Current trends and challenges in deciphering single molecule resolution maps of single cell transcriptomes.

Authors

David Schaeper, Upol Chowdhury, Sarath Chandra Janga

Published in

Briefings in bioinformatics. Volume 27. Issue 3. May 04, 2026.

Abstract

Advancements in single-cell RNA sequencing (scRNA-seq) techniques have expanded the study of cellular heterogeneity and transcriptional dynamics. Early methods relied on manual cell isolation followed by barcode introduction, but subsequent approaches integrated automated cell isolation with cellular barcoding to increase throughput. While most current single-cell RNA-seq methods aim to capture transcripts at the single-cell level in a high-throughput manner using short-read sequencing, such efforts frequently prevent assignment of full-length transcripts to individual cells, limiting insight into isoform diversity and complete mutational profiles. Recent advances in long-read sequencing accuracy are starting to enable integration of full-length transcript coverage with high-throughput barcoding. This review traces the evolution of scRNA-seq from early manual isolation methods to today's high-throughput short-read droplet- and combinatorial barcoding-based platforms. Then, the review discusses recent advances stemming from the adaptation of high-throughput scRNA-seq protocols for use with long-read sequencing and addresses key challenges such as accurate barcode identification despite lower base-calling accuracy and efforts to compensate for reduced throughput relative to short-read technologies. In parallel, the review highlights the development of computational tools tailored to long-read scRNA-seq, including methods for cell barcode and unique molecular index recovery, variant detection, and complete end-to-end workflows, emphasizing both their shared and unique advantages. Finally, applications of long-read scRNA-seq are shown to provide novel insights, spanning cancer genomics, neurology, early development, and disease contexts. By integrating technical, computational, and biological perspectives, the transformative potential of long-read scRNA-seq is shown, advancing our understanding of cellular heterogeneity.

PMID:
42308418
Bibliographic data and abstract were imported from PubMed on 18 Jun 2026.

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