Spatial Visual Proteomics: Insights into Tumor Microenvironment Dynamics

The tumor microenvironment, a dynamic network of cancer, immune, stromal, and extracellular matrix components, dictates tumor progression and therapeutic resistance. Traditional proteomics, while informative, homogenizes tissues, obscuring the spatial context critical for deciphering tumor heterogeneity and cellular crosstalk. This review explores advancements in spatial visual proteomic technologies that preserve spatial architecture to decode tumor microenvironment complexity. Key methodologies encompass laser capture microdissection for precise cell segmentation, advanced antibody-based platforms for multiplexed proteomic profiling, and emerging innovations such as tissue expansion microscopy for enhanced resolution, and chemical biology-based probes for cell-type-specific proteome profiling. Innovations in sample preparation, such as microfluidic systems and automated workflows, enhance sensitivity and scalability, enabling single-cell resolution. Computational tools address challenges in cell segmentation, data deconvolution, and multi-omics integration, bridging proteomic, genomic, and transcriptomic datasets. Applications highlight tumor heterogeneity, stromal-immune interactions, and spatially resolved metabolic reprogramming, offering insights into immune evasion and therapeutic resistance. We underscore the transformative potential of spatial proteomics in precision oncology, enabling biomarker discovery, prognostic stratification, and tailored therapies. Future directions emphasize enhancing multimodal integration, refining AI-driven tools, and translating findings into clinical practice.

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Publication Details

Journal
Genomics Proteomics & Bioinformatics
Published
2026-06-16
DOI
https://doi.org/10.1093/gpbjnl/qzag045
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00
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article

Spatial Visual Proteomics: Insights into Tumor Microenvironment Dynamics

Changying Fu, Ruijun Tian, Peiwu Huang, Qian Kong
Genomics Proteomics & Bioinformatics
Single-cell and spatial transcriptomics
article

Spatial Visual Proteomics: Insights into Tumor Microenvironment Dynamics

Changying Fu, Ruijun Tian, Peiwu Huang, Qian Kong
article en

Abstract

The tumor microenvironment, a dynamic network of cancer, immune, stromal, and extracellular matrix components, dictates tumor progression and therapeutic resistance. Traditional proteomics, while informative, homogenizes tissues, obscuring the spatial context critical for deciphering tumor heterogeneity and cellular crosstalk. This review explores advancements in spatial visual proteomic technologies that preserve spatial architecture to decode tumor microenvironment complexity. Key methodologies encompass laser capture microdissection for precise cell segmentation, advanced antibody-based platforms for multiplexed proteomic profiling, and emerging innovations such as tissue expansion microscopy for enhanced resolution, and chemical biology-based probes for cell-type-specific proteome profiling. Innovations in sample preparation, such as microfluidic systems and automated workflows, enhance sensitivity and scalability, enabling single-cell resolution. Computational tools address challenges in cell segmentation, data deconvolution, and multi-omics integration, bridging proteomic, genomic, and transcriptomic datasets. Applications highlight tumor heterogeneity, stromal-immune interactions, and spatially resolved metabolic reprogramming, offering insights into immune evasion and therapeutic resistance. We underscore the transformative potential of spatial proteomics in precision oncology, enabling biomarker discovery, prognostic stratification, and tailored therapies. Future directions emphasize enhancing multimodal integration, refining AI-driven tools, and translating findings into clinical practice.

Genomics Proteomics & Bioinformatics
Sun Yat-sen University (CN), Southern University of Science and Technology (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 12%
Single-cell and spatial transcriptomics
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