Deep Visual Proteomics defines single-cell identity and heterogeneity

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 Adam Eckert, Ernst Lengyel, Christian Gnann, Emma Lundberg, Peter Horvath*Matthias Mann*

*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskriftArtikelForskningpeer review

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.

OriginalsprogEngelsk
Sider (fra-til)1231-1240
Antal sider10
TidsskriftNature Biotechnology
Vol/bind40
Udgave nummer8
Tidlig onlinedato19 maj 2022
DOI
StatusUdgivet - aug. 2022

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© 2022. The Author(s).

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