Deep Visual Proteomics defines single-cell identity and heterogeneity

作者：Andreas Mund, Fabian Coscia, András Kriston, Réka Hollandi, Ferenc Kovács, Andreas‐David Brunner, Ede Migh, Lisa Schweizer, Alberto Santos, Michael Bzorek, Soraya Naimy, Lise Mette Rahbek Gjerdrum, Beatrice Dyring‐Andersen, Jutta Bulkescher, Claudia Lukas, Mark A. Eckert, Ernst Lengyel, Christian Gnann, Emma Lundberg, Péter Horváth, Matthias Mann · 发表于：Nature Biotechnology · 年份：2022 · DOI：10.1038/s41587-022-01302-5 · 被引用次数：583 · 研究领域：Cell Image Analysis Techniques、Single-cell and spatial transcriptomics、Advanced Proteomics Techniques and Applications

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.