Using Transcriptional Signatures to Assess Immune Cell Function: From Basic Mechanisms to Immune-Related Disease

Review IMF

YJMBI-64751; No. of pages: 12; 4C: 3, 4, 5, 6

Using Transcriptional Signatures to Assess Immune Cell Function: From Basic Mechanisms to Immune-Related Disease Maxime Touzot 1, 2, 4 , Alix Dahirel 1, 2, 4 , Antonio Cappuccio 1, 2, 3, 4 , Elodie Segura 1, 2 , Philippe Hupé 2, 3, 5 and Vassili Soumelis 1, 2, 4 1 2 3 4 5

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INSERM U932, 26 rue d'Ulm, 75005 Paris, France Institut Curie, Section Recherche, 26 rue d'Ulm, 75005 Paris, France Service de Bioinformatique, INSERM U900, Institut Curie, 26 rue d'Ulm, 75248 Paris, France Laboratoire d'Immunologie Clinique, Institut Curie, 26 rue d'Ulm, 75005 Paris, France CNRS UMR 144

Correspondence to Vassili Soumelis: [email protected]. http://dx.doi.org/10.1016/j.jmb.2015.05.006 Edited by M. Yaniv

Abstract Assessing human immune response remains a challenge as it involves multiple cell types in specific tissues. The use of microarray-based expression profiling as a tool for assessing the immune response has grown increasingly over the past decade. Transcriptome analyses provide investigators with a global perspective of the complex molecular and cellular events that unfold during the development of an immune response. In this review, we will detail the broad use of gene expression profiling to decipher the complexity of immune responses from disease biomarkers identification to cell activation, polarisation or functional specialisation. We will also describe how such data-driven strategies revealed the flexibility of immune function with common and specific transcriptional programme under multiple stimuli. © 2015 Published by Elsevier Ltd.

Introduction Assessing human immune response remains a challenge as it involves multiple cell types in specific tissues representing complex microenvironments. Immune cell function is determined by the integration of multiple signals that activate complex transcriptional programmes regulating multiple gene networks and functional pathways. Despite the limits of working at the mRNA level and possible discrepancies with protein levels, the use of microarray-based expression profiling as a tool for assessing the immune response has grown increasingly over the past decade. Transcriptome analyses provide investigators with a global perspective of the complex molecular and cellular events that unfold during the development of an immune response. Measuring a large number of cellular outputs increases the probability of capturing meaningful biological behaviours, which typically occur for only part of the outputs. Furthermore, the emergence of new bioinformatics tools and a growing number of available databases 0022-2836/© 2015 Published by Elsevier Ltd.

simplified the visualisation, analysis and functional interpretation of transcriptional signatures. Gene expression profiling of mixed cells from various tissues has emerged as a novel way to study the immune response in patients and to identify clinically relevant biomarkers in immune-mediated diseases. When applied to a given cell type, gene expression can be a powerful tool to analyse how cells compute and integrate multiple inputs to generate functional outputs. Such an approach theoretically allows better discrimination of the contribution of intrinsic (cell-specific) or extrinsic factors (microenvironment) that shape the immune response. In this review, we will detail the broad use of gene expression profiling to decipher the complexity of immune responses from disease biomarkers identification to cell activation, polarisation or functional specialisation. We will also describe how such data-driven strategies revealed the flexibility of immune function with common and specific transcriptional programme under multiple stimuli. J Mol Biol (2015) xx, xxx–xxx

Please cite this article as: Touzot Maxime, et al, Using Transcriptional Signatures to Assess Immune Cell Function: From Basic Mechanisms to Immune-Related Disease, J Mol Biol (2015), http://dx.doi.org/10.1016/j.jmb.2015.05.006

