A general approach for detecting expressed mutations in AML cells using single cell RNA-sequencing
Allegra A. Petti,
Stephen R. Williams,
Christopher A. Miller,
Ian T. Fiddes,
Sridhar N. Srivatsan,
David Y. Chen,
Catrina C. Fronick,
Robert S. Fulton,
Deanna M. Church and
Timothy J. Ley ()
Additional contact information
Allegra A. Petti: Washington University School of Medicine
Stephen R. Williams: 10x Genomics, Inc.
Christopher A. Miller: Washington University School of Medicine
Ian T. Fiddes: 10x Genomics, Inc.
Sridhar N. Srivatsan: Washington University School of Medicine
David Y. Chen: Washington University School of Medicine
Catrina C. Fronick: Washington University School of Medicine
Robert S. Fulton: Washington University School of Medicine
Deanna M. Church: Inscripta, Inc.
Timothy J. Ley: Washington University School of Medicine
Nature Communications, 2019, vol. 10, issue 1, 1-16
Abstract:
Abstract Virtually all tumors are genetically heterogeneous, containing mutationally-defined subclonal cell populations that often have distinct phenotypes. Single-cell RNA-sequencing has revealed that a variety of tumors are also transcriptionally heterogeneous, but the relationship between expression heterogeneity and subclonal architecture is unclear. Here, we address this question in the context of Acute Myeloid Leukemia (AML) by integrating whole genome sequencing with single-cell RNA-sequencing (using the 10x Genomics Chromium Single Cell 5’ Gene Expression workflow). Applying this approach to five cryopreserved AML samples, we identify hundreds to thousands of cells containing tumor-specific mutations in each case, and use the results to distinguish AML cells (including normal-karyotype AML cells) from normal cells, identify expression signatures associated with subclonal mutations, and find cell surface markers that could be used to purify subclones for further study. This integrative approach for connecting genotype to phenotype is broadly applicable to any sample that is phenotypically and genetically heterogeneous.
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
https://www.nature.com/articles/s41467-019-11591-1 Abstract (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11591-1
Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/
DOI: 10.1038/s41467-019-11591-1
Access Statistics for this article
Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie
More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().