scPolyA enables efficient, interactive and agent-guided alternative polyadenylation analysis in single-cell and spatial transcriptomes

Alternative polyadenylation (APA) changes which regulatory sequences an RNA retains, creating a layer of cellular variation that gene-level expression profiles cannot resolve. Here we present scPolyA, a unified, AI-enabled framework that transforms conventional single-cell and spatial 3'-end transcriptomes into quantitative maps of RNA-end choice. scPolyA connects de novo poly(A)-site discovery to cell- and spot-resolved measurements of terminal-site usage, 3' untranslated region length and intronic polyadenylation, enabling differential APA analysis across cell states and tissue regions. Its integrated deep-learning module, DeepCPS, improves recognition of sparsely supported poly(A) sites and predicts cleavage positions at nucleotide resolution. An integrated graphical workspace brings parameter exploration, efficient rerunning and genome browsing together, while an APA-specialised AI agent translates natural-language research questions into executed analyses, verified outputs and evidence-guided interpretation. Across diverse single-cell protocols and spatial platforms, scPolyA achieves higher reference-atlas agreement than most evaluated de novo methods, while single-cell benchmarks demonstrate competitive runtime and memory use. Applied to cutaneous squamous cell carcinoma, scPolyA reveals recurrent 3' untranslated region shortening in tumour keratinocytes across all ten patients and corresponding patterns in tumour-enriched regions, with tissue-level contrasts shaped by cellular composition. scPolyA extends conventional transcriptomics from gene abundance to RNA-end regulation, linking transcript choices to cellular states and tissue organisation.