A universal toolkit for single-cell and spatial long-read transcriptomic sequencing data
Year: 2026
Type: conference-abstract
Abstract: Recent advances in single-cell and spatial transcriptomics combined with long-read sequencing have opened new possibilities for isoform-level profiling and studying alternative splicing. However, upstream processing of such data poses distinct algorithmic challenges, including detecting barcodes in error-prone reads using extremely large whitelists, resolving molecule concatenation artifacts and PCR duplicates, accurately quantifying and discovering complex isoforms, as well as a lack of methods for detecting spatially variable isoforms. Here we present Spl-IsoQuant —a universal toolkit for processing long-read RNA-seq data obtained with various single-cell and spatial protocols. Our software extends the IsoQuant framework with high-precision barcode calling that also handles cDNA concatenation typical for some protocols and ONT sequencing. Spl- IsoQuant performs UMI-based PCR deduplication and accurately quantifies genes and isoforms in individual cells, cell types, and user-defined conditions. It supports multiple protocols, such as 10x single-cell, 10x Visium and Visium HD, Stereo-seq and Curio. Moreover, it can process long-read data from any custom protocol via a user-specified molecule description format. We complement the toolkit with Spl-IsoFind, which discovers spatially variable isoforms using Moran’s I spatial autocorrelation. Combined with region-wise isoform tests, this framework detects both known patterns and signals not aligned with predefined anatomical regions. Applying our toolkit to several mouse brain spatial long-read Stereo-seq and 10x Visium HD datasets with 500nm vs 2μm resolution respectively, we demonstrate spatially variable isoform discovery at single-cell level and reproducibility across protocols, establishing a general-purpose pipeline for single-cell and spatial longread RNA-seq analysis.
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7204019026