SCReadCounts: Estimation of cell-level SNVs from scRNA-seq data

Prashant, N M; Alomran, Nawaf; Chen, Yu; Liu, Hongyu; Bousounis, Pavlos; Movassagh, Mercedeh; Edwards, Nathan; Horvath, Anélia

doi:10.1101/2020.11.23.394569

Cited by 4 publications

(13 citation statements)

References 43 publications

(66 reference statements)

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“…In this study we find 20 significant cis-scReQTLs. This number is expected given the input size (up to 70 SNVs and up to 3000 cells per dataset), and, based on our previous studies is likely to be significantly higher in larger datasets [11,18].…”

Section: Discussionmentioning

confidence: 67%

“…To estimate the expression of the scSNVs we applied SCReadCounts as previously described [18]. For each cell, SCReadCounts tabulates the reference and variant counts of sequencing reads (nref and nvar, respectively) for genomic positions of interest, and computes the expressed Variant Allele Fraction (VAFRNA = nvar / (nvar + nref)) at a desired depth threshold (minimum number of reads covering the position, minR).…”

Section: Scsnvs Expressionmentioning

confidence: 99%

“…To explore if cells bearing certain scSNVs have related gene expression features, we assessed the scSNV expression in the individual cells after graph-based cell-clustering. For this analysis we processed the scRNA-seq datasets as we have previously described [11,18]. Briefly, after alignment with STARsolo [12] and quality filtering, the gene-expression matrices were processed using Seurat [24] to normalize gene expression and correct for batch-and cell-cycle effects; the normalized gene expression values were then used to assign likely cell types using SingleR [25] (Methods).…”

Section: Scsnvs Expressionmentioning

confidence: 99%

“…To compare SNV assessments from single cells to those from pooled and bulk datasets, we utilized matched genome, exome, and scRNA-seq data from multiple time-points during anticancer treatment. We assessed SNV calls by three popular variant callers -GATK, Strelka2 and Mutect2 [14][15][16], in combination with a recently developed in our lab method for cell level tabulation of the sequencing read counts bearing SNV alleles from barcoded scRNA-seq alignments -SCReadCounts [18]. We then analyzed the SNVs called exclusively in the single cell alignments (single cell exclusive SNVs, scSNVs) for cellular distribution and annotations and correlated their allele expression to the expression level of their harboring gene.…”

Section: Introductionmentioning

confidence: 99%

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Improved SNV discovery in barcode-stratified scRNA-seq alignments

Prashant

Liu

Dillard

et al. 2021

Preprint

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Single cell SNV analysis is an emerging and promising strategy to connect cell-level genetic variation to cell phenotypes. At the present, SNV detection from 10x Genomics scRNA-seq data is typically performed on the pooled sequencing reads across all cells in a sample. Here, we assess the gain of information of SNV assessments from individual cell scRNA-seq data, where the alignments are split by barcode prior to the variant call. For our analyses we use publicly available sequencing data on the human breast cancer cell line MCF7 cell line generated at consequent time-points during anti-cancer treatment. We analyzed SNV calls by three popular variant callers – GATK, Strelka2 and Mu-tect2, in combination with a method for cell-level tabulation of the sequencing read counts bearing SNV alleles – SCReadCounts. Our analysis shows that variant calls on individual cell alignments identify at least two-fold higher number of SNVs as compared to the pooled scRNA-seq. We demonstrate that scSNVs exclusively called in the single cell alignments (scSNVs) are substantially enriched in novel genetic variants and in coding functional annotations, in particular, stop-codon and missense substitutions. Furthermore, we find that the expression of some scSNVs correlates with the expression of their harbouring gene (cis-scReQTLs).Overall, our study indicates an immense potential of SNV calls from individual cell scRNA-seq data and emphasizes on the need of cell-level variant detection approaches and tools. Given the growing accumulation of scRNA-seq datasets, cell-level variant assessments are likely to significantly contribute to the understanding of the cellular heterogeneity and the relationship between genetics variants and functional phenotypes. In addition, cell-level variant assessments from scRNA-seq can be highly informative in cancer where they can help elucidate somatic mutations evolution and functionality.

