Tissue homeostasis and disease emerge from cell–cell interactions operating across spatial scales: from autocrine and juxtacrine signals within micrometers to paracrine gradients coordinating responses across tissues. While these can be read out from spatial transcriptomics, existing computational methods capture either local adjacency-based or long-range dependencies, but rarely both within a single framework. We introduce InterScale, a graph-transformer approach that jointly models local and global cellular interactions from spatial transcriptomics data. By integrating a Graph Convolutional Network as a local component with a global transformer encoder, InterScale learns multi-scale representations of cellular communication. A downstream workflow enables scale-resolved interpretation of interactions from gene to tissue level. Applied to Sonic Hedgehog morphogen patterning in neural organoids, InterScale resolves spatially restricted neuronal differentiation programs and broader progenitor regulatory states along the morphogen gradient. In a human pancreatic dataset contrasting healthy and type 1 diabetic tissue, it reveals disease-associated spatial reorganization and tissue remodeling. InterScale’s modular architecture supports diverse spatial transcriptomics platforms and provides a scalable, unbiased, and biologically interpretable framework for studying cellular interactions across scales.
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