Decrypting lysine deacetylase inhibitor action and protein modifications by dose-resolved proteomics.

Lysine deacetylase inhibitors (KDACis) are approved drugs for cutaneous T cell lymphoma (CTCL), peripheral T cell lymphoma (PTCL), and multiple myeloma, but many aspects of their cellular mechanism of action (MoA) and substantial toxicity are not well understood. To shed more light on how KDACis elicit cellular responses, we systematically measured dose-dependent changes in acetylation, phosphorylation, and protein expression in response to 21 clinical and pre-clinical KDACis. The resulting 862,000 dose-response curves revealed, for instance, limited cellular specificity of histone deacetylase (HDAC) 1, 2, 3, and 6 inhibitors; strong cross-talk between acetylation and phosphorylation pathways; localization of most drug-responsive acetylation sites to intrinsically disordered regions (IDRs); an underappreciated role of acetylation in protein structure; and a shift in EP300 protein abundance between the cytoplasm and the nucleus. This comprehensive dataset serves as a resource for the investigation of the molecular mechanisms underlying KDACi action in cells and can be interactively explored online in ProteomicsDB.

Deep Visual Proteomics defines single-cell identity and heterogeneity.

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.

Spatiotemporal dissection of the cell cycle with single-cell proteogenomics.

The cell cycle, over which cells grow and divide, is a fundamental process of life. Its dysregulation has devastating consequences, including cancer1-3. The cell cycle is driven by precise regulation of proteins in time and space, which creates variability between individual proliferating cells. To our knowledge, no systematic investigations of such cell-to-cell proteomic variability exist. Here we present a comprehensive, spatiotemporal map of human proteomic heterogeneity by integrating proteomics at subcellular resolution with single-cell transcriptomics and precise temporal measurements of individual cells in the cell cycle. We show that around one-fifth of the human proteome displays cell-to-cell variability, identify hundreds of proteins with previously unknown associations with mitosis and the cell cycle, and provide evidence that several of these proteins have oncogenic functions. Our results show that cell cycle progression explains less than half of all cell-to-cell variability, and that most cycling proteins are regulated post-translationally, rather than by transcriptomic cycling. These proteins are disproportionately phosphorylated by kinases that regulate cell fate, whereas non-cycling proteins that vary between cells are more likely to be modified by kinases that regulate metabolism. This spatially resolved proteomic map of the cell cycle is integrated into the Human Protein Atlas and will serve as a resource for accelerating molecular studies of the human cell cycle and cell proliferation.

Illuminating Non-genetic Cellular Heterogeneity with Imaging-Based Spatial Proteomics.

Cellular heterogeneity is an important biological phenomenon observed across space and time in human tissues. Imaging-based spatial proteomic technologies can provide fruitful new readouts of phenotypic states for individual cells at subcellular resolution, which may help unravel the roles of non-genetic cellular heterogeneity in tumorigenesis and drug resistance.

A Sample Preparation Protocol for High Throughput Immunofluorescence of Suspension Cells on an Adherent Surface.

Imaging is a powerful approach for studying protein expression and has the advantage over other methodologies in providing spatial information in situ at single cell level. Using immunofluorescence and confocal microscopy, detailed information of subcellular distribution of proteins can be obtained. While adherent cells of different tissue origin are relatively easy to prepare for imaging applications, non-adherent cells from hematopoietic origin, present a challenge due to their poor attachment to surfaces and subsequent loss of a substantial fraction of the cells. Still, these cell types represent an important part of the human proteome and express genes that are not expressed in adherent cell types. In the era of cell mapping efforts, overcoming the challenge with suspension cells for imaging applications would enable systematic profiling of hematopoietic cells. In this work, we successfully established an immunofluorescence protocol for preparation of suspension cell lines, peripheral blood mononucleated cells (PBMC) and human platelets on an adherent surface. The protocol is based on a multi-well plate format with automated sample preparation, allowing for robust high throughput imaging applications. In combination with confocal microscopy, the protocol enables systematic exploration of protein localization to all major subcellular structures.