359-PLATO：高空间分辨率组织蛋白质组学重构框架

这项研究介绍了一种名为 PLATO 的创新性空间蛋白质组学技术，旨在克服传统方法在通量和组织覆盖范围上的局限。该技术结合了微流控芯片平行采样与名为 Flow2Spatial 的深度学习算法，能够以 25微米的高分辨率重建全组织的蛋白质分布。研究人员通过对小鼠小脑、大鼠肠绒毛以及人类乳腺癌样本的分析，证明了该方法在识别组织异质性和临床亚型方面的卓越性能。Flow2Spatial 算法利用迁移学习，即使在蛋白质与对应 mRNA 相关性较低的情况下，也能实现精确的空间制图。与传统技术相比，PLATO 显著减少了所需的检测次数，为基础生物学研究和临床诊断提供了一种高效、高分辨率且易于操作的工具。References: Hu B, He R, Pang K, et al. High-resolution spatially resolved proteomics of complex tissues based on microfluidics and transfer learning[J]. Cell, 2025, 188(3): 734-748. e22.前往小宇宙评论区与主播互动

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359-PLATO：高空间分辨率组织蛋白质组学重构框架

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