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Single-cell Long-read Transcriptomics: From Technologies to Biological Insights.

Created on 22 Jul 2026

Authors

Ze-Hui Ren, Wenteng Liu, Jianhua Yin, Chuanyu Liu

Published in

GigaScience. Jul 22, 2026. Epub Jul 22, 2026.

Abstract

Single-cell long-read transcriptomics (scLR-seq) extends single-cell analysis beyond gene abundance by resolving full-length transcript structures in individual cells. It can directly interrogate isoform usage, alternative splicing, and transcription start and end site selection, thereby revealing regulatory variation that is often obscured by short-read measurements. In this review, we examine the experimental and computational foundations of scLR-seq, including platform selection, library design, cell barcode and unique molecular identifier (UMI) recovery, transcript discovery, and isoform quantification. We discuss how these choices influence the reliability of downstream biological interpretation, and summarize emerging insights into isoform usage, alternative splicing, transcription start and end site selection, allele-specific expression, fusion transcripts, transposable element-derived transcripts, and RNA modifications. Finally, we highlight applications of scLR-seq in diverse biological systems, such as the immune system, neural development, and tumor microenvironments, and consider future opportunities and challenges in integrating multi-omics data to decode cellular programs and disease evolution.

PMID:
42484606
Bibliographic data and abstract were imported from PubMed on 22 Jul 2026.

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