Advances in spatial multi-omics technologies

Spatial multi-omics is an emerging interdisciplinary field that integrates imaging, omics, spatial analysis, sequencing, mass spectrometry, and bioinformatics to simultaneously acquire and analyze multimodal data, encompassing genomics, transcriptomics, epigenomics, proteomics, metabolomics, and radiomics. By preserving the spatial context of cells, this approach provides comprehensive insights into cellular diversity, functionality, intercellular interactions, and microenvironmental dynamics. This review synthesizes the evolution and recent advancements in spatial omics and multi-omics integration technologies, emphasizing key parameters such as analytical throughput, spatial resolution, and detection sensitivity. It underscores the transformative role of spatial multi-omics in resolving cellular spatial heterogeneity and highlights its impactful applications across critical biomedical domains, including oncology, neuroscience, reproductive biology, and organ aging and regeneration. By enabling integrated multimodal analysis, spatial multi-omics has significantly advanced the understanding of disease mechanisms and biological processes, positioning itself as a key driver of digital medicine.The review outlines the development of spatial single-omics technologies, from early fluorescence microscopy-based techniques, such as multiplexed fluorescence in situ hybridization (FISH) and immunohistochemistry, to cutting-edge mass spectrometry imaging (MSI) and next-generation sequencing (NGS)-based methods. These innovations have progressively overcome limitations in resolution and throughput, enabling precise molecular profiling within spatial contexts. Notable advancements, such as MERFISH and seqFISH+, have achieved single-molecule transcriptomic resolution, while MSI methods like MALDI and DESI have enabled high-throughput, non-invasive, label-free metabolic profiling. The integration of these single-omics approaches into spatial multi-omics frameworks facilitates the concurrent profiling of multiple molecular layers, such as transcriptomes and proteomes, within the same tissue section, thereby preserving spatial relationships and minimizing batch effects.This review highlights spatial multi-omics integrative analysis techniques, encompassing spatial (epi)genomics and transcriptomics, spatial transcriptomics and proteomics, spatial transcriptomics and metabolomics, spatial proteomics and metabolomics, imaging genomics, and spatial multi-omics data analysis, demonstrating its capacity to yield profound biological insights. Additionally, representative applications of spatial multi-omics technologies are presented. In oncology, spatial multi-omics has been employed to elucidate tumor microenvironment heterogeneity, identifying distinct cellular interactions at tumor-normal interfaces and informing precision therapeutics. In neuroscience, it has achieved mapping of brain regions with unprecedented molecular detail, revealing developmental and pathological dynamics. In reproductive biology, spatial multi-omics has uncovered distinct spatial gene expression patterns during gametogenesis, providing insights into infertility mechanisms. In studies of aging and regeneration, it has identified spatially defined molecular signatures, guiding regenerative medicine strategies. Additionally, the integration of radiogenomics, combining imaging modalities like CT and MRI with genomic data, has enabled correlations between macroscopic imaging features and molecular profiles, particularly in cancer research.Despite its potential, spatial multi-omics faces challenges, including trade-offs between resolution, sensitivity, and throughput, as well as complexities in data integration and standardization. High-resolution techniques often compromise throughput, and signal interference between modalities can limit multiplexing capabilities. The analysis of large, multimodal datasets demands advanced computational frameworks to ensure accurate spatial alignment across omics layers. Emerging resources, such as STOmicsDB and CROST databases, alongside analytical tools like SpatialData and SpatialGlue, are addressing these challenges by standardizing data formats and enabling integrative analyses.Looking ahead, spatial multi-omics is poised to advance biological research at both microscopic and macroscopic scales. At the subcellular level, it can enhance resolution to probe chromatin dynamics, while at the tissue level, improved computational efficiency can support large-scale 3D modeling, deepening the understanding of functional networks. These developments promise to revolutionize precision medicine and digital biology, establishing spatial multi-omics as a cornerstone for decoding the spatial complexity of biological systems.