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:

Determining cell type abundance and expression from bulk tissues with digital cytometry

Abstract

Single-cell RNA-sequencing has emerged as a powerful technique for characterizing cellular heterogeneity, but it is currently impractical on large sample cohorts and cannot be applied to fixed specimens collected as part of routine clinical care. We previously developed an approach for digital cytometry, called CIBERSORT, that enables estimation of cell type abundances from bulk tissue transcriptomes. We now introduce CIBERSORTx, a machine learning method that extends this framework to infer cell-type-specific gene expression profiles without physical cell isolation. By minimizing platform-specific variation, CIBERSORTx also allows the use of single-cell RNA-sequencing data for large-scale tissue dissection. We evaluated the utility of CIBERSORTx in multiple tumor types, including melanoma, where single-cell reference profiles were used to dissect bulk clinical specimens, revealing cell-type-specific phenotypic states linked to distinct driver mutations and response to immune checkpoint blockade. We anticipate that digital cytometry will augment single-cell profiling efforts, enabling cost-effective, high-throughput tissue characterization without the need for antibodies, disaggregation or viable cells.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

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

Fig. 1: Framework for in silico cell enumeration and purification.
Fig. 2: Bulk tissue deconvolution with single-cell reference profiles.
Fig. 3: Purification of representative cell-type-specific transcriptome profiles from a group of specimens.
Fig. 4: High-resolution purification of cell-type-specific expression from synthetic mixtures.
Fig. 5: High-resolution expression profiling of bulk tumor biopsies.
Fig. 6: Cellular signatures of melanoma driver mutation status and immunotherapy response.

Similar content being viewed by others

Data availability

All expression datasets analyzed in this work, including accession codes, file names and web links (if available), are listed in Supplementary Table 1. Expression data generated in this study are available at http://cibersortx.stanford.edu and through the Gene Expression Omnibus with accession code GSE127472.

Code availability

CIBERSORTx v.1.0 was used to generate the results in this work and is freely available for academic research use at http://cibersortx.stanford.edu.

References

  1. Wagner, A., Regev, A. & Yosef, N. Revealing the vectors of cellular identity with single-cell genomics. Nat. Biotech. 34, 1145–1160 (2016).

    Article  CAS  Google Scholar 

  2. Shen-Orr, S. S. & Gaujoux, R. Computational deconvolution: extracting cell type-specific information from heterogeneous samples. Curr. Opin. Immunol. 25, 571–578 (2013).

    Article  CAS  Google Scholar 

  3. Newman, A. M. & Alizadeh, A. A. High-throughput genomic profiling of tumor-infiltrating leukocytes. Curr. Opin. Immunol. 41, 77–84 (2016).

    Article  CAS  Google Scholar 

  4. Aran, D., Hu, Z. & Butte, A. J. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 18, 220 (2017).

    Article  Google Scholar 

  5. Racle, J., de Jonge, K., Baumgaertner, P., Speiser, D. E. & Gfeller, D. Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data. eLife 6, e26476 (2017).

    Article  Google Scholar 

  6. Quon, G. et al. Computational purification of individual tumor gene expression profiles leads to significant improvements in prognostic prediction. Genome Med. 5, 29 (2013).

    Article  Google Scholar 

  7. Angelova, M. et al. Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy. Genome Biol. 16, 64 (2015).

    Article  Google Scholar 

  8. Becht, E. et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 17, 218 (2016).

    Article  Google Scholar 

  9. Puram, S. V. et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171, 1611–1624 (2017).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  11. Baron, M., et al. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell populationstructure. Cell Syst. 3, 346–360.e4 (2016).

    Article  Google Scholar 

  12. Lappalainen, T. & Greally, J. M. Associating cellular epigenetic models with human phenotypes. Nat. Rev. Genet. 18, 441–451 (2017).

    Article  CAS  Google Scholar 

  13. He, Z. et al. Comprehensive transcriptome analysis of neocortical layers in humans, chimpanzees and macaques. Nat. Neurosci. 20, 886–895 (2017).

    Article  CAS  Google Scholar 

  14. Schelker, M. et al. Estimation of immune cell content in tumour tissue using single-cell RNA-seq data. Nat. Commun. 8, 2032 (2017).

    Google Scholar 

  15. Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).

    Article  CAS  Google Scholar 

  16. Ziegenhain, C. et al. Comparative analysis of single-cell RNA sequencing methods. Mol. Cell 65, 631–643.e634 (2017).

    Article  CAS  Google Scholar 

  17. Cancer Genome Atlas Network. Genomic classification of cutaneous melanoma. Cell 161, 1681–1696 (2015).

    Article  Google Scholar 

  18. Dvinge, H. et al. Sample processing obscures cancer-specific alterations in leukemic transcriptomes. Proc. Natl Acad. Sci. USA 111, 16802–16807 (2014).

