Gene expression profiling in mitochondrial disease: assessment of microarray accuracy by high-throughput Q-PCR

Mitochondrial diseases are a heterogeneous array of disorders with a complex etiology. Use of microarrays as a tool to investigate complex human disease is increasingly common, however, a principle drawback of microarrays is their limited dynamic range, due to the poor quantification of weak signals. Although it is generally understood that low-intensity microarray 'spots' may be unreliable, there exists little documentation of their accuracy. Quantitative PCR (Q-PCR) is frequently used to validate microarray data, yet few Q-PCR validation studies have focused on the accuracy of low-intensity microarray signals. Hence, we have used Q-PCR to systematically assess microarray accuracy as a function of signal strength in a mouse model of mitochondrial disease, the superoxide dismutase 2 (SOD2) nullizygous mouse. We have focused on a unique category of data--spots with only one weak signal in a two-dye comparative hybridization--and show that such 'high-low' signal intensities are common for differentially expressed genes. This category of differential expression may be more important in mitochondrial disease in which there are often mosaic expression patterns due to the idiosyncratic distribution of mutant mtDNA in heteroplasmic individuals. Using RNA from the SOD2 mouse, we found that when spotted cDNA microarray data are filtered for quality (low variance between many technical replicates) and spot intensity (above a negative control threshold in both channels), there is an excellent quantitative concordance with Q-PCR (R2 = 0.94). The accuracy of gene expression ratios from low-intensity spots (R2 = 0.27) and 'high-low' spots (R2 = 0.32) is considerably lower. Our results should serve as guidelines for microarray interpretation and the selection of genes for validation in mitochondrial disorders.     

Integral protein microarrays for the identification of lung cancer antigens in sera that induce a humoral immune response

The identification of biomarkers (both molecules and profiles) in patient sera offers enormous interest for the diagnosis of cancers. In this context, the detection of antibodies to tumor cell autologous antigens possesses great potential. The humoral immune response represents a form of biological amplification of signals that are otherwise weak because of very low concentrations of antigen, especially in the early stages of cancers. Herein we present the use of integral microarrays spotted with tumor-derived proteins to investigate the antibody repertoire in the sera of lung cancer patients and controls. The use of two-dimensional liquid chromatography allowed us to separate proteins from the lung adenocarcinoma cell line A549 into 1760 fractions, which were printed in duplicate, along with various controls, onto nitrocellulose coated slides. The sensitivity and specificity of the microarrays to detect singular antibodies in fluids were first validated through the recognition of fractions containing a lung marker antigen by antibody probing. Twenty fractions were initially selected as highly reactive against the anti-PGP9.5 antibody, and subsequent mass spectrometry analyses confirmed the identity of PGP9.5 protein in four of them. As a result, the importance of neighboring fractions in microarray detection was revealed due to the spreading of proteins during the separation process. Next, the microarrays were individually incubated with 14 serum samples from patients with lung cancer patients, 14 sera from colon cancer patients, and 14 control sera from normal subjects. The reactivity of the selected fractions was analyzed, and the level of immunoglobulin bound to each fraction by each serum sample was quantified. Eight of the 20 fractions offered p values < 0.01 and were recognized by an average of four reacting patients, whereas no serum from normal individuals was positive for those fractions. Protein microarrays from tumor-derived fractions hold the diagnostic potential of uncovering antigens that induce an immune response in patients with certain types of cancers.     

Development of a fluorescent and colorimetric detection methods-based protein microarray for serodiagnosis of TORCH infections

We developed a protein microarray methodology that has the ability of serodiagnosis of IgM antibodies directed against TORCH pathogens. Six chemical surface modifications were validated by a dimension atomic force microscope (AFM) and contact angle measurement, agarose modified surface of which offered an appropriate platform for detecting IgM antibody. Further, signal amplification sensitivities on agarose modified microarrays were detected by Cy3-labeled biotin-streptavidin and immunogold-based assays. The detection limits of IgM antibody on the microarrays were 0.48 and 0.24mug/ml, quantitatively equal to 0.25 and 12.5pg, respectively, on each spot as ascertained by the two assays. Satisfactory linear correlations between the signal intensity and the logarithm of the IgM concentration were obtained. Finally, 60 serum samples characterized by a commercial ELISA were evaluated by the protein microarray. There were good concordances between the results of the protein microarray and ELISA assay for sorting of the TORCH infected sera (95.0% by fluorescence-based assay and 96.7% by immunogold-based assay). Clearly, the potential application of this protein microarray format facilitates clinical detection of not only the antibodies directed against TORCH pathogens but also other autoimmune diseases.     