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Transcriptome Analysis: From Sample to Data Mining Overview of available technologies to assess transcriptomic profiles DNA microarray is the most common tool to assess gene expression profiles. The technology relies on oligonucleotide probes to capture complementary target sequences present in biological samples at various concentrations. However, they are not a fully quantitative assay as changes in transcript abundance must be measured in reference to control samples included in each study. Although microarrays allow us to measure transcript abundance on a genome-wide scale, it should be noted that their sensitivity are lower than quantitative PCR (qPCR) [1]. In the past years, new technologies such as Nanostring, Open RNA and RNA sequencing (RNAseq) that combine high-throughput technologies with the absolute quantification readout and sensitivity of qPCR have emerged [2,3]. The Nanostring technology is an alternative to reverse-transcription qPCR that detects transcript abundance for up to 500 transcripts with high sensitivity [2]. The RNA-seq technology, also called the “Whole Transcriptomic Shotgun Sequence”, provides not only full information on transcript abundance but also information on gene splicing transcripts, post-transcriptional modifications, gene fusion, profiles of non-coding RNA species and genetic polymorphisms [4–6]. However, RNA-seq started as an expensive method, and its use in the field of immunology is still new. The choice of sample: Purified versus mixed cell populations? Assessing immune cell function in human remains a challenge as it involves multiple cell types present in complex tissue microenvironment. The choice of the sample type is critical when considering immune cell function studies. One may indeed investigate the immune function of a global system composed of mixed cell populations present in a specific tissue. Alternatively, one may focus on the specific immune response of a given cell type, independently of the tissue and/or disease, to define the cell identity and cell-intrinsic properties. When investigating immune-mediated diseases, transcriptional profiles have been obtained from many human tissues including, for instance, the skin [7,8], muscle [9], liver [10] and kidney [11,12]. However, access to relevant tissues in specific disease settings, such as the brain in multiple sclerosis, kidney in systemic lupus erythematosus (SLE) or the joints in rheumatoid arthritis (RA), still remains a major limitation due to the size and the quality of sample. In the past

years, blood profiling has therefore emerged as a simple and cost-effective way to study the immune response in different pathologies, such as infection and autoimmunity [13]. Blood acts as a pipeline for the immune system, as it carries educated immune cells from one site to another. Blood transcriptional profiling consists in high-throughput assessment of RNA transcript abundance in circulating mononuclear cells [peripheral blood molecular cells (PBMCs)] or whole blood RNA. Changes in transcript abundance can result from exposure to host or pathogen-derived immunogenic factors and/or changes in relative cellular composition. Biological interpretation of such changes can thus be problematic. One strategy to circumvent these issues is to profile purified populations of cells. Such strategy allows the identification of cell-intrinsic expression signatures, detection of transcripts expressed at low levels and the detection of differences in expression that would otherwise be drowned in whole blood gene expression profiles. Therefore, such strategy optimises the identification of gene signatures with better discrimination than in PBMCs. However, it also contains several limitations, including potential bias introduced by the isolation process, and the time and cost of the sampling and purification process, in particular, for rare cell populations [14]. Data mining A large number of approaches have been developed for the analysis of genome-wide transcriptional profiling data. There is hardly a one-size-fits-all approach to microarray data analysis. Indeed, what works in one situation may not be universally applicable. A standard data mining primer/basic step used for analysing microarray data has been reviewed elsewhere and will not be detailed here [15]. We will mostly focus on three commonly used approaches, which have proven useful in characterising immune cell function in various model systems (Fig. 1):

(1) A gene-centric approach This traditional analytical approach is widely used. It involves either feature selection (group comparison) or dimension reduction (e.g., principal component analysis PCA, hierarchical and k-means clustering). These approaches are effective methods for dimension reduction and facilitate data visualisation. (2) A knowledge-based modular-centric approach This approach is based on functional annotations or gene-enrichment analysis using prior knowledge, that is, published observations. It assesses the

Please cite this article as: Touzot Maxime, et al, Using Transcriptional Signatures to Assess Immune Cell Function: From Basic Mechanisms to Immune-Related Disease, J Mol Biol (2015), http://dx.doi.org/10.1016/j.jmb.2015.05.006

Gene centric approach

Knowledge base modular centric approach

Hierarchical Clustering

GSEA Enrichment analysis

Modular centric approach

Assessment of Immune Cell Function

M6 M5 M4 M3 M2 M1

M6 M5 M4 M3 M2 M1 Control Disease

Please cite this article as: Touzot Maxime, et al, Using Transcriptional Signatures to Assess Immune Cell Function: From Basic Mechanisms to Immune-Related Disease, J Mol Biol (2015), http://dx.doi.org/10.1016/j.jmb.2015.05.006

Transcriptomic

Fig. 1. Three approaches for data mining are commonly used: The gene-centric approach such as hierarchical clustering allows classical group comparison and facilitates data visualisation. The knowledge-based modular-centric approach such as Gene Set Enrichment Analysis provides gene-enrichment analysis using published knowledge. This approach better discriminates small changes between large groups of genes as compared to traditional gene-centric comparisons. Finally, the modular-centric approach identifies modules of transcripts co-regulated across a large reference datasets using clustering algorithms. The results are displayed through modular fingerprints that represent over- or under-expression of each module (adapted from Obermeser and coll [65]).

