Aim This session aims to explore emerging strategies for resolving spatial relationships within complex omics datasets, including genomics, transcriptomics, proteomics, and multi-omics integration. As technologies such as spatial transcriptomics, imaging mass cytometry, and single-cell sequencing mature, the ability to contextualize molecular data within physical space has become essential. The session will focus on conceptual frameworks, computational methodologies, and experimental innovations that enable researchers to map molecular profiles to tissue architecture, cellular neighborhoods, and dynamic biological systems.

Scope The session will cover

• Advances in spatially resolved technologies (e.g., spatial transcriptomics platforms, multiplexed imaging, in situ sequencing)
• Computational models for spatial pattern detection, cell–cell interaction inference, and neighborhood analysis
• Integration of spatial omics with single-cell and bulk datasets
• Data harmonization, normalization, and multimodal alignment challenges
• Machine learning and AI approaches for spatial inference and predictive modeling
• Topology aware and Topological Data Analysis based techniques
• Applications in developmental biology, cancer microenvironment research, immunology, and precision medicine
• Standards, benchmarking, and reproducibility in spatial omics analysis. Both methodological and application-focused contributions are welcome.

Impact Understanding spatial relationships in omics data is transforming how we interpret biological systems. By resolving where molecular events occur, and how they interact across spatial contexts, researchers can uncover mechanisms that remain hidden in bulk analyses. This session will catalyze cross-disciplinary collaboration between experimentalists, computational and data scientists. Ultimately, the session seeks to contribute to the continued development of spatially informed omics research, supporting a more integrated understanding of health and disease.