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Existing deconvolution methods that integrate single\u2010cell RNA sequencing (scRNA\u2010seq) data with ST often overlook the biological principle that cells in communication with each other tend to be closer spatially. Here we introduce SPADE, a deep learning framework that aligns scRNA\u2010seq data to spatial locations by jointly modeling expression similarity between scRNA\u2010seq and ST data and concordance between the spot distance and cell\u2013cell communication (CCC) patterns. SPADE also enables quantitative characterization of CCC across spots and regions. Evaluations on 55 simulated and real datasets show that SPADE achieves strong performance in recovering region\u2010specific cell\u2010type patterns and enhancing spatial gene expression profiles compared with existing methods. In the breast cancer datasets, SPADE demonstrates a unique advantage in identifying tumor\u2010infiltrating immune cells and tertiary lymphoid structures. In the colorectal cancer liver metastasis dataset, SPADE distinguishes tumor heterogeneity with region\u2010specific CCC events and describes the general CCC landscape in the tissue. Overall, SPADE highlights the key role of spatially constrained CCC in shaping tissue organization and enables biological interpretation of spatial transcriptomics data.<\/jats:p>","DOI":"10.1002\/advs.76142","type":"journal-article","created":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T19:24:27Z","timestamp":1781810667000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SPADE: A Deep Learning Framework for Spatial Mapping and Quantitative Cell\u2013Cell Interaction Inference"],"prefix":"10.1002","volume":"13","author":[{"given":"Xinyi","family":"Li","sequence":"first","affiliation":[{"name":"Department of Immunology School of Basic Medical Sciences Health Science Center Peking University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Immunology School of Basic Medical Sciences Health 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