Identification of T cell-restricted genes, and signatures for different T cell responses, using a comprehensive collection of microarray datasets

Tatyana Chtanova, Rebecca Newton, Sue Liu, Lilach Weininger, Timothy Young, Diego Silva, Francesco Bertoni, Andrea Rinaldi, Stephane Chappaz, Federica Sallusto, Michael Rolph, Charles Mackay

Research output: Contribution to journalArticle

87 Citations (Scopus)

Abstract

We used a comprehensive collection of Affymetrix microarray datasets to ascertain which genes or molecules distinguish the known major subsets of human T cells. Our strategy allowed us to identify the genes expressed in most T cell subsets: TCR αβ+ and γδ+, three effector subsets (Th1, Th2, and T follicular helper cells), T central memory, T effector memory, activated T cells, and others. Our genechip dataset also allowed for identification of genes preferentially or exclusively expressed by T cells, compared with numerous non-T cell leukocyte subsets profiled. Cross-comparisons between microarray datasets revealed important features of certain subsets. For instance, blood γδ T cells expressed no unique gene transcripts, but did differ from αβ T cells in numerous genes that were down-regulated. Hierarchical clustering of all the genes differentially expressed between T cell subsets enabled the identification of precise signatures. Moreover, the different T cell subsets could be distinguished at the level of gene expression by a smaller subset of predictor genes, most of which have not previously been associated directly with any of the individual subsets. T cell activation had the greatest influence on gene regulation, whereas central and effector memory T cells displayed surprisingly similar gene expression profiles. Knowledge of the patterns of gene expression that underlie fundamental T cell activities, such as activation, various effector functions, and immunological memory, provide the basis for a better understanding of T cells and their role in immune defense
Original languageEnglish
Pages (from-to)7837-7847
Number of pages11
JournalJournal of Immunology
Volume175
DOIs
Publication statusPublished - 2005
Externally publishedYes

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