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Mixed cell population

Transcriptomic signature

Functional outcomes Pathophysiology insight

= Healthy

= Disease

Clinical biomarkers * Disease activity * Response to treatment

Response to stimuli * Pathogen * Vaccine agent

Fig. 2. Transcriptional signatures obtained from mixed cell populations (e.g. blood profiling) are commonly used to study immune-mediated disease. The gene signatures allow identifying new biomarkers related to the pathogenesis, diagnosis and severity of disease and identifying therapeutic targets for human disease.

enrichment of a given set of differentially expressed genes across canonical gene sets corresponding to a known pathway or ontology. This approach allows identifying and interpreting small changes between large groups of genes that would be missed with the traditional genecentric comparisons. As it is based on published data, it also may introduce bias due to the difference in biological system and stimuli. (3) A modular-centric approach Knowledgebased modular-centric approach and modular-centric approaches fundamentally differ in the nature of the sets of genes, which differential expression between conditions is assessed. The modular-centric approach makes no assumption about known connectivity between transcripts. It first identifies clusters (modules) of transcripts co-regulated across a large reference datasets using clustering algorithms. Compari-

sons between groups of interest are then carried out on a module-by-module basis [16]. The results of these comparisons are displayed through heatmaps or modular fingerprints that represent over- or underexpression of each module. A first set of PBMCs' transcriptional modules, which have been validated in immune or infectious diseases, has been published recently [13,17–19]. This modular view facilitates functional interpretation and enables comparative analyses across multiple datasets and diseases. Lastly, it can improve robustness of identified biomarker signatures [16]. For both strategies (knowledge-based modular approaches or modular-centric), several bioinformatics tools are available such as the Database for Annotation, Visualisation and Integrated Discovery; Ingenuity Pathway Analysis; Gene Set Enrichment Analysis; or the Weight Gene Correlation Network Analysis [20–23].

Please cite this article as: Touzot Maxime, et al, Using Transcriptional Signatures to Assess Immune Cell Function: From Basic Mechanisms to Immune-Related Disease, J Mol Biol (2015), http://dx.doi.org/10.1016/j.jmb.2015.05.006

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Purified cell population

Transcriptomic signature

Functional outcomes Cell differentiation

Naïve T cell

Effector T cell

State of cell activation Activation 1

Activation 2 Resting cells

Activation 3

Ontogeny links

Fig. 3. Transcriptional signature obtained from purified cells population is used to better characterise cells in terms of activation, differentiation and functional specialisation. Comparison of genes signature from different cells may reveal ontogeny links.

Transcriptional Signatures From Mixed Cell Populations to Assess Immune Cell Function Gene expression profiling of mixed populations obtained from tissue has been widely used in immunology to identify global or specific transcriptional signature. In this chapter, we will detail how such transcriptional signatures can be used as a source of transcriptional biomarkers to understand the pathogenesis, improve diagnosis and find novel therapeutic targets for human immune-mediated diseases. Finally, we will illustrate how it has changed our global view of immune response during infection (Fig. 2). Insight into disease pathophysiology The field of autoimmunity has proven a fertile ground for transcriptomic studies, especially using PBMC profiling [24–41]. Ten years ago, an increased

expression of type I interferon IFN-regulated genes, called “IFN signature”, was identified in PBMCs of SLE patients [24]. These findings emphasised the deleterious effect of IFN pathway activation and the critical role of IFN in the pathophysiology of SLE. This led to the development of specific anti-IFN therapies that are currently evaluated in clinical trials in SLE [25,26]. A “IFN signature” was also found in other systemic diseases such as primary Sjögren's syndrome [37], myositis [42] and systemic sclerosis [43] and in a subgroup of patients with RA [36]. Systemic onset juvenile arthritis (SoJIA) is another disease with systemic involvement that greatly benefited from the study of blood transcriptional profiles, thanks to the development of both diagnostic and therapeutic modalities [29,30]. Finally, in renal transplantation, gene signatures obtained from kidney graft biopsies shed new light on the pathophysiology of allograft rejection, thanks to the identification of a specific role of NK cell signatures in late humoral rejection [44,45].

