The goal of this project is to obtain transcriptome data from human islets using either hybridization arrays or RNA sequencing technology. We plan to obtain complete information about relative expression of all genes in islets from a number of different organ donor groups with an emphasis on: 1) donors with no autoantibodies or diabetes, 2) donors with autoantibodies but no diabetes, 3) donors with newly diagnosed type 1 diabetes, 4) donors with long-standing (over 5 years) type 1 diabetes, and, 5) donors with type 2 diabetes. The dataset will be analyzed to identify genes whose expression differs significantly between one or more of these donor groups. The goal is to define what pathways and processes uniquely define islets in each donor group, in order to gain new insights into the pathophysiology of diabetes and discover possible new drug targets. To further that goal each list of differentially expressed genes (distinguishing islets in one group from one or more of the other groups) will be subjected to a data mining process designed to define if the list is significantly enriched in genes that: 1) belong to specific pathways or gene ontologies; 2) are regulated by specific transcription factors; 3) are known to be influenced by specific drugs, proteins or other molecules. This will provide a global (systems level) definition of gene expression patterns in islets from each of these donor groups. However, we also intend to make this dataset available to other investigators who may want to examine the data for expression of genes relevant to their specific studies.