Abstract：
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 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.
摘要：
大多数哺乳动物基因有多个polyA位点，代表了由切割和聚腺苷酸化（CPA）机制调节的转录物多样性的重要来源。为了更好地理解这些蛋白质如何控制polyA位点的选择，我们引入CPA-Perturb-seq，42个CPA调节因子的多重扰动筛选数据集，具有3'scRNA-seq读数，可在转录组范围内推断polyA位点的使用。我们开发了一个框架来检测聚腺苷酸化的扰动依赖性变化，并表征共同调节的polyA位点的模块。我们发现一组内含子polyA位点受核RNA生命周期的不同成分调节，包括伸长率，拼接,终止,和监视。我们训练并验证了一个深度神经网络(APARENT-Perturb)，用于串联polyA位点的使用，描绘一个预测扰动响应并揭示调控复合物之间相互作用的顺式调控代码。我们的工作强调了多重单细胞扰动筛选的潜力，以进一步我们对转录后调控的理解。
