Optimizing Single-Cell Long-Read Sequencing for Enhanced Isoform Detection in Pancreatic Islets

This study addresses the limitations of current single-cell long-read RNA sequencing technologies in detecting full-length transcripts and isoform diversity, particularly in pancreatic islets. We demonstrate that optimizing single-cell library preparation protocols reproducibly enhances read length and transcript identification in pancreatic islets. Combined with targeted insulin depletion and extended reverse transcription, 5' capture methods significantly improved read length and isoform detection compared with standard protocols, while maximizing the number of informative reads. These improvements yield longer reads in single-cell experiments, substantially enhancing transcript identification and enabling more accurate analysis of isoform diversity.