Please cite this article as: Touzot Maxime, et al, Using Transcriptional Signatures to Assess Immune Cell Function: From Basic Mechanisms to Immune-Related Disease, J Mol Biol (2015), http://dx.doi.org/10.1016/j.jmb.2015.05.006

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A Input

Cell

Signal integration

Signal 1 Signal 2 Signal 3 Signal 4

Mode 1 Mode 2 Mode 3 Mode 4

Signal X

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B Input

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Pathological context 1 Pathological context 2

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Response 1 Response 2 Response 3 Response 4

YES NO NO YES

Response X

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Fig. 4. Transcriptional signatures allow discriminating the contribution of intrinsic (cell specificity) or extrinsic factors (microenvironment) that shape the immune response. The combination of multiple signals (signal 1 to X) will be integrated according to different modes by the cells; the latter will give rise to multiple transcriptional signatures with different functional outcome. (B) This approach could be used to identify transcriptional signatures predictive of response to therapy in various pathological contexts and determine the efficacy of a given therapy.

Transcriptional signatures as a source of biomarkers

Transcriptional signatures to evaluate the global immune response following vaccine

For clinicians, the identification of transcriptional signatures provides new relevant biomarkers to assess disease severity or response to therapy. In autoimmune disease, a prominent IFN signature was associated with active disease for a specific subgroup of patient of SLE or RA [27,35,46]. Other biomarkers identified from transcriptional signatures can also predict response to biotherapy [47–51]. Blood transcriptional signatures have been described in several infectious contexts such as sepsis [52], acute bacterial infection [53] and chronic bacterial infection [54] as possible biomarkers that reflect the severity of disease. Microarray data have been used to discriminate responders and non-responders to hepatitis C virus treatment, thereby predicting viral infection outcome [55,56]. Blood signatures have also been obtained from solid organ transplant recipients in the context of both tolerance [57,58] and graft rejection [11,12,44,59,60]. Such signatures may serve as a minimally invasive monitoring for guidance immunosuppression titration.

Transcriptional signatures obtained from mixed cells populations also give critical information about global response of cells to a single stimulus. It allows us to investigate the dynamic of immune response and the contribution of each cell type. For example, the application of blood transcriptional profiling to investigate the response to vaccines and adjuvants changed our current view of vaccination physiology and the application of vaccine strategy [19,61–67]. Indeed, microarray data of PBMCs before and after yellow fever and influenza vaccination revealed both expected and novel response signatures and led to novel hypothesis on the genetic regulation of the CD8 + T cells' response. Similarly, a comprehensive analysis of blood transcriptomic responses to different seasonal vaccines (pneumococcal and influenza) was recently established [65]. By using multiple time points, the authors investigated the short- and long-term responses to various vaccines. Using the modular framework described earlier, they identified several

Please cite this article as: Touzot Maxime, et al, Using Transcriptional Signatures to Assess Immune Cell Function: From Basic Mechanisms to Immune-Related Disease, J Mol Biol (2015), http://dx.doi.org/10.1016/j.jmb.2015.05.006

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important qualitative differences in the transcriptional signatures observed for each vaccine. For example, the humoral response was more sustained with the pneumococcal vaccine as compared to the influenza vaccine. These information combined with transcriptional signatures of microbial vaccines allow the formulation of testable hypothesis of the role of new molecular pathways in vaccination response [19] Taken together, transcriptional signatures obtained from mixed population revealed that (1) multiple diseases can share similar components of the blood transcriptional profiles such as inflammation or IFN signatures; (2) whilst no single element of the profile may be specific to any given disease, it is the combination of those elements that makes a signature unique; and (3) transcriptional signatures from mixed cell populations can uncover the quality of cells immune function.

Transcriptional Signatures From Purified Cells Emphasise the Flexibility of Transcriptional Programmes Regulating Cell Functions The immune function of specific cell types is controlled by complex transcriptional programmes regulated by multiple gene networks. In the past decade, transcriptomic analysis has emerged as an efficient and appropriate tool to understand and characterise immune response at both global and cell-specific levels. For example, the Immunological Genome Project recently published several studies documenting the transcriptional programmes of mouse immune cells (α/β CD4 + T cells, CD8 + T cells, DC, macrophages, NK cells), at various stage of activation and tissue location [68–73]. The transcriptional signatures identified provide new molecular definition of cell identity, functional specialisation and lineage commitment. We will focus here on some examples of how gene signatures can help to better characterise cell populations in terms of activation, differentiation and functional specialisation. We will also describe how gene expression profiling reveals multiple dynamic transcriptional programmes that emphasise the flexibility of immune function (Fig. 3). Transcriptional signatures identify key steps in T helper cell differentiation Recently, gene expression profiling has been used to unravel mechanisms controlling T helper (Th) cell fate decision. Naive CD4 + T cells differentiate into functionally distinct lineages (Th subset) in response to environmental cues and interaction with antigen presenting cells [74]. Although studies in mice and human have identified key master transcription factor

