Single-cell long-read targeted sequencing reveals transcriptional variation in ovarian cancer

Single-cell RNA sequencing predominantly employs short-read sequencing to characterize cell types, states and dynamics; however, it is inadequate for comprehensive characterization of RNA isoforms. Long-read sequencing technology enables single-cell RNA isoform detection but is hampered by lower throughput and unintended sequencing of artifacts. Here we developed a hybridization-capture method called scTaILoR-seq (Single-cell Targeted Isoform Long-Read Sequencing) which targets over a thousand genes of interest, improving median gene detection sensitivity 10-fold. We used this approach to identify and quantify RNA isoforms from ovarian cancer cell lines and primary tumors, yielding 10,796 single-cell transcriptomes. Using long-read variant calling we revealed associations of single nucleotide variants (SNVs) with diverse transcript structures. In addition, phasing of SNVs across transcripts facilitated measurement of allelic imbalance within distinct cell populations. In sum, we developed a robust long-read single-cell RNA sequencing method and analytical framework for targeted RNA isoform detection, quantification and structural characterization.