丹麦哥本哈根大学Matthias Mann，Andreas Mund和芬兰赫尔辛基大学Peter Horvath共同合作近期取得重要研究进展。他们研究开发了深度视觉蛋白质组学（DVP）的方法来定义单细胞的身份和异质性。相关研究成果2022年5月19日在线发表于《自然—生物技术》杂志上。

在这里，研究人员介绍了深度视觉蛋白质组学 (DVP)的方法，它将人工智能驱动的细胞表型图像分析与自动单细胞或单核激光显微切割和超高灵敏度质谱法相结合。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，研究人员将不同的细胞状态与由已知和未表征的蛋白质定义的蛋白质组图谱进行分类。

在存档的原发性黑色素瘤组织中，DVP确定了正常黑色素细胞过渡到完全侵袭性黑色素瘤时空间分辨率的蛋白质组变化，揭示了随着癌症进展在空间上发生变化的途径，如转移性垂直生长中的mRNA剪接失调与干扰素信号和抗原表达的减少相吻合。DVP在组织环境中保留精确空间蛋白质组信息的能力对临床样本的分子谱分析具有重要意义。

据了解，尽管空间蛋白质组学已有基于成像和质谱的方法，但将图像与单细胞分辨率的蛋白质丰度测量联系起来仍然是一个关键的挑战。

附：英文原文

Title: Deep Visual Proteomics defines single-cell identity and heterogeneity

Author: Mund, Andreas, Coscia, Fabian, Kriston, Andrs, Hollandi, Rka, Kovcs, Ferenc, Brunner, Andreas-David, Migh, Ede, Schweizer, Lisa, Santos, Alberto, Bzorek, Michael, Naimy, Soraya, Rahbek-Gjerdrum, Lise Mette, Dyring-Andersen, Beatrice, Bulkescher, Jutta, Lukas, Claudia, Eckert, Mark Adam, Lengyel, Ernst, Gnann, Christian, Lundberg, Emma, Horvath, Peter, Mann, Matthias

Issue&Volume: 2022-05-19

Abstract: 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. Deep Visual Proteomics combines machine learning, automated image analysis and single-cell proteomics.

DOI: 10.1038/s41587-022-01302-5

Source: https://www.nature.com/articles/s41587-022-01302-5