Beyond counting: how single-cell long-read sequencing turns transcriptome complexity into precision targets

Single-cell RNA sequencing (scRNA-seq) has emerged as a critical tool in oncology research, revealing key aspects of immune infiltration, tumor heterogeneity, and the tumor microenvironment. However, most scRNA-seq experiments capture only a portion of the 5'- or 3'-end of the gene due to limitations in sequencing read length. This limits short-read scRNA-seq to a method that quantifies gene expression but falls short of understanding the full complexity of the transcriptome. Since single nucleotide variants (SNVs), structural variants (SVs), and aberrant splicing are known drivers of tumor development, it is critical to be able to understand their heterogeneity at the single cell level. Long-read RNA-seq is capable of sequencing full-length molecules, simplifying the process of identifying these types of alterations. This review examines how single-cell long-read sequencing (scLRS) technologies are overcoming the limitations of short-read platforms to resolve the complexity of the cancer transcriptome. We highlight key applications that leverage full-length information, including the identification of novel tumor-specific neo-antigens and fusion genes. By linking genotype information with transcript expression, this technology holds the potential for developing highly specific, isoform-selective therapies that minimize off-target effects. We also describe the application of scLRS to sensitively trace tumor clone subtypes using isoform profiles and to identify clonal evolution through tracing SNV variation within single cells. Furthermore, we discuss the current state of the scLRS field and how it can be applied for multimodal analysis, which integrates full-length transcriptomics with genomic, spatial and proteomic data to create a more comprehensive profile of the tumor micro-environment. Finally, we outline the current technological and computational challenges of scLRS, including cost, throughput, and the need for standardized bioinformatic tools, providing a roadmap for future advancements. As these limitations are overcome, we foresee scLRS as an indispensable tool for uncovering the transcriptomic complexity within the tumor microenvironment, accelerating the development of precision oncology therapies.