis of nasopharyngeal carcinoma cells. Cell Cycle 18, 3044–3053 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Brabletz, T., Kalluri, R., Nieto, M. A. & Weinberg, R. A. EMT in cancer. Nat. Rev. Cancer 18, 128–134 (2018).

    Article  CAS  PubMed  Google Scholar 

  38. Pastushenko, I. et al. Identification of the tumour transition states occurring during EMT. Nature 556, 463–468 (2018).

    Article  CAS  PubMed  Google Scholar 

  39. Gutschner, T. et al. The noncoding RNA MALAT1 is a critical regulator of the metastasis phenotype of lung cancer cells. Cancer Res. 73, 1180–1189 (2013).

    Article  CAS  PubMed  Google Scholar 

  40. Murrow, L. M. et al. Mapping the complex paracrine response to hormones in the human breast at single-cell resolution. Preprint at bioRxiv https://doi.org/10.1101/430611 (2020).

  41. Diggle, P. J. Statistical Analysis of Spatial Point Patterns (Academic, 1983).

  42. Park, Y. G. et al. Protection of tissue physicochemical properties using polyfunctional crosslinkers. Nat. Biotechnol. 37, 73–83 (2019).

    Article  CAS  Google Scholar 

  43. Azaripour, A. et al. A survey of clearing techniques for 3D imaging of tissues with special reference to connective tissue. Prog. Histochem. Cytochem. 51, 9–23 (2016).

    Article  PubMed  Google Scholar 

  44. Kreso, A. & Dick, J. E. Evolution of the cancer stem cell model. Cell Stem Cell 14, 275–291 (2014).

    Article  CAS  PubMed  Google Scholar 

  45. Nassar, D. & Blanpain, C. Cancer stem cells: basic concepts and therapeutic implications. Annu Rev. Pathol. 11, 47–76 (2016).

    Article  CAS  PubMed  Google Scholar 

  46. O’Brien, C. A., Pollett, A., Gallinger, S. & Dick, J. E. A human colon cancer cell capable of initiating tumour growth in immunodeficient mice. Nature 445, 106–110 (2007).

    Article  PubMed  CAS  Google Scholar 

  47. Al-Hajj, M., Wicha, M. S., Benito-Hernandez, A., Morrison, S. J. & Clarke, M. F. Prospective identification of tumorigenic breast cancer cells. Proc. Natl Acad. Sci. USA 100, 3983–3988 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Lathia, J. D. & Liu, H. Overview of cancer stem cells and stemness for community oncologists. Target Oncol. 12, 387–399 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Batlle, E. & Clevers, H. Cancer stem cells revisited. Nat. Med. 23, 1124–1134 (2017).

    Article  CAS  PubMed  Google Scholar 

  50. Schulenburg, A. et al. Cancer stem cells in basic science and in translational oncology: can we translate into clinical application? J. Hematol. Oncol. 8, 16 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Desai, A., Yan, Y. & Gerson, S. L. Concise reviews: cancer stem cell targeted therapies: toward clinical success. Stem Cells Transl. Med. 8, 75–81 (2019).

    Article  PubMed  Google Scholar 

  52. Prasad, S., Ramachandran, S., Gupta, N., Kaushik, I. & Srivastava, S. K. Cancer cells stemness: a doorstep to targeted therapy. Biochim. Biophys. Acta Mol. Basis Dis. 1866, 165424 (2020).

    Article  CAS  PubMed  Google Scholar 

  53. Klonisch, T. et al. Cancer stem cell markers in common cancers—therapeutic implications. Trends Mol. Med. 14, 450–460 (2008).

    Article  CAS  PubMed  Google Scholar 

  54. Enderling, H. Cancer stem cells: small subpopulation or evolving fraction? Integr. Biol. 7, 14–23 (2015).

    Article  Google Scholar 

  55. Hoefflin, R. et al. Spatial niche formation but not malignant progression is a driving force for intratumoural heterogeneity. Nat. Commun. 7, ncomms11845 (2016).

  56. Neumeister, V., Agarwal, S., Bordeaux, J., Camp, R. L. & Rimm, D. L. In situ identification of putative cancer stem cells by multiplexing ALDH1, CD44, and cytokeratin identifies breast cancer patients with poor prognosis. Am. J. Pathol. 176, 2131–2138 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Yang, F. et al. Evaluation of breast cancer stem cells and intratumor stemness heterogeneity in triple-negative breast cancer as prognostic factors. Int. J. Biol. Sci. 12, 1568–1577 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Ricci-Vitiani, L. et al. Tumour vascularization via endothelial differentiation of glioblastoma stem-like cells. Nature 468, 824–828 (2010).

    Article  CAS  PubMed  Google Scholar 

  59. Lancaster, M. A. & Knoblich, J. A. Generation of cerebral organoids from human pluripotent stem cells. Nat. Protoc. 9, 2329–2340 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Camp, J. G. et al. Human cerebral organoids recapitulate gene expression programs of fetal neocortex development. Proc. Natl Acad. Sci. USA 112, 15672–15677 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Uhlin, E. et al. Derivation of human iPS cell lines from monozygotic twins in defined and xeno free conditions. Stem Cell Res. 18, 22–25 (2017).

    Article  CAS  PubMed  Google Scholar 

  62. Sato, T. et al. Long-term expansion of epithelial organoids from human colon, adenoma, adenocarcinoma, and Barrett’s epithelium. Gastroenterology 141, 1762–1772 (2011).

