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Assessing the human immune system through blood transcriptomics

Damien Chaussabel, Virginia Pascual, Jacques Banchereau

BMC Biology · 2010 · Review

doi:10.1186/1741-7007-8-84

Abstract

Blood is the pipeline of the immune system. Assessing changes in transcript abundance in blood on a genome-wide scale affords a comprehensive view of the status of the immune system in health and disease. This review summarizes the work that has used this approach to identify therapeutic targets and biomarker signatures in the field of autoimmunity and infectious disease. Recent technological and methodological advances that will carry the blood transcriptome research field forward are also discussed.

Full text

From PubMed Central, U.S. National Library of Medicine, as current on Oct 20, 2026. Paragraph numbers are ours, so an answer can point at one.

Profiling the human immune system

The immune system plays a central role not only in health maintenance but also in pathogenesis: excess immunity is associated, for instance, with auto-immune diseases (for example, multiple sclerosis, type 1 diabetes, psoriasis, lupus, rheumatoid arthritis), inflammation (sepsis, inflammatory bowel disease) and allergy, as well as cell and organ rejection; deficient immunity is, on the other hand, linked to cancer or susceptibility to infection.

When investigating immune-mediated diseases in humans, restricted access to relevant tissue(s) for sampling, such as the brain in multiple sclerosis or the joints in rheumatoid arthritis, constitutes a major limitation. Cells of the immune system, however, become educated and implement their functions by recirculating between central and peripheral lymphoid organs as well as by migrating to and from sites of injury via the blood (Figure 1). As blood flows throughout the body, carrying naïve and educated immune cells from one site to another, it acts as a pipeline for the immune system. Indeed, it is the preferred route for immune cells to reach the lymph nodes where antigen-specific immune responses develop. After exiting these nodes through outgoing lymphatic vessels, the cells again reach the bloodstream to be transported to tissues throughout the body. Upon patrolling these tissues, they gradually drift back into the lymphatic system to re-enter the blood and begin the cycle all over again. The complex patterns of recirculation depend on the state of cell activation, the adhesion molecules expressed by immune and endothelial cells, and the presence of chemotactic molecules that selectively attract particular populations of blood cells. Circulating immune cells are, in addition, exposed to factors that are released systemically.

A wide range of molecular and cellular profiling assays is currently available for the study of the human immune system (Figure 2). The level of sophistication of instruments such as polychromatic flow cytometers, one of the immunologist's favorite tools, has increased over the past few years. Major technological breakthroughs have also occurred in the fields of genomics and proteomics, thus creating today a unique opportunity for the study of human beings in health and disease where inherent heterogeneity dictates that large collections of samples be analyzed. Among the high-throughput molecular profiling technologies available today, genomic approaches are the most scalable, have the most breadth and robustness, and therefore are best suited for the study of human populations.

The human genome can be investigated from two different angles that consist of either determining its make up or measuring its output. Sequence variation can be detected using, for instance, single nucleotide polymorphism (SNP) chips, which permit the identification of common polymorphisms or rare mutations associated with diseases. Hundreds of thousands of SNPs can be typed using these platforms, yielding a genome-wide, hypothesis-free scan of genetic associations for a given phenotype of interest. Many such genome-wide association studies (often referred to as GWAS) have been published in recent years, a number of them investigating the genetic underpinning of immune-related diseases. Notably, such studies have been useful to pinpoint genes and pathways that may be involved in the pathogenesis of autoimmune diseases. Associations between common genetic variants and resistance to infection have also been reported. However, parameters measured by this approach are determined by heredity and will not change throughout the life of an individual. This is in contrast to transcript abundance, which is the parameter measured by the second genome-wide profiling approach. Transcriptional activity is largely dependent on environmental factors and, as a result, RNA abundance will change dynamically over time. For instance, sets of transcripts may be induced in response to an infectious challenge and return to baseline levels following pathogen clearance. Dynamic changes in the cellular make up of a tissue will also effect changes in transcript abundance that will be measured on a genome-wide scale.

Transcriptional profiles have been obtained from many human tissues -including, for instance, the skin, muscle, liver, kidney or brain - but the status of the immune system can be best monitored by profiling transcript abundance in blood. Indeed, profiling transcript abundance in blood provides a 'snap shot' of the complex immune networks that operate throughout the entire body. However, while this has proven to be a valid approach to finding clues about pathogenesis as well as to identifying potential biomarkers, a number of challenges and limitations exist. Data interpretation is one of them. Firstly, the volume of data generated from such studies can be overwhelming, and it is necessary to integrate information from a multitude of sources (study design, quality control data, sample information, and importantly clinical information) in order for the results to be interpretable. Secondly, the changes in transcript abundance observed in complex tissues such as blood can be caused not only by regulation of gene transcriptional activity but also by relative changes in abundance of cell populations expressing transcripts at constant levels. Thirdly, in addition to pathogenic processes, a number of factors may affect blood transcript abundance and confound the analysis. Medications and co-morbidities are two such factors that often restrict patient selection and complicate data interpretation. This review will discuss some of the strategies recently developed that will address some of these limitations.

Transcriptome profiling: a technology primer

Real-time PCR technology is currently considered the gold standard for the analysis of gene expression. However, it can be used to measure abundance of only a limited number of transcripts. Introduced over 10 years ago, DNA microarrays are now in routine use and can measure transcript abundance on a genome-wide scale. This technology relies on dense arrays of oligonucleotide probes that will capture complementary sequences present in biological samples at various concentrations. The probes can be deposited on a solid surface (printed microarrays), synthesized in situ (Affymetrix GeneChips), or bound to glass beads lodged into wells etched in the surface of a glass slide (Illumina BeadArrays). The labeled material captured by the microarray is imaged and relative abundance determined based on the strength of the signal produced by each oligonucleotide feature. It should be noted that, while they provide a means to survey transcript abundance on a genome-wide scale, the sensitivity of microarray assays is low compared to other approaches such as real-time PCR. A microarray is not a fully quantitative assay and changes in transcript abundance must be measured in reference to control samples that need to be included in each study. However, some of these limitations may be lifted by methods relying on high-throughput sequencing for the genome-wide measurement of RNA abundance. Building on the legacy of the SAGE (serial analysis of gene expression) technology introduced in the 1990s, RNA sequencing (RNA-seq) uses either total or fractionated RNA, for example poly(A)+, as a starting point. This material is converted to a library of cDNA fragments. High throughput sequencing of such fragments yields short sequences or reads that are typically 30 to 400 bp in length, depending on the technology platform used. For a given sample, tens of millions of such sequences will then be uniquely mapped against a reference genome. The higher the level of expression of a given gene, the higher the number of reads that will be aligned against it (Figure 3). Thus, this approach does not rely on probe design and provides several types of information, including not only transcript abundance but also transcriptome structure (splice variants), profiles of non-coding RNA species, and genetic polymorphisms. RNA-seq is expected to become sufficiently cost-effective and practical that it will eventually supersede microarray technologies.

Other technologies should be considered for the profiling of focused sets of genes. Nanostring technology can, for instance, detect the abundance of up to 500 transcripts with high sensitivity. The approach is 'digital' since it counts individual RNA molecules using strings of fluorochromes as reporters to identify the different RNA species. Other technology platforms developed by, among others, Luminex, High Throughput Genomics or Fluidigm round up the offering for 'sub-genome' transcript profiling.

Profiling autoimmune diseases

The field of autoimmunity has proven a fertile ground for blood transcriptional studies. Alterations in transcript abundance in the blood of patients reflect the sustained response against self-antigens and, more generally, uncontrolled inflammatory processes. Such diseases often present with recurring-remitting patterns of activity, with episodes of flaring that may be reflected by fluctuations in transcript abundance. The work has initially focused on diseases with clear systemic involvement such as systemic lupus erythematosus (SLE). Multiple cell types and soluble mediators, including IL10 and IFNγ, have been proposed to be at the center of lupus pathogenesis. While some scattered evidence indicated the potential role of type I interferon in lupus, several observations did not support the hypothesis: first, not every SLE patient has detectable serum type I IFN levels; second, dysregulation of type-I IFN production is not found in most murine SLE-models; and third, genetic linkage and association studies had not identified candidate lupus susceptibility genes within the IFN pathway. However, in one of our earliest microarray studies we demonstrated that all but one of the pediatric patients exhibited upregulation of IFN-inducible genes, and the only patient lacking this signature had been in remission for over 2 years. In addition, it was found that treating SLE patients with high dose IV steroids, which are used to control disease flares, results in the silencing of the IFN signature. A surprise from these initial studies was the absence of type I IFN gene transcripts in the face of an abundance of IFN-inducible ones in the blood cells of SLE patients. A likely explanation is that the cells producing type I IFN, and therefore transcribing these genes, migrate to sites of injury. Altogether, results from microarray studies played a key role in convincing the community of the potential importance of type I IFN in SLE pathogenesis. A phase Ia trial to evaluate the safety, pharmacokinetics, and immunogenicity of anti-IFN monoclonal antibody (mAb) therapy in adult SLE patients was recently conducted. The antibody elicited a specific and dose-dependent inhibition of overexpression of type I IFN-inducible genes in both whole blood and skin lesions from SLE patients, at both the transcript and protein levels. As expected, overexpression of BLyS/BAFF, a type I IFN-inducible gene, also decreased with treatment. Thus, this first trial supports the proposed central role of type I IFN in human SLE.

