Multiplexed single-cell characterization of alternative polyadenylation regulators (3PRACE)

Most mammalian genes have multiple polyA sites, representing a substantial source of transcript diversity regulated by the cleavage and polyadenylation (CPA) machinery. To better understand how these proteins govern polyA site choice we introduce CPA-Perturb-seq, a multiplexed perturbation screen dataset of 42 CPA regulators with a 3' scRNA-seq readout that enables transcriptome-wide inference of polyA site usage. We develop a framework to detect perturbation-dependent changes in polyadenylation and characterize modules of co-regulated polyA sites. We find groups of intronic polyA sites regulated by distinct components of the nuclear RNA life cycle including elongation, splicing, termination, and surveillance. We train and validate a multi-task deep neural network (APARENT-Perturb) for tandem polyA site usage, delineating a cis-regulatory code that predicts perturbation response and reveals interactions between regulatory complexes. Our work highlights the potential for multiplexed single-cell perturbation screens to further our understanding of post-transcriptional regulation. Overall design: We conducted a pooled single-cell CRISPR screen (in both HEK293FT and K562 cell lines) targeting 42 genes previously implicated in regulating cleavage and polyadenylation. We inferred polyA site usage at single-cell resolution from a scRNA-seq readout, and determine how each of these perturbations effects polyA site usage. We validate our polyA site quantifications by performing 3'RACE followed by Illumina sequencing at 7 loci in NT and NUDT21-perturbed cells. We also built a sequence-based deep learning model (APARENT-Perturb) to describe how sequence affects perturbation, and design an MPRA to validate these sequence-based predictions on polyA site usage.