Despite the availabilty of imaging-based and mass-spectrometry-based methods for spatial proteomics, a key challenge remains connecting images with single-cell-resolution protein abundance measurements. Here, we introduce Deep Visual Proteomics (DVP), which combines artificial-intelligence-driven image analysis of cellular phenotypes with automated single-cell or single-nucleus laser microdissection and ultra-high-sensitivity mass spectrometry. DVP links protein abundance to complex cellular or subcellular phenotypes while preserving spatial context. By individually excising nuclei from cell culture, we classified distinct cell states with proteomic profiles defined by known and uncharacterized proteins. In an archived primary melanoma tissue, DVP identified spatially resolved proteome changes as normal melanocytes transition to fully invasive melanoma, revealing pathways that change in a spatial manner as cancer progresses, such as mRNA splicing dysregulation in metastatic vertical growth that coincides with reduced interferon signaling and antigen presentation. The ability of DVP to retain precise spatial proteomic information in the tissue context has implications for the molecular profiling of clinical samples.

尽管基于成像和基于质谱的方法可用于空间蛋白质组学，但关键挑战仍然是将图像与单细胞分辨率蛋白质丰度测量联系起来。在这里，我们介绍了深度视觉蛋白质组学 (DVP)，它将人工智能驱动的细胞表型图像分析与自动化单细胞或单核激光显微切割和超高灵敏度质谱相结合。DVP 将蛋白质丰度与复杂的细胞或亚细胞表型联系起来，同时保留空间背景。通过从细胞培养物中单独切除细胞核，我们对不同的细胞状态进行了分类，这些细胞状态具有由已知和未表征的蛋白质定义的蛋白质组学特征。在存档的原发性黑色素瘤组织中，DVP 确定了空间分辨的蛋白质组变化，因为正常的黑色素细胞转变为完全侵袭性黑色素瘤，揭示了随着癌症进展以空间方式变化的途径，例如转移性垂直生长中的 mRNA 剪接失调与干扰素信号传导和抗原呈递减少相吻合。DVP 在组织环境中保留精确空间蛋白质组学信息的能力对临床样本的分子谱分析具有重要意义。