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Deep Visual Proteomics defines single-cell identity and heterogeneity

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Item Type:Article
Title:Deep Visual Proteomics defines single-cell identity and heterogeneity
Creators Name:Mund, A. and Coscia, F. and Kriston, A. and Hollandi, R. and Kovács, F. and Brunner, A.D. and Migh, E. and Schweizer, L. and Santos, A. and Bzorek, M. and Naimy, S. and Rahbek-Gjerdrum, L.M. and Dyring-Andersen, B. and Bulkescher, J. and Lukas, C. and Eckert, M.A. and Lengyel, E. and Gnann, C. and Lundberg, E. and Horvath, P. and Mann, M.
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.
Source:Nature Biotechnology
ISSN:1087-0156
Publisher:Nature Publishing Group
Date:19 May 2022
Official Publication:https://doi.org/10.1038/s41587-022-01302-5
PubMed:View item in PubMed

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