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High-resolution spatially resolved proteomics of complex tissues based on microfluidics and transfer learning
Beiyu Hu1, Ruiqiao He2, Kun Pang2
1Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China.
Cell
|January 24, 2025
Summary
A new framework called PLATO (parallel-flow projection and transfer learning across omics data) enables high-resolution spatial proteomics. This method maps thousands of proteins in whole tissues, offering new insights into disease complexity.
Area of Science:
- Proteomics
- Systems Biology
- Bioinformatics
Background:
- Spatial proteomics faces challenges in achieving high-resolution protein mapping across entire tissues.
- Existing imaging- and antibody-based methods have limitations in depth and scale.
Purpose of the Study:
- To introduce PLATO (parallel-flow projection and transfer learning across omics data), an integrated framework for high-resolution spatial proteomics.
- To demonstrate PLATO's capability in mapping thousands of proteins in whole tissue sections.
Main Methods:
- PLATO combines microfluidics with deep learning for protein mapping.
- The framework was validated using mouse cerebellum, rat villus, and human breast cancer samples.
Main Results:
- PLATO achieved a spatial resolution of 25 μm.
- Successfully mapped 2,564 protein groups in mouse cerebellum in a single run.
- Uncovered proteomic dynamics in disease states, revealing spatially distinct tumor subtypes and key dysregulated proteins.
Conclusions:
- PLATO is a transformative platform for exploring spatial proteomic regulation.
- The framework provides novel insights into the tumor microenvironment and its interplay with genetic/environmental factors.
