Gene model correction for PVRIG in single cell and bulk sequencing data enables accurate detection and study of its functional relevance
Single cell RNA sequencing (scRNA-seq) has gained increased popularity in recent years and has revolutionized the study of cell populations; however, this technology presents several caveats regarding specific gene expression measurement. Here we examine the expression levels of several immune checkpoint genes, which are currently assessed in clinical studies. We find that unlike in most bulk sequencing studies, PVRIG, a novel immune-modulatory receptor in the DNAM-1 axis, suffers from poor detection in 10x Chromium scRNA-seq and other types of assays that utilize the GENCODE transcriptomic reference (gene model). We show that the default GENCODE gene model, typically used in the analysis of such data, is incorrect in the PVRIG genomic region and demonstrate that fixing the gene model recovers genuine PVRIG expression levels. We explore computational strategies for resolving multi-gene mapped reads, such as those implemented in RSEM and STARsolo and find that they provide a partial solution to the problem. Our study provides means to better interrogate the expression of PVRIG in scRNA-seq and emphasizes the importance of optimizing gene models and alignment algorithms to enable accurate gene expression measurement in scRNA-seq and bulk sequencing. The methodology applied here for PVRIG can be applied to other genes with similar issues.