Cell-cell interactions are essential for maintaining tissue homeostasis, and their dysregulation is closely associated with the development of a wide range of diseases, including cancer and autoimmune disorders. Therefore, a comprehensive understanding of the underlying molecular mechanisms is critical for the development of novel therapeutic strategies.
Ligand proteins, which play a central role in cell–cell communication, bind to cell surface receptors and activate intracellular signaling pathways. While interactions between neighboring cells have been extensively validated experimentally, the spatial range over which secreted ligands exert their effects remains insufficiently understood.
In this study, we first used Visium spatial transcriptomics data to model the spatial diffusion of ligands. Assuming a Gaussian distribution, we constructed a model to describe the relationship between the spatial spread of ligand signals and target gene expression. By setting target gene expression levels as the response variable and estimating the diffusion distance parameter, we estimated the effective range of each ligand.
Furthermore, by utilizing high-resolution Stereo-seq data, we enabled analysis at the single-cell level. This advancement has made it possible to elucidate in greater detail the spatial dynamics of intercellular signaling and the influence of the microenvironment on signaling pathways.
Our analytical framework successfully detected known interactions between plasmacytoid dendritic cells (pDCs) and their neighboring cells. Furthermore, we present examples of this method applied to both normal and tumor tissues.

Co-authors: Yasuhiro Kojima, Shuto Hayashi, Teppei Shimamura