Systemic onset juvenile arthritis (SoJIA) is another disease with systemic involvement that greatly benefited from the study of blood transcriptional profiles with the development of both therapeutic and diagnostic modalities. Diseases with specific organ involvement have also been the subject of significant, yet not always extensive, blood profiling efforts. Blood signatures have, for instance, been obtained from patients with multiple sclerosis. Given the inaccessibility of the brain, blood constitutes a particularly attractive source of surrogate molecular markers for this disease. These efforts have yielded a systemic signature and identified potential predictive markers of clinical relapse and response to treatment. Transcriptional signatures have also been generated in the context of dermatologic diseases. In this case, the target organ being readily accessible, efforts have been focusing on profiling transcript abundance in skin tissues. However, systemic involvement has been recognized in recent years to be an important component of autoimmune skin diseases and unique blood transcriptional profiles have also been identified in patients with, for example, psoriasis.

Blood transcriptional profiles have been generated in the context of many other autoimmune diseases. Indeed, the range of autoimmune/autoinflammatory diseases that have been investigated encompasses SLE, juvenile idiopathic arthritis, multiple sclerosis, rheumatoid arthritis, Sjogren's syndrome, diabetes, inflammatory bowel disease, psoriasis and psoriatic arthritis, inflammatory myopathies, scleroderma, vasculitis and anti-phospholipid syndrome. The body of work produced that focuses on blood transcript profiling in the context of autoimmune diseases has been covered at length in a recent review.

Profiling infectious diseases

Global changes in transcript abundance have also been measured in the blood of patients with infectious diseases. In this context, alterations of blood transcriptional profiles are a reflection of the immunological response mounted by the host against pathogens. This response is initiated by specialized receptors expressed at the surface of host cells recognizing pathogen-associated molecular patterns. Different classes of pathogens signal through different combinations of receptors, eliciting in turn different types of immune responses. This translates experimentally into distinct transcriptional programs being induced upon exposure of immune cells in vitro to distinct classes of infectious agents. Similarly, patterns of transcript abundance measured in the blood of patients with infections caused by different etiological agents were found to be distinct.

Predictably, dramatic changes were observed in the blood of patients with systemic infections (for example, sepsis). However, profound alterations in patterns of transcript abundance were also found in patients with localized infections (for example, upper respiratory tract infection, urinary tract infections, pulmonary tuberculosis, skin abscesses). Measuring changes in host transcriptional profiles may therefore prove of diagnostic value even in situations where the causative pathogenic agent is not present in the test sample. Importantly, it may also help ascertain the severity of the infection and monitor its course.

Infections often present as acute clinical events; thus, it is important to capture dynamic changes in transcript abundance that occur during the course of the infection from the time of initial exposure. Blood signatures have been described in the context of acute infections caused by a wide range of pathogenic parasites, viruses and bacteria, including Plasmodium, respiratory viruses (influenza, rhinovirus, respiratory syncytial virus), dengue virus, and adenovirus, as well as Salmonella, Mycobacterium tuberculosis, Staphylococcus aureus, Burkholderia pseudomallei and the general context of bacterial sepsis. Some of those pathogens will persist and establish chronic infections (for example, human immunodeficiency virus and Plasmodium) that may lead to a state of latency (for example, tuberculosis), and transcript profiling may be used in those situations as a surveillance tool for monitoring disease progression or reactivation.

Blood profiling of infectious diseases remains limited in scale. In particular, additional studies will be necessary to ascertain dynamic changes occurring over time.

Profiling other diseases

In addition to autoimmune and infectious diseases, blood transcript profiling studies have been carried out in the cancer research field. While hematological malignancies have led the way (reviewed in), blood profiles have also been obtained more recently from patients with solid organ tumors. Notably, these signatures can reflect not only the immunological or physiological changes effected by cancers but also the presence of rare tumor cells in the circulation.

Blood signatures have also been obtained from solid organ transplant recipients in the context of both tolerance and graft rejection. While such signatures can also be detected in biopsy material, blood offers the distinct advantage of being accessible for safely monitoring molecular changes on a routine basis.

Some work has also been done in the context of cardiovascular diseases where inflammation is known to play an important role. Hence, profiles have been identified in a wide range of conditions, including stroke, chronic heart failure or acute coronary syndrome.

The body of published work is too large to be cited in this review - and it is likely to be only the tip of the iceberg, with a lot more unpublished data scattered throughout public and private repositories. Other efforts have yielded, for instance, blood transcriptional signatures in patients with neurodegenerative diseases, and those associated with disease exacerbation or responsiveness to glucocorticoids in patients with asthma, and with responses to environmental exposure, exercise or even laughter. Unfortunately, too many published studies are underpowered and sometimes lack even the most rudimentary validation steps. All too often primary data are not available for reanalysis either, reflecting a lack of enforcement of editorial policies, or the absence thereof in some journals. Hence, one of the main challenges for this field is to move beyond the proof of principle stage and consolidate the wealth of data being generated.

Collectively, studies published thus far demonstrate that alterations in transcript abundance can be detected on a genome-wide scale in the blood of patients with a wide range of diseases. This statement is far from trivial given the skepticism that initially met studies investigating the blood transcriptome of patients. We have also learned that: 1) multiple diseases can share components of the blood transcriptional profile - for instance, the case for inflammation or interferon signatures; 2) while 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 finally, 3) the work accomplished to date highlights the importance of carrying out analyses aiming at directly comparing transcriptional profiles across diseases. Indeed, much can be learned, for instance, about autoimmunity from studying responses to infection, and vice versa. Furthermore, such efforts may eventually lead us closer to a molecular classification of diseases. First, however, technological and methodological advances are necessary for the blood transcriptome research field to move beyond the proof of principle stage.

Moving forward

Recent progress in blood transcriptome research has been possible thanks to the development of robust sample collection techniques and the introduction of high throughput gene expression microarray platforms. Such advances have been necessary but the margin for progression in the field is still very significant. We describe here some of the current hurdles and discuss potential solutions for overcoming them.

Data management

For years the scale of blood transcriptional studies has been constrained by the cost of the technology. With the price tag on a commercial whole genome microarray below the $100 US mark, this is not the case anymore. Thus, data management has now become the first essential step to making large scale molecular profiling a viable proposition. Beyond storing the output of microarray instruments, data management must capture and organize information that is essential for the interpretation of the results (Figure 4). This includes sample information, data quality metrics, clinical information collected at the time of sampling, details about the experimental design, and materials and methods. Capturing such information ensures that the large volumes of data generated, which are often not published immediately, will remain exploitable for years to come. This point has become critical given the fact that results from genome-wide profiling studies can never be exploited to their fullest extent and possess considerable cumulative value when re-analyzed collectively. Notably, the results generated by other cellular and molecular profiling platforms will also need to be integrated in order to complete the picture. Therefore, implementing effective data management solutions and practices is essential to sustain the necessary increase in the scale of blood transcriptional studies (Figure 3). Unfortunately, implementing data management solutions in the laboratory is often an expensive proposition, requiring customization of off-the-shelf products or development of custom software adapted to handle specific workflows. Managing data also takes time and requires dedicated personnel. Thus, while the need is widely perceived, the commitment and steps necessary to implement effective data management solutions and practices are rarely adopted.

