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 deep neural network (APARENT-Perturb) for tandem polyA site usage, delineating a -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.

中文翻译：

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替代聚腺苷酸化调节剂的多重单细胞表征


大多数哺乳动物基因具有多个多聚腺苷酸位点，代表了受切割和多聚腺苷酸化 (CPA) 机制调节的转录多样性的重要来源。为了更好地了解这些蛋白质如何控制 PolyA 位点选择，我们引入了 CPA-Perturb-seq，这是一个由 42 个 CPA 调节因子组成的多重扰动筛选数据集，具有 3' scRNA-seq 读数，可以在转录组范围内推断 PolyA 位点的使用情况。我们开发了一个框架来检测聚腺苷酸化中扰动依赖性的变化，并表征共同调节的聚腺苷酸位点的模块。我们发现内含子多聚A位点组受到核RNA生命周期的不同组成部分的调节，包括延伸、剪接、终止和监视。我们训练并验证了用于串联多聚腺苷酸位点使用的深度神经网络（APARENT-Perturb），描绘了预测扰动响应并揭示调节复合物之间相互作用的调节代码。我们的工作强调了多重单细胞扰动筛选的潜力，以进一步加深我们对转录后调控的理解。