Doctor of Philosophy, University Of East Anglia (2017)
Bachelor of Science, University Alexandre Ioan Cuza of Iasi (2011)
Multiomic single cell analysis of normal human bone marrow identifies a unique stem and progenitor population that expands in AML
Proceedings of the Annual Meeting of the American Association for Cancer Research 2020
View details for DOI 10.1158/1538-7445.AM2020-3779
- Determining cell type abundance and expression from bulk tissues with digital cytometry NATURE BIOTECHNOLOGY 2019; 37 (7): 773-+
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.
View details for PubMedID 31061481