This review examines the experimental and computational foundations of scLR-seq, including platform selection, library design, cell barcode and unique molecular identifier recovery, transcript discovery, and isoform quantification, 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.
Abstract
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 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.
Abstract Summary Long-read RNA sequencing technologies, including Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT), enable direct characterization of full-length transcripts and transcriptome complexity. However, analysis of long-read RNA-seq data remains fragmented across multiple tools, limiting the ability to obtain a unified view of transcript structure, expression, and regulatory variation in long-read transcriptomes. We present NextLongIso, a scalable and reproducible Nextflow pipeline that enables coordinated analysis of multiple layers of transcript regulation. Rather than focusing solely on transcript reconstruction, NextLongIso integrates transcript discovery with downstream regulatory analyses to jointly characterize alternative splicing, isoform switching, transcript boundary dynamics (including alternative promoters and polyadenylation), and transposable element-associated transcription from both PacBio and ONT datasets. By eliminating complex cross-tool data harmonization, this unified framework facilitates the transition from transcript identification to functional interpretation of transcriptomic variation. Availability and Implementation NextLongIso is implemented in Nextflow and is freely available at github: https://github.com/YidanSunResearchLab/nf-LongIso.git and Zenodo: https://doi.org/10.5281/zenodo.21049837.
Single-cell RNA sequencing has transformed our understanding of cellular systems, yet the reliance on short-read sequencing restricts analysis to gene-level quantification and obscures the immense biological diversity generated by alternative splicing. While long-read sequencing technologies can capture full-length RNA and resolve transcript isoforms, current platforms remain constrained by throughput and high per-base costs, rendering them impractical for modern million-cell applications. To address this critical limitation, we developed and optimized sequencing-by-expansion (SBX) chemistry for high-throughput single-cell RNA isoform profiling. Integrated within the AXELIOS 1 sequencing platform, SBX employs a unique biochemical conversion process that transforms complementary DNA into expanded surrogate high signal-to-noise polymers called Xpandomers which are sequenced via translocation through a dense nanopore array yielding over 9.5 billion reads in a two-hour run. To leverage this unique data type for long-read single-cell RNA isoform sequencing, we developed the Consensus UMI Deduplication using Longest Length (CUDLL) algorithm, which computationally consolidates variable-length raw SBX reads into single, high-fidelity consensus reads, elevating sequence accuracy to 99.83% and maximizing per transcript read length. We demonstrate that this consensus approach successfully captures the vast isoform diversity of single-cell libraries and enables the accurate measure of differential isoform expression across distinct cell types in peripheral blood mononuclear cells. Furthermore, SBX coupled with CUDLL efficiently resolves T-cell and B-cell receptor clonotypes directly from whole-transcriptome libraries without the need for VDJ-specific target enrichment. Ultimately, this work establishes SBX and the AXELIOS 1 as a transformative platform for high-scale single-cell isoform sequencing.
Christophe H. Georgescu, Ghamdan Al-Eryani, Allison Brookhart et al.· bioRxiv· 0 citations
A practical framework for isoform-level analysis in single-cell CRISPR screens is provided, identifying current capabilities and limitations, and providing a practical framework for isoform-level analysis in perturbation studies scale.
Retinal development has been extensively studied and key transcriptional regulators that drive fate decisions have been identified for major cell classes. These findings were confirmed and deepened in recent years with the advance of single cell RNA sequencing (Scrase). However, many processes that guide progenitor to postmitotic cell differentiation remain elusive, especially since some genes seem to yield different cell populations without apparent correlation with expression level or timing. Here, differential transcript isoform usage might play a role in diversifying the function of developmental genes. In short-read based scRNAseq, isoforms can only be identified if a read maps to a unique sequence or exon junction. However, due to the sparsity and very short reads, these events are extremely rare. We combined a commercial scRNAseq kit, that produces barcoded, full-length cDNA with Oxford Nanopore Technologies based long-read sequencing to generate the first single cell long-read sequencing dataset of fetal human retina. It can help elucidate the role of alternative splicing in retinal development and guide the design of transcript-specific gene therapies for retinal regeneration.
BACKGROUND AND AIMS
Long non-coding RNAs (lncRNAs) are important regulators of cellular processes, but their analysis at single-cell resolution remains challenging because lncRNA prediction, quantification, cell-type-specific characterization and downstream functional interpretation are often performed using separate tools. Although single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) provide cellular-resolution transcriptomic profiles, reproducible workflows for lncRNA-focused analysis, particularly in plant systems, remain limited. To address this need, we developed scLncR, an open-source, modular and reproducible framework for lncRNA analysis in single-cell and single-nucleus transcriptomic data.
METHODS
scLncR is a versatile framework incorporating multiple functional modules: lncRNA prediction, independent expression matrix processing, cell-type specific expression analysis, snRNA-seq/scRNA-seq expression enrichment analysis, weighted gene co-expression network analysis (WGCNA), pseudotime trajectory, and functional enrichment. It supports both command-line operation (for server-based customization) and a Shiny-based graphical user interface for user-friendly access.
RESULTS
By connecting discrete analytical steps, scLncR enables a seamless transition from candidate lncRNA discovery to biological interpretation. Benchmarking revealed that our independent lncRNA matrix processing strategy enhances lncRNA signal visibility while preserving high concordance with established preprocessing methods at both cell-type and cluster levels. Notably, application to Arabidopsis root datasets prioritized three lncRNA candidates linked to root-hair cellular states and distinct genetic contexts.
CONCLUSIONS
scLncR serves as an open-source workflow resource designed to systematize and streamline lncRNA-focused analyses for single-cell and single-nucleus transcriptomic data. The source code, configuration files and documentation available at https://github.com/Lilab-SNNU/scLncR, release v1.0.0.
Shuwei Yin, Yi Lu, Wenyu Yan et al.· Annals of Botany· 0 citations
Motivation Single-cell RNA sequencing (scRNA-seq) allows for the detailed analysis of dynamic cellular processes. In particular, this has been enabled by the estimation of RNA velocity, the derivative of gene expression, from separate count matrices for different splice states, which provides information about a cell’s immediate future even in snapshot data. Useful velocity estimates strongly depend on accurate counts for spliced and unspliced transcripts. Velocyto remains the standard tool for spliced and unspliced mRNA molecule quantification. However, despite considerable advances in scRNA-seq protocols, velocyto has not been updated to account for peculiarities of new protocols, such as popular approaches based on 5’ chemistry. Results To address this shortcoming, we present tidesurf, a command line tool for the quantification of spliced and unspliced transcript molecules from scRNA-seq libraries. Employing it on four different publicly available 10x Genomics Chromium datasets, we show the accuracy on various datasets generated with either 3’ or 5’ chemistry, whereas velocyto’s results are highly erroneous for the latter. Considering broader applicability, our results highlight tidesurf as a potential replacement for velocyto. Availability and implementation A Python implementation of tidesurf is available from PyPI and at github.com/janschleicher/tidesurf. Code for reproducing the analyses is available at github.com/janschleicher/tidesurf projects.
Jan T. Schleicher, M. Claassen· bioRxiv· 1 citation