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However, existing analytical frameworks primarily focus on identifying cellular states and characterizing differentially expressed molecular features across these states. The relational spatial organization of molecular features and its dynamics across biological contexts therefore remain largely unexplored. Here, we present GeneSPOT, a gene-centric computational framework for characterizing the spatial relationships among genes and quantifying their reorganization across biological conditions. Although gene-centric in design, GeneSPOT generalizes to other molecular features. Across diverse applications, GeneSPOT resolved tissue structures, integrated paired and unpaired spatial multi-omics data, unveiled the spatial convergence of gene programs during embryonic development, and identified aging-associated spatial reorganization among genes whose expression did not change significantly with age. GeneSPOT thus complements conventional cell-centric analysis, providing a framework for deciphering how spatial relationships among molecular features are reorganized across biological conditions and over time.<\/jats:p>","DOI":"10.64898\/2026.09.24.753111","type":"posted-content","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:25:51Z","timestamp":1790727951000},"source":"Crossref","is-referenced-by-count":0,"title":["Deciphering the gene-centric spatial dynamics across tissue and time with GeneSPOT"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8662-5874","authenticated-orcid":false,"given":"Qi","family":"Zou","sequence":"first","affiliation":[{"name":"Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China"},{"name":"Center of Intelligent Medicine, School of Control Science and 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