Spatial multi-omics in precision medicine: Integrating biological insights through multidisciplinary collaboration

Abstract

Spatial multi-omics has emerged as a transformative approach in biomedical research, enabling the integration of diverse molecular modalities while preserving their native spatial contexts. This review provides an overview of spatial multi-omics technologies, focusing on data acquisition, quality management, and integration strategies across transcriptomic, genomic, epigenomic, proteomic, and metabolomic layers. Spatial transcriptomics is highlighted as a foundational framework for aligning multi-omics data with histological and cellular architecture. We emphasize its applications in elucidating tumor heterogeneity, immune-stromal interactions, and metabolic or epigenetic dynamics within the tumor microenvironment, which are crucial for understanding disease progression and therapeutic response. The review further discusses key challenges such as technical noise, batch effects, and the complexity of high-dimensional data integration, along with optimization strategies for sampling and analysis in both clinical and research settings. Ethical and regulatory considerations, including patient data privacy and responsible implementation of artificial intelligence, are also examined in the context of clinical translation. Taken together, this review offers an integrative synthesis of spatial multi-omics technologies and their applications in cancer biology, providing a balanced perspective to help researchers and clinicians navigate this rapidly evolving field and recognize its translational potential for advancing precision medicine.

Keywords: Artificial intelligence in biomedicine; Multi-omics data integration; Precision medicine; Spatial multi-omics; Spatial transcriptomics; Tumor microenvironment.