About Deep Visual Proteomics (DVP)
DVP [1] links high-resolution, data-rich imaging of cell cultures or biobank tissues with deep-learning-based cell segmentation and machine-learning-based identification of cell types and states. Cellular or subcellular features of interest classified by artificial intelligence (AI) undergo automated laser microdissection (LMD) [1] and proteomic profiling via mass spectroscopy (MS) [2]. Subsequent bioinformatics data analysis enables data mining and the discovery of protein signatures, providing molecular insights into proteome variation concerning health and disease states at the single-cell level. The DVP concept and workflow is shown in figure 1.
Identifying single cells for cellular heterogeneity
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 identified spatially resolved proteome changes as normal melanocytes transition to fully invasive melanoma. It revealed 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.
For this research, cells or nuclei were excised using a LMD7 laser microdissection microscope which was adapted for automated single-cell automation [1] (refer to figures 2-4).
To find out more about using DVP, with AI-guided imaging, LMD, and MS, for identifying single cells, 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
https://www.nature.com/articles/s41587-022-01302-5
