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The evolving landscape of spatial proteomics technologies in the AI age
Beiyu Hu1,2, Junjie Zhu1,2, Fangqing Zhao1,2
1Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.
Fundamental Research
|February 6, 2026
Summary
Spatial proteomics offers a powerful new way to study how proteins form tissues, overcoming limitations of single-cell and spatial transcriptomics. This review details current techniques and future directions for spatial protein analysis in biology and medicine.
Area of Science:
- Proteomics
- Cell Biology
- Tissue Engineering
Background:
- Single-cell technologies reveal cellular complexity but not tissue formation.
- Spatial transcriptomics offers in situ tissue analysis but indirect cellular state measurement.
- Proteins, not genes, primarily control biological processes, necessitating spatial proteomics.
Purpose of the Study:
- To review recent technical advancements in spatial proteomics.
- To discuss the strengths, weaknesses, and challenges of current spatial proteomics methods.
- To explore the potential of artificial intelligence in advancing spatial proteomics.
Main Methods:
- Categorization of spatial proteomics techniques into antibody-based, LC-MS/MS-based, and imaging mass spectrometry-based.
- Detailed description and comparative analysis of each method's capabilities and limitations.
- Exploration of emerging artificial intelligence applications for spatial proteomics data analysis.
Main Results:
- Spatial proteomics provides direct, high-resolution insights into protein localization and function within tissues.
- Current methods offer complementary approaches, each with specific advantages for different research questions.
- Artificial intelligence presents significant opportunities for enhanced data interpretation and discovery in spatial proteomics.
Conclusions:
- Spatial proteomics is crucial for understanding tissue architecture and function, complementing genomic approaches.
- Continued technological development is needed to address current challenges and expand applications.
- The integration of spatial proteomics with AI promises to revolutionize biological and clinical research.
Keywords:
Artificial intelligenceLaser capture microdissectionMass spectrometryMulti-omicsSpatial proteomicsMore Related Videos
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