# Deep Visual Proteomics defines single-cell identity and heterogeneity

“Deep Visual Proteomics defines single-cell identity and heterogeneity” (2022), by Andreas Mund et al. Chemistry research paper. Original abstract below, unchanged. A free PDF is available.

- Authors: Andreas Mund, Fabian Coscia, András Kriston, Réka Hollandi, Ferenc Kovács, Andreas‐David Brunner, Ede Migh, Lisa Schweizer, Alberto Santos, Michael Bzorek et al.
- Published: May 19, 2022
- Field: Chemistry
- DOI: https://doi.org/10.1038/s41587-022-01302-5
- Free PDF: https://www.nature.com/articles/s41587-022-01302-5.pdf

## 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.

Canonical page: https://nerdi.pages.dev/paper/W4280598306
Read three real papers a day in Nerdi (iPhone): https://apps.apple.com/app/apple-store/id6813826387?pt=128757794&ct=web-paper&mt=8