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.

中文翻译：

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基于微流控和迁移学习的复杂组织高分辨率空间分辨蛋白质组学

尽管在成像和抗体基方法方面取得了近期进展，但在整个组织中实现深度、高分辨率的蛋白质映射仍然是空间蛋白质组学中的一个重大挑战。在这里，我们提出了平行流投影和跨组学数据迁移学习（PLATO），这是一个结合微流控和深度学习的集成框架，能够实现对整个组织切片中数千种蛋白质的高分辨率映射。我们通过分析小鼠小脑的空间蛋白质组，验证了 PLATO 框架，在一次运行中识别出 2,564 个蛋白质组。然后，我们将 PLATO 应用于大鼠绒毛和人类乳腺癌样本，实现了 25 μm 的空间分辨率，并揭示了与疾病状态相关的蛋白质组学动态。这种方法揭示了空间上不同的肿瘤亚型，确定了关键失调的蛋白质，并提供了对肿瘤微环境复杂性的新见解。我们相信，PLATO 代表了一个探索空间蛋白质组学调控及其与遗传和环境因素相互作用的变革性平台。