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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.

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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.

Additional information

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

References

  1. 1.

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

  2. 2.

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

  3. 3.

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

  4. 4.

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

  5. 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).

  6. 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).

  7. 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).

  8. 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).

  9. 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).

  10. 10.

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

  11. 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).

  12. 12.

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

  13. 13.

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

  14. 14.

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

  15. 15.

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

  16. 16.

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

  17. 17.

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

  18. 18.

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

  19. 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).

  20. 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).

  21. 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).

  22. 22.

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

  23. 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).

  24. 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).

  25. 25.

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

  26. 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).

  27. 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).

  28. 28.

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

  29. 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).

  30. 30.

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

  31. 31.

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

  32. 32.

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

  33. 33.

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

  34. 34.

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

  35. 35.

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

  36. 36.

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

  37. 37.

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

  38. 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).

  39. 39.

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

  40. 40.

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

  41. 41.

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

  42. 42.

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

  43. 43.

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

  44. 44.

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

  45. 45.

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

  46. 46.

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

  47. 47.

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

  48. 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).

  49. 49.

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

  50. 50.

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

  51. 51.

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

  52. 52.

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

  53. 53.

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

  54. 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).

  55. 55.

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

  56. 56.

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

  57. 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).

  58. 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).

  59. 59.

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

  60. 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).

  61. 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).

  62. 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).

  63. 63.

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

  64. 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).

  65. 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).

  66. 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).

  67. 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).

  68. 68.

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

  69. 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).

  70. 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).

  71. 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).

  72. 72.

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

  73. 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. 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. 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).

  76. 76.

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

  77. 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. 78.

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

  79. 79.

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

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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

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.

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.

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

Supplementary information

  1. Supplementary Information

    Supplementary Figs. 1–18 and Supplementary Notes 1 and 2

  2. Reporting Summary

  3. Supplementary Table 1

    Inventory of expression datasets and patient samples analyzed in this work

  4. Supplementary Table 2

    Signature matrices and cell groupings for expression purification

  5. Supplementary Table 3

    CREBBP genotyping results in FL tumors

  6. Supplementary Table 4

    DEGs identified by high-resolution CIBERSORTx

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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.