In this four year two phase (R21/R33) project we will apply advanced proteomic and metabolomic nanoflow liquid chromatography-Fourier transform ion cyclotron resonance mass spectrometry technologies in the study of both plasma and serum from Type 1 diabetes patients and isolated human pancreatic islets. The overall approach endeavors to advance the study of Type 1 diabetes and human islet transplantation by identifying biomarkers at the level of the proteome and metabolome that are predictive of both Type 1 diabetes and islet performance in vivo. Our approach will utilize proteome-wide stable isotope labeling of peptides, as well as quantitative cysteine-peptide enrichment technology (QCET) and N-linked glycopeptide enrichment strategies to obtain broad proteome coverage and enhance quantitation. We will also utilize very low nanoflow LC separations to minimize ionization suppression and eliminate background ions originating from the solvent, thereby improving normalization of metabolite peak intensities and improve quantitation. This approach will be capable of rapidly identifying and measuring expression levels for thousands of peptides or concentrations of metabolites in a single analysis. Phase 1 of this project will (a) define the sample processing and LC separation conditions necessary for broad metabolome and proteome coverage in human plasma/serum and pancreatic islets, (b) establish accurate mass and time tag databases for both peptides and metabolites detected in human plasma/serum and pancreatic islets, and (c) demonstrate the ability of the technology to distinguish plasma/serum from healthy control or recently diagnosed Type 1 diabetic patients. The refinement of this technological approach will provide the basis for high throughput studies of large numbers of samples. The application of this technology in Phase 2 of the project will involve (a) the high throughput studies of complete sample sets from the Diabetes Autoantibody Standardization Program (DASP), (b) validation of potential biomarkers via analysis of a blind DASP sample set, and (c) comparative studies of multiple human pancreatic islet preparations to identify potential biomarkers predictive of islet performance in vivo.
