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Nat Biotechnol. 2019 Jul;37(7):773-782. doi: 10.1038/s41587-019-0114-2. Epub 2019 May 6.

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

Newman AM1,2, Steen CB3,4, Liu CL5,3, Gentles AJ6,3,7,8, Chaudhuri AA9,10, Scherer F3,11, Khodadoust MS3, Esfahani MS3,7,10, Luca BA8, Steiner D3, Diehn M5,8,10, Alizadeh AA12,13,14,15,16.

Author information

1
Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA. amnewman@stanford.edu.
2
Department of Biomedical Data Science, Stanford University, Stanford, CA, USA. amnewman@stanford.edu.
3
Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
4
Department of Informatics, University of Oslo, Oslo, Norway.
5
Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA.
6
Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
7
Center for Cancer Systems Biology, Stanford University, Stanford, CA, USA.
8
Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA.
9
Department of Radiation Oncology, Stanford University, Stanford, CA, USA.
10
Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
11
Division of Hematology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
12
Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA. arasha@stanford.edu.
13
Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA. arasha@stanford.edu.
14
Center for Cancer Systems Biology, Stanford University, Stanford, CA, USA. arasha@stanford.edu.
15
Stanford Cancer Institute, Stanford University, Stanford, CA, USA. arasha@stanford.edu.
16
Division of Hematology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA. arasha@stanford.edu.

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.

Comment in

PMID:
31061481
PMCID:
PMC6610714
[Available on 2019-11-06]
DOI:
10.1038/s41587-019-0114-2

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