Single-Cell Allele-Specific Gene Expression Analysis

Dong, Meichen; Jiang, Y.

doi:10.1007/978-1-4939-9057-3_11

Cited by 6 publications

(8 citation statements)

References 36 publications

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“…The in vivo protein work is a necessary complement to RNA studies because, in some scenarios, there can be little correlation between protein and RNA levels, as in (39), and reviewed in 2012 here (40) and in 2016 here (41). Additionally, studies of allele bias at the RNA level can sometimes have trouble distinguishing between transcriptional bursting (42) and actual allele bias that manifests at the protein level (13), because they often lack means to quantify the distinct allelic protein products. Specifically, it can be technically difficult or sometimes impossible to generate antibodies to distinguish two allelic variants of the same gene.…”

Section: Discussionmentioning

confidence: 99%

“…In some scenarios, there can be little correlation between protein and RNA levels, as in 44 , and reviewed in 2012 here 45 and in 2016 here 46 . Additionally, studies of allele bias at the RNA level can sometimes have trouble distinguishing between transcriptional bursting 47 and actual allele bias that manifests at the protein level 13 . Detecting allele specific expression at the protein level by means other than those described here remain challenging.…”

Section: Discussionmentioning

confidence: 99%

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Introns Control Stochasticity in Metazoan Gene Expression

Sands

Shi

2019

Preprint

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Understanding sources of variation in gene expression is important because they underlie variation in traits. Studies in microbes and cell culture have revealed significant, intrinsic nongenetic, nonenvironmental axes of variation in gene expression. Stochastic autosomal allele bias (including monoallelic expression), which can be quantified as intrinsic noise, is one of these natural axes. Higher intrinsic noise means a higher chance of observing a cell with allelically biased expression. Here, we surveyed intrinsic noise in the tissues of C. elegans using fluorescent reporter alleles controlled by the hsp-90 promoter. We saw visually striking allele bias present in several distinct cell types, including nerves, muscles and intestines. Because intron sequences can both alter chromatin markings and increase expression level, we hypothesized that introns increase the probability of fully expressing both alleles of a gene, thereby decreasing intrinsic noise. We found that intrinsic noise decreases by an order of magnitude when alleles contain synthetic or natural introns. We found that this is true in both diploid muscle cells and polyploid intestine cells. Our results show introns control intrinsic noise for other distinctly regulated genes (vit-2 and hsp-16.2). As in prior studies in yeast, we found these distinct promoters also impact intrinsic noise. Finally, we found that introns control intrinsic noise using a 5'-position dependent mechanism. Intron control of intrinsic noise may help explain why some genes have lost introns, why so many genes have introns, and why deep intronic mutations can result in allele silencing and disease.3

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Introns Control Stochasticity in Metazoan Gene Expression

Sands

Shi

2019

Preprint

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“…By enabling cell-level transcriptome analyses, scRNA-seq exhibits a major advantage over the conventional averaged bulk RNA-seq, which is the ability to assess intracellular relationships between molecular features. With the emerging advances in scRNA-seq technologies, estimations of genetic variation from scRNA-seq data are becoming more reliable [4][5][6]. Recent studies have demonstrated the usefulness of scRNA-seq single nucleotide variant (SNV) assessments for a variety of applications, including random monoallelic expression (RME), transcriptional burst kinetics [7][8][9][10][11], haplotype inference [12], chromosome X inactivation [13,14], genetic heterogeneity in cancer [15][16][17][18][19], aneuploidy [20], quantitative trait loci (QTL) assessments [21], and demultiplexing [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Estimating the Allele-Specific Expression of SNVs From 10× Genomics Single-Cell RNA-Sequencing Data

Prashant

Liu

Bousounis

et al. 2020

Genes

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With the recent advances in single-cell RNA-sequencing (scRNA-seq) technologies, the estimation of allele expression from single cells is becoming increasingly reliable. Allele expression is both quantitative and dynamic and is an essential component of the genomic interactome. Here, we systematically estimate the allele expression from heterozygous single nucleotide variant (SNV) loci using scRNA-seq data generated on the 10×Genomics Chromium platform. We analyzed 26,640 human adipose-derived mesenchymal stem cells (from three healthy donors), sequenced to an average of 150K sequencing reads per cell (more than 4 billion scRNA-seq reads in total). High-quality SNV calls assessed in our study contained approximately 15% exonic and >50% intronic loci. To analyze the allele expression, we estimated the expressed variant allele fraction (VAFRNA) from SNV-aware alignments and analyzed its variance and distribution (mono- and bi-allelic) at different minimum sequencing read thresholds. Our analysis shows that when assessing positions covered by a minimum of three unique sequencing reads, over 50% of the heterozygous SNVs show bi-allelic expression, while at a threshold of 10 reads, nearly 90% of the SNVs are bi-allelic. In addition, our analysis demonstrates the feasibility of scVAFRNA estimation from current scRNA-seq datasets and shows that the 3′-based library generation protocol of 10×Genomics scRNA-seq data can be informative in SNV-based studies, including analyses of transcriptional kinetics.

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“…By enabling cell-level transcriptome analyses, scRNA-seq brings a major advantage over the conventional averaged bulk RNA-seq: the ability to assess intracellular relationships between molecular features. With the recent advances in the scRNA-seq technologies, estimations of genetic variation from scRNA-seq data are becoming reliable [4,5] and several studies have demonstrated their usefulness in addressing key biological and clinical questions [6][7][8][9][10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Estimating allele-specific expression of SNVs from 10x Genomics Single-Cell RNA-Sequencing Data

Liu

Bousounis

et al. 2019

Preprint

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With the recent advances in single-cell RNA-sequencing (scRNA-seq) technologies, estimation of allele expression from single cells is becoming increasingly reliable. Allele expression is both quantitative and dynamic and is an essential component of the genomic interactome. Here, we systematically estimate allele expression from heterozygous single nucleotide variant (SNV) loci using scRNA-seq data generated on the 10x Genomics platform. We include in the analysis 26,640 human adipose-derived mesenchymal stem cells (from three healthy donors), with an average sequencing reads over 120K/cell (more than 4 billion scRNA-seq reads total). High quality SNV calls assessed in our study contained approximately 15% exonic and >50% intronic loci. To analyze the allele expression, we estimate the expressed Variant Allele Fraction (VAFRNA) from SNV-aware alignments and analyze its variance and distribution (mono-and bi-allelic) at different cutoffs for required minimal number of sequencing reads. Our analysis shows that when assessing SNV loci covered by a minimum of 3 unique sequencing reads, over 50% of the heterozygous SNVs show biallelic expression, while at minimum of 10 reads, nearly 90% of the SNVs are bi-allelic. Consistent with single cell studies on RNA velocity and models of transcriptional burst kinetics, we observe a substantially higher rate of monoallelic expression among intronic SNVs, signifying the usefulness of scVAFRNA to assess dynamic cellular processes. Our analysis demonstrates the feasibility of scVAFRNA estimation from current scRNA-seq datasets and shows that the 3'-based library generation protocol of 10x Genomics scRNA-seq data can be highly informative in SNV-based analyses.

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Single-Cell Allele-Specific Gene Expression Analysis

Cited by 6 publications

References 36 publications

Introns Control Stochasticity in Metazoan Gene Expression

Introns Control Stochasticity in Metazoan Gene Expression

Estimating the Allele-Specific Expression of SNVs From 10× Genomics Single-Cell RNA-Sequencing Data

Estimating allele-specific expression of SNVs from 10x Genomics Single-Cell RNA-Sequencing Data

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