Data mining

A myriad of approaches have been developed for the analysis of genome-wide transcriptional profiling data. However, there is no silver bullet when it comes to microarray data analysis. The challenges encountered are several fold: 1) dimensionality, or how to cope with the fact that the number of parameters measured exceeds by several orders of magnitude the number of conditions included in most experiments; 2) noise - a direct consequence of the first point is that results from microarray analyses are particularly permissive to noise (false discovery); 3) 'seeing' the data - data visualization is critical as it helps promote insight and supports data interpretation; 4) biological context - it is important to keep the biology in sight at all times. Indeed, while it is easy to become absorbed by the data, it is essential to use biological knowledge when designing analysis strategies. Finally, there is hardly a one-size-fits-all approach to microarray data analysis and what works in one situation may not be universally applicable. Indeed, the most common response from experts when questioned on the best way to analyze a given dataset is that 'it depends...': it depends, for instance, on the extent of the differences being observed or on the variability inherent to a given disease or study population; it depends on what questions are being asked; or it can depend on whether follow-up confirmatory experiments are planned. In Table 1 we provide a data mining primer that explains the basic steps involved in microarray data analysis and the considerations that arise. Ad hoc data mining approaches can be developed to meet specific needs. For instance, we have developed a data mining strategy for the specific purpose of analyzing blood transcriptional profiles. This approach simply consists of a priori grouping of sets of genes with similar transcriptional patterns. This is repeated for several different datasets and subsequently, when comparing the cluster membership of all the genes across those datasets, the genes with similar membership are grouped together to form what we have termed a transcriptional module. Structuring the data permits focusing downstream statistical testing on these sets of transcripts that form coherent transcriptional and functional modular units. This is in contrast with more traditional approaches that rely on iterative statistical testing for thousands of individual transcripts that are treated as independent variables. The modular transcriptional framework that we have developed reduces the number of variables by collapsing sets of coordinately expressed genes into a new entity, the module. Reducing data dimensionality as such can: 1) facilitate functional interpretation; 2) enable comparative analyses across multiple datasets and diseases; 3) minimize noise and improve robustness of biomarker signatures; and 4) yield multivariate metrics that can be used at the bedside. Data visualization is also of critical importance for the interpretation of large-scale datasets. We have devised a straightforward visualization scheme for mapping global transcriptional changes for individual diseases on a modular basis (Figure 5).Briefly, differences in expression levels between study groups are displayed for each module on a grid. Each position on the grid is assigned to a given module; a red spot indicates an increase and a blue spot a decrease in transcript abundance. The spot intensity is determined by the proportion of transcripts reaching significance for a given module. A posteriori, biological interpretation has linked several modules to immune cells or pathways (see legend of Figure 5). Hence, in the example provided in Figure 5, patients with S. aureus infection demonstrate significant over-expression of genes in modules related to innate immunity, including myeloid (M1.5, M2.6), neutrophil (M2.2), and inflammation (M3.2, M3.3) modules, and under-expression of genes regulating adaptive immunity such as those in B cell (M1.3), cytotoxic cell (M2.1), and T-cell-specific (M2.8) modules. It should also be noted that no changes were observed for other modules, such as module M3.1, which includes interferon-inducible genes, abundance of which would be increased in the context of a viral infection.

Beyond mRNA: profiling microRNAs

MicroRNA (miRNA) control has emerged as a critical regulatory circuit of the immune system. Measuring changes in miRNA abundance in the blood of human subjects in health and disease is therefore a promising new field of investigation. These short non-coding single-stranded RNAs about 22 nucleotides in length have been found to play essential regulatory roles. These molecules exhibit highly specific, regulated patterns of expression and control protein expression by translational repression, mRNA cleavage, or promotion of mRNA decay. Interestingly, thanks to their small size, miRNA molecules are stable and can be measured not only in blood cells but also in circulation in the serum. They are thus not only potentially important contributors to immune function, but also potential sources of biomarkers.

Deconvoluting blood transcriptional signatures

Blood transcriptome research will also benefit from conceptual advances that may help address shortcomings inherent to whole blood profiling.

First, blood is a complex tissue and changes in transcript abundance can be attributed to either transcriptional regulation or relative changes in composition of leukocyte populations. Two approaches exist for 'deconvoluting' these two phenomena. First, one can isolate and individually profile different cell populations present in the blood. This approach may also permit the identification of transcripts expressed at low levels or the detection of differences in expression that would otherwise be drowned in whole blood. However, isolation methods may introduce technical bias, and require extensive sample processing. A second approach consists of deconvoluting whole blood transcriptional profiles 'in silico'. This type of analysis attempts to deduce cellular composition or cell-specific levels of gene expression using statistical methodologies.

Finally, we must also keep in mind that the immune status of a human subject is not entirely reflected by its blood profile obtained at the steady state. Indeed, an individual's capacity to respond to innate as well as antigen-specific immune signals may also provide useful and complementary information.

In conclusion, blood transcript profiling has earned its place in the molecular and cellular profiling armamentarium used to study the human immune system. Changes in transcript abundance recapitulate the influence of genetic, epigenetic, cellular and environmental factors. Initially considered to belong to the 'cutting edge', this approach has become both robust and practical. As discussed in this review, it has become a mainstay for the study of immune function in patients with a wide range of diseases. Furthermore, recent studies have demonstrated the utility of blood transcriptome profiling for monitoring immune responses to drugs or vaccines. Thus, blood transcript profiling is developing into a mainstream tool for the assessment of the status of the human immune system.

References 143

  1. 1.

    Gregersen PK Olsson LM Recent advances in the genetics of autoimmune diseaseAnnu Rev Immunol2009 27363391

    Not in our library10.1146/annurev.immunol.021908.132653

  2. 2.

    Zhernakova A van Diemen CC Wijmenga C Detecting shared pathogenesis from the shared genetics of immune-related diseasesNat Rev Genet2009 104355

    Not in our library10.1038/nrg2489

  3. 3.

    Fellay J Shianna KV Ge D Colombo S Ledergerber B Weale M Zhang K Gumbs C Castagna A Cozzi-Lepri A De Luca A Easterbrook P Günthard HF Mallal S Mussini C Dalmau J Martinez-Picado J Miro JM Obel N Wolinsky SM Martinson JJ Detels R Margolick JB Jacobson LP Descombes P Antonarakis SE Beckmann JS O'Brien SJ A whole-genome association study of major determinants for host control of HIV-1Science2007 317944947

    Not in our library10.1126/science.1143767

  4. 4.

    Tanaka Y Nishida N Sugiyama M Kurosaki M Matsuura K Sakamoto N Nakagawa M Korenaga M Hino K Hige S Ito Y Mita E Tanaka E Mochida S Murawaki Y Honda M Sakai A Hiasa Y Nishiguchi S Koike A Sakaida I Imamura M Ito K Yano K Masaki N Sugauchi F Izumi N Tokunaga K Mizokami M Genome-wide association of IL28B with response to pegylated interferon-alpha and ribavirin therapy for chronic hepatitis CNat Genet2009 4111051109

    Not in our library10.1038/ng.449

  5. 5.

    Greco JA Pollins AC Boone BE Levy SE Nanney LB A microarray analysis of temporal gene expression profiles in thermally injured human skinBurns2010 36192204

    Not in our library10.1016/j.burns.2009.06.211

  6. 6.

    Cole J Tsou R Wallace K Gibran N Isik F Comparison of normal human skin gene expression using cDNA microarraysWound Repair Regen2001 97785

    Not in our library10.1046/j.1524-475x.2001.00077.x

  7. 7.

    Berchtold LA Larsen CM Vaag A Faulenbach M Workman CT Kruhoffer M Donath M Mandrup-Poulsen T IL-1 receptor antagonism and muscle gene expression in patients with type 2 diabetesEur Cytokine Netw2009 208187

    Not in our library10.1684/ecn.2009.0152

  8. 8.

    Frueh FW Hayashibara KC Brown PO Whitlock JP Jr Use of cDNA microarrays to analyze dioxin-induced changes in human liver gene expressionToxicol Lett2001 122189203

    Not in our library10.1016/s0378-4274(01)00364-2

  9. 9.

    Flanagan JM Steward S Hankins JS Howard TM Neale G Ware RE Microarray analysis of liver gene expression in iron overloaded patients with sickle cell anemia and beta-thalassemiaAm J Hematol2009 84328334

    Not in our library10.1002/ajh.21407

  10. 10.

    Flechner SM Kurian SM Head SR Sharp SM Whisenant TC Zhang J Chismar JD Horvath S Mondala T Gilmartin T Cook DJ Kay SA Walker JR Salomon DR Kidney transplant rejection and tissue injury by gene profiling of biopsies and peripheral blood lymphocytesAm J Transplant2004 414751489

    Not in our library10.1111/j.1600-6143.2004.00526.x

  11. 11.

