A principal goal of microarray studies is to identify the genes showing differential expression under distinct conditions. In such studies, the selection of an optimal test statistic is a crucial challenge, which depends on the type and amount of data under analysis. While previous studies on simulated or spike-in datasets do not provide practical guidance on how to choose the best method for a given real dataset, we introduce an enhanced reproducibility-optimization procedure, which enables the selection of a suitable gene- anking statistic directly from the data. In comparison with existing ranking methods, the reproducibilityoptimized statistic shows good performance consistently under various simulated conditions and on Affymetrix spike-in dataset. Further, the feasibility of the novel statistic is confirmed in a practical research setting using data from an in-house cDNA microarray study of asthma-related gene expression changes. These results suggest that the procedure facilitates the selection of an appropriate test statistic for a given dataset without relying on a priori assumptions, which may bias the findings and their interpretation. Moreover, the general reproducibilityoptimization procedure is not limited to detecting differential expression only but could be extended to a wide range of other applications as well.

T helper (Th) cells differentiate into functionally distinct effector cell subsets of which Th1 and Th2 cells are best characterized. Besides T cell receptor signaling, IL-12-induced STAT4 and T-bet-and IL-4-induced STAT6 and GATA3 signaling pathways are the major players regulating the Th1 and Th2 differentiation process, respectively. However, there are likely to be other yet unknown factors or pathways involved. In this study we used quantitative proteomics exploiting cleavable ICAT labeling and LC-MS/MS to identify IL-4-regulated proteins from the microsomal fractions of CD4 ؉ cells extracted from umbilical cord blood. We were able to identify 557 proteins of which 304 were also quantified. This study resulted in the identification of the down-regulation of small GTPases GIMAP1 and GIMAP4 by IL-4 during Th2 differentiation. We also showed that both GIMAP1 and GI-

(GIMAPs) GTPase of the immunity associated protein family are a novel protein family of putative small GTPases. GIMAPs are mainly expressed in the cells of the immune system and have been associated with immunological functions, such as thymocyte development, apoptosis of peripheral lymphocytes and T helper cell differentiation. GIMAPs have also been linked to immunological diseases, such as T cell lymphopenia, leukemia and autoimmune diseases. In this review we examine the role of GIMAP proteins in T-lymphocyte biology.