Profiling humoral autoimmune repertoire of dilated cardiomyopathy (DCM) patients and development of a disease-associated protein chip

Dilated cardiomyopathy (DCM) is a myocardial disease characterized by progressive depression of myocardial contractile function and ventricular dilatation. Thirty percent of DCM patients belong to the inherited genetic form; the rest may be idiopathic, viral, autoimmune, or immune-mediated associated with a viral infection. Disturbances in humoral and cellular immunity have been described in cases of myocarditis and DCM. A number of autoantibodies against cardiac cell proteins have been identified in DCM. In this study, we have profiled the autoantibody repertoire of plasma from DCM patients against a human protein array consisting of 37,200 redundant, recombinant human proteins and performed qualitative and quantitative validation of these putative autoantigens on protein microarrays to identify novel putative DCM specific autoantigens. In addition to analyzing the whole IgG autoantibody repertoire, we have also analyzed the IgG3 antibody repertoire in the plasma samples to study the characteristics of IgG3 subclass antibodies. By combining screening of a protein expression library with protein microarray technology, we have detected 26 proteins identified by the IgG antibody repertoire and 6 proteins bound by the IgG3 subclass. Several of these autoantibodies found in plasma of DCM patients, such as the autoantibody against the Kv channel-interacting protein, are associated with heart failure.     

Application of protein microarrays for multiplexed detection of antibodies to tumor antigens in breast cancer

There is strong preclinical evidence that cancer, including breast cancer, undergoes immune surveillance. This continual monitoring, by both the innate and the adaptive immune systems, recognizes changes in protein expression, mutation, folding, glycosylation, and degradation. Local immune responses to tumor antigens are amplified in draining lymph nodes, and then enter the systemic circulation. The antibody response to tumor antigens, such as p53 protein, are robust, stable, and easily detected in serum; may exist in greater concentrations than their cognate antigens; and are potential highly specific biomarkers for cancer. However, antibodies have limited sensitivities as single analytes, and differences in protein purification and assay characteristics have limited their clinical application. For example, p53 autoantibodies in the sera are highly specific for cancer patients, but are only detected in the sera of 10-20% of patients with breast cancer. Detection of p53 autoantibodies is dependent on tumor burden, p53 mutation, rapidly decreases with effective therapy, but is relatively independent of breast cancer subtype. Although antibodies to hundreds of other tumor antigens have been identified in the sera of breast cancer patients, very little is known about the specificity and clinical impact of the antibody immune repertoire to breast cancer. Recent advances in proteomic technologies have the potential for rapid identification of immune response signatures for breast cancer diagnosis and monitoring. We have adapted programmable protein microarrays for the specific detection of autoantibodies in breast cancer. Here, we present the first demonstration of the application of programmable protein microarray ELISAs for the rapid identification of breast cancer autoantibodies.     

Microarrays as validation strategies in clinical samples: tissue and protein microarrays

The widespread use of DNA microarrays has led to the discovery of many genes whose expression profile may have significant clinical relevance. The translation of this data to the bedside requires that gene expression be validated as protein expression, and that annotated clinical samples be available for correlative and quantitative studies to assess clinical context and usefulness of putative biomarkers. We review two microarray platforms developed to facilitate the clinical validation of candidate biomarkers: tissue microarrays and reverse-phase protein microarrays. Tissue microarrays are arrays of core biopsies obtained from paraffin-embedded tissues, which can be assayed for histologically-specific protein expression by immunohistochemistry. Reverse-phase protein microarrays consist of arrays of cell lysates or, more recently, plasma or serum samples, which can be assayed for protein quantity and for the presence of post-translational modifications such as phosphorylation. Although these platforms are limited by the availability of validated antibodies, both enable the preservation of precious clinical samples as well as experimental standardization in a high-throughput manner proper to microarray technologies. While tissue microarrays are rapidly becoming a mainstay of translational research, reverse-phase protein microarrays require further technical refinements and validation prior to their widespread adoption by research laboratories.     

