Selective activation of T helper (Th) cell subsets plays an important role in immune response to pathogens as well as in the pathogenesis of human allergy and inflammatory diseases. Th1 cells along with the recently discovered Th17 cells play a role in the pathogenesis of autoimmune diseases. Th2 cytokines lead to series of inflammatory processes characteristic for asthma and other atopic diseases. To understand the pathogenesis of immune-mediated diseases it is crucial to dissect pathways and regulatory networks leading to the development of distinct Th subsets. Such knowledge may lead to better strategies for developing diagnostics and therapies for these diseases. The differentiation of Th1, Th2, and Th17 effector cells is driven by signals originating from T cell and costimulatory receptors as well as cytokines in the surroundings of activated naive T helper cells. There are several proteins involved in the regulation of this differentiation process. Most of the data on T helper cell differentiation have been acquired using mouse. In this review, we have summarized what is known about human T helper differentiation. In addition, selected differences between human and mouse will be discussed.

There is an urgent need for bioinformatic methods that allow integrative analysis of multiple microarray data sets. While previous studies have mainly concentrated on reproducibility of gene expression levels within or between different platforms, we propose a novel meta-analytic method that takes into account the vast amount of available probe-level information to combine the expression changes across different studies. We first show that the comparability of relative expression changes and the consistency of differentially expressed genes between different Affymetrix array generations can be considerably improved by determining the expression changes at the probe-level and by considering the latest information on probe-level sequence matching instead of the probe annotations provided by the manufacturer. With the improved probe-level expression change estimates, data from different generations of Affymetrix arrays can be combined more effectively. This will allow for the full exploitation of existing results when designing and analyzing new experiments.

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