Brunner, Andreas-David

Bulkescher, Jutta

Bzorek, Michael

Coscia, Fabian

Dyring-Andersen, Beatrice

Eckert, Mark Adam

Gnann, Christian

Hollandi, Reka

Horvath, Peter

Kovacs, Ferenc

Kriston, Andras

Lengyel, Ernst

Lukas, Claudia

Lundberg, Emma

Mann, Matthias

Migh, Ede

Mund, Andreas

Naimy, Soraya

Rahbek-Gjerdrum, Lise Mette

Santos, Alberto

Schweizer, Lisa

English

PubMed

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.</p

Kriston, András

Hollandi, Réka

Kovács, Ferenc

Copenhagen University Research Information System

Deep Visual Proteomics defines single-cell identity and heterogeneity

Helsingin yliopiston digitaalinen arkisto

Deep Visual Proteomics combines machine learning, automated image analysis and single-cell proteomics. 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

Mund, A.

Coscia, F.

Kriston, A.

Hollandi, R.

Kovacs, F.

Brunner, A.

Migh, E.

Schweizer, L.

Santos, A.

Bzorek, M.

Naimy, S.

Rahbek-Gjerdrum, L.

Dyring-Andersen, B.

Bulkescher, J.

Lukas, C.

Eckert, M.

Lengyel, E.

Gnann, C.

Lundberg, E.

Horvath, P.

Mann, M.

MPG.PuRe

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 defines single-cell identity and heterogeneity

Abstract

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Copenhagen University Research Information System

Helsingin yliopiston digitaalinen arkisto

MPG.PuRe

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PubMed Central