Analysis of the humoral immune response to immunoselected phage-displayed peptides by a microarray-based method

We describe a novel approach for high-throughput analysis of the immune response in cancer patients using phage-based microarray technology. The recombinant phages used for fabricating phage arrays were initially selected via the use of random peptide phage libraries and breast cancer patient serum antibodies. The peptides displayed by the phages retained their ability to be recognized by serum antibodies after immobilization. The recombinant phage microarrays were screened against either breast cancer or healthy donor serum antibodies. A model-based statistical method is proposed to estimate significant differences in serum antibody reactivity between patients and normals. A significant tumor effect was found with most of the selected phage-displayed peptides, suggesting that recombinant phage microarrays can serve as a tool in monitoring humoral responses towards phage-displayed peptides.     

A consolidated approach to analyzing data from high-throughput protein microarrays with an application to immune response profiling in humans.

Motivation: DNA microarrays are a well-known and established technology in biological and pharmaceutical research providing a wealth of information essential for understanding biological processes and aiding drug development. Protein microarrays are quickly emerging as a follow-up technology, which will also begin to experience rapid growth as the challenges in protein to spot methodologies are overcome. Like DNA microarrays, their protein counterparts produce large amounts of data that must be suitably analyzed in order to yield meaningful information that should eventually lead to novel drug targets and biomarkers. Although the statistical management of DNA microarray data has been well described, there is no available report that offers a successful consolidated approach to the analysis of high-throughput protein microarray data. We describe the novel application of a statistical methodology to analyze the data from an immune response profiling assay using human protein microarray with over 5000 proteins on each chip.     

A pre-processing pipeline to quantify,visualize, and reduce technical variation in protein microarray studies

Technical variation, or variation from non-biological sources, is present in most laboratory assays. Correcting for this variation enables analysts to extract a biological signal that informs questions of interest. However, each assay has different sources and levels of technical variation, and the choice of correction methods can impact downstream analyses. Compared to similar assays such as DNA microarrays, relatively few methods have been developed and evaluated for protein microarrays, a versatile tool for measuring levels of various proteins in serum samples. Here, we propose a pre-processing pipeline to correct for some common sources of technical variation in protein microarrays. The pipeline builds upon an existing normalization method by using controls to reduce technical variation. We evaluate our method using data from two protein microarray studies and by simulation. We demonstrate that pre-processing choices impact the fluorescent-intensity based ranks of proteins, which in turn, impact downstream analysis.     

Optimized normalization for antibody microarrays and application to serum-protein profiling

The measurements of coordinated patterns of protein abundance using antibody microarrays could be used to gain insight into disease biology and to probe the use of combinations of proteins for disease classification. The correct use and interpretation of antibody microarray data requires proper normalization of the data, which has not yet been systematically studied. Therefore we undertook a study to determine the optimal normalization of data from antibody microarray profiling of proteins in human serum specimens. Forty-three serum samples collected from patients with pancreatic cancer and from control subjects were probed in triplicate on microarrays containing 48 different antibodies, using a direct labeling, two-color comparative fluorescence detection format. Seven different normalization methods representing major classes of normalization for antibody microarray data were compared by their effects on reproducibility, accuracy, and trends in the data set. Normalization with ELISA-determined concentrations of IgM resulted in the most accurate, reproducible, and reliable data. The other normalization methods were deficient in at least one of the criteria. Multiparametric classification of the samples based on the combined measurement of seven of the proteins demonstrated the potential for increased classification accuracy compared with the use of individual measurements. This study establishes reliable normalization for antibody microarray data, criteria for assessing normalization performance, and the capability of antibody microarrays for serum-protein profiling and multiparametric sample classification.     

Profiling the humoral immune response of acute and chronic Q fever by protein microarray

Antigen profiling using comprehensive protein microarrays is a powerful tool for characterizing the humoral immune response to infectious pathogens. Coxiella burnetii is a CDC category B bioterrorist infectious agent with worldwide distribution. In order to assess the antibody repertoire of acute and chronic Q fever patients we have constructed a protein microarray containing 93% of the proteome of Coxiella burnetii, the causative agent of Q fever. Here we report the profile of the IgG and IgM seroreactivity in 25 acute Q fever patients in longitudinal samples. We found that both early and late time points of infection have a very consistent repertoire of IgM and IgG response, with a limited number of proteins undergoing increasing or decreasing seroreactivity. We also probed a large collection of acute and chronic Q fever patient samples and identified serological markers that can differentiate between the two disease states. In this comparative analysis we confirmed the identity of numerous IgG biomarkers of acute infection, identified novel IgG biomarkers for acute and chronic infections, and profiled for the first time the IgM antibody repertoire for both acute and chronic Q fever. Using these results we were able to devise a test that can distinguish acute from chronic Q fever. These results also provide a unique perspective on isotype switch and demonstrate the utility of protein microarrays for simultaneously examining the dynamic humoral immune response against thousands of proteins from a large number of patients. The results presented here identify novel seroreactive antigens for the development of recombinant protein-based diagnostics and subunit vaccines, and provide insight into the development of the antibody response.     

