Microarray analysis of clinical samples has provided useful insight into the biological processes and diversity of cell types in complex diseases that includes systemic autoimmune disease. Analysis of tissues such as skin, tumors and peripheral blood cells allows the quantitative characterization of the diversity in sample phenotypes as well as the biological pathways that are deregulated in the disease. The analysis of tumors has demonstrated its ability to quantitatively and reproducibly separate tumors into subtypes with different prognoses, to identify pathways deregulated in the disease and to reveal infiltrating cells. Application of microarray technology to scleroderma skin biopsies, isolated scleroderma cell lines and peripheral blood samples has shown that it will be an important tool for understanding the diversity in rheumatic diseases, as well as changes to the underlying gene expression pathways. In this core, we will use proven methods that have been developed and already successfully implemented in the core Pi's laboratory to analyze skin biopsy samples and peripheral blood mononuclear cells taken from patients with systemic sclerosis and normal controls. High quality RNA will be prepared, hybridized to Agilent technologies whole-genome DNA microarrays by established protocols. Using these established methods, the core Pi's lab has hybridized more than 1000 Agilent microarrays over the past three years. All microarrays are normalized using standard methods and analyzed using a combination of algorithms that include testing for differential expression and pathway analysis. The goals of this core are: Aim 1. Generate quality controlled DNA microarray hybridizations for each sample and process the resulting data using a standard analysis pipeline. Each sample will be hybridized to Agilent 44,000 element DNA microarrays, scanned on an Axon Instruments GenePix Scanner and submitted to a research microarray database. Aim 2. Analysis of the resulting data for differentially expressed genes, gene expression signatures predictive of clinical endpoints and deregulated pathways.