Precise Spatial Proteomic Information in Tissues

Single-cell identify and cellular heterogeneity with deep visual proteomics, combining laser microdissection, artificial intelligence (AI), and mass spectrometry

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Despite the availability of imaging-based and mass-spectrometry-based methods for spatial proteomics, a key challenge remains connecting images with single-cell-resolution protein abundance measurements. Deep Visual Proteomics (DVP), a recently introduced method, 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.

Read the full article:

A. Mund, F. Coscia, A. Kriston, R. Hollandi, F. Kovács, A.-D. Brunner, E. Migh, L. Schweizer, A. Santos, M. Bzorek, S. Naimy, L.M. Rahbek-Gjerdrum, B. Dyring-Andersen, J. Bulkescher, C. Lukas, M.A. Eckert, E. Lengyel, C. Gnann, E. Lundberg, P. Horvath, M. Mann:

Deep Visual Proteomics defines single-cell identity and heterogeneity

Nature Biotechnology (2022) vol. 40, pp.1231–1240
DOI: 10.1038/s41587-022-01302-5

About the article

By individually excising nuclei from cell culture, distinct cell states can be classified with proteomic profiles defined by known and uncharacterized proteins. In an archived primary melanoma tissue, DVP [1] 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 signalling 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.

For this research, cells or nuclei were excised using a LMD7 laser microdissection microscope which was adapted for automated single-cell automation [1].

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