SUMMARY The Single Cell and Spatial Proteomics Core will provide access to highly multiplexed single cell proteomic assays to reveal aging-related regulatory cell-states and their phenotypic identification and localization. Based on innovative technologies pioneered in our lab, single cell mass cytometry (i.e. CyTOF, for cell suspensions)1-2 and Multiplexed Ion Beam Imaging (i.e. MIBI, for nanometer-scale spatial tissue imaging)3-5, the Core will provide deep cell state characterization of cell preparations and tissues derived from the research projects during this grant cycle. Using elemental mass isotopes as reporters, these technologies facilitate robust, inexpensive, single cell analyses quantifying 50+ features simultaneously on millions of cells per experiment. We have had extensive experience applying these technologies to derive deep phenotypic profiles of immune and intrinsic cells from numerous tissues, including the hematopoietic1-2,6-9, central nervous system5,10-12, and muscle13-14. Besides cells composition of these systems, we have routinely created and applied single cell assays to capture functional and regulatory cell states, including: signaling1-2,6,8,15-17, cell cycle18-19, metabolism20, and regulatory chromatin content4-5,14,21-22. All these established assays will be available to the research projects and performed by the Core. The Single Cell and Spatial Proteomics Core will work with each project to create (spatial) progenitor cell tissue atlases that captures deep cellular phenotypes and function with age, focusing on single cell metabolic status and chromatin content. Overall, the products of these new and integrated single cell assays will support the aims of Projects 1-3 for this funding cycle. These include: integration of single cell metabolic and chromatin states with spatial localization in muscle progenitor cells as they age (Project 1, Rando); spatial profiling of neural stem cell phenotypes with metabolic use and chromatin modifications (Project 2, Brunet); tracking the phenotype and augmented metabolism in aging and expanding hematopoietic progenitor cells along with the identity and localization of their myeloid derivatives (Project 3 – Goodell). With novel single cell assay technologies, we have also established extensive computational methods for single-cell analysis, including the first uses of principle component analysis (PCA)1, stochastic neighborhood embedding (tSNE)23, spanning tree analysis of density normalized events (SPADE, clustering/graphing)1,24, pseudo-time ordering (i.e. `cell clocks', Wanderlust)2, and spatial enrichment approximation in imaging on highly multiplexed single cell data4,25-27. We will work closely with Core C (Bioinformatics) to leverage the latest single cell interpretation tools to answer questions about the various cell states identified and their relationship to aging perturbations.