Bioinformatics frameworks for single-cell long-read sequencing: unlocking isoform-level resolution

Alternative splicing (AS) plays a key role in regulating gene expression, and its dysregulation is implicated in numerous human diseases, including cancer. While bulk RNA sequencing has advanced our understanding of AS, it cannot capture cellular heterogeneity or reliably reconstruct full-length isoforms, both of which underpin disease mechanisms and therapeutic responses. Single-cell RNA sequencing (scRNA-seq) is an established and a powerful approach to examine AS landscapes at single-cell resolution, enabling the identification of cell-specific aberrant splicing events that may contribute to disease. However, conventional scRNA-seq is limited by short read lengths, often preventing an accurate reconstruction of full-length transcript isoforms. This limitation is addressed by long-read RNA-seq (lrRNA-seq), which can sequence full-length RNA molecules, some exceeding 100 000 nucleotides in length. Thereby, lrRNA-seq enables more accurate characterization of isoform diversity, identification of novel splice variants, quantification of percent spliced-in values, and detection of fusion transcripts. The convergence of single-cell resolution and third-generation sequencing technologies has led to the development of single-cell long-read sequencing (SCLR-seq), a powerful approach that addresses the key constraints of bulk short-read RNA-Seq by providing isoform-level resolution and cell-type specificity. This review explores the growing utility of SCLR-seq, highlighting recent developments in bioinformatics tools and pipelines designed for SCLR-seq data analysis. We discuss how this emerging technology is transforming our understanding of isoform regulation and aberrant splicing in human diseases, and its potential to uncover novel diagnostic and therapeutic targets.