a. Faculty of Life and Health Sciences, Shenzhen University of Advanced Technology, Shenzhen 518107, China;
b. Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA;
c. Center for Cell Lineage and Development, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China;
d. State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, Hubei 430070, China;
e. College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, Hubei 430070, China;
f. Bioinformatics Institute, Agency for Science, Technology, and Research, Singapore 138671, Singapore;
g. Digital Analytics Lab, ImmunoQs Pte Ltd, Singapore 139952, Singapore;
h. Faculty of Life and Health Sciences, and Shenzhen-Hong Kong Institute of Brain Science and The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China;
i. NMPA Key Laboratory for Research and Evaluation of Viral Vector Technology in Cell and Gene Therapy Medicinal Products, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China
Funds:
This work was supported by the National Natural Science Foundation of China (32171022, 32221005 and 32401246).
While conventional FISH and IHC methods struggle to decode complex tissue heterogeneity and comprehensive molecular diagnosis due to low-throughput spatial information, spatial omics technologies enable high-throughput molecular mapping across tissue microenvironments. These technologies are emerging as transformative tools in molecular diagnostics and medical research. By integrating histopathological morphology with spatial multi-omics profiling (genome, transcriptome, epigenome, and proteome), spatial omics technologies open an avenue for understanding disease progression, therapeutic resistance mechanisms, and precise diagnosis. It particularly enhances tumor microenvironment analysis by mapping immune cell distributions and functional states, which may greatly facilitate tumor molecular subtyping, prognostic assessment, and predicting the efficacy of radiotherapy and chemotherapy. Despite the substantial advancements in spatial omics, the translation of spatial omics into clinical applications remains challenging due to robustness, efficacy, clinical validation, and cost constraints. In this review, we will summarize the current progress and prospects of spatial omics technologies, particularly in medical research and diagnostic applications.
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