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

Petti, Allegra A.; Williams, Stephen R.; Miller, Christopher A.; Fiddes, Ian T.; Srivatsan, Sridhar Nonavinkere; Chen, David Y.; Fronick, Catrina C.; Fulton, Robert S.; Church, Deanna M.; Ley, Timothy J.

doi:10.1038/s41467-019-11591-1

Cited by 175 publications

(179 citation statements)

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“…Recent single cell RNA-sequencing work has demonstrated that leukemia can exist along a continuum of differentiation states and as such, we sought to determine if the genetic clonal architecture we identified above was associated with particular differentiation states 20,21 . We performed simultaneous single cell DNA molecular profiling and cell surface protein expression analysis on AML samples (n = 17) using a DNA + protein single cell sequencing adaptation of the Tapestri platform.…”

Section: Resultsmentioning

confidence: 99%

Single cell mutational profiling delineates clonal trajectories in myeloid malignancies

Miles

Bowman

Merlinsky

et al. 2020

Preprint

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1Myeloid malignancies, including acute myeloid leukemia (AML), arise from the proliferation and 2 expansion of hematopoietic stem and progenitor cells which acquire somatic mutations. Bulk 3 molecular profiling studies on patient samples have suggested that somatic mutations are obtained 4 in a step-wise fashion, where mutant genes with high variant allele frequencies (VAFs) are 5 proposed to occur early in disease development and mutations with lower VAFs are thought to be 6 acquired later in disease progression 1-3 . Although bulk sequencing informs leukemia biology and 7 prognostication, it cannot distinguish which mutations occur in the same clone(s), accurately 8 measure clonal complexity and clone size, or offer definitive evidence of mutational order. To 9 elucidate the clonal framework of myeloid malignancies, we performed single cell mutational 10 profiling on 146 samples from 123 patients. We found AML is most commonly comprised of a 11 small number of dominant clones, which in many cases harbor co-occurring mutations in 12 epigenetic regulators. Conversely, mutations in signaling genes often occur more than once in 13 distinct subclones consistent with increasing clonal diversity. We also used these data to map the 14 clonal trajectory of each patient and found that specific mutation combinations (FLT3-ITD + 15 NPM1 c ) synergize to promote clonal expansion and dominance. We combined cell surface protein 16 expression with single cell mutational analysis to map somatic genotype and clonal architecture 17 with immunophenotype. Our studies of clonal architecture at a single cell level provide novel 18 insights into the pathogenesis of myeloid transformation and how clonal complexity contributes 19 to disease progression. 20 21 22 23 2 Results 24The genomic landscape of myeloid malignancies has been well described, with a near complete 25 catalogue of putative mutant driver genes [3][4][5][6][7] . While combinations of mutations have been 26 functionally investigated in mouse models of disease, there remains uncertainty about the co-27 occurrence of somatic mutations within the same clone. The composition and combinatorial 28 distribution of mutations across cell types and states continues to be poorly understood. To analyze 29 the clonal architecture in clinical isolates from patients with myeloid malignancies, we performed 30 single cell DNA sequencing on the Tapestri platform using a custom 109 amplicon panel covering 31 31 of the most frequently mutated genes in myeloid malignancies (Extended Table 1) 8 . We 32 sequenced 740,529 cells from 146 samples from 123 patients with myeloid malignancies, 33 including clonal hematopoiesis (CH), myeloproliferative neoplasms (MPN), and AML (Figure 341A). Samples from patients at diagnosis and at relapse were queried, with the majority of samples 35 being from patients with relapsed/refractory disease (Figure 1B; Extended Table 2). The most 36 common mutations identified in single cell profiling were found in DNMT3A (n=62 patients), 37 TET2 (n=58 patients), NPM1 (...

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Section: Resultsmentioning

confidence: 99%

Single cell mutational profiling delineates clonal trajectories in myeloid malignancies

Miles

Bowman

Merlinsky

et al. 2020

Preprint

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“…We demonstrate that HLA genes present cell-type specific expression (Boegel et al 2018) and that HLA loss of expression can be evaluated per-cell and per-cluster. Using five AML samples published in (Petti et al 2019) for which HLA class I and class II genotypes were provided by the authors, we demonstrate the ability to find cell type specific allele bias when cell types have been annotated using marker genes. We also analyze data from two Merkel cell carcinoma (MCC) patients published in (Yost et al 2019) and extend their finding that HLA class I expression is lost, to show that this expression loss may be allele-specific.…”

Section: Resultsmentioning

confidence: 99%

“…10x Genomics Chromium 5’ GEX library data derived from five subjects with AML, as described in (Petti et al 2019) was reanalyzed. Genotypes for HLA-A, -B, -C, -DRB1, and -DQB1 at two-field resolution were provided by the authors.…”

Section: Resultsmentioning

confidence: 99%

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scHLAcount: Allele-specific HLA expression from single-cell gene expression data

Darby

Stubbington

Marks

et al. 2019

Preprint

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AbstractStudies in bulk RNA sequencing data suggest cell-type and allele-specific expression of the human leukocyte antigen (HLA) genes. These loci are extremely diverse and they function as part of the major histocompatibility complex (MHC) which is responsible for antigen presentation. Mutation and or misregulation of expression of HLA genes has implications in diseases, especially cancer. Immune responses to tumor cells can be evaded through HLA loss of function. However, bulk RNA-seq does not fully disentangle cell type specificity and allelic expression. Here we present scHLAcount, a workflow for computing allele-specific molecule counts of the HLA genes in single cells an individualized reference. We demonstrate that scHLAcount can be used to find cell-type specific allelic expression of HLA genes in blood cells, and detect different allelic expression patterns between tumor and normal cells in patient biopsies. scHLAcount is available at https://github.com/10XGenomics/scHLAcount.

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“…Given recent reports of using 10× Genomics single-cell RNA-seq data for calling variants within genes and nearby regions, 25 we next examined the overall distribution of variants in the ASD and control samples. For this purpose, we carried out variant calling at bulk level (see "Methods"), obtaining a list of variants present in each genomic region.…”

Section: Snv Analysismentioning

confidence: 99%

Single-cell sperm transcriptomes and variants from fathers of children with and without autism spectrum disorder

et al. 2020

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The human sperm is one of the smallest cells in the body, but also one of the most important, as it serves as the entire paternal genetic contribution to a child. Investigating RNA and mutations in sperm is especially relevant for diseases such as autism spectrum disorders (ASD), which have been correlated with advanced paternal age. Historically, studies have focused on the assessment of bulk sperm, wherein millions of individual sperm are present and only high-frequency variants can be detected. Using 10× Chromium single-cell sequencing technology, we assessed the transcriptome from >65,000 single spermatozoa across six sperm donors (scSperm-RNA-seq), including two who fathered multiple children with ASD and four fathers of neurotypical children. Using RNA-seq methods for differential expression and variant analysis, we found clusters of sperm mutations in each donor that are indicative of the sperm being produced by different stem cell pools. Finally, we have shown that genetic variations can be found in single sperm.npj Genomic Medicine (2020) 5:14 ; https://doi.

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

Cited by 175 publications

References 73 publications

Single cell mutational profiling delineates clonal trajectories in myeloid malignancies

Single cell mutational profiling delineates clonal trajectories in myeloid malignancies

scHLAcount: Allele-specific HLA expression from single-cell gene expression data

Single-cell sperm transcriptomes and variants from fathers of children with and without autism spectrum disorder

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