Decoding functional cell–cell communication events by multi-view graph learning on spatial transcriptomics

Cell–cell communication events (CEs) mediated by multiple ligand–receptor pairs construct a complex intercellular signaling network. Usually only a subset of CEs directly works for a specific downstream response in certain microenvironments. We call them functional communication events (FCEs). Spatial transcriptomic methods can profile the spatial distribution of gene expression levels of ligands, receptors, and their downstream genes. This provides a new possibility for revealing the holographic network of cell–cell communication. We developed HoloNet, a computational method for decoding FCEs using spatial transcriptomic data. We modeled CEs as a multi-view network, developed an attention-based graph learning model on the network to predict the target gene expression, and decoded the FCEs for specific downstream genes by interpreting the trained model. We applied HoloNet on two breast cancer Visium datasets to reveal the communication landscapes in breast cancer microenvironments. It detected ligand–receptor signals triggering the expression changes of invasion-related genes in stromal cells surrounding tumors. The experiments showed that HoloNet is a powerful tool on spatial transcriptomic data to help understand the shaping of cellular phenotypes through cell–cell communication in a microenvironment.