    Bunnag S Einecke G Reeve J Jhangri GS Mueller TF Sis B Hidalgo LG Mengel M Kayser D Kaplan B Halloran PF Molecular correlates of renal function in kidney transplant biopsiesJ Am Soc Nephrol2009 2011491160

    Not in our library10.1681/asn.2008080863

  12. 12.

    Glatt SJ Everall IP Kremen WS Corbeil J Sasik R Khanlou N Han M Liew CC Tsuang MT Comparative gene expression analysis of blood and brain provides concurrent validation of SELENBP1 up-regulation in schizophreniaProc Natl Acad Sci USA2005 1021553315538

    Not in our library10.1073/pnas.0507666102

  13. 13.

    Ramilo O Allman W Chung W Mejias A Ardura M Glaser C Wittkowski KM Piqueras B Banchereau J Palucka AK Chaussabel D Gene expression patterns in blood leukocytes discriminate patients with acute infectionsBlood2007 10920662077

    Not in our library10.1182/blood-2006-02-002477

  14. 14.

    Pascual V Allantaz F Arce E Punaro M Banchereau J Role of interleukin-1 (IL-1) in the pathogenesis of systemic onset juvenile idiopathic arthritis and clinical response to IL-1 blockadeJ Exp Med2005 20114791486

    Not in our library10.1084/jem.20050473

  15. 15.

    Chaussabel D Quinn C Shen J Patel P Glaser C Baldwin N Stichweh D Blankenship D Li L Munagala I Bennett L Allantaz F Mejias A Ardura M Kaizer E Monnet L Allman W Randall H Johnson D Lanier A Punaro M Wittkowski KM White P Fay J Klintmalm G Ramilo O Palucka AK Banchereau J Pascual V A modular analysis framework for blood genomics studies: application to systemic lupus erythematosusImmunity2008 29150164

    Not in our library10.1016/j.immuni.2008.05.012

  16. 16.

    Allantaz F Chaussabel D Allantaz F Chaussabel D Stichweh D Bennett L Allman W Mejias A Ardura M Chung W Smith E Wise C Palucka K Ramilo O Punaro M Banchereau J Pascual V Blood leukocyte microarrays to diagnose systemic onset juvenile idiopathic arthritis and follow the response to IL-1 blockadeJ Exp Med2007 20421312144

    Not in our library10.1084/jem.20070070

  17. 17.

    Wold B Myers RM Sequence census methods for functional genomicsNat Methods2008 51921

    Not in our library10.1038/nmeth1157

  18. 18.

    Sultan M Schulz MH Richard H Magen A Klingenhoff A Scherf M Seifert M Borodina T Soldatov A Parkhomchuk D Schmidt D O'Keeffe S Haas S Vingron M Lehrach H Yaspo ML A global view of gene activity and alternative splicing by deep sequencing of the human transcriptomeScience2008 321956960

    Not in our library10.1126/science.1160342

  19. 19.

    Geiss GK Bumgarner RE Birditt B Dahl T Dowidar N Dunaway DL Fell HP Ferree S George RD Grogan T James JJ Maysuria M Mitton JD Oliveri P Osborn JL Peng T Ratcliffe AL Webster PJ Davidson EH Hood L Dimitrov K Direct multiplexed measurement of gene expression with color-coded probe pairsNat Biotechnol2008 26317325

    Not in our library10.1038/nbt1385

  20. 20.

    Bennett L Palucka AK Arce E Cantrell V Borvak J Banchereau J Pascual V Interferon and granulopoiesis signatures in systemic lupus erythematosus bloodJ Exp Med2003 197711723

    Not in our library10.1084/jem.20021553

  21. 21.

    Baechler EC Batliwalla FM Karypis G Gaffney PM Ortmann WA Espe KJ Shark KB Grande WJ Hughes KM Kapur V Gregersen PK Behrens TW Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupusProc Natl Acad Sci USA2003 10026102615

    Not in our library10.1073/pnas.0337679100

  22. 22.

    Llorente L Zou W Levy Y Richaud-Patin Y Wijdenes J Alcocer-Varela J Morel-Fourrier B Brouet JC Alarcon-Segovia D Galanaud P Emilie D Role of interleukin 10 in the B lymphocyte hyperactivity and autoantibody production of human systemic lupus erythematosusJ Exp Med1995 181839844

    Not in our library10.1084/jem.181.3.839

  23. 23.

    Llorente L Richaud-Patin Y García-Padilla C Claret E Jakez-Ocampo J Cardiel MH Alcocer-Varela J Grangeot-Keros L Alarcón-Segovia D Wijdenes J Galanaud P Emilie D Clinical and biologic effects of anti-interleukin-10 monoclonal antibody administration in systemic lupus erythematosusArthritis Rheum2000 4317901800

    Not in our library10.1002/1529-0131(200008)43:8<1790::aid-anr15>3.0.co;2-2

  24. 24.

    Yokoyama H Takabatake T Takaeda M Wada T Naito T Ikeda K Goshima S Takasawa K Tomosugi N Kobayashi K Kida H Up-regulated MHC-class II expression and gamma-IFN and soluble IL-2R in lupus nephritisKidney Int1992 42755763

    Not in our library10.1038/ki.1992.344

  25. 25.

    Haas C Ryffel B Le Hir M IFN-gamma is essential for the development of autoimmune glomerulonephritis in MRL/Ipr miceJ Immunol1997 15854845491

    Not in our library

  26. 26.

    Horwitz DA Gray JD Behrendsen SC Kubin M Rengaraju M Ohtsuka K Trinchieri G Decreased production of interleukin-12 and other Th1-type cytokines in patients with recent-onset systemic lupus erythematosusArthritis Rheum1998 41838844

    Not in our library10.1002/1529-0131(199805)41:5<838::aid-art10>3.0.co;2-s

  27. 27.

    Preble OT Black RJ Friedman RM Klippel JH Vilcek J Systemic lupus erythematosus: presence in human serum of an unusual acid-labile leukocyte interferonScience1982 216429431

    Not in our library10.1126/science.6176024

  28. 28.

    Liu K Mohan C What do mouse models teach us about human SLE?Clin Immunol2006 119123130

    Not in our library10.1016/j.clim.2006.01.014

  29. 29.

    Tsao BP Update on human systemic lupus erythematosus geneticsCurr Opin Rheumatol2004 16513521

    Not in our library10.1097/01.bor.0000132648.62680.81

  30. 30.

    Sandrin-Garcia P Junta CM Fachin AL Mello SS Baião AM Rassi DM Ferreira MC Trevisan GL Sakamoto-Hojo ET Louzada-Júnior P Passos GA Donadi EA Shared and unique gene expression in systemic lupus erythematosus depending on disease activityAnn N Y Acad Sci2009 1173493500

    Not in our library10.1111/j.1749-6632.2009.04636.x

  31. 31.

    Petri M Singh S Tesfasyone H Dedrick R Fry K Lal P Williams G Bauer J Gregersen P Behrens T Baechler E Longitudinal expression of type I interferon responsive genes in systemic lupus erythematosusLupus2009 18980989

    Not in our library10.1177/0961203309105529

  32. 32.

    Nikpour M Dempsey AA Urowitz MB Gladman DD Barnes DA Association of a gene expression profile from whole blood with disease activity in systemic lupus erythaematosusAnn Rheum Dis2008 6710691075

    Not in our library10.1136/ard.2007.074765

  33. 33.

    Nakou M Knowlton N Frank MB Bertsias G Osban J Sandel CE Papadaki H Raptopoulou A Sidiropoulos P Kritikos I Tassiulas I Centola M Boumpas DT Gene expression in systemic lupus erythematosus: bone marrow analysis differentiates active from inactive disease and reveals apoptosis and granulopoiesis signaturesArthritis Rheum2008 5835413549

    Not in our library10.1002/art.23961

  34. 34.

    Bauer JW Petri M Batliwalla FM Koeuth T Wilson J Slattery C Panoskaltsis-Mortari A Gregersen PK Behrens TW Baechler EC Interferon-regulated chemokines as biomarkers of systemic lupus erythematosus disease activity: a validation studyArthritis Rheum2009 6030983107

    Not in our library10.1002/art.24803

  35. 35.

