GSE210670
Comparison of single-nuclei 5' versus 3'-RNA-seq approaches: utility for somatic variant detection
Somatic mosaic variants are a major cause of human disease, including cancer and focal epilepsies, but can be challenging to study due to their mosaicism in bulk tissue biopsies. Coupling single-cell genotype and transcriptomic data has potential to provide insight into the role somatic variants play in disease etiology, such as by determining what cell types are affected or how the mutations affect gene expression. Here, we asked whether commonly used single-cell 3'- or 5'-RNA-sequencing assays can be used to derive single-cell genotype data for a priori known variants that are located near to either end of a transcript. To that end, we compared performance of commercially available single-cell 3'- and 5'- gene expression kits using resected brain samples from three pediatric patients with focal epilepsy. We quantified the ability to detect genetic variants in single-cell datasets depending on distance from the transcript end. Finally, we demonstrated the ability to identify affected cell types in a patient with a RHEB somatic variant causing an epilepsy-associated cortical malformation. Our results demonstrate that native single-cell 3' or 5'-RNA-sequencing data can be used successfully to genotype single-cells for somatic variants that are expressed within proximity to a transcript end. Overall design: We established a workflow to compare performance between the 10x Genomics Chromium NextGEM Single-Cell 3' and 5' gene expression kits and evaluate detection of known germline and somatic variants in the resulting datasets (Fig. 1). For these analyses, we obtained frozen resected brain tissue from three pediatric patients treated for focal epilepsy and enrolled in an IRB-approved research protocol. In a previous study, germline and somatic variants were identified in bulk tissue samples via exome sequencing analysis [7]. In this study, we performed single-nuclei RNA-sequencing (snRNA-seq) on remaining material cut from the same tissue section (Fig. 1a). To evaluate performance of the 3' vs. 5' gene expression kits, we compared QC metrics, gene expression, cell type clustering, and marker gene efficacy. For variant analysis we evaluated the detection rate of both germline and somatic variants depending on distance from the transcript end and expression level of each gene of interest (Fig. 1b). Finally, as a proof-of-concept, we identified cell types expressing a disease-causing RHEB variant using single-nuclei 5'-RNA-seq data (Fig. 1c).