    Article  CAS  Google Scholar 

  19. Kadić, E., Moniz, R. J., Huo, Y., Chi, A. & Kariv, I. Effect of cryopreservation on delineation of immune cell subpopulations in tumor specimens as determined by multiparametric single cell mass cytometry analysis. BMC Immunol. 18, 6 (2017).

    Article  Google Scholar 

  20. Chen, P.-L., et al. Analysis of immune signatures in longitudinal tumor samples yields insight into biomarkers of response and mechanisms of resistance to immune checkpoint blockade. Cancer Discov. 6, 827–837 (2016).

    Article  Google Scholar 

  21. Segerstolpe, A. et al. Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes. Cell Metab. 24, 593–607 (2016).

    Article  CAS  Google Scholar 

  22. Gaujoux, R. & Seoighe, C. CellMix: a comprehensive toolbox for gene expression deconvolution. Bioinformatics 29, 2211–2212 (2013).

    Article  CAS  Google Scholar 

  23. Liebner, D. A., Huang, K. & Parvin, J. D. MMAD: microarray microdissection with analysis of differences is a computational tool for deconvoluting cell type-specific contributions from tissue samples. Bioinformatics 30, 682–689 (2014).

    Article  CAS  Google Scholar 

  24. Moffitt, R. A. et al. Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma. Nat. Genet. 47, 1168–1178 (2015).

    Article  CAS  Google Scholar 

  25. Shen-Orr, S. S. et al. Cell type-specific gene expression differences in complex tissues. Nat. Methods 7, 287–289 (2010).

    Article  CAS  Google Scholar 

  26. Zhong, Y., Wan, Y. W., Pang, K., Chow, L. M. & Liu, Z. Digital sorting of complex tissues for cell type-specific gene expression profiles. BMC Bioinformatics 14, 89 (2013).

    Article  Google Scholar 

  27. Zuckerman, N. S., Noam, Y., Goldsmith, A. J. & Lee, P. P. A self-directed method for cell-type identification and separation of gene expression microarrays. PLoS Comput. Biol. 9, e1003189 (2013).

    Article  CAS  Google Scholar 

  28. Onuchic, V. et al. Epigenomic deconvolution of breast tumors reveals metabolic coupling between constituent cell types. Cell Rep. 17, 2075–2086 (2016).

    Article  CAS  Google Scholar 

  29. Green, M. R. et al. Mutations in early follicular lymphoma progenitors are associated with suppressed antigen presentation. Proc. Natl Acad. Sci. USA 112, E1116–E1125 (2015).

    Article  CAS  Google Scholar 

  30. Gentles, A. J. et al. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat. Med. 21, 938–945 (2015).

    Article  CAS  Google Scholar 

  31. Thorsson, V. et al. The immune landscape of cancer. Immunity 48, 812–830.e814 (2018).

    Article  CAS  Google Scholar 

  32. Ahn, J. et al. DeMix: deconvolution for mixed cancer transcriptomes using raw measured data. Bioinformatics 29, 1865–1871 (2013).

    Article  CAS  Google Scholar 

  33. Wang, Z. et al. Transcriptome deconvolution of heterogeneous tumor samples with immune infiltration. iScience 9, 451–460 (2018).

    Article  CAS  Google Scholar 

  34. Alizadeh, A. A. et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000).

    Article  CAS  Google Scholar 

  35. Golub, T. R. et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999).

    Article  CAS  Google Scholar 

  36. Bild, A. H. et al. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 439, 353–357 (2006).

    Article  CAS  Google Scholar 

  37. Lenz, G. et al. Stromal gene signatures in large-B-cell lymphomas. N. Engl. J. Med. 359, 2313–2323 (2008).

    Article  CAS  Google Scholar 

  38. Whitney, A. R. et al. Individuality and variation in gene expression patterns in human blood. Proc. Natl Acad. Sci. USA 100, 1896–1901 (2003).

    Article  CAS  Google Scholar 

  39. Jiang, Y. et al. CREBBP inactivation promotes the development of HDAC3-dependent lymphomas. Cancer Discov. 7, 38–53 (2017).

    Article  CAS  Google Scholar 

  40. Cancer Genome Atlas Research Network. Comprehensive genomic characterization of squamous cell lung cancers. Nature 489, 519–525 (2012).

    Article  Google Scholar 

  41. Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature 511, 543–550 (2014).

    Article  Google Scholar 

  42. Lambrechts, D., et al. Phenotype molding of stromal cells in the lung tumor microenvironment. Nat. Med. 24, 1277–1289 (2018).