    Yao Y Richman L Higgs BW Morehouse CA de los Reyes M Brohawn P Zhang J White B Coyle AJ Kiener PA Jallal B Neutralization of interferon-alpha/beta-inducible genes and downstream effect in a phase I trial of an anti-interferon-alpha monoclonal antibody in systemic lupus erythematosusArthritis Rheum2009 6017851796

    Not in our library10.1002/art.24557

  36. 36.

    Pascual V Allantaz F Patel P Palucka AK Chaussabel D Banchereau J How the study of children with rheumatic diseases identified interferon-alpha and interleukin-1 as novel therapeutic targetsImmunol Rev2008 2233959

    Not in our library10.1111/j.1600-065x.2008.00643.x

  37. 37.

    Allantaz F Chaussabel D Banchereau J Pascual V Microarray-based identification of novel biomarkers in IL-1-mediated diseasesCurr Opin Immunol2007 19623632

    Not in our library10.1016/j.coi.2007.10.003

  38. 38.

    Bomprezzi R Ringnér M Kim S Bittner ML Khan J Chen Y Elkahloun A Yu A Bielekova B Meltzer PS Martin R McFarland HF Trent JM Gene expression profile in multiple sclerosis patients and healthy controls: identifying pathways relevant to diseaseHum Mol Genet2003 1221912199

    Not in our library10.1093/hmg/ddg221

  39. 39.

    Achiron A Gurevich M Friedman N Kaminski N Mandel M Blood transcriptional signatures of multiple sclerosis: unique gene expression of disease activityAnn Neurol2004 55410417

    Not in our library10.1002/ana.20008

  40. 40.
    Pharmacogenomics of interferon-beta therapy in multiple sclerosis: baseline IFN signature determines pharmacological differences between patients. · 2008
    Not checked
  41. 41.

    Gurevich M Tuller T Rubinstein U Or-Bach R Achiron A Prediction of acute multiple sclerosis relapses by transcription levels of peripheral blood cellsBMC Med Genomics2009 246

    Not in our library10.1186/1755-8794-2-46

  42. 42.

    Achiron A Gurevich M Snir Y Segal E Mandel M Zinc-ion binding and cytokine activity regulation pathways predicts outcome in relapsing-remitting multiple sclerosisClin Exp Immunol2007 149235242

    Not in our library10.1111/j.1365-2249.2007.03405.x

  43. 43.

    Nomura I Gao B Boguniewicz M Darst MA Travers JB Leung DY Distinct patterns of gene expression in the skin lesions of atopic dermatitis and psoriasis: a gene microarray analysisJ Allergy Clin Immunol2003 11211951202

    Not in our library10.1016/j.jaci.2003.08.049

  44. 44.

    de Jongh GJ Zeeuwen PL Kucharekova M Pfundt R van der Valk PG Blokx W Dogan A Hiemstra PS van de Kerkhof PC Schalkwijk J High expression levels of keratinocyte antimicrobial proteins in psoriasis compared with atopic dermatitisJ Invest Dermatol2005 12511631173

    Not in our library10.1111/j.0022-202x.2005.23935.x

  45. 45.

    Stoeckman AK Baechler EC Ortmann WA Behrens TW Michet CJ Peterson EJ A distinct inflammatory gene expression profile in patients with psoriatic arthritisGenes Immun2006 7583591

    Not in our library10.1038/sj.gene.6364334

  46. 46.

    Koczan D Guthke R Thiesen HJ Ibrahim SM Kundt G Krentz H Gross G Kunz M Gene expression profiling of peripheral blood mononuclear leukocytes from psoriasis patients identifies new immune regulatory moleculesEur J Dermatol2005 15251257

    Not in our library

  47. 47.

    Batliwalla FM Li W Ritchlin CT Xiao X Brenner M Laragione T Shao T Durham R Kemshetti S Schwarz E Coe R Kern M Baechler EC Behrens TW Gregersen PK Gulko PS Microarray analyses of peripheral blood cells identifies unique gene expression signature in psoriatic arthritisMol Med2005 112129

    Not in our library10.2119/2006-00003.gulko

  48. 48.

    Crow MK Wohlgemuth J Microarray analysis of gene expression in lupusArthritis Res Ther2003 5279287

    Not in our library10.1186/ar1015

  49. 49.

    Han GM Chen SL Shen N Ye S Bao CD Gu YY Analysis of gene expression profiles in human systemic lupus erythematosus using oligonucleotide microarrayGenes Immun2003 4177186

    Not in our library10.1038/sj.gene.6363966

  50. 50.

    Pascual V Allantaz F Arce E Punaro M Banchereau J Role of interleukin-1 (IL-1) in the pathogenesis of systemic onset juvenile idiopathic arthritis and clinical response to IL-1 blockadeJ Exp Med2005 20113491350

    Not in our library10.1084/jem.20050473

  51. 51.

    Ogilvie EM Khan A Hubank M Kellam P Woo P Specific gene expression profiles in systemic juvenile idiopathic arthritisArthritis Rheum2007 5619541965

    Not in our library10.1002/art.22644

  52. 52.

    Fall N Barnes M Thornton S Luyrink L Olson J Ilowite NT Gottlieb BS Griffin T Sherry DD Thompson S Glass DN Colbert RA Grom AA Gene expression profiling of peripheral blood from patients with untreated new-onset systemic juvenile idiopathic arthritis reveals molecular heterogeneity that may predict macrophage activation syndromeArthritis Rheum2007 5637933804

    Not in our library10.1002/art.22981

  53. 53.

    Barnes MG Grom AA Thompson SD Griffin TA Pavlidis P Itert L Fall N Sowders DP Hinze CH Aronow BJ Luyrink LK Srivastava S Ilowite NT Gottlieb BS Olson JC Sherry DD Glass DN Colbert RA Subtype-specific peripheral blood gene expression profiles in recent-onset juvenile idiopathic arthritisArthritis Rheum2009 6021022112

    Not in our library10.1002/art.24601

  54. 54.

    Achiron A Feldman A Mandel M Gurevich M Impaired expression of peripheral blood apoptotic-related gene transcripts in acute multiple sclerosis relapseAnn N Y Acad Sci2007 1107155167

    Not in our library10.1196/annals.1381.017

  55. 55.

    Singh MK Scott TF LaFramboise WA Hu FZ Post JC Ehrlich GD Gene expression changes in peripheral blood mononuclear cells from multiple sclerosis patients undergoing beta-interferon therapyJ Neurol Sci2007 2585259

    Not in our library10.1016/j.jns.2007.02.034

  56. 56.

    Edwards CJ Feldman JL Beech J Shields KM Stover JA Trepicchio WL Larsen G Foxwell BM Brennan FM Feldmann M Pittman DD Molecular profile of peripheral blood mononuclear cells from patients with rheumatoid arthritisMol Med2007 134058

    Not in our library10.2119/2006-00056.edwards

  57. 57.

    van der Pouw Kraan TC Wijbrandts CA van Baarsen LG Voskuyl AE Rustenburg F Baggen JM Ibrahim SM Fero M Dijkmans BA Tak PP Verweij CL Rheumatoid arthritis subtypes identified by genomic profiling of peripheral blood cells: assignment of a type I interferon signature in a subpopulation of patientsAnn Rheum Dis2007 6610081014

    Not in our library10.1136/ard.2006.063412

  58. 58.

    Lequerré T Gauthier-Jauneau AC Bansard C Derambure C Hiron M Vittecoq O Daveau M Mejjad O Daragon A Tron F Le Loët X Salier JP Gene profiling in white blood cells predicts infliximab responsiveness in rheumatoid arthritisArthritis Res Ther2006 8R105

    Not in our library10.1186/ar1924

  59. 59.

    Batliwalla FM Baechler EC Xiao X Li W Balasubramanian S Khalili H Damle A Ortmann WA Perrone A Kantor AB Gulko PS Kern M Furie R Behrens TW Gregersen PK Peripheral blood gene expression profiling in rheumatoid arthritisGenes Immun2005 6388397

    Not in our library10.1038/sj.gene.6364209

  60. 60.

    Emamian ES Leon JM Lessard CJ Grandits M Baechler EC Gaffney PM Segal B Rhodus NL Moser KL Peripheral blood gene expression profiling in Sjogren's syndromeGenes Immun2009 10285296

    Not in our library10.1038/gene.2009.20

  61. 61.

    Kaizer EC Glaser CL Chaussabel D Banchereau J Pascual V White PC Gene expression in peripheral blood mononuclear cells from children with diabetesJ Clin Endocrinol Metab2007 9237053711

    Not in our library10.1210/jc.2007-0979

  62. 62.

