A general approach for detecting expressed mutations in AML cells using single cell RNA-sequencing
Open accessAbstract
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.
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Vartrix avoids reference bias due to ambiguous support such as alignment end effects and corrects bases with duplicate reads via the UMI.
“Next, we count alleles per cell with vartrix14(Fig. 1c) which avoids reference bias due to ambiguous support such as alignment end effects and corrects bases with duplicate reads via the UMI.”
From Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes (2020) · cites this paper as [14]
The Methods section (‘Single-cell mutation identification and analysis’; page unavailable) identifies VarTrix as a tool for automated cell-variant assignment. It also describes resolving discordant duplicate reads by UMI, but attributes that procedure to the authors’ Pysam-based workflow, not to VarTrix. Our copy does not say that VarTrix avoids reference bias or alignment-end effects. (LLM, conf=0.85)
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Ultra-Deep Sequencing Reveals the Mutational Landscape of Classical Hodgkin Lymphoma
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- Authors
- Petti, A. A., Williams, S. R., Miller, C. A., Fiddes, I. T., Srivatsan, S. N., Chen, D. Y., Fronick, C. C., Fulton, R. S., Church, D. M., & Ley, T. J.
- Affiliations
- Washington University in St. Louis (US)
- 10X Genomics (United States) (US)
- Corresponding author
- Ley, Timothy J.
- ORCID
- Venue
- Nature Communications
- Published
- 2019-08-14
- Volume
- 10
- Issue
- 1
- Pages
- 3660-3660
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC6694122
- OpenAlex
- W2968098426
- Cited by
- 215
- References
- 76
- Citations by year
- 2026: 14 · 2025: 26 · 2024: 35 · 2023: 44 · 2022: 32 · 2021: 40 · 2020: 22 · 2019: 2
- FWCI
- 17.12
- Type
- journal article
- Language
- en
- Update notices
- correction (2022-07-21)
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Commons@Becker (Washington University School of Medicine) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (CA101937, CA197561, CA167540, CA211782, K12CA167540, R50CA211782)
- Topics
- Health Sciences › Medicine › Hematology › Acute Myeloid Leukemia Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Keywords
- acute myeloid leukemia, subclonal heterogeneity, cancer mutations, transcriptional heterogeneity, single-cell RNA sequencing, genotype-phenotype correlation, cell surface markers, normal karyotype AML, tumor cell identification
- MeSH terms
- Whole Genome Sequencing, Genotype, Humans, Mutation, Phenotype, RNA, Leukemia, Myeloid, Acute, Sequence Analysis, RNA, Polymorphism, Single Nucleotide, Gene Expression Profiling, Genomics, Single-Cell Analysis
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