TY - JOUR
T1 - Deep Visual Proteomics defines single-cell identity and heterogeneity
AU - Mund, Andreas
AU - Coscia, Fabian
AU - Kriston, András
AU - Hollandi, Réka
AU - Kovács, Ferenc
AU - Brunner, Andreas-David
AU - Migh, Ede
AU - Schweizer, Lisa
AU - Santos, Alberto
AU - Bzorek, Michael
AU - Naimy, Soraya
AU - Rahbek-Gjerdrum, Lise Mette
AU - Dyring-Andersen, Beatrice
AU - Bulkescher, Jutta
AU - Lukas, Claudia
AU - Eckert, Mark Adam
AU - Lengyel, Ernst
AU - Gnann, Christian
AU - Lundberg, Emma
AU - Horvath, Peter
AU - Mann, Matthias
N1 - © 2022. The Author(s).
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
U2 - 10.1038/s41587-022-01302-5
DO - 10.1038/s41587-022-01302-5
M3 - Journal article
C2 - 35590073
VL - 40
SP - 1231
EP - 1240
JO - Nature Biotechnology
JF - Nature Biotechnology
SN - 1087-0156
ER -