High-resolution spatially resolved proteomics of complex tissues based on microfluidics and transfer learning

作者：Beiyu Hu, Ruiqiao He, Kun Pang, Guibin Wang, Ning Wang, Wenzhuo Zhu, Xin Sui, Huajing Teng, Tianxin Liu, Junjie Zhu, Zewen Jiang, Jinyang Zhang, Zhenqiang Zuo, Weihu Wang, Peifeng Ji, Fangqing Zhao · 发表于：Cell · 年份：2025 · DOI：10.1016/j.cell.2024.12.023 · 被引用次数：62 · 研究领域：Cell Image Analysis Techniques、Single-cell and spatial transcriptomics、Advanced Biosensing Techniques and Applications

Despite recent advances in imaging- and antibody-based methods, achieving in-depth, high-resolution protein mapping across entire tissues remains a significant challenge in spatial proteomics. Here, we present parallel-flow projection and transfer learning across omics data (PLATO), an integrated framework combining microfluidics with deep learning to enable high-resolution mapping of thousands of proteins in whole tissue sections. We validated the PLATO framework by profiling the spatial proteome of the mouse cerebellum, identifying 2,564 protein groups in a single run. We then applied PLATO to rat villus and human breast cancer samples, achieving a spatial resolution of 25 μm and uncovering proteomic dynamics associated with disease states. This approach revealed spatially distinct tumor subtypes, identified key dysregulated proteins, and provided novel insights into the complexity of the tumor microenvironment. We believe that PLATO represents a transformative platform for exploring spatial proteomic regulation and its interplay with genetic and environmental factors.