Jutta Bulkescher

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.

Chromosome segregation and cell division are essential, highly ordered processes that depend on numerous protein complexes. Results from recent RNA interference screens indicate that the identity and composition of these protein complexes is incompletely understood. Using gene tagging on bacterial artificial chromosomes, protein localization, and tandem-affinity purification-mass spectrometry, the MitoCheck consortium has analyzed about 100 human protein complexes, many of which had not or had only incompletely been characterized. This work has led to the discovery of previously unknown, evolutionarily conserved subunits of the anaphase-promoting complex and the gamma-tubulin ring complex--large complexes that are essential for spindle assembly and chromosome segregation. The approaches we describe here are generally applicable to high-throughput follow-up analyses of phenotypic screens in mammalian cells.