In our cells, a limited number of RNA binding proteins (RBPs) are responsible for all aspects of RNA metabolism across the entire transcriptome. To accomplish this, RBPs form regulatory units that act on specific target regulons. However, the landscape of RBP combinatorial interactions remains poorly explored. Here, we perform a systematic annotation of RBP combinatorial interactions via multimodal data integration. We build a large-scale map of RBP protein neighborhoods by generating in vivo proximity-dependent biotinylation datasets of 50 human RBPs. In parallel, we use CRISPR interference with single-cell readout to capture transcriptomic changes upon RBP knockdowns. By combining these physical and functional interaction readouts, along with the atlas of RBP mRNA targets from eCLIP assays, we generate an integrated map of functional RBP interactions. We then use this map to match RBPs to their context-specific functions and validate the predicted functions biochemically for four RBPs. This study provides a detailed map of RBP interactions and deconvolves them into distinct regulatory modules with annotated functions and target regulons. This multimodal and integrative framework provides a principled approach for studying post-transcriptional regulatory processes and enriches our understanding of their underlying mechanisms.

在我们的细胞中，有限数量的 RNA 结合蛋白 （RBP） 负责整个转录组中 RNA 代谢的各个方面。为了实现这一目标，RBP 形成了作用于特定目标调节子的监管单位。然而，RBP 组合相互作用的前景仍然没有得到充分探索。在这里，我们通过多模态数据集成对 RBP 组合交互进行系统注释。我们通过生成 50 个人类 RBP 的体内邻近依赖性生物素化数据集来构建 RBP 蛋白邻域的大规模图谱。同时，我们使用 CRISPR 干扰和单细胞读数来捕获 RBP 敲低后的转录组变化。通过结合这些物理和功能相互作用读数，以及来自 eCLIP 检测的 RBP mRNA 靶标图谱，我们生成了功能性 RBP 相互作用的综合图谱。然后，我们使用此图将 RBP 与其上下文特定功能相匹配，并验证四个 RBP 的预测功能。本研究提供了 RBP 相互作用的详细图谱，并将它们解卷积为具有注释功能和目标调节子的不同调节模块。这种多模式和综合框架为研究转录后调控过程提供了一种原则性的方法，并丰富了我们对其潜在机制的理解。