Decoding Condition-Specific Cellular Crosstalk in Spatial Omics via Bilinear Edge Classification

Tissues are multicellular structured communities whose function emerges from a combination of individual cellular characteristics along with their corresponding spatial configuration, affecting their interactions and response patterns. During processes such as disease progression or aging, tissues can undergo structural reorganization, including changes in co-localization of different cell types, assembly or destruction of functional niches, and disruption of intercellular communication axes. Such changes can manifest primarily in the spatial reorganization of cells rather than in the transcriptional states of individual cells. While computational tools for spatial transcriptomics have made significant progress in characterizing tissue architecture, most approaches for characterizing changes in tissue states across biological conditions operate at the level of individual cells or rely on discrete cell type labels, thus limiting the ability to detect coordinated transcriptional changes between neighboring cells that distinguish one condition from another. We present CO_SCPLOWASEIC_SCPLOW, a bilinear classification framework operating on cellular proximity graphs, which directly models condition-specific cell-cell interactions in spatial omics data by focusing on interactions (edges), rather than cells (nodes), as the fundamental unit of biological inference. To capture such condition-specific signals, we leverage a model whose inductive bias aligns with cellular interactions through coordinated gene-gene relationships of neighboring cells. CO_SCPLOWASEIC_SCPLOW enables the discovery of condition-associated multicellular interactions and spatial expression programs, and characterizes the loss of multicellular function and structure. Applied to mammalian liver fibrosis, atherosclerosis, and brain aging, CO_SCPLOWASEIC_SCPLOW reveals biologically meaningful spatial reorganization, including the shift from endothelial-to macrophage-dominated networks in atherosclerotic plaques, disruption of hepatocyte zonation in fibrosis, and oligodendrocyte-microglia crosstalk in aging white matter.