RoCK and ROI: Single-cell transcriptomics with multiplexed enrichment of selected transcripts and region-specific sequencing

Abstract

AbstractVarious tools have been developed to reliably identify, trace and analyze single cells in complex tissues. In recent years, these technologies have been combined with transcriptomic profiling approaches to explore molecular mechanisms that drive development, health, and disease. However, current methods still fall short of profiling single cell transcriptomes comprehensively, with one major challenge being high non-detection rates of specific transcripts and transcript regions. Such information is often crucial to understanding the biology of cells or tissues and includes lowly expressed transcripts, sequence variations and exon junctions. Here, we developed a scRNAseq workflow, RoCK and ROI (Robust Capture of Key transcripts and Regions Of Interest), that tackles these limitations. RoCKseq uses targeted capture to enrich for key transcripts, thereby supporting the detection and identification of cell types and complex phenotypes in scRNAseq experiments. ROIseq directs a subset of reads to a specific region of interest via selective priming to ensure detection. Importantly, RoCK and ROI guarantees efficient retrieval of specific sequence information without compromising overall single cell transcriptome information and our workflow is supported by a novel bioinformatics pipeline to analyze the multimodal information. RoCK and ROI represents a significant enhancement over non-targeted single cell sequencing, particularly when cell categorization depends on transcripts that are missed in standard scRNAseq experiments. In addition, it also allows exploration of biological questions that require assessment of specific sequence elements along the targets to be addressed.