    Takamura T Honda M Sakai Y Ando H Shimizu A Ota T Sakurai M Misu H Kurita S Matsuzawa-Nagata N Uchikata M Nakamura S Matoba R Tanino M Matsubara K Kaneko S Gene expression profiles in peripheral blood mononuclear cells reflect the pathophysiology of type 2 diabetesBiochem Biophys Res Commun2007 361379384

    Not in our library10.1016/j.bbrc.2007.07.006

  63. 63.

    Burczynski ME Peterson RL Twine NC Zuberek KA Brodeur BJ Casciotti L Maganti V Reddy PS Strahs A Immermann F Spinelli W Schwertschlag U Slager AM Cotreau MM Dorner AJ Molecular classification of Crohn's disease and ulcerative colitis patients using transcriptional profiles in peripheral blood mononuclear cellsJ Mol Diagn2006 85161

    Not in our library10.2353/jmoldx.2006.050079

  64. 64.

    Greenberg SA Pinkus JL Pinkus GS Burleson T Sanoudou D Tawil R Barohn RJ Saperstein DS Briemberg HR Ericsson M Park P Amato AA Interferon-alpha/beta-mediated innate immune mechanisms in dermatomyositisAnn Neurol2005 57664678

    Not in our library10.1002/ana.20464

  65. 65.

    Baechler EC Bauer JW Slattery CA Ortmann WA Espe KJ Novitzke J Ytterberg SR Gregersen PK Behrens TW Reed AM An interferon signature in the peripheral blood of dermatomyositis patients is associated with disease activityMol Med2007 135968

    Not in our library10.2119/2006-00085.baechler

  66. 66.

    Tan FK Zhou X Mayes MD Gourh P Guo X Marcum C Jin L Arnett FC Jr Signatures of differentially regulated interferon gene expression and vasculotrophism in the peripheral blood cells of systemic sclerosis patientsRheumatology2006 45694702

    Not in our library10.1093/rheumatology/kei244

  67. 67.

    York MR Nagai T Mangini AJ Lemaire R van Seventer JM Lafyatis R A macrophage marker, Siglec-1, is increased on circulating monocytes in patients with systemic sclerosis and induced by type I interferons and toll-like receptor agonistsArthritis Rheum2007 5610101020

    Not in our library10.1002/art.22382

  68. 68.

    Alcorta DA Barnes DA Dooley MA Sullivan P Jonas B Liu Y Lionaki S Reddy CB Chin H Dempsey AA Jennette JC Falk RJ Leukocyte gene expression signatures in antineutrophil cytoplasmic autoantibody and lupus glomerulonephritisKidney Int2007 72853864

    Not in our library10.1038/sj.ki.5002371

  69. 69.

    Potti A Bild A Dressman HK Lewis DA Nevins JR Ortel TL Gene-expression patterns predict phenotypes of immune-mediated thrombosisBlood2006 10713911396

    Not in our library10.1182/blood-2005-07-2669

  70. 70.

    Pascual V Chaussabel D Banchereau J A genomic approach to human autoimmune diseasesAnnu Rev Immunol2010 28535571

    Not in our library10.1146/annurev-immunol-030409-101221

  71. 71.

    Janeway CA Jr Medzhitov R Innate immune recognitionAnnu Rev Immunol2002 20197216

    Not in our library10.1146/annurev.immunol.20.083001.084359

  72. 72.

    Aderem A Ulevitch RJ Toll-like receptors in the induction of the innate immune responseNature2000 406782787

    Not in our library10.1038/35021228

  73. 73.

    Nau GJ Richmond JF Schlesinger A Jennings EG Lander ES Young RA Human macrophage activation programs induced by bacterial pathogensProc Natl Acad Sci USA2002 9915031508

    Not in our library10.1073/pnas.022649799

  74. 74.

    Huang Q Liu D Majewski P Schulte LC Korn JM Young RA Lander ES Hacohen N The plasticity of dendritic cell responses to pathogens and their componentsScience2001 294870875

    Not in our library10.1126/science.294.5543.870

  75. 75.

    Chaussabel D Semnani RT McDowell MA Sacks D Sher A Nutman TB Unique gene expression profiles of human macrophages and dendritic cells to phylogenetically distinct parasitesBlood2003 102672681

    Not in our library10.1182/blood-2002-10-3232

  76. 76.
    Genomic transcriptional profiling identifies a candidate blood biomarker signature for the diagnosis of septicemic melioidosis · 2009
    No notice
  77. 77.

    Tang BM McLean AS Dawes IW Huang SJ Lin RC Gene-expression profiling of peripheral blood mononuclear cells in sepsisCrit Care Med2009 37882888

    Not in our library10.1097/ccm.0b013e31819b52fd

  78. 78.

    Jacobsen M Repsilber D Gutschmidt A Neher A Feldmann K Mollenkopf HJ Ziegler A Kaufmann SH Candidate biomarkers for discrimination between infection and disease caused by Mycobacterium tuberculosisJ Mol Med2007 85613621

    Not in our library10.1007/s00109-007-0157-6

  79. 79.

    Griffiths MJ Shafi MJ Popper SJ Hemingway CA Kortok MM Wathen A Rockett KA Mott R Levin M Newton CR Marsh K Relman DA Kwiatkowski DP Genomewide analysis of the host response to malaria in Kenyan childrenJ Infect Dis2005 19115991611

    Not in our library10.1086/429297

  80. 80.

    Franklin BS Parroche P Ataíde MA Lauw F Ropert C de Oliveira RB Pereira D Tada MS Nogueira P da Silva LH Bjorkbacka H Golenbock DT Gazzinelli RT Malaria primes the innate immune response due to interferon-gamma induced enhancement of toll-like receptor expression and functionProc Natl Acad Sci USA2009 10657895794

    Not in our library10.1073/pnas.0809742106

  81. 81.
    Expression profile of immune response genes in patients with Severe Acute Respiratory Syndrome · 2005
    No notice
  82. 82.

    Popper SJ Watson VE Shimizu C Kanegaye JT Burns JC Relman DA Gene transcript abundance profiles distinguish Kawasaki disease from adenovirus infectionJ Infect Dis2009 200657666

    Not in our library10.1086/603538

  83. 83.

    Franklin BS Parroche P Ataíde MA Lauw F Ropert C de Oliveira RB Pereira D Tada MS Nogueira P da Silva LH Bjorkbacka H Golenbock DT Gazzinelli RT Gene expression signatures diagnose influenza and other symptomatic respiratory viral infections in humansCell Host Microbe2009 6207217

    Not in our library10.1016/j.chom.2009.07.006

  84. 84.

    Thach DC Agan BK Olsen C Diao J Lin B Gomez J Jesse M Jenkins M Rowley R Hanson E Tibbetts C Stenger DA Walter E Epidemic Outbreak Surveillance (EOS)Surveillance of transcriptomes in basic military trainees with normal, febrile respiratory illness, and convalescent phenotypesGenes Immun2005 6588595

    Not in our library10.1038/sj.gene.6364244

  85. 85.

    Ubol S Masrinoul P Chaijaruwanich J Kalayanarooj S Charoensirisuthikul T Kasisith J Differences in global gene expression in peripheral blood mononuclear cells indicate a significant role of the innate responses in progression of dengue fever but not dengue hemorrhagic feverJ Infect Dis2008 19714591467

    Not in our library10.1086/587699

  86. 86.
    Gene expression profiling during early acute febrile stage of dengue infection can predict the disease outcome. · 2009
    Not checked
  87. 87.

    Thompson LJ Dunstan SJ Dolecek C Perkins T House D Dougan G Nguyen TH Tran TP Doan CD Le TP Nguyen TD Tran TH Farrar JJ Monack D Lynn DJ Popper SJ Falkow S Transcriptional response in the peripheral blood of patients infected with Salmonella enterica serovar TyphiProc Natl Acad Sci USA2009 1062243322438

    Not in our library10.1073/pnas.0912386106

  88. 88.

    Ardura MI Banchereau R Mejias A Di Pucchio T Glaser C Allantaz F Pascual V Banchereau J Chaussabel D Ramilo O Enhanced monocyte response and decreased central memory T cells in children with invasive Staphylococcus aureus infectionsPLoS One2009 4e5446

    Not in our library10.1371/journal.pone.0005446

  89. 89.

