Genes and Immunity (2005) 6, 319–331 & 2005 Nature Publishing Group All rights reserved 1466-4879/05 $30.00 www.nature.com/gene FULL PAPER Immune response in silico (IRIS): immune-specific genes identified from a compendium of microarray expression data AR Abbas1, D Baldwin1,YMa1, W Ouyang2, A Gurney2,3, F Martin2, S Fong2, M van Lookeren Campagne2, P Godowski2, PM Williams3, AC Chan2 and HF Clark1,2 1Department of Bioinformatics, Genentech, Inc., South San Francisco, CA, USA; 2Department of Immunology, Genentech, Inc., South San Francisco, CA, USA; 3Department of Molecular Biology, Genentech, Inc., South San Francisco, CA, USA Immune cell-specific expression is one indication of the importance of a gene’s role in the immune response. We have compiled a compendium of microarray expression data for virtually all human genes from six key immune cell types and their activated and differentiated states. Immune Response In Silico (IRIS) is a collection of genes that have been selected for specific expression in immune cells. The expression pattern of IRIS genes recapitulates the phylogeny of immune cells in terms of the lineages of their differentiation. Gene Ontology assignments for IRIS genes reveal significant involvement in inflammation and immunity. Genes encoding CD antigens, cytokines, integrins and many other gene families playing key roles in the immune response are highly represented. IRIS also includes proteins of unknown function and expressed sequence tags that may not represent genes. The predicted cellular localization of IRIS proteins is evenly distributed between cell surface and intracellular compartments, indicating that immune specificity is important at many points in the signaling pathways of the immune response. IRIS provides a resource for further investigation into the function of the immune system and immune diseases. Genes and Immunity (2005) 6, 319–331. doi:10.1038/sj.gene.6364173 Published online 24 March 2005 Keywords: microarray expression; specific expression; immune response Introduction described, this has not yet led to a complete under- standing of immune diseases. The immune system in higher eukaryotes has evolved Genome-wide microarray expression profiling of im- into a complex network involving many specialized cell mune cells provides opportunities for identifying other types with multiple points of regulation. This enables the genes that may function in the immune response. immune response to attack foreign invaders while Microarray experiments have been carried out on recognizing and tolerating self-antigens. Two distinct various immune cell subsets by a number of different lineages of immune cells have evolved with cell types investigators in order to understand gene expression that specialize in each of the many roles that are required differences during differentiation and activation.1–17 for this immune response. The myeloid lineage of These studies have provided invaluable insight into monocytes, macrophages, dendritic cells and neutrophils comprehensive gene expression profiles that define carries out the innate immune response, recognizing many different immune cell subsets and states of microbial pathogens typically by carbohydrates found differentiation and activation. In particular, a recent only in bacterial proteins. The lymphoid lineage of T study detailed elegantly the genes expressed specifically cells, B cells and natural killer (NK) cells enables in many subsets of the T-cell lineage.18 Comparison of adaptive immunity by distinguishing self from non-self gene expression profiles across a broad range of immune antigens and also providing a memory of foreign cell types and nonimmune tissues is necessary, however, proteins seen before. Other immune cells, such as to fully appreciate which gene expression differences are eosinophils, basophils and mast cells, also participate unique to each immune cell subset, which are found in in the immune response. Although a large repertoire of multiple subsets or across immune cell lineages, and genes that play key roles in the differentiation, function which are found more widely across the many cell types and regulation of these immune cells is already well- in the human body and thus may be involved in more general cellular processes. Differences among microarray platforms and experimental protocols used by different Correspondence: Dr HF Clark, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA. E-mail: [email protected] investigators often confound such comparison. Here, a Received 29 July 2004; revised 1 November 2004; accepted 8 compendium of microarray expression data from a broad December 2004; published online 24 March 2005 representation of isolated immune cell subsets has been Immune response in silico AR Abbas et al 320 generated on the same microarray platform and analysis highest mean expression level of any immune cell subset of gene expression profile across the major cell types of within each cell type as defined in Table 1 is calculated the immune system is made possible. Moreover, gene for each IRIS gene. Hierarchical clustering, a statistical expression in all other major tissues allows determina- method for grouping genes by the similarity of their tion of immune-specific expression. Traditional methods expression profiles, reveals patterns of gene expression of determining expression are performed on a gene-by- that are distinct among immune cell lineages. The gene basis, and single microarray experiments show dendrogram of the relationships of these genes mimics differential expression between just a few immune cell the evolutionary relationships of the lineages of immune subsets. Here, genes fitting a complex expression cell types. The heat map showing all IRIS genes also criterion are identified on a genomic scale. illustrates the complexity of expression profiles, with Immune cell-specific expression is one indication of most genes expressed at some level in more than one the importance of a gene’s role in the function of the immune cell type. immune system. ‘Cluster of differentiation’ (CD) anti- gens are used experimentally as specific markers for IRIS categories immune cell subsets and they often play a key role in the While clustering is a useful method for identifying function of that cell. For example, the T-cell receptor is patterns of gene expression, it fails to clearly define the expressed only on T lymphocytes and is responsible for parameters of these patterns. Therefore, cutoff values of self-antigen recognition, a primary function of this gene expression signatures in various immune cell types immune cell.19 Here, immune cell specifically expressed were approximated and used to define the IRIS genes are identified by a survey of expression profiles categories that were observed in the clustering (Table 1) across all the immune cells and major non-immune and these parameters are defined in the Cell & Lineage tissues by a method termed Immune Response In Silico Assignment section of Materials and methods. The (IRIS). Immune cell specificity is determined generally by probesets are categorized by their profiles according to higher expression in any immune cell than expression in the degree of specificity within immune cells. Profiles any nonimmune cell tissue. Finer characterization of specific to one cell type are assigned to the categories T specificity within an immune cell type, such as T cells, cell, NK cell, B cell, monocyte, dendritic or neutrophil. and an immune cell lineage, such as lymphoid cells, is Profiles specific to more than one cell type within a also determined. Furthermore, expression profiles within lineage are assigned to lymphoid or myeloid categories. subsets of an immune cell, such as classes of T cells Profiles specific to cell types across lineages, as well as expressing CD4 or CD8 antigens, memory and helper T probesets with expression levels equivalent in all cells and resting vs activated cells, further refine the immune cells, are assigned to the multiple category. specificity of immune-specific genes. IRIS has identified Genes represented by several probesets may appear in both well-characterized immune genes and a number of more than one category. A gene is best described as highly immune-specific genes with unknown function. specific to the most exclusive category to which it has been assigned, that is, the highest expressing probeset may be in the T-cell category and more weakly Results expressing probesets for that gene may fall into the lymphoid or multiple categories because of the strin- IRIS identifies genes more highly expressed in immune gency of cutoff levels used. cells than in any of the major organs of the body. Gene expression is surveyed across a compendium of samples, Patterns of expression profiles within each lineage including immune cell subsets (Table 1) and a compre- Cell types within a lineage share some immune-specific hensive range of normal tissues. The immune cells have genes, suggesting that they confer common functions been isolated from normal human blood by purifying among those cells. The expression profiles are very each cell type via its specific cell surface markers. diverse and complex, but some general patterns emerge. Activated and differentiated subsets were developed by As described above, the IRIS categories attempt to group in vitro stimulations. RNA samples were labeled and run genes within a cell type, or, if specific to more than one on microarray chips that include probesets for virtually cell type, within