Optimizing Single-Cell Long-Read Sequencing for Enhanced Isoform Detection in Pancreatic Islets
Maria Skjøtt HanseniD, Christopher J Hill, Lori SusseliD, Kristen L. WellsiD
Abstract
Alternative splicing is an essential mechanism for generating protein diversity by producing distinct isoforms from a single gene. Dysregulation of splicing that affects pancreatic function and immune tolerance has been linked to both type 1 and type 2 diabetes. Next-generation sequencing technologies, with their short read lengths, are limited in their ability to accurately detect splice variants. Long-read sequencing technologies offer the potential to overcome these limitations by providing full-length transcript information; however, their application in single-cell RNA sequencing has been hindered by technical challenges, including insufficient read lengths and higher error rates. Furthermore, cell types that produce high levels of a single transcript, such as islet endocrine cells, can obscure identification of lower abundance transcripts. In this study, we optimized a protocol for single-cell long-read sequencing in pancreatic islets to improve read length and transcript detection. Our findings demonstrate that 5 library preparation protocols outperform 3 protocols, resulting in better transcript identification. Furthermore, we show that targeted depletion of insulin transcripts enhances the detection of informative reads, highlighting the utility of transcript depletion strategies. This optimized protocol enables isoform-specific gene expression analysis and reveals differential transcript usage across the various cell types in pancreatic islets. By leveraging this approach, we gain deeper insights into the transcriptomic complexity and cellular heterogeneity within pancreatic islets. Article HighlightsO_LIThis study addresses the limitations of current single-cell long-read RNA-sequencing (sclrRNA-seq) technologies in detecting full-length transcripts and isoform diversity, particularly in pancreatic islets. C_LIO_LIWe demonstrate that optimizing single-cell library preparation protocols reproducibly enhances read length and transcript identification in pancreatic islets. C_LIO_LI5 capture methods, combined with targeted insulin depletion and extended reverse transcription, significantly improved read length and isoform detection compared to standard protocols, while maximizing the number of informative reads. C_LIO_LIThese improvements yield longer reads in single-cell experiments, substantially enhancing transcript identification and enabling more accurate analysis of isoform diversity. C_LI
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 3
- FWCI
- 6.20
- Field percentile
- top 5%
- Organism
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- A comparison of long-read single-cell transcriptomic approaches
Anita Scoones, Yuxuan Lan, … Iain C. Macaulay·bioRxiv·Jul 3, 2025
- Integrating short-read and long-read single-cell RNA sequencing for comprehensive transcriptome profiling in mouse retina
Meng Wang, Yumei Li, … Rui Chen·Genome ResearchPeer-reviewed·Apr 1, 2025
- High-throughput RNA isoform sequencing using programmable cDNA concatenation
Aziz M. Al’Khafaji, Jonathan T. Smith, … Nir Hacohen·Nature BiotechnologyPeer-reviewed·Jun 8, 2023
- Comprehensive characterization of single cell full-length isoforms in human and mouse with long-read sequencing
Luyi Tian, Jafar S. Jabbari, … Matthew E. Ritchie·Genome biologyPeer-reviewed·Nov 11, 2021
- HIT-scISOseq: High-throughput and High-accuracy Single-cell Full-length Isoform Sequencing for Corneal Epithelium
Yingfeng Zheng, Zhi-Chao Chen, … Chuan‐Le Xiao·bioRxiv·Jul 27, 2020
- Comparison of Single-cell Long-read and Short-read Transcriptome Sequencing of Patient-derived Organoid Cells of ccRCC: Quality Evaluation of the MAS-ISO-seq Approach
Natalia Zajac, Qin Zhang, … Hubert Rehrauer·bioRxiv·Mar 14, 2024