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Section: Discussionmentioning

confidence: 67%

Section: Scsnvs Expressionmentioning

confidence: 99%

Section: Scsnvs Expressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved SNV discovery in barcode-stratified scRNA-seq alignments

Prashant

Liu

Dillard

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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“…VAF RNA is assessed from the individual cell alignments at the positions of interest using SCReadCounts [ 35 ]. For each position, SCReadCounts estimates the number of sequencing reads bearing the variant and the reference nucleotide (n var and n ref , respectively), calculates VAF RNA (VAF RNA = n var / (n var + n ref )) and outputs the values in an SNV-barcode matrix.…”

Section: Resultsmentioning

confidence: 99%

scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets

et al. 2021

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Background Recently, pioneering expression quantitative trait loci (eQTL) studies on single cell RNA sequencing (scRNA-seq) data have revealed new and cell-specific regulatory single nucleotide variants (SNVs). Here, we present an alternative QTL-related approach applicable to transcribed SNV loci from scRNA-seq data: scReQTL. ScReQTL uses Variant Allele Fraction (VAFRNA) at expressed biallelic loci, and corelates it to gene expression from the corresponding cell. Results Our approach employs the advantage that, when estimated from multiple cells, VAFRNA can be used to assess effects of SNVs in a single sample or individual. In this setting scReQTL operates in the context of identical genotypes, where it is likely to capture RNA-mediated genetic interactions with cell-specific and transient effects. Applying scReQTL on scRNA-seq data generated on the 10 × Genomics Chromium platform using 26,640 mesenchymal cells derived from adipose tissue obtained from three healthy female donors, we identified 1272 unique scReQTLs. ScReQTLs common between individuals or cell types were consistent in terms of the directionality of the relationship and the effect size. Comparative assessment with eQTLs from bulk sequencing data showed that scReQTL analysis identifies a distinct set of SNV-gene correlations, that are substantially enriched in known gene-gene interactions and significant genome-wide association studies (GWAS) loci. Conclusion ScReQTL is relevant to the rapidly growing source of scRNA-seq data and can be applied to outline SNVs potentially contributing to cell type-specific and/or dynamic genetic interactions from an individual scRNA-seq dataset. Availability:https://github.com/HorvathLab/NGS/tree/master/scReQTL

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Allelic Transcription Factor binding shape transcriptional kinetics observed with scRNA-seq in human cell lines

Jin

Feng

Bush

2022

Preprint

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Gene expression from bulk RNA-seq studies is an average measurement between two chromosomes and across cell populations. Both allelic and cell-to-cell heterogeneity in gene expression results from promoter bursting patterns that repeatedly alternate between an activated and inactivated state. Increased cell-to-cell heterogeneity in gene expression has been shown as a hallmark of aging, which is potentially induced by the change of promoter bursting patterns. Despite their importance in humans, bursting kinetics studies have been studied only in a limited number of genes within selected model organisms due to technical restrictions in measuring multiple transcript levels over time. Here, we construct a transcriptomic kinetics map using single-cell RNA-seq (scRNA-seq) data and systematically investigate the regulatory effect of genetic variants and transcription factor (TF) binding on transcriptional kinetics. We obtain allelic level expression from scRNA-seq data with phased genotypes for two clonal cell lines and estimate transcriptional bursting kinetics for a single chromosome. We found that among transcriptional kinetics, the transcription initiation rate and burst frequency correlate most with eQTL effect sizes from bulk RNA-seq studies, suggesting that eQTLs affect average gene expression mainly through altering the transcription initiation rate and burst frequency. Notably, eQTL studies focused on mean expression changes cannot identify scenarios where burst size and frequency are regulated in opposite directions, which is common amongst genes from LCL. We further found that ~90% of the variance of burst frequency can be explained by TF occupancy within the core promoter and allele-specific binding events of individual TFs are typically associated with the change of transcription initiation rate and burst frequency. Finally, we demonstrate how a genetic variant alters TF binding, resulting in changes to promoter burst kinetics of HLA-DQA1 to influence multiple hematological traits.

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SCReadCounts: Estimation of cell-level SNVs from scRNA-seq data

Cited by 4 publications

References 43 publications

Improved SNV discovery in barcode-stratified scRNA-seq alignments

Improved SNV discovery in barcode-stratified scRNA-seq alignments

scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets

Allelic Transcription Factor binding shape transcriptional kinetics observed with scRNA-seq in human cell lines

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