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

Abstract

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.

STARsolo: accurate, fast and versatile mapping/quantification of single-cell and single-nucleus RNA-seq data

“…By analyzing the data from multiple tissues in the Tabula Muris dataset, we showed that despite strong 3 -end bias, it is possible to detect cell-type-specific splice junction usage in droplet-based scRNA-seq. STARsolo can also output a standard BAM file containing read alignments and error-corrected CBs and UMIs, which can be used for a variety of downstream analyses, such as differential splicing [36], alternative polyadenylation [39], allele specific expression [38,40], fusion detection, etc.…”

“…Characterizing these transcriptomic features in a cell-typespecific manner may enable discovery of interesting biological phenomena beyond gene expression. However, most analyses of droplet-based scRNA-seq data are focused on gene expression, with a few notable exceptions: [35][36][37][38][39][40].…”

Examining parent-of-origin effects on transcription and RNA methylation in mediating aggressive behavior in honey bees (Apis mellifera)

“…Targeted studies of the functional consequences of parent-speci c transcription of individual genes in these networks would be highly informative. It is also possible that these observed biases in parent-speci c transcript abundance are isolated to speci c cell types [79], and our study was limited in that we sequenced material from pools of two transcriptionally distinct tissues (heads and abdomens) [71]. Therefore, future studies should utilize single tissues and/or single cell sequencing technologies for ner resolution [80,81].…”

Examining parent-of-origin effects on transcription and RNA methylation in mediating aggressive behavior in honey bees (Apis mellifera)

“…Targeted studies of the functional consequences of parent-specific transcription of individual genes in these networks would be highly informative. It is also possible that these observed biases in parent-specific transcript abundance are isolated to specific cell types [ 83 ], and our study was limited in that we sequenced material from pools of two transcriptionally distinct tissues (heads and abdomens) [ 75 ]. Therefore, future studies should utilize single tissues and/or single cell sequencing technologies for finer resolution [ 84 , 85 ].…”