Decoding the causal drivers of spatial cellular topology

Decoding how cells influence and communicate with each other in space is fundamental for understanding tissue organization. However, existing approaches either overlook spatial context entirely or rely solely on local cell-cell adjacency, failing to capture how global tissue topology shapes cellular communication. Here, we present GLACIER, which introduces spatial Granger causality to infer transcriptional and signaling relationships that emerge from tissue organization. By combining GASTONs global isodepth coordinate with Veloramas graph-based causal inference framework, we enable bidirectional inference of regulatory relationships along spatial axes, identifying transcription factortarget interactions and ligand-receptor pairs that operate across spatial domains. Applying GLACIER to single-cell spatial transcriptomics data from the mouse cerebellum, we identify both continuous within-cell-type regulatory gradients and discontinuous drivers at layer interfaces, while distinguishing between forward and backward cellular communication along the isodepth axis. Our approach reveals how tissue architecture directs patterns of cellular communication, providing a framework for understanding spatially-encoded regulatory programs. Software availabilityhttps://github.com/rohitsinghlab/glacier.