Identification and quantification of alternative polyadenylation sites in single cell RNA-seq data using scPAISO

Alternative polyadenylation (APA) is a critical posttranscriptional mechanism that generates transcriptomic diversity through the production of mRNA isoforms with distinct 3’ UTRs or coding sequences. Current APA analysis based on single-cell RNA sequencing (scRNA-seq) for establishing cell type-specific APA landscapes primarily rely on Read2 data, which lacks precise cleavage site (CS) information. This limitation restricts their ability to achieve precise de novo mapping of polyadenylation sites (PASs). Here, we present s ingle- c ell P oly A denylation ISO form quantification (scPAISO), a computational pipeline designed for de novo identification of PAS and quantification of PAS isoforms in scRNA-seq data, by leveraging the often-discarded Read1 from 3’ tag-based scRNA-seq protocols. Unlike existing tools, scPAISO directly captures mRNA 3’ end cleavage sites, enabling superior performance in motif enrichment (stronger AAUAAA signal) and peak precision (sharper PAS peaks). Moreover, the smaller peak widths enhance the spatial resolution, enabling more accurate detection of closely spaced PASs in the genome. By integrating Read1 and Read2 data, scPAISO achieves isoform-level quantification with an assignment accuracy exceeding 95%. We demonstrate the robustness of scPAISO in identifying PASs and quantifying APA events across diverse biological contexts, including hematopoiesis, systemic sclerosis (SSc), and mouse tissues. We identified stage-specific 3’ UTR lengthening in hematopoietic progenitors, global 3’ UTR remodeling in SSc and tissue-specific polyadenylation (PA) preference along with RNA-binding proteins in mice. Overall, scPAISO represents a significant advancement in the analysis of APA at single-cell resolution and provides a powerful tool for exploring the regulatory landscape of APA, offering new insights into transcriptome complexity and gene regulation in both health and disease.