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

Alternative polyadenylation (APA) generates transcript diversity by producing mRNA isoforms with distinct 3' untranslated regions (3' UTRs) or coding sequences. Existing single-cell RNA sequencing (scRNA-seq) methods for APA analysis primarily rely on Read2 data, which lacks precise cleavage site (CS) information and limits accurate polyadenylation site (PAS) identification. Here, we present single-cell PolyAdenylation ISOform quantification (scPAISO), a computational pipeline that leverages the often-discarded Read1 from 3' tag-based scRNA-seq to enable de novo PAS identification and PAS isoform quantification. Unlike existing approaches, scPAISO directly captures mRNA 3' end cleavage sites, resulting in stronger AAUAAA motif enrichment, sharper PAS peaks, and improved spatial resolution for resolving closely spaced PASs. Across multiple datasets and biological systems, scPAISO robustly identified PASs and quantified APA dynamics, revealing stage-specific 3' UTR lengthening during hematopoietic differentiation, widespread 3' UTR remodeling in systemic sclerosis, and tissue-specific polyadenylation preferences associated with distinct RNA-binding protein programs in mice. scPAISO provides an accurate and scalable framework for single-cell APA analysis, enabling high-resolution characterization of post-transcriptional regulation and transcriptome diversity in development, physiology, and disease.