Various 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. A remaining challenge is that important information relevant for understanding the biology of cells or tissues, such as lowly expressed transcripts, sequence variations or exon junctions, remains undetected. We developed an scRNAseq workflow, RoCK and ROI (Robust Capture of Key transcripts and Region Of Interest), that tackles these limitations. RoCKseq uses targeted capture to enrich for key transcripts, thereby enhancing the detection, identification and tracking of cell types in scRNAseq experiments. ROIseq directs a subset of reads to a specific region of interest via selective priming. This allows specific sequence information to be retrieved for mRNAs of interest, enabling, for example, the inspection of sequence variations. Importantly, the targeted information obtained with RoCK and ROI is recorded together with standard transcriptome readouts. To analyze the multimodal information provided by RoCK and ROI, we developed a novel pipeline. The entire workflow increases the information obtained for lowly expressed genes and enables the detection of individual sequence variations and the exploration of the biological relevance and consequences of the respective variation for the cells expressing it.