    Wong HR Cvijanovich N Allen GL Lin R Anas N Meyer K Freishtat RJ Monaco M Odoms K Sakthivel B Shanley TP Genomics of Pediatric SIRS/Septic Shock InvestigatorsGenomic expression profiling across the pediatric systemic inflammatory response syndrome, sepsis, and septic shock spectrumCrit Care Med2009 3715581566

    Not in our library10.1097/ccm.0b013e31819fcc08

  90. 90.

    Payen D Lukaszewicz AC Belikova I Faivre V Gelin C Russwurm S Launay JM Sevenet N Gene profiling in human blood leucocytes during recovery from septic shockIntensive Care Med2008 3413711376

    Not in our library10.1007/s00134-008-1048-1

  91. 91.

    Johnson SB Lissauer M Bochicchio GV Moore R Cross AS Scalea TM Gene expression profiles differentiate between sterile SIRS and early sepsisAnn Surg2007 245611621

    Not in our library10.1097/01.sla.0000251619.10648.32

  92. 92.

    Staratschek-Jox A Classen S Gaarz A Debey-Pascher S Schultze JL Blood-based transcriptomics: leukemias and beyondExpert Rev Mol Diagn2009 9271280

    Not in our library10.1586/erm.09.9

  93. 93.

    Aaroe J Lindahl T Dumeaux V Saebo S Tobin D Hagen N Skaane P Lonneborg A Sharma P Borresen-Dale AL Gene expression profiling of peripheral blood cells for early detection of breast cancerBreast Cancer Res2010 12R7

    Not in our library10.1186/bcr2472

  94. 94.

    Findeisen P Rockel M Nees M Roder C Kienle P Von Knebel Doeberitz M Kalthoff H Neumaier M Systematic identification and validation of candidate genes for detection of circulating tumor cells in peripheral blood specimens of colorectal cancer patientsInt J Oncol2008 3310011010

    Not in our library

  95. 95.

    Hayes DC Secrist H Bangur CS Wang T Zhang X Harlan D Goodman GE Houghton RL Persing DH Zehentner BK Multigene real-time PCR detection of circulating tumor cells in peripheral blood of lung cancer patientsAnticancer Res2006 2615671575

    Not in our library

  96. 96.

    Martin KJ Graner E Li Y Price LM Kritzman BM Fournier MV Rhei E Pardee AB High-sensitivity array analysis of gene expression for the early detection of disseminated breast tumor cells in peripheral bloodProc Natl Acad Sci USA2001 9826462651

    Not in our library10.1073/pnas.041622398

  97. 97.

    Martínez-Llordella M Lozano JJ Puig-Pey I Orlando G Tisone G Lerut J Benítez C Pons JA Parrilla P Ramírez P Bruguera M Rimola A Sánchez-Fueyo A Using transcriptional profiling to develop a diagnostic test of operational tolerance in liver transplant recipientsJ Clin Invest2008 11828452857

    Not in our library10.1172/jci35342

  98. 98.

    Kawasaki M Iwasaki M Koshiba T Fujino M Hara Y Kitazawa Y Kimura H Uemoto S Li XK Tanaka K Gene expression profile analysis of the peripheral blood mononuclear cells from tolerant living-donor liver transplant recipientsInt Surg2007 92276286

    Not in our library

  99. 99.

    Brouard S Mansfield E Braud C Li L Giral M Hsieh SC Baeten D Zhang M Ashton-Chess J Braudeau C Hsieh F Dupont A Pallier A Moreau A Louis S Ruiz C Salvatierra O Soulillou JP Sarwal M Identification of a peripheral blood transcriptional biomarker panel associated with operational renal allograft toleranceProc Natl Acad Sci USA2007 1041544815453

    Not in our library10.1073/pnas.0705834104

  100. 100.

    Lin D Hollander Z Ng RT Imai C Ignaszewski A Balshaw R Freue GC Wilson-McManus JE Qasimi P Meredith A Mui A Triche T McMaster R Keown PA McManus BM Biomarkers in Transplantation Team; NCE CECR Centre of Excellence for the Prevention of Organ FailureWhole blood genomic biomarkers of acute cardiac allograft rejectionJ Heart Lung Transplant2009 28927935

    Not in our library10.1016/j.healun.2009.04.025

  101. 101.

    Alakulppi N Seikku P Jaatinen T Holmberg C Laine J Feasibility of diagnosing subclinical renal allograft rejection in children by whole blood gene expression analysisTransplantation2008 8612221228

    Not in our library10.1097/tp.0b013e3181883fb0

  102. 102.

    ueller TF Einecke G Reeve J Sis B Mengel M Jhangri GS Bunnag S Cruz J Wishart D Meng C Broderick G Kaplan B Halloran PF Microarray analysis of rejection in human kidney transplants using pathogenesis-based transcript setsAm J Transplant2007 727122722

    Not in our library10.1111/j.1600-6143.2007.02005.x

  103. 103.

    Sarwal M Chua MS Kambham N Hsieh SC Satterwhite T Masek M Salvatierra O Jr Molecular heterogeneity in acute renal allograft rejection identified by DNA microarray profilingN Engl J Med2003 349125138

    Not in our library10.1056/nejmoa035588

  104. 104.

    Scherer A Krause A Walker JR Korn A Niese D Raulf F Early prognosis of the development of renal chronic allograft rejection by gene expression profiling of human protocol biopsiesTransplantation2003 7513231330

    Not in our library10.1097/01.tp.0000068481.98801.10

  105. 105.

    Tang Y Lu A Aronow BJ Sharp FR Blood genomic responses differ after stroke, seizures, hypoglycemia, and hypoxia: blood genomic fingerprints of diseaseAnn Neurol2001 50699707

    Not in our library10.1002/ana.10042

  106. 106.

    Nakayama M Kudoh T Kaikita K Yoshimura M Oshima S Miyamoto Y Takeya M Ogawa H Class A macrophage scavenger receptor gene expression levels in peripheral blood mononuclear cells specifically increase in patients with acute coronary syndromeAtherosclerosis2008 198426433

    Not in our library10.1016/j.atherosclerosis.2007.09.006

  107. 107.

    Moore DF Li H Jeffries N Wright V Cooper RA Jr Elkahloun A Gelderman MP Zudaire E Blevins G Yu H Goldin E Baird AE Using peripheral blood mononuclear cells to determine a gene expression profile of acute ischemic stroke: a pilot investigationCirculation2005 111212221

    Not in our library10.1161/01.cir.0000152105.79665.c6

  108. 108.

    Cappuzzello C Napolitano M Arcelli D Melillo G Melchionna R Di Vito L Carlini D Silvestri L Brugaletta S Liuzzo G Crea F Capogrossi MC Gene expression profiles in peripheral blood mononuclear cells of chronic heart failure patientsPhysiol Genomics2009 38233240

    Not in our library10.1152/physiolgenomics.90364.2008

  109. 109.

    Maes OC Xu S Yu B Chertkow HM Wang E Schipper HM Transcriptional profiling of Alzheimer blood mononuclear cells by microarrayNeurobiol Aging2007 2817951809

    Not in our library10.1016/j.neurobiolaging.2006.08.004

  110. 110.

    Lovrecic L Kastrin A Kobal J Pirtosek Z Krainc D Peterlin B Gene expression changes in blood as a putative biomarker for Huntington's diseaseMov Disord2009 2422772281

    Not in our library10.1002/mds.22477

  111. 111.

    Borovecki F Lovrecic L Zhou J Jeong H Then F Rosas HD Hersch SM Hogarth P Bouzou B Jensen RV Krainc D Genome-wide expression profiling of human blood reveals biomarkers for Huntington's diseaseProc Natl Acad Sci USA2005 1021102311028

    Not in our library10.1073/pnas.0504921102

  112. 112.

    Hakonarson H Bjornsdottir US Halapi E Bradfield J Zink F Mouy M Helgadottir H Gudmundsdottir AS Andrason H Adalsteinsdottir AE Kristjansson K Birkisson I Arnason T Andresdottir M Gislason D Gislason T Gulcher JR Stefansson K Profiling of genes expressed in peripheral blood mononuclear cells predicts glucocorticoid sensitivity in asthma patientsProc Natl Acad Sci USA2005 1021478914794

    Not in our library10.1073/pnas.0409904102

  113. 113.

    Aoki T Matsumoto Y Hirata K Ochiai K Okada M Ichikawa K Shibasaki M Arinami T Sumazaki R Noguchi E Expression profiling of genes related to asthma exacerbationsClin Exp Allergy2009 39213221

    Not in our library10.1111/j.1365-2222.2008.03186.x

  114. 114.