for Th lineages, the molecular networks controlling Th differentiation remain poorly characterised. The first genome-wide transcriptome profiling during human Th17 differentiation was published recently [75]. Microarray analyses were performed on purified CD4 + naïve T cells stimulated with Th17-polarising cytokines at multiple time point of differentiation (from 0.5 to 72 h). This first detailed kinetic analysis of large-scale gene expression profiles revealed early and late transcriptional signatures that specifically shifted the CD4 + naive T cells to a Th17 profile. Beside the common mouse or human Th17-related genes, new target molecules (transcription factors, membrane proteins, enzymes) were identified at the RNA level. A similar work was performed in mouse Th17 differentiation, but the authors used shRNA interference technology to construct and validate a dynamic transcriptional network during Th17 differentiation [76]. They could identify two modules of regulators that explain the balance from Th17 to regulatory T cell phenotypes and validate some regulatory factors at the protein level. Thus, the transcription signatures obtained in those studies not only provided an overview of the genes and pathways regulated in response to induction of Th17 differentiation but also provided a starting point for further functional validation and identification of pharmacological targets in Th17 differentiation. Transcriptional signatures reveal a broad spectrum of cell activation states in macrophages Classical view of cell activation has been reduced to few phenotypes from steady state to classically or alternatively activated cells. For example, activation of macrophages may result either in to classically (M1) or alternatively (M2) activated cells that represented two polar extremes of signals computed by macrophages [77]. The dichotomy M1 and M2 has been very helpful in describing specific immune response during infection, asthma and allergies [78]. However, this concept has been recently challenged by a study of the transcriptional activation programme of macrophages. Gene expression profiling of human macrophages under different stimuli (either alone or in combinations) revealed how macrophages compute and integrate signals from their environment. The authors observed a much broader activation spectrum during macrophage polarisation [79]. They identified 49 co-expression modules that were used to compare and characterise the different phenotypes and functions of macrophages activated by different stimuli. According to these modules, they were able to define not two but ten activation states of macrophages. All these activation states share common denominators of macrophage activation but also exert regulators related to stimuli-specific programmes. A similar but more extensive approach has been used to explore

Please cite this article as: Touzot Maxime, et al, Using Transcriptional Signatures to Assess Immune Cell Function: From Basic Mechanisms to Immune-Related Disease, J Mol Biol (2015), http://dx.doi.org/10.1016/j.jmb.2015.05.006

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the regulatory system that controls cell states in human haematopoiesis [80]. Transcriptional signature reveals ontogeny links in dendritic cells lineage Gene expression profiling can also promote a better characterisation of cell lineage and/or ontogeny. A comprehensive transcriptional map of mouse DC ontogeny has been released recently by the Immunological Genome Project that deciphers the complexity of DC population. Assessing the ontogeny of human cells is far more complex, but identification of specific transcriptional signatures may give some clues. For example, it has been proposed that mouse and human DC subsets constitute a unique haematopoietic lineage and likely share the same ontology, based on transcriptional signature [81]. In a recent paper, we used gene expression profiling to better characterise a novel human DC subset present in inflammatory environments termed inflammatory DC (infDC). [82]. We generated transcriptional signature form purified infDC and four others cell populations. The global analysis of the transcriptional signatures revealed that the infDC are closely related to both inflammatory macrophages and blood CD1c + DC. We then compared the immune function of infDC and the others cell populations with four different functional modules derived from the original infDC signatures. We were able to characterise at the global and functional levels the infDC that represent a specific DC subset. Transcriptome analysis showed that human infDCs express transcription factors involved in both DC and macrophage development, suggesting that infDCs have a specific developmental pathway. In addition, infDCs were specifically enriched for the monocyte-derived DC gene signature and are therefore most likely derived from monocytes rather than from DC precursors. Thus, transcriptional signature obtained from purified cells enables to decipher the complexity of the transcriptional machinery involved in various immune processes. It also reveals a much broader flexibility of transcriptional programme.