    Article  CAS  PubMed  Google Scholar 

  63. Chung, K. et al. Structural and molecular interrogation of intact biological systems. Nature 497, 332–337 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Tanaka, N. et al. Mapping of the three-dimensional lymphatic microvasculature in bladder tumours using light-sheet microscopy. Br. J. Cancer 118, 995–999 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Tomer, R., Ye, L., Hsueh, B. & Deisseroth, K. Advanced CLARITY for rapid and high-resolution imaging of intact tissues. Nat. Protoc. 9, 1682–1697 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Bria, A. & Iannello, G. TeraStitcher—a tool for fast automatic 3D-stitching of teravoxel-sized microscopy images. BMC Bioinformatics 13, 316 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Rueden, C. T. et al. ImageJ2: ImageJ for the next generation of scientific image data. BMC Bioinformatics 18, 529 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank A. Östman for discussions. This study was supported by the Swedish Research Council (grant nos. 2009-3364, 2013-3189 and 2017-00815, to P.U.), the Swedish Cancer Society (grant nos. CAN 2016-801, 19 0544 Pj and 19 0545 Us, to P.U.), the Swedish Childhood Cancer Foundation (grant no. PR2018-0123, to P.U.), the Swedish Brain Foundation (grant nos. FO2018-0209, to P.U., and FO2018-0281, to A.F.), the Olle Engkvist foundation (to P.U.), the David and Astrid Hagelén Foundation (to N.T.), the Karolinska Institutet Research Foundation (to N.T., S.K. and P.U.), the Takeda Science Foundation (to N.T.), Grant-in-Aid for Scientific Research (KAKENHI 18H04906, 18K19482 and 19H03792, to N.T.), the Kobayashi Foundation for Cancer Research (to N.T.), the Keio Gijuku Academic Development Funds (to N.T.), the Scandinavia-Japan Sasakawa Foundation (to N.T., K.F. and S.K.), and the Wenner-Gren Foundation (to S.K.). The light-sheet microscopy infrastructure used in this research received grants from the Strategic Research Area in Neuroscience (StratNeuro) and the Strategic Research Area in Stem Cells and Regenerative Medicine (StratRegen), supported by the Swedish government.

Author information

Author notes

  1. These authors contributed equally: Nobuyuki Tanaka, Shigeaki Kanatani, Dagmara Kaczynska.

Authors and Affiliations

  1. Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden

    Nobuyuki Tanaka, Shigeaki Kanatani, Dagmara Kaczynska, Keishiro Fukumoto, Lauri Louhivuori, Ayako Miyakawa & Per Uhlén

  2. Department of Urology, Keio University School of Medicine, Tokyo, Japan

    Nobuyuki Tanaka, Keishiro Fukumoto, Naoya Niwa, Kazuhiro Matsumoto & Mototsugu Oya

  3. Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands

    Tomohiro Mizutani, Oded Kopper & Hans Clevers

  4. Department of Pathology, University of Turku, Turku, Finland

    Pauliina Kronqvist

  5. Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden

    Stephanie Robertson, Claes Lindh, Lorand Kis & Johan Hartman

  6. Department of Clinical Pathology and Cytology, Karolinska University Hospital, Stockholm, Sweden

    Stephanie Robertson, Claes Lindh, Lorand Kis & Johan Hartman

  7. Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden

    Robin Pronk & Anna Falk

  8. Department of Molecular Medicine and Surgery, Karolinska University Hospital, Stockholm, Sweden

    Ayako Miyakawa

  9. Turku Bioscience Centre, Åbo Akademi University and University of Turku, Turku, Finland

    Cecilia Sahlgren

  10. Faculty of Science and Engineering, Åbo Akademi University, Turku, Finland

    Cecilia Sahlgren

  11. Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands

    Cecilia Sahlgren

Authors
  1. Nobuyuki Tanaka
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shigeaki Kanatani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dagmara Kaczynska
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Keishiro Fukumoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lauri Louhivuori
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tomohiro Mizutani
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Oded Kopper
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Pauliina Kronqvist
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Stephanie Robertson
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Claes Lindh
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Lorand Kis
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Robin Pronk
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Naoya Niwa
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Kazuhiro Matsumoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Mototsugu Oya
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Ayako Miyakawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Anna Falk
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Johan Hartman
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Cecilia Sahlgren
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Hans Clevers
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Per Uhlén
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

N.T., S.K. and P.U. conceived the study and designed experiments. N.T., S.K., D.K., K.F., L.L. and K.M. performed the experiments and data analyses. N.T., S.K. and D.K. performed 3D image processing and 3D data analyses. A.M., P.K., S.R., C.L., L.K., N.N., K.M., J.H. and C.S. provided mouse and human tumour samples. T.M., O.K., R.P., A.F. and H.C. provided human organoids. M.O. and A.M. provided conceptual advice on clinical aspects. S.K., C.S., L.L. and H.C. reviewed and commented on the manuscript. N.T. and P.U. performed data interpretation and wrote the manuscript. P.U. supervised the research.

Corresponding authors

Correspondence to Nobuyuki Tanaka or Per Uhlén.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–10 and captions for Supplementary Videos 1–6.

Reporting Summary

Supplementary Video 1

Volume rendering of a mouse embryo.

Supplementary Video 2

Cell-by-cell analysis of human breast cancer tissue.

Supplementary Video 3

Parvalbumin RNA and protein staining of a mouse cortical section.

Supplementary Video 4

Volume rendering of a human organoid.

Supplementary Video 5

Cell-by-cell analysis of human colon cancer tissue.

Supplementary Video 6

Spatial niche analysis of human breast cancer tissue.

Supplementary Dataset 1

Key resources.

Supplementary Dataset 2

Probe sequences and accession numbers of the targeted RNAs.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tanaka, N., Kanatani, S., Kaczynska, D. et al. Three-dimensional single-cell imaging for the analysis of RNA and protein expression in intact tumour biopsies. Nat Biomed Eng 4, 875–888 (2020). https://doi.org/10.1038/s41551-020-0576-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41551-020-0576-z

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer