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

Cell-cell communication events (CEs) are mediated by multiple ligand-receptor pairs. Usually only a particular subset of CEs directly works for a specific downstream response in a particular microenvironment. We name them as functional communication events (FCEs) of the target responses. Decoding the FCE-target gene relations is important for understanding the machanisms of many biological processes, but has been intractable due to the mixing of multiple factors and the lack of direct observations. We developed a method HoloNet for decoding FCEs using spatial transcriptomic data by integrating ligand-receptor pairs, cell-type spatial distribution and downstream gene expression into a deep learning model. We modeled CEs as a multiview network, developed an attention-based graph learning method to train the model for generating target gene expression with the CE networks, and decoded the FCEs for specific downstream genes by interpreting the trained model. We applied HoloNet on three Visium datasets of breast cancer or liver cancer. It revealed the communication landscapes in tumor microenvironments, and uncovered how various ligand-receptor signals and cell types affect specific biological processes. We also validated the stability of HoloNet in a Slideseq-v2 dataset. The experiments showed that HoloNet is a powerful tool on spatial transcriptomic data to help revealing specific cell-cell communications in a microenvironment that shape cellular phenotypes.