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      Deciphering the transcriptional network of the DC lineage

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          Abstract

          Although, much progress has been made in our understanding of DC ontogeny and function, the transcriptional regulation of DC lineage commitment and functional specialization in vivo is poorly understood. We performed a comprehensive comparative analysis of CD8+, CD103+, CD11b+, and plasmacytoid DC subsets and the recently identified Macrophage DC precursors and Common DC precursors across the entire immune system. Here we characterize candidate transcriptional activators involved in myeloid progenitor commitment to the DC lineage and predicted regulators of DC functional diversity in tissues. We identify a molecular signature that distinguishes tissue DC from macrophages. We also identify a transcriptional program expressed specifically during steady-state tissue DC migration to the draining lymph nodes that may control tolerance to self-tissue antigens.

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          Most cited references 64

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          Dendritic cells and the control of immunity.

          B and T lymphocytes are the mediators of immunity, but their function is under the control of dendritic cells. Dendritic cells in the periphery capture and process antigens, express lymphocyte co-stimulatory molecules, migrate to lymphoid organs and secrete cytokines to initiate immune responses. They not only activate lymphocytes, they also tolerize T cells to antigens that are innate to the body (self-antigens), thereby minimizing autoimmune reactions. Once a neglected cell type, dendritic cells can now be readily obtained in sufficient quantities to allow molecular and cell biological analysis. With knowledge comes the realization that these cells are a powerful tool for manipulating the immune system.
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            Summaries of Affymetrix GeneChip probe level data.

            High density oligonucleotide array technology is widely used in many areas of biomedical research for quantitative and highly parallel measurements of gene expression. Affymetrix GeneChip arrays are the most popular. In this technology each gene is typically represented by a set of 11-20 pairs of probes. In order to obtain expression measures it is necessary to summarize the probe level data. Using two extensive spike-in studies and a dilution study, we developed a set of tools for assessing the effectiveness of expression measures. We found that the performance of the current version of the default expression measure provided by Affymetrix Microarray Suite can be significantly improved by the use of probe level summaries derived from empirically motivated statistical models. In particular, improvements in the ability to detect differentially expressed genes are demonstrated.
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              GenePattern 2.0.

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                Author and article information

                Affiliations
                [1 ]Immunology Institute, Mount Sinai School of Medicine New York, NY, USA
                [2 ]Department of Oncological sciences, Mount Sinai School of Medicine New York, NY, USA
                [3 ]Institute for Genomics and Multiscale Biology and Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine New York, NY, USA
                [4 ]Broad Institute, Cambridge, MA 02142, USA
                [5 ]Department of Regenerative Biology, Mount Sinai School of Medicine New York, NY, USA
                [6 ]Department of Pathology & Immunology, Washington University in St. Louis, St. Louis, MO, USA
                [7 ]Computer Science department, Stanford University, Stanford, CA
                [9 ]Cancer Immunology and AIDS, Dana Farber Cancer Institute, Boston, MA, USA
                [10 ]Albert Einstein College of Medicine, Bronx, NY
                Author notes
                Corresponding author: Miriam Merad, M.D., Ph.D., 1425 Madison Avenue, Box 1496, Tel: 212 659 8276, Miriam.merad@ 123456mssm.edu
                [8]

                Present address: Department of Computer Science, UNC, Chapel Hill, NC

                Journal
                100941354
                21750
                Nat Immunol
                Nat. Immunol.
                Nature immunology
                1529-2908
                1529-2916
                23 January 2014
                15 July 2012
                September 2012
                14 April 2014
                : 13
                : 9
                : 888-899
                NIHMS384129
                10.1038/ni.2370
                3985403
                22797772

                Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

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                Immunology

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