Project description:Single-cell sequencing methodologies such as scRNA-seq and scATAC-seq have become widespread and effective tools to interrogate tissue composition. Increasingly, variant callers are being applied to these methodologies to resolve the genetic heterogeneity of a sample, especially in the case of detecting the clonal architecture of a tumor. Typically, traditional bulk DNA variant callers are applied to the pooled reads of a single-cell library to detect candidate mutations. Recently, multiple studies have applied such callers on reads from individual cells, with some citing the ability to detect rare variants with higher sensitivity. Many studies apply these two approaches to the Chromium (10x Genomics) scRNA-seq and scATAC-seq methodologies. However, Chromium-based libraries may offer additional challenges to variant calling compared to existing single-cell methodologies, raising questions for the validity of variants obtained from such a workflow. To determine the merits and challenges of various variant-calling approaches on Chromium scRNA-seq and scATAC-seq libraries, we use sample libraries with matched bulk whole-genome-sequencing to evaluate the performance of callers. We review caller performance, finding that bulk callers applied on pooled reads significantly outperform individual-cell approaches. We also evaluate variants unique to scRNA-seq and scATAC-seq methodologies, finding patterns of noise but also potential capture of RNA-editing events. Finally, we review the notion that variant calling at the single-cell level can detect rare somatic variants, providing empirical results that suggest resolving such variants is infeasible in single-cell Chromium libraries.
Project description:Single-cell sequencing methodologies such as scRNA-seq and scATAC-seq have become widespread and effective tools to interrogate tissue composition. Increasingly, variant callers are being applied to these methodologies to resolve the genetic heterogeneity of a sample, especially in the case of detecting the clonal architecture of a tumor. Typically, traditional bulk DNA variant callers are applied to the pooled reads of a single-cell library to detect candidate mutations. Recently, multiple studies have applied such callers on reads from individual cells, with some citing the ability to detect rare variants with higher sensitivity. Many studies apply these two approaches to the Chromium (10x Genomics) scRNA-seq and scATAC-seq methodologies. However, Chromium-based libraries may offer additional challenges to variant calling compared to existing single-cell methodologies, raising questions for the validity of variants obtained from such a workflow. To determine the merits and challenges of various variant-calling approaches on Chromium scRNA-seq and scATAC-seq libraries, we use sample libraries with matched bulk whole-genome-sequencing to evaluate the performance of callers. We review caller performance, finding that bulk callers applied on pooled reads significantly outperform individual-cell approaches. We also evaluate variants unique to scRNA-seq and scATAC-seq methodologies, finding patterns of noise but also potential capture of RNA-editing events. Finally, we review the notion that variant calling at the single-cell level can detect rare somatic variants, providing empirical results that suggest resolving such variants is infeasible in single-cell Chromium libraries.
Project description:High-throughput single-cell RNA sequencing (scRNA-seq) workflows produce libraries that demand extensive sequencing. However, standard next-generation sequencing (NGS) methods remain expensive, contributing to the high running costs of single-cell experiments and often negatively affecting the sample numbers and statistical strength of such projects. In recent years, a plethora of new sequencing technologies have become available to researchers through several manufacturers, often providing lower-cost alternatives to standard NGS. In this study, we compared data generated from scRNA-seq libraries sequenced with both standard Illumina sequencing by synthesis (Illumina SBS) and MGI’s DNA nanoball sequencing (DNBSEQ). Our findings reveal similar overall performance using both technologies. DNBSEQ exhibited mildly superior sequence quality compared to Illumina SBS, as evidenced by higher Phred scores, lower read duplication rates, and a greater number of genes mapping to the reference genome. Yet, these improvements did not translate into meaningful differences in single-cell analysis parameters of our experiments, including detection of additional genes within cells, gene expression saturation levels, and numbers of identified cells, with both technologies demonstrating equally robust performance in these aspects. The data produced by both sequencing platforms also produced comparable analytical outcomes for single-cell analysis. No significant difference in the annotation of cells into different cell types was observed and the same top genes were differentially expressed between populations and experimental conditions. Overall, our data demonstrate that alternative technologies can be applied to sequence scRNA-seq libraries, generating virtually undiscernible results compared to standard methods, and providing cost-effective alternatives.
Project description:We compare the performance of two library preparation protocols (poly(A) and exome capture) in in vitro degraded RNA samples VcaP cell were grown, and treated with MDV3100 (enzalutamide) or DHT (dihydrotestosterone), intact RNA was isolated and samples were prepared in technical triplicates using two library preparation protocol. Also cells were subject to in vitro degradation through incubation of the whole cell lysate in 37C for increasing amounts of time. Following incbation paired capture and poly(A) libraries were prepared.
Project description:To compare the performance of Illumina and BGI sequencing technologies for high-throughput single cell sequencing, four Chromium single cell libraries of the following human cell types: Induced Pluripotent Stem Cells (hIPSC), cultured Trabecular MeshWork Cells (TMWC) and Peripheral Blood Mononuclear Cells (PBMCs), were sequenced on Illumina sequencers (NextSeq 500, NovaSeq 6000) and a BGI sequencer (MGISEQ-2000). The technologies were benchmarked based on sequencing quality, characterisation of cell populations within samples and for specific single cell analyses such as variant calling and detection of guide RNAs from pooled CRISPR screens.