Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference

Huang, Yuanhua; McCarthy, Davis J.; Stegle, Oliver

doi:10.1101/598748

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…To demultiplex the data, as well as to identify droplets containing ambient mRNA (empty droplets) or two cells ('doublets'), we developed a computational method based on SNP-fingerprinting, which classifies single cells with negligible error rates, even for cells with low sequencing depth. During the course of this project, several other methods for SNP-based demultiplexing have been published 20,47,48 . In particular, Demuxlet applies a similar approach, using pre-computed reference SNP profiles for the samples being pooled to identify single cells and detect doublets.…”

Section: Discussionmentioning

confidence: 99%

Multiplexed single-cell profiling of post-perturbation transcriptional responses to define cancer vulnerabilities and therapeutic mechanism of action

McFarland

Warren

et al. 2019

Preprint

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Assays to study cancer cell responses to pharmacologic or genetic perturbations are typically restricted to using simple phenotypic readouts such as proliferation rate or the expression of a marker gene. Information-rich assays, such as gene-expression profiling, are generally not amenable to efficient profiling of a given perturbation across multiple cellular contexts. Here, we developed MIX-Seq, a method for multiplexed transcriptional profiling of post-perturbation responses across a mixture of samples with single-cell resolution, using SNP-based computational demultiplexing of single-cell RNAsequencing data. We show that MIX-Seq can be used to profile responses to chemical or genetic perturbations across pools of 100 or more cancer cell lines, and combine it with Cell Hashing to further multiplex additional experimental conditions, such as multiple post-treatment time points or drug doses. Analyzing the high-content readout of scRNA-seq reveals both shared and context-specific transcriptional response components that can identify drug mechanism of action and can be used to predict long-term cell viability from short-term transcriptional responses to treatment.

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Section: Discussionmentioning

confidence: 99%

Multiplexed single-cell profiling of post-perturbation transcriptional responses to define cancer vulnerabilities and therapeutic mechanism of action

McFarland

Warren

et al. 2019

Preprint

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“…Replicates were subsequently separated and compared based on SNVs using Vireo, a tool for deconvolution of pooled single-cell data based on genome signatures. 36 This showed high correlation between replicates pooled for sequencing from WT and gata2b +/− KMs, where only the lowly abundant cell populations showed disproportionate distribution (Figure S3). After filtering out low-quality cells, we obtained 17 215 cells (WT = 10 176, gata2b +/− = 7039) for scRNA-seq analysis (Figure 2B).…”

Section: Single-cell Rna-sequencing Analysis Reveals a Cell Maturatio...mentioning

confidence: 92%

GATA2 heterozygosity causes an epigenetic feedback mechanism resulting in myeloid and erythroid dysplasia

Gioacchino,

Zhang,

Koyunlar

et al. 2024

Br J Haematol

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SummaryThe transcription factor GATA2 has a pivotal role in haematopoiesis. Heterozygous germline GATA2 mutations result in a syndrome characterized by immunodeficiency, bone marrow failure and predispositions to myelodysplastic syndrome (MDS) and acute myeloid leukaemia. Clinical symptoms in these patients are diverse and mechanisms driving GATA2‐related phenotypes are largely unknown. To explore the impact of GATA2 haploinsufficiency on haematopoiesis, we generated a zebrafish model carrying a heterozygous mutation of gata2b (gata2b+/−), an orthologue of GATA2. Morphological analysis revealed myeloid and erythroid dysplasia in gata2b+/− kidney marrow. Because Gata2b could affect both transcription and chromatin accessibility during lineage differentiation, this was assessed by single‐cell (sc) RNA‐seq and single‐nucleus (sn) ATAC‐seq. Sn‐ATAC‐seq showed that the co‐accessibility between the transcription start site (TSS) and a −3.5–4.1 kb putative enhancer was more robust in gata2b+/− zebrafish HSPCs compared to wild type, increasing gata2b expression and resulting in higher genome‐wide Gata2b motif use in HSPCs. As a result of increased accessibility of the gata2b locus, gata2b+/− chromatin was also more accessible during lineage differentiation. scRNA‐seq data revealed myeloid differentiation defects, that is, impaired cell cycle progression, reduced expression of cebpa and cebpb and increased signatures of ribosome biogenesis. These data also revealed a differentiation delay in erythroid progenitors, aberrant proliferative signatures and down‐regulation of Gata1a, a master regulator of erythropoiesis, which worsened with age. These findings suggest that cell‐intrinsic compensatory mechanisms, needed to obtain normal levels of Gata2b in heterozygous HSPCs to maintain their integrity, result in aberrant lineage differentiation, thereby representing a critical step in the predisposition to MDS.

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“…When insufficient reads satisfy this condition-for example when the input datasets are shallow or target different genomic regions (i.e different transcription factors), the power to evaluate sample relatedness is compromised. Many NGS-based studies now integrate multiple types of assays [8][9][10][11][12][13] and multiplex experiments from several samples to reduce cost at the expense of read-depth. We therefore set out to develop a method for quantifying sample-relatedness that could be systematically applied to modern large-scale projects.…”

Section: Mainmentioning

confidence: 99%

Detecting sample swaps in diverse NGS data types using linkage disequilibrium

Javed

Farjoun

Fennell

et al. 2020

Preprint

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As the number of genomics datasets grows rapidly, sample mislabeling has become a high stakes issue. We present CrosscheckFingerprints (Crosscheck), a tool for quantifying sample-relatedness and detecting incorrectly paired sequencing datasets from different donors. Crosscheck outperforms similar methods and is effective even when data are sparse or from different assays. Application of Crosscheck to 8851 ENCODE ChIP-, RNA-, and DNase-seq datasets enabled us to identify and correct dozens of mislabeled samples and ambiguous metadata annotations, representing ~1% of ENCODE datasets.

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Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference

Cited by 7 publications

References 24 publications

Multiplexed single-cell profiling of post-perturbation transcriptional responses to define cancer vulnerabilities and therapeutic mechanism of action

Multiplexed single-cell profiling of post-perturbation transcriptional responses to define cancer vulnerabilities and therapeutic mechanism of action

GATA2 heterozygosity causes an epigenetic feedback mechanism resulting in myeloid and erythroid dysplasia

Detecting sample swaps in diverse NGS data types using linkage disequilibrium

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