    Article  CAS  Google Scholar 

  43. Davies, H. et al. Mutations of the BRAF gene in human cancer. Nature 417, 949–954 (2002).

    Article  CAS  Google Scholar 

  44. Akbani, R., et al. Genomic classification of cutaneous melanoma. Cell 161, 1681–1696.

  45. Curtin, J. A. et al. Distinct sets of genetic alterations in melanoma. N. Engl. J. Med 353, 2135–2147 (2005).

    Article  CAS  Google Scholar 

  46. Wherry, E. J. & Kurachi, M. Molecular and cellular insights into T cell exhaustion. Nat. Rev. Immunol. 15, 486–499 (2015).

    Article  CAS  Google Scholar 

  47. Postow, M. A., Callahan, M. K. & Wolchok, J. D. Immune checkpoint blockade in cancer therapy. J. Clin. Oncol.. 33, 1974–1982 (2015).

    Article  CAS  Google Scholar 

  48. Anderson, A. C., Joller, N. & Kuchroo, V. K. Lag-3, Tim-3, and TIGIT: co-inhibitory receptors with specialized functions in immune regulation. Immunity 44, 989–1004 (2016).

    Article  CAS  Google Scholar 

  49. Baitsch, L. et al. Exhaustion of tumor-specific CD8+ T cells in metastases from melanoma patients. J. Clin. Invest.. 121, 2350–2360 (2011).

    Article  CAS  Google Scholar 

  50. Van Allen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015).

    Article  Google Scholar 

  51. Redman, J. M., Gibney, G. T. & Atkins, M. B. Advances in immunotherapy for melanoma. BMC Med. 14, 20 (2016).

    Article  Google Scholar 

  52. Tumeh, P. C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568–571 (2014).

    Article  CAS  Google Scholar 

  53. Kvistborg, P. et al. Anti-CTLA-4 therapy broadens the melanoma-reactive CD8+ T cell response. Sci. Transl. Med. 6, 254ra128 (2014).

    Article  Google Scholar 

  54. Daud, A. I. et al. Tumor immune profiling predicts response to anti–PD-1 therapy in human melanoma. J. Clin. Invest. 126, 3447–3452 (2016).

    Article  Google Scholar 

  55. Nathanson, T. et al. Somatic mutations and neoepitope homology in melanomas treated with CTLA-4 blockade. Cancer Immunol. Res. 5, 84–91 (2017).

    Article  CAS  Google Scholar 

  56. Cao, J. et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357, 661–667 (2017).

    Article  CAS  Google Scholar 

  57. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411 (2018).

    Article  CAS  Google Scholar 

  58. Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36, 421 (2018).

    Article  CAS  Google Scholar 

  59. Chakravarthy, A. et al. Pan-cancer deconvolution of tumour composition using DNA methylation. Nat. Commun. 9, 3220 (2018).

    Article  Google Scholar 

  60. Corces, M. R., et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nat. Genet. 48, 1193–1203 (2016).

    Article  CAS  Google Scholar 

  61. Abbas, A. R. et al. Immune response in silico (IRIS): immune-specific genes identified from a compendium of microarray expression data. Genes Immun. 6, 319–331 (2005).

    Article  CAS  Google Scholar 

  62. Subramanian, A. 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).

    Article  CAS  Google Scholar 

  63. Levy, R. et al. Active idiotypic vaccination versus control immunotherapy for follicular lymphoma. J. Clin. Oncol. 32, 1797–1803 (2014).

    Article  CAS  Google Scholar 

  64. Allantaz, F. et al. Expression profiling of human immune cell subsets identifies miRNA-mRNA regulatory relationships correlated with cell type specific expression. PLoS ONE 7, e29979 (2012).

    Article  CAS  Google Scholar 

  65. Compagno, M. et al. Mutations of multiple genes cause deregulation of NF-kappaB in diffuse large B-cell lymphoma. Nature 459, 717–721 (2009).

    Article  CAS  Google Scholar 

  66. Jourdan, M. et al. An in vitro model of differentiation of memory B cells into plasmablasts and plasma cells including detailed phenotypic and molecular characterization. Blood 114, 5173–5181 (2009).

    Article  CAS  Google Scholar 

  67. Kiaii, S. et al. Follicular lymphoma cells induce changes in T-cell gene expression and function: potential impact on survival and risk of transformation. J. Clin. Oncol. 31, 2654–2661 (2013).

    Article  CAS  Google Scholar 

  68. Nakaya, H. I. et al. Systems biology of vaccination for seasonal influenza in humans. Nat. Immunol. 12, 786–795 (2011).

    Article  CAS  Google Scholar 

  69. Tatlow, P. J. & Piccolo, S. R. A cloud-based workflow to quantify transcript-expression levels in public cancer compendia. Sci. Rep. 6, 39259 (2016).

    Article  CAS  Google Scholar 

  70. Milpied, P. et al. Germinal center program de-synchronization and intra-patient heterogeneity in follicular lymphoma B-cells revealed by integrative single-cell analysis. Blood 130, 41–41 (2017).

