Regulatory T cells (Tregs), which are characterized by expression of the transcription factor Foxp3, are a dynamic and heterogeneous population of cells that control immune responses and prevent autoimmunity. We recently identified a subset of Tregs in murine skin with properties typical of memory cells and defined this population as memory Tregs (mTregs). Due to the importance of these cells in regulating tissue inflammation in mice, we analyzed this cell population in humans and found that almost all Tregs in normal skin had an activated memory phenotype. Compared with mTregs in peripheral blood, cutaneous mTregs had unique cell surface marker expression and cytokine production. In normal human skin, mTregs preferentially localized to hair follicles and were more abundant in skin with high hair density. Sequence comparison of TCRs from conventional memory T helper cells and mTregs isolated from skin revealed little homology between the two cell populations, suggesting that they recognize different antigens. Under steady-state conditions, mTregs were nonmigratory and relatively unresponsive; however, in inflamed skin from psoriasis patients, mTregs expanded, were highly proliferative, and produced low levels of IL-17. Taken together, these results identify a subset of Tregs that stably resides in human skin and suggest that these cells are qualitatively defective in inflammatory skin disease.

Motivation: Primary data analysis methods are of critical importance in second generation DNA sequencing. Improved methods have the potential to increase yield and reduce the error rates. Openly documented analysis tools enable the user to understand the primary data, this is important for the optimization and validity of their scientific work.Results: In this article, we describe Swift, a new tool for performing primary data analysis on the Illumina Solexa Sequencing Platform. Swift is the first tool, outside of the vendors own software, which completes the full analysis process, from raw images through to base calls. As such it provides an alternative to, and independent validation of, the vendor supplied tool. Our results show that Swift is able to increase yield by 13.8%, at comparable error rate.Availability and Implementation: Swift is implemented in C++and supported under Linux. It is supplied under an open source license (LGPL3), allowing researchers to build upon the platform. Swift is available from http://swiftng.sourceforge.net.Contact: new@sgenomics.org; nava.whiteford@nanoporetech.comSupplementary information: Supplementary data are available at Bioinformatics online.