Large-Scale Transcriptomics Analysis of Multiple Signal Integration When considering immune cell function, most studies in immunology have focused on the effects of a single signal (e.g., pathogen, cytokine) on a given cell type or have studied combinations of signal on a limited number of output responses. However, one cell type can respond to a diversity of signal (simultaneously or sequentially). This creates the possibility that immune cell function and underlying transcriptional programmes would be shaped by one or multiple signals and their interactions. Thus, we may anticipate

a specific cellular response (intrinsic factors) and context-dependant response (extrinsic factors), resulting from the interaction of a given signal and other signals from the microenvironment. Here, we will show how transcriptional profile of multiple signal helps in better identify, define and/or discover immune cell function under different contexts (Fig. 4). Transcriptomic studies uncover functional flexibility of cytokines Cytokines are key modulators of immune cell function, activation, differentiation, proliferation and survival [83,84]. Some cytokines such as IFN, IL-2 or TGF-β are known for their pleiotropic and sometimes paradoxical effects [85,86]. The latter may be partially explained by the cell-specific response to a given cytokine (intrinsic effect). However, function of a given cytokine may be potentially modulated by the integration of multiple signals from complex inflammatory microenvironments. Using a systems-level approach, we recently showed that a given cytokine (IFN-α) generates multiple transcriptional signatures, including distinct functional modules (chemokine, cytokine and antiviral) of variable flexibility, when acting in four Th cytokine microenvironments [87]. We could identify a core of conserved context-independent IFN-induced genes (ISGs), but also demonstrate the emergence of important IFN functions driven by specific cytokine environments that were validated at the protein level. The identification of a flexible transcriptional signature induced by a given cytokine revealed a new level of complexity in cytokine responses. It also suggests that each disease-specific microenvironment (extrinsic factors) may drive the specific cytokine response, which may underlie the diversity of effects observed of a given cytokine. Deciphering the large-scale integration of multiple stimuli Combinatorial effect of multiple signals may lead to different network involvement and cellular response. Multiple signals can induce synergistic or antagonistic interactions, currently considered as homogenous behaviours. This classification of interactions (synergistic or antagonistic) has been extensively used to analyse the effect of combinations of biological stimuli and drugs. We recently challenged this concept by using an innovative systems-level approach associating mathematical and data-driven analysis of transcriptomic data using innate immune cells. We showed that 10 interaction modes define cellular responses to combined stimuli, revealing a much greater complexity than is recognised by current classifications into synergistic or antagonistic interactions [88]. We could uncover an unexpected inhibition of cytokine-induced pDC activation effects by TLR

Please cite this article as: Touzot Maxime, et al, Using Transcriptional Signatures to Assess Immune Cell Function: From Basic Mechanisms to Immune-Related Disease, J Mol Biol (2015), http://dx.doi.org/10.1016/j.jmb.2015.05.006

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ligands, although both of these pDC stimuli exert strong activating effects when used as single agents. Such strategy may also be applied to the integration of pharmacological agents in order to optimise drug combination strategies targeting specific cell types, such as in cancer or immune-mediated diseases. Taken together, transcriptional signatures of combined stimuli unravel the complexity and flexibility of immune function. Understanding the common and specific cellular response of immune cells will improve our current understanding of immune cells function.

Conclusions and Perspectives Studies of the past 5 years have established transcriptomic analysis of immune cells as a powerful method to characterise immune cell function, diversity, ontogeny and potential underlying mechanisms. Many of the approaches reviewed here can be applied to a diversity of cellular systems and disease. We anticipate that improved accessibility and cost decrease of these methods will promote their diffusion and use by an increasing number of groups. In particular, dynamic transcriptomic data may help derive mathematical models of immune response and immune cell differentiation, potentially leading to predictive algorithms. Extensive studies of the effect and integration of multiple stimuli may shed new light on the adaptation of immune cells to complex inflammatory environments. Generation and prospective validation of transcriptomic signatures in patient cohorts should accelerate their transfer to clinical use. Recent developments in tools that allow data sharing, mining and representation may also help the discovery process by facilitating the exploitation of data produced by various groups [65]. In order to promote these important and challenging developments and efficient and coordinated interaction between immunologists, computational biologists and physicians will be a key success factor. Received 24 February 2015; Received in revised form 4 May 2015; Accepted 5 May 2015 Available online xxxx

Keywords: transcriptional signature; immunology; immune mediated disease; system biology

Abbreviations used: qPCR quantitative PCR; RNA-seq RNA sequencing; SLE systemic lupus erythematosus; PBMC peripheral blood molecular cell; RA rheumatoid arthritis; Th T helper.

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Please cite this article as: Touzot Maxime, et al, Using Transcriptional Signatures to Assess Immune Cell Function: From Basic Mechanisms to Immune-Related Disease, J Mol Biol (2015), http://dx.doi.org/10.1016/j.jmb.2015.05.006

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