Spatial proteomics in precision medicine: technologies, bioinformatics, and translational applications

Abstract

Protein function is inherently spatial: the same molecule can elicit distinct biological outcomes depending on its localization, interacting partners, and surrounding microenvironment. Spatial proteomics enables systematic in situ characterization of protein localization, abundance, and interactions across subcellular to tissue scales, surpassing the resolution and contextual information accessible to conventional bulk proteomics. Recent technological advances including DNA-barcoded multiplexing methods, cyclic fluorescence platforms, and mass spectrometry imaging have substantially increased multiplexing capacity, sensitivity, and spatial accuracy. These capabilities directly support clinically relevant applications, such as tumor immune microenvironment analysis, mapping of protein aggregation in neurodegeneration, growth factor dynamics during tissue repair, patient stratification, pharmacodynamic mapping, and target-engagement assessment. Computational innovations, including graph neural networks, self-supervised embeddings, and workflow management tools (e.g. Snakemake, Nextflow), further enhance cell segmentation, noise reduction, and multi-modal data integration, enabling extraction of robust, spatially resolved proteomic information from complex tissues. Future research will aim to standardize protocols, enable real-time clinical analysis, and develop 3D spatial proteome maps to advance spatial proteomics toward precision diagnostics and targeted therapies.

Keywords

spatial proteomics / multi-omics / mass spectrometry / imaging / precision medicine

Acknowledgements

This research was supported by the National Natural Science Foundation of China (grant Nos. 82174238,82405163), the Beijing Natural Science Foundation (grant No. 7242254), and the Scientific and Technological Innovation Project of China Academy of Chinese Medical Sciences (grant Nos. CI2023C069YLL, NLTS2025011, XTCX2023001).

Author contributions

Yiwen Li (Funding acquisition, Writing – original draft), Yusheng Zhang (Data curation, Resources), Ying Zhang (Software), Qing Wang (Writing – review & editing), Boyang Ji (Writing – review & editing), Hongjun Yang (Conceptualization), and Xianyu Li (Conceptualization, Funding acquisition, Writing – review & editing).

Conflicts of interest

All authors have declared no conflicts of interest.

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