Recovery of missing single-cell RNA-sequencing data with optimized transcriptomic references

Single-cell RNA-sequencing (scRNA-seq) is an indispensable tool for characterizing cellular diversity and generating hypotheses throughout biology. Droplet-based scRNA-seq datasets often lack expression data for genes that can be detected with other methods. Here we show that the observed sensitivity deficits stem from three sources: (1) poor annotation of 3 & PRIME; gene ends; (2) issues with intronic read incorporation; and (3) gene overlap-derived read loss. We show that missing gene expression data can be recovered by optimizing the reference transcriptome for scRNA-seq through recovering false intergenic reads, implementing a hybrid pre-mRNA mapping strategy and resolving gene overlaps. We demonstrate, with a diverse collection of mouse and human tissue data, that reference optimization can substantially improve cellular profiling resolution and reveal missing cell types and marker genes. Our findings argue that transcriptomic references need to be optimized for scRNA-seq analysis and warrant a reanalysis of previously published datasets and cell atlases. This paper presents an improved approach for mapping single-cell RNA-seq reads with optimized transcriptomic references, which markedly recovers previously missing gene expression data.

Automated summary

This study presents an improved approach for mapping single-cell RNA-seq reads using optimized transcriptomic references. It addresses the issue of missing gene expression data in droplet-based scRNA-seq datasets and demonstrates improved cellular profiling resolution. The findings emphasize the importance of optimizing transcriptomic references for scRNA-seq analysis and suggest a reanalysis of previously published datasets and cell atlases.