Deep Visual Proteomics defines single-cell identity and heterogeneity.
IF: 68.164
Cited by: 91


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.


Spatial Proteomics
Spatial Transcriptomics

MeSH terms

Laser Capture Microdissection
Mass Spectrometry


Mund, Andreas
Coscia, Fabian
Kriston, András
Hollandi, Réka
Kovács, 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

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