The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Determining cell type abundance and expression from bulk tissues with digital cytometry
Open accessAbstract
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. CIBERSORTx, a suite of computational tools, enables inference of cell type abundance and cell-type-specific gene expression profiles from bulk RNA profiles.
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Braun, D. A., Hou, Y., Bakouny, Z., Ficial, M., Sant’ Angelo, M., Forman, J., Ross-Macdonald, P., Berger, A. C., Jegede, O. A., Elagina, L., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0839-y · Cited by 992
Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics
Longo, S. K., Guo, M. G., Ji, A. L., & Khavari, P. A. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00370-8 · Cited by 1,059
Gay, C. M., Stewart, C. A., Park, E. M., Diao, L., Groves, S. M., Heeke, S., Nabet, B. Y., Fujimoto, J., Solis, L. M., Lu, W., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2020.12.014 · Cited by 962
Deep learning in cancer diagnosis, prognosis and treatment selection
Tran, K. A., Kondrashova, O., Bradley, A., Williams, E. D., Pearson, J. V., & Waddell, N. (2021) · Genome Medicine · doi:10.1186/s13073-021-00968-x · Cited by 932
Metadata
- Authors
- Newman, A. M., Steen, C. B., Liu, C. L., Gentles, A. J., Chaudhuri, A. A., Scherer, F., Khodadoust, M. S., Esfahani, M. S., Luca, B. A., Steiner, D., Diehn, M., & Alizadeh, A. A.
- Venue
- Nature Biotechnology
- Published
- 2019-05-06
- Volume
- 37
- Issue
- 7
- Pages
- 773-782
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W2942610007
- Cited by
- 4,478
- References
- 27
- FWCI
- 107.92
- Type
- journal article
- Language
- en
- Open access
- green
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- Newman, A. M., Steen, C. B., Liu, C. L., Gentles, A. J., Chaudhuri, A. A., Scherer, F., Khodadoust, M. S., Esfahani, M. S., Luca, B. A., Steiner, D., Diehn, M., & Alizadeh, A. A. (2019). Determining cell type abundance and expression from bulk tissues with digital cytometry. Nature Biotechnology. https://doi.org/10.1038/s41587-019-0114-2
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