Deep Visual Proteomics defines single-cell identity and heterogeneity.
Deep Visual Proteomics defines single-cell identity and heterogeneity.
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DOI：
10.1038/s41587-022-01302-5
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发表时间：
2022-08
影响因子：
46.9
通讯作者：
Mann, Matthias
中科院分区：
文献类型：
--
作者：
Mund, Andreas;Coscia, Fabian;Kriston, Andras;Hollandi, Reka;Kovacs, 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
尽管空间蛋白质组学的基于成像和基于质谱的方法的可用性，一个关键的挑战仍然是连接图像与单细胞分辨率的蛋白质丰度测量。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学谱进行分类。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向完全侵袭性黑色素瘤转变时的空间分辨蛋白质组变化，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，这与干扰素信号传导和抗原呈递减少一致。DVP在组织背景中保留精确的空间蛋白质组学信息的能力对临床样品的分子谱分析具有意义。深度视觉蛋白质组学结合了机器学习、自动图像分析和单细胞蛋白质组学。
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.
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影响因子：
48
作者：
Conrad, Christian;Wuensche, Annelie;Tan, Tze Heng;Bulkescher, Jutta;Sieckmann, Frank;Verissimo, Fatima;Edelstein, Arthur;Walter, Thomas;Liebel, Urban;Pepperkok, Rainer;Ellenberg, Jan
通讯作者：
Ellenberg, Jan
影响因子：
8.8
作者：
Kumar PR;Moore JA;Bowles KM;Rushworth SA;Moncrieff MD
通讯作者：
Moncrieff MD
影响因子：
12.3
作者：
Carpenter AE;Jones TR;Lamprecht MR;Clarke C;Kang IH;Friman O;Guertin DA;Chang JH;Lindquist RA;Moffat J;Golland P;Sabatini DM
通讯作者：
Sabatini DM
影响因子：
1.6
作者：
Kokkat, Theresa J.;Patel, Miral S.;Baloch, Zubair W.
通讯作者：
Baloch, Zubair W.
DOI：
10.1146/annurev-biodatasci-080917-013328
发表时间：
2019-01-01
期刊：
ANNUAL REVIEW OF BIOMEDICAL DATA SCIENCE, VOL 2, 2019
影响因子：
--
作者：
Heriche, Jean-Karim;Alexander, Stephanie;Ellenberg, Jan
通讯作者：
Ellenberg, Jan