To better understand the biology of longevity, the Proteomics Project will quantitatively profile both long-lived model systems as well as human cohorts in the 98th percentile of longevity to identify proteomic signatures for longevity and aging by using recently developed multi-dimensional deep proteomic profiling. We will perform comprehensive quantitative proteome analyses through use of mass spectrometric (MS) and ELISA-based methods that enable precise protein measurements. Improved computational proteomics, instrument performance and sample preparation will provide robust quantification of a large fraction of endogenous proteins in serum or tissues. The project has three aims: 1). We will use discovery proteomics by data-dependent analysis (DDA) and innovative quantitative digital proteomics analysis (data-independent analysis (DIA or SWATH-MS)) of serum samples from human longevity cohorts, tissues from long-lived and short-lived species of birds, primates, and rodents, as well as long-lived mutant mice and mice treated with drugs that extend longevity, to determine what longevity-associated proteomic signatures are present across experimental models. 2). We will employ extensive assessments of the post-translational modifications that are associated with longevity in human serum and mouse models. 3). We will use highly sensitive and specific targeted proteomics, combined with longevity biomarker candidate assessment, to verify proteomic signatures of longevity that are identified in Aims 1 and 2, and through a systems biology analysis from our other Longevity Consortium projects and cores. To provide robust confidence in the elucidation of associations with longevity and related phenotypes, our statistical pipelines for processing and analyzing population proteomic data will include (a) rigorous methods to estimate and test protein- and peptide-level associations, and (b) prioritization of candidate biomarkers. We work in close collaboration with the other project and core components of the Longevity Consortium to consolidate data, incorporate the consortiums data into a systems biology analysis including external data relevant to longevity and refine the multi-omic data to derive signatures of longevity. Well characterized longevity associated proteins and/or post-translational modifications derived from these relationships contributed by the Consortium will allow external users to utilize the data obtained by this consortium and allow additional systems-level analyses and ultimately replication and/or discovery of additional signatures of longevity.