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

## TL;DR

Spatial proteomics provides detailed insights into protein locations and interactions, enabling better understanding and treatment of diseases through advanced technologies and computational methods.

## Contribution

The paper reviews recent technological and computational advances in spatial proteomics and their translational applications in precision medicine.

## Key findings

- Technologies like DNA-barcoded multiplexing and mass spectrometry imaging improve spatial proteomics resolution and sensitivity.
- Computational tools such as graph neural networks enhance data integration and analysis of spatial proteomic data.
- Spatial proteomics supports clinical applications like tumor analysis and neurodegeneration research.

## 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.