Protein–protein interactions (PPIs) regulate signalling pathways and cell phenotypes, and the visualization of spatially resolved dynamics of PPIs would thus shed light on the activation and crosstalk of signalling networks. Here we report a method that leverages a sequential proximity ligation assay for the multiplexed profiling of PPIs with up to 47 proteins involved in multisignalling crosstalk pathways. We applied the method, followed by conventional immunofluorescence, to cell cultures and tissues of non-small-cell lung cancers with a mutated epidermal growth-factor receptor to determine the co-localization of PPIs in subcellular volumes and to reconstruct changes in the subcellular distributions of PPIs in response to perturbations by the tyrosine kinase inhibitor osimertinib. We also show that a graph convolutional network encoding spatially resolved PPIs can accurately predict the cell-treatment status of single cells. Multiplexed proximity ligation assays aided by graph-based deep learning can provide insights into the subcellular organization of PPIs towards the design of drugs for targeting the protein interactome.

蛋白质-蛋白质相互作用 （PPI） 调节信号通路和细胞表型，因此 PPI 空间分辨动力学的可视化将阐明信号网络的激活和串扰。在这里，我们报道了一种方法，该方法利用顺序邻位连接测定对 PPI 进行多重分析，其中多达 47 种蛋白质参与多信号串扰通路。我们将该方法应用于具有突变表皮生长因子受体突变的非小细胞肺癌的细胞培养物和组织，以确定 PPI 在亚细胞体积中的共定位，并重建 PPI 亚细胞分布的变化响应酪氨酸激酶抑制剂奥希替尼的扰动。我们还表明，编码空间解析 PPI 的图卷积网络可以准确预测单个细胞的细胞处理状态。在基于图形的深度学习的辅助下，多重邻近连接分析可以深入了解 PPI 的亚细胞组织，从而设计靶向蛋白质相互作用组的药物。