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


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 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

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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.