A general approach for detecting expressed mutations in AML cells using single cell RNA-sequencing

Basic information
Cell
89,827
Sample
5

Technology
10X Genomics
Omics
scRNA-seq
Source
Bone Marrow

Dataset ID
31413257
Platform
Illumina NovaSeq
Species
Human
Disease
Acute myeloid leukemia (AML)
Age range
38 - 60
Update date
2019-08-14
Summary

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.

Overall design

To be supplemented.

Contributors

Allegra A Petti # 1 2, Stephen R Williams # 3, Christopher A Miller 1 2, Ian T Fiddes 3, Sridhar N Srivatsan 1, David Y Chen 4, Catrina C Fronick 2, Robert S Fulton 2, Deanna M Church 5, Timothy J Ley 6 7 8

Contact

timley@wustl.edu.(Timothy J Ley)

snRNA-Seq
Sample nameSample titleDiseaseGenderAgeSourceTreatmentTechnologyPlatformOmicsSample IDDataset IDAction
No data available