To test this hypothesis, samples taken from human renal tissue with or without DN were compared and contrasted using conventional and single probe analysis. A common data set of CEL files was analyzed using standard techniques followed by significance analysis of microarrays . In parallel single probe analysis was performed using Epoxomicin default settings and yielded comparable numbers of regulated genes. However, gene ontology analysis by Database for Annotation, Visualization and Integrated Discovery of both data sets showed a clearly improved representation of biological processes linked to the Afatinib development and progression of DN for the single probe-based approach. The single probe methodology allowed the unique detection of Wntpathway activation in DN. Inflammatory processes may underlie important events in the pathogenesis of DN . We previously demonstrated activation of the inflammatory transcriptional regulators nuclear factorkappa B and interferon regulatory factor linked to the progression of DN . This observation prompted us to look more closely for evidence of inflammatory events in DN. Biopsy samples from patients presenting with advanced DN were examined by immunohistochemistry for specific inflammatory cell types. Staining for T cells , B cells and monocytes/macrophages showed a prominent infiltration in the renal tubulo-interstitium of patients with advanced DN . Although histological characterization clearly demonstrated inflammatory processes at work in samples of advanced DN, RMA based array and gene ontology analysis could identify only limited regulation of GO categories associated with inflammation suggesting a more sensitive approach was needed . After elimination of probes that could cross-hybridize to other transcripts, CI using the identical CEL files identified 39,933 significantly regulated individual probes using the default settings . With default settings of a minimum of 3 probes matching to a de novo annotated transcript, 6,533 transcripts were found to be significantly regulated, corresponding to 2,626 genes. The initial analysis summarized in Fig. 2 identified 1,466 genes found in common by both approaches. CI uniquely identified 1,160 genes and 884 genes were unique to the RMA analysis. While this shows a solid common core of regulated genes, gene lists per se are not suitable for evaluation of the biologically meaningful differences between the resulting lists. As our goal was to identify biological processes involved in DN, we mapped the genes from each approach onto GO. A relative ranking of the association of the various GO-categories with respect to the gene lists was carried out employing DAVID. The DAVID tool was developed for GOranking, and is independent of methodological differences between the microarray analyses tools used in this study.