Kontext: Kernel-based method bridging spatial domain identification and cell-cell communication

Abstract

The spatial organization of biological tissues is characterized by spatial domains: regions with coherent transcriptional and anatomical properties, heavily influenced by local cell-cell interactions. Although identifying spatial domains is a common task in spatial transcriptomics, most existing methods provide limited mechanistic insight into their organization. We present Kontext, a kernel-based framework that jointly represents cells and their local environments, enabling the identification of spatial domains grounded in their underlying ligand-receptor interactions. This representation further provides complementary outputs for characterizing cellular microenvironments, spatial niches, and sender-receiver communication patterns. Kontext outperforms state-of-the-art methods in recovering reference domain annotations across several spatial transcriptomics technologies and biological models, while offering improved computational efficiency and scalability. Applying Kontext on a Xenium temporal mouse heart cryoinjury dataset, we characterize the spatiotemporal evolution of the injury and identify an undescribed sustained activity of the complement system in the communication between fibroblasts and macrophages during scar maturation. In a million-cell three-dimensional Open-ST dataset of human metastatic lymph node tissue, we combine spatial domain identification and niche analysis to define the boundary region between the tumor and its microenvironment, revealing distinct communication patterns between this interface and the tumor core.