    Peretz A Peck EC Bammler TK Beyer RP Sullivan JH Trenga CA Srinouanprachnah S Farin FM Kaufman JD Diesel exhaust inhalation and assessment of peripheral blood mononuclear cell gene transcription effects: an exploratory study of healthy human volunteersInhal Toxicol2007 1911071119

    Not in our library10.1080/08958370701665384

  115. 115.

    McHale CM Zhang L Lan Q Li G Hubbard AE Forrest MS Vermeulen R Chen J Shen M Rappaport SM Yin S Smith MT Rothman N Changes in the peripheral blood transcriptome associated with occupational benzene exposure identified by cross-comparison on two microarray platformsGenomics2009 93343349

    Not in our library10.1016/j.ygeno.2008.12.006

  116. 116.

    Bushel PR Heinloth AN Li J Huang L Chou JW Boorman GA Malarkey DE Houle CD Ward SM Wilson RE Fannin RD Russo MW Watkins PB Tennant RW Paules RS Blood gene expression signatures predict exposure levelsProc Natl Acad Sci USA2007 1041821118216

    Not in our library10.1073/pnas.0706987104

  117. 117.

    Kawai T Morita K Masuda K Nishida K Sekiyama A Teshima-Kondo S Nakaya Y Ohta M Saito T Rokutan K Physical exercise-associated gene expression signatures in peripheral bloodClin J Sport Med2007 17375383

    Not in our library10.1097/jsm.0b013e31814c3e4f

  118. 118.

    Connolly PH Caiozzo VJ Zaldivar F Nemet D Larson J Hung SP Heck JD Hatfield GW Cooper DM Effects of exercise on gene expression in human peripheral blood mononuclear cellsJ Appl Physiol2004 9714611469

    Not in our library10.1152/japplphysiol.00316.2004

  119. 119.

    Hayashi T Tsujii S Iburi T Tamanaha T Yamagami K Ishibashi R Hori M Sakamoto S Ishii H Murakami K Laughter up-regulates the genes related to NK cell activity in diabetesBiomed Res2007 28281285

    Not in our library10.2220/biomedres.28.281

  120. 120.

    Chaussabel D Ueno H Banchereau J Quinn C Data management: it starts at the benchNat Immunol2009 1012251227

    Not in our library10.1038/ni1209-1225

  121. 121.

    Mootha VK Bunkenborg J Olsen JV Hjerrild M Wisniewski JR Stahl E Bolouri MS Ray HN Sihag S Kamal M Patterson N Lander ES Mann M Integrated analysis of protein composition, tissue diversity, and gene regulation in mouse mitochondriaCell2003 115629640

    Not in our library10.1016/s0092-8674(03)00926-7

  122. 122.

    Segal E Yelensky R Koller D Genome-wide discovery of transcriptional modules from DNA sequence and gene expressionBioinformatics2003 19Suppl 1i273282

    Not in our library10.1093/bioinformatics/btg1038

  123. 123.

    Allison DB Cui X Page GP Sabripour M Microarray data analysis: from disarray to consolidation and consensusNat Rev Genet2006 75565

    Not in our library10.1038/nrg1749

  124. 124.
    Geometric Interpretation of Gene Coexpression Network Analysis · 2008
    No notice
  125. 125.

    Benjamini Y Hochberg Y Controlling the false discovery rate: a practical and powerful approach to multiple testingJ R Stat Soc Ser B1995 57289300

    Not in our library

  126. 126.

    Dobbin KK Zhao Y Simon RM How large a training set is needed to develop a classifier for microarray data?Clin Cancer Res2008 14108114

    Not in our library10.1158/1078-0432.ccr-07-0443

  127. 127.

    Jorstad TS Midelfart H Bones AM A mixture model approach to sample size estimation in two-sample comparative microarray experimentsBMC Bioinformatics2008 9117

    Not in our library10.1186/1471-2105-9-117

  128. 128.

    Pawitan Y Michiels S Koscielny S Gusnanto A Ploner A False discovery rate, sensitivity and sample size for microarray studiesBioinformatics2005 2130173024

    Not in our library10.1093/bioinformatics/bti448

  129. 129.

    Yang MC Yang JJ McIndoe RA She JX Microarray experimental design: power and sample size considerationsPhysiol Genomics2003 162428

    Not in our library10.1152/physiolgenomics.00037.2003

  130. 130.

    Ambros V The functions of animal microRNAsNature2004 431350355

    Not in our library10.1038/nature02871

  131. 131.

    Bartel DP MicroRNAs: genomics, biogenesis, mechanism, and functionCell2004 116281297

    Not in our library10.1016/s0092-8674(04)00045-5

  132. 132.

    Bushati N Cohen SM microRNA functionsAnnu Rev Cell Dev Biol2007 23175205

    Not in our library10.1146/annurev.cellbio.23.090506.123406

  133. 133.

    Cortez MA Calin GA MicroRNA identification in plasma and serum: a new tool to diagnose and monitor diseasesExpert Opin Biol Ther2009 9703711

    Not in our library10.1517/14712590902932889

  134. 134.

    Haining WN Ebert BL Subrmanian A Wherry EJ Eichbaum Q Evans JW Mak R Rivoli S Pretz J Angelosanto J Smutko JS Walker BD Kaech SM Ahmed R Nadler LM Golub TR Identification of an evolutionarily conserved transcriptional signature of CD8 memory differentiation that is shared by T and B cellsJ Immunol2008 18118591868

    Not in our library10.4049/jimmunol.181.3.1859

  135. 135.

    Baron C Somogyi R Greller LD Rineau V Wilkinson P Cho CR Cameron MJ Kelvin DJ Chagnon P Roy DC Busque L Sékaly RP Perreault C Prediction of graft-versus-host disease in humans by donor gene-expression profilingPLoS Med2007 4e23

    Not in our library10.1371/journal.pmed.0040023

  136. 136.

    Wang M Master SR Chodosh LA Computational expression deconvolution in a complex mammalian organBMC Bioinformatics2006 7328

    Not in our library10.1186/1471-2105-7-328

  137. 137.

    Lu P Nakorchevskiy A Marcotte EM Expression deconvolution: a reinterpretation of DNA microarray data reveals dynamic changes in cell populationsProc Natl Acad Sci USA2003 1001037010375

    Not in our library10.1073/pnas.1832361100

  138. 138.

    Lahdesmaki H Shmulevich L Dunmire V Yli-Harja O Zhang W In silico microdissection of microarray data from heterogeneous cell populationsBMC Bioinformatics2005 654

    Not in our library10.1186/1471-2105-6-54

  139. 139.
    Deconvolution of Blood Microarray Data Identifies Cellular Activation Patterns in Systemic Lupus Erythematosus · 2009
    No notice
  140. 140.

    Repsilber D Kern S Telaar A Walzl G Black GF Selbig J Parida SK Kaufmann SH Jacobsen M Biomarker discovery in heterogeneous tissue samples - taking the in-silico deconfounding approachBMC Bioinformatics1127

    Not in our library10.1186/1471-2105-11-27

  141. 141.

    Shen-Orr SS Tibshirani R Khatri P Bodian DL Staedtler F Perry NM Hastie T Sarwal MM Davis MM Butte AJ Cell type-specific gene expression differences in complex tissuesNat Methods2010 7287289

    Not in our library10.1038/nmeth.1439

  142. 142.

    Gaucher D Therrien R Kettaf N Angermann BR Boucher G Filali-Mouhim A Moser JM Mehta RS Drake DR Castro E Akondy R Rinfret A Yassine-Diab B Said EA Chouikh Y Cameron MJ Clum R Kelvin D Somogyi R Greller LD Balderas RS Wilkinson P Pantaleo G Tartaglia J Haddad EK Sékaly RP Yellow fever vaccine induces integrated multilineage and polyfunctional immune responsesJ Exp Med2008 20531193131

    Not in our library10.1084/jem.20082292

  143. 143.

    Querec TD Akondy RS Lee EK Cao W Nakaya HI Teuwen D Pirani A Gernert K Deng J Marzolf B Kennedy K Wu H Bennouna S Oluoch H Miller J Vencio RZ Mulligan M Aderem A Ahmed R Pulendran B Systems biology approach predicts immunogenicity of the yellow fever vaccine in humansNat Immunol2009 10116125

    Not in our library10.1038/ni.1688

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