Screening cell-cell communication in spatial transcriptomics via collective optimal transport

Spatial transcriptomic technologies and spatially annotated single-cell RNA sequencing datasets provide unprecedented opportunities to dissect cell-cell communication (CCC). However, incorporation of the spatial information and complex biochemical processes required in the reconstruction of CCC remains a major challenge. Here, we present COMMOT (COMMunication analysis by Optimal Transport) to infer CCC in spatial transcriptomics, which accounts for the competition between different ligand and receptor species as well as spatial distances between cells. A collective optimal transport method is developed to handle complex molecular interactions and spatial constraints. Furthermore, we introduce downstream analysis tools to infer spatial signaling directionality and genes regulated by signaling using machine learning models. We apply COMMOT to simulation data and eight spatial datasets acquired with five different technologies to show its effectiveness and robustness in identifying spatial CCC in data with varying spatial resolutions and gene coverages. Finally, COMMOT identifies new CCCs during skin morphogenesis in a case study of human epidermal development. This work presents a computational framework, COMMOT, to spatially infer cell-cell communication from transcriptomics data based on a variant of optimal transport (OT).

Automated summary

COMMOT is a computational framework for spatially inferring cell-cell communication from transcriptomics data based on a variant of optimal transport (OT). It can handle complex molecular interactions and spatial constraints, and infer spatial signaling directionality and genes regulated by signaling using machine learning models.