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Volume 11
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10.3390/genes11030240
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Article

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

by
Prashant N. M.
1,†,
Hongyu Liu
1,2,†,
Pavlos Bousounis
1,
Liam Spurr
1,3,4,
Nawaf Alomran
1,
Helen Ibeawuchi
1,
Justin Sein
1,
Dacian Reece-Stremtan
5 and
Anelia Horvath
1,6,7,*
1
McCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington University, Washington, DC 20037, USA
2
Chinese Medicine Toxicological Laboratory, Institute of Traditional Chinese Medicine, Heilongjiang University of Chinese Medicine, Harbin 150040, China
3
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
4
Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
5
Computer Applications Support Services, School of Medicine and Health Sciences, The George Washington University, Washington, DC 20037, USA
6
Department of Biochemistry and Molecular Medicine, Department of Biostatistics and Bioinformatics, School of Medicine and Health Sciences, George Washington University, Washington, DC 20037, USA
7
Department of Pharmacology and Physiology, School of Medicine and Health Sciences, The George Washington University, Washington, DC 20037, USA
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Genes 2020, 11(3), 240; https://doi.org/10.3390/genes11030240
Submission received: 22 December 2019 / Revised: 10 February 2020 / Accepted: 19 February 2020 / Published: 25 February 2020
(This article belongs to the Special Issue Genetic Variation and Splicing from Single Cell RNA-Sequencing)
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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.
Keywords: single cell; VAFRNA; sc-VAFRNA; sc-RNA-seq; monoallelic expression; SNV; genetic variation; RNA-seq; single-cell RNA-sequencing single cell; VAFRNA; sc-VAFRNA; sc-RNA-seq; monoallelic expression; SNV; genetic variation; RNA-seq; single-cell RNA-sequencing

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MDPI and ACS Style

N. M., P.; Liu, H.; Bousounis, P.; Spurr, L.; Alomran, N.; Ibeawuchi, H.; Sein, J.; Reece-Stremtan, D.; Horvath, A. Estimating the Allele-Specific Expression of SNVs From 10× Genomics Single-Cell RNA-Sequencing Data. Genes 2020, 11, 240. https://doi.org/10.3390/genes11030240

AMA Style

N. M. P, Liu H, Bousounis P, Spurr L, Alomran N, Ibeawuchi H, Sein J, Reece-Stremtan D, Horvath A. Estimating the Allele-Specific Expression of SNVs From 10× Genomics Single-Cell RNA-Sequencing Data. Genes. 2020; 11(3):240. https://doi.org/10.3390/genes11030240

Chicago/Turabian Style

N. M., Prashant, Hongyu Liu, Pavlos Bousounis, Liam Spurr, Nawaf Alomran, Helen Ibeawuchi, Justin Sein, Dacian Reece-Stremtan, and Anelia Horvath. 2020. "Estimating the Allele-Specific Expression of SNVs From 10× Genomics Single-Cell RNA-Sequencing Data" Genes 11, no. 3: 240. https://doi.org/10.3390/genes11030240

APA Style

N. M., P., Liu, H., Bousounis, P., Spurr, L., Alomran, N., Ibeawuchi, H., Sein, J., Reece-Stremtan, D., & Horvath, A. (2020). Estimating the Allele-Specific Expression of SNVs From 10× Genomics Single-Cell RNA-Sequencing Data. Genes, 11(3), 240. https://doi.org/10.3390/genes11030240

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