    Google Scholar 

  71. Vallejos, C. A., Risso, D., Scialdone, A., Dudoit, S. & Marioni, J. C. Normalizing single-cell RNA sequencing data: challenges and opportunities. Nat. Methods 14, 565–571 (2017).

    Article  CAS  Google Scholar 

  72. Stegle, O., Teichmann, S. A. & Marioni, J. C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015).

    Article  CAS  Google Scholar 

  73. Hicks, S. C., Townes, F. W., Teng, M. & Irizarry, R. A. Missing data and technical variability in single-cell RNA-sequencing experiments. Biostatistics, 9, 562–578 (2018).

  74. Venet, D., Pecasse, F., Maenhaut, C. & Bersini, H. Separation of samples into their constituents using gene expression data. Bioinformatics 17 (Suppl. 1), S279–S287 (2001).

  75. Abbas, A. R., Wolslegel, K., Seshasayee, D., Modrusan, Z. & Clark, H. F. Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus. PLoS ONE 4, e6098 (2009).

    Article  Google Scholar 

  76. Zhong, Y. & Liu, Z. Gene expression deconvolution in linear space. Nat. Methods 9, 8–9 (2012); author reply 9, 9 (2012).

  77. Lee, D. D. & Seung, H. S. Algorithms for non-negative matrix factorization. In Proc. 13th International Conference on Neural Information Processing Systems (eds. Leen, T.K. et al.) 535–541 (MIT Press, 2000).

  78. Bacher, R. et al. SCnorm: robust normalization of single-cell RNA-seq data. Nat. Methods 14, 584 (2017).

    Article  CAS  Google Scholar 

  79. Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

    Article  Google Scholar 

Download references

Acknowledgements

We are grateful to R. Levy, S.K. Plevritis, B. Chen, B. Nabet and M. Matusiak for assistance with this study. This work was supported by grants from the National Cancer Institute (A.M.N., R00CA187192; A.A.A., U01CA194389; A.A.A. and M.D., R01CA188298; S.K.P., U01CA154969), the Stinehart-Reed foundation (A.M.N., A.A.A.), the Stanford Bio-X Interdisciplinary Initiatives Seed Grants Program (IIP) (A.M.N.), the Virginia and D.K. Ludwig Fund for Cancer Research (A.M.N., A.A.A.), the US Department of Defense (A.M.N., W81XWH-12-1-0498), the Shanahan and Bronzini Family Funds (A.A.A.), anonymous donors (A.A.A., A.M.N.), the V Foundation for Cancer Research (A.A.A.), the Leukemia and Lymphoma Society (A.A.A.), the Damon Runyon Cancer Research Foundation (A.A.A.) and the American Society of Hematology (A.A.A.).

Author information

Authors and Affiliations

Authors

Contributions

A.M.N. and A.A.A. conceived of CIBERSORTx, developed strategies for related experiments, and wrote the paper with input from C.L.L., C.B.S., A.J.G., M.S.E. and M.D. A.M.N. developed and implemented CIBERSORTx and analyzed the data. C.L.L. and C.B.S. implemented web infrastructure. C.B.S. assisted with CIBERSORTx software development and validation experiments. A.J.G. assisted in the development of CIBERSORTx. A.A.C. and M.S.K. performed flow cytometry and single-cell profiling. F.S. performed targeted DNA sequencing of FL tumor specimens. B.A.L. assisted with validation studies. D.S. assisted with data acquisition. M.D. assisted in the collection and expression profiling of patient specimens. All authors commented on the manuscript at all stages.

Corresponding authors

Correspondence to Aaron M. Newman or Ash A. Alizadeh.

Ethics declarations

Competing interests

A.M.N. has patent filings related to expression deconvolution and cancer biomarkers and has served as a consultant for Roche, Merck and CiberMed. A.A.A. has patent filings related to expression deconvolution and cancer biomarkers and has served as a consultant or advisor for Roche, Genentech, Janssen, CiberMed, Pharmacyclics, Gilead and Celgene. M.D. has patent filings related to cancer biomarkers and has served as a consultant for Roche, Novartis, CiberMed and Quanticel Pharmaceuticals. No potential conflicts of interest were disclosed by the other authors.

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. 1–18 and Supplementary Notes 1 and 2

Reporting Summary

Supplementary Table 1

Inventory of expression datasets and patient samples analyzed in this work

Supplementary Table 2

Signature matrices and cell groupings for expression purification

Supplementary Table 3

CREBBP genotyping results in FL tumors

Supplementary Table 4

DEGs identified by high-resolution CIBERSORTx

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Newman, A.M., Steen, C.B., Liu, C.L. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol 37, 773–782 (2019). https://doi.org/10.1038/s41587-019-0114-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41587-019-0114-2

This article is cited by

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