Improved SNV Discovery in Barcode-Stratified scRNA-seq Alignments.

Prashant N M,Helen Ibeawuchi,Christian Dillard,Hongyu Liu,Anelia Dafinova Horvath,Hang Chan,Turkey Alsaeedy

doi:10.3390/genes12101558

Prashant N M, Helen Ibeawuchi + Show 5 more

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https://doi.org/10.3390/genes12101558

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Journal: Genes	Publication Date: Sep 30, 2021
Citations: 8	License type: CC BY 4.0

Affiliation: George Washington University, McCormick (United States)

Abstract

Currently, the detection of single nucleotide variants (SNVs) from 10 x Genomics single-cell RNA sequencing data (scRNA-seq) is typically performed on the pooled sequencing reads across all cells in a sample. Here, we assess the gaining of information regarding SNV assessments from individual cell scRNA-seq data, wherein the alignments are split by cellular barcode prior to the variant call. We also reanalyze publicly available data on the MCF7 cell line during anticancer treatment. We assessed SNV calls by three variant callers—GATK, Strelka2, and Mutect2, in combination with a method for the cell-level tabulation of the sequencing read counts bearing variant alleles–SCReadCounts (single-cell read counts). Our analysis shows that variant calls on individual cell alignments identify at least a two-fold higher number of SNVs as compared to the pooled scRNA-seq; these SNVs are enriched in novel variants and in stop-codon and missense substitutions. Our study indicates an immense potential of SNV calls from individual cell scRNA-seq data and emphasizes the need for cell-level variant detection approaches and tools, which can contribute to the understanding of the cellular heterogeneity and the relationships to phenotypes, and help elucidate somatic mutation evolution and functionality.

Highlights

In single-cell studies, single nucleotide variant (SNV) analysis is an emerging and promising strategy to connect cell-level genetic variation to phenotypes and to interrogate the lineage relationships in heterogeneous cell populations
To compare SNV assessments from single cells to those from pooled and bulk datasets, we utilized the matched genome, exome, and scRNA-seq data from multiple time-points during anticancer treatment of the human breast cancer cell line MCF7; the data was previously generated as a part of a separate study and publicly available
ScRNA-seq MCF7 was generated at four different time-points during bortezomib treatment: before treatment (t0) and after 12 h (t12), 48 h (t48), and 72 h of exposure, followed by a drug wash and 24 h of recovery (t96) [19], and accompanied by matched whole-genome sequencing (WGS) and deep targeted exon sequencing (TES)

Summary

Introduction

In single-cell studies, single nucleotide variant (SNV) analysis is an emerging and promising strategy to connect cell-level genetic variation to phenotypes and to interrogate the lineage relationships in heterogeneous cell populations. To detect single-cell SNVs from DNA, genome and exome sequencing experiments can be performed [1,2,3,4,5]. These studies have revealed enormous amounts of knowledge on cell-level genetic heterogeneity; they face challenges related to sample availability, unequal coverage, and amplification bias, and are relatively costly for large-scale applications. SNV assessments from single-cell RNA sequencing (scRNA-seq) experiments have started to emerge [6,7,8,9]. SNVs from scRNA-seq studies can provide crucial information on SNV functionality through studying allele-specific dynamics and their correlation to phenotype features, such as gene expression and splicing [10,11,12]

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