Protein function is inherently spatial—the same molecule can elicit distinct biological outcomes depending on its localization, interacting partners, and surrounding microenvironment. Conventional bulk proteomics loses critical spatial information, limiting insights into complex biological processes and disease mechanisms. For researchers and clinicians, a tool to map protein distribution in native tissue contexts has long been sought, and spatial proteomics (SP) has emerged as the transformative solution.
As recognized by Nature Methods as the 2024 “Method of the Year”, SP has undergone remarkable technological evolution. Two core platform categories drive progress: imaging-based approaches (e.g., DNA-barcoded multiplexing, fluorophore-based cyclic imaging) preserve histological context with high multiplexity (20–100 markers), aligning seamlessly with clinical pathology workflows; mass spectrometry (MS)-based methods (e.g., MALDI-MSI, DESI) enable label-free, unbiased proteome discovery and post-translational modification detection, offering broad molecular coverage. Complementary advances in sample preparation, such as tissue clearing and protein stabilization technologies, have further enhanced SP’s sensitivity and applicability to clinically relevant specimens like FFPE biopsies.
Computational innovations are pivotal to unlocking SP’s potential. AI tools including graph neural networks, self-supervised embeddings, and deep generative models address key analytical challenges: cell segmentation, noise reduction, spatial context modeling, and cross-modal integration. Workflow management tools like Snakemake and Nextflow ensure reproducibility, while multi-omics integration frameworks (e.g., DBiT-seq, inClust+) enable synergistic analysis of SP with transcriptomics, metabolomics, and epigenomics, providing panoramic views of cellular and tissue organization.
The translational impact of SP spans core areas of precision medicine. In disease stratification, it transcends traditional histologic classification by decoding protein spatial gradients and cellular niches—for instance, MALDI-MSI achieves 100% sensitivity and 96% specificity in thyroid nodule cytomolecular classification. In therapeutic target discovery, SP uncovers microenvironment-specific targets, such as immunosuppressive cellular neighborhoods in hepatocellular carcinoma and JAK/STAT pathway activation in lethal skin disease. For drug development, it enables precise prediction of therapeutic responses, characterization of pharmacodynamic mechanisms, and identification of resistance niches, accelerating personalized treatment strategies.
This work provides a comprehensive overview of SP’s technological landscape, computational tools, and clinical applications, highlighting its role as a transformative force in precision medicine. By bridging molecular insights with spatial context, SP addresses longstanding gaps in understanding disease biology and offers new avenues for diagnostic innovation and targeted therapy. The work entitled “Spatial proteomics in precision medicine: technologies, bioinformatics, and translational applications” was published on Precision Clinical Medicine (published on Jan. 8, 2026).

DOI:10.1093/pcmedi/pbaf040