A cancer protein microarray platform using antibody fragments and its clinical applications

Antibody microarrays have shown great potential for measurement of either a spectrum of target proteins in proteomics or disease-associated antigens in molecular diagnostics. Despite its importance, the applications of antibody microarrays are still limited by a variety of fundamental problems. Among them, cross-reactivity significantly limits the multiplexing ability in parallel sandwich immunoassays. As a result, it is very important to design new capture probes in order to incorporate a universal label into the assay configuration. In this report, an antibody fragments (F(ab')2) microarray platform for serum tumor markers was developed. Each antigen was detected at different concentrations to assemble its calibration curve, and combinations of different markers were tested to examine the specificity of simultaneous detection based on the F(ab')2 microarrays. Diagnostics of serum samples with this cancer antibody microarray platform and immunoradiometric assays (IRMA) were also performed. Wide range calibration curves (0-1280 U mL(-1)) were obtained for each tumor marker. Comparative studies demonstrated that such F(ab')2 microarrays exhibited both moderately improved sensitivity and better specificity than full-sized monoclonal antibody microarrays. It is also demonstrated that this microarray platform is quantitative, highly specific and reasonably sensitive. More importantly, clinical applications of our F(ab')2 microarray platform for upwards of 100 patient serum samples clearly show its potential in cancer diagnostics.     

Novel type of protein chip for multiplex detection of autoantibodies

Evaluation of: Akada J, Kamei S, Ito A et al. A new type of protein chip to detect hepatocellular carcinoma-related autoimmune antibodies in the sera of hepatitis C virus-positive patients. Proteome Sci. 11(1), 33 (2013).

Unlocking the proteome and delivering biomarkers to the clinic will be critical for early and improved diagnosis and prognosis. Conventional protein microarrays have evolved as a promising proteomic technology with great potential for protein expression profiling in health and disease. In this study, Akada et al. explore a new type of protein chip, interfaced with a dual-color fluorescence-based read-out, for screening of autoantibodies in serum. Uniquely, the recombinant antigens were microarray adapted by molecular design to contain a five-cysteine tag for immobilization and green fluorescent protein for detection (color 1). The engineered antigens were immobilized on in-house-designed maleimide-incorporated diamond-like carbon substrates and subsequently heat treated in a solution of denaturing and reducing agents before any specifically bound serum autoantibodies were detected (color 2). The authors used a 4-plex array targeting hepatocellular carcinoma-related autoantibodies in the sera of hepatitis C virus-positive patients as model system to demonstrate proof-of-concept.     

Molecular profiling of the immune response in colon cancer using protein microarrays: occurrence of autoantibodies to ubiquitin C-terminal hydrolase L3

We implemented a protein microarray approach to identify proteins that induce a humoral response in colon cancer. Solubilized proteins from the LoVo colon adenocarcinoma cell line were separated into 1760 fractions, arrayed onto nitrocellulose-coated slides, and hybridized with individual sera from 15 newly diagnosed patients with colon cancer, 15 with lung cancer, and 15 healthy subjects. 39/1760 fractions showed enhanced reactivity with sera from patients with colon cancer (p < 0.01) relative to healthy controls. A distinct pattern of reactivity was observed with sera from colon cancer relative to lung cancer. One fraction that exhibited reactivity with 9/15 colon cancer sera was subjected to mass spectrometry leading to the identification of ubiquitin C-terminal hydrolase isozyme 3 (UCH-L3) as a constituent. To validate the occurrence of autoantibodies to UCH-L3, independent analysis was done by means of Western blots. UCH-L3 antibodies were detected in 19/43 sera from patients with colon cancer, and in 0/54 sera from subjects with lung cancer (24), colon adenoma (15) or otherwise healthy (15). Our findings indicate the occurrence of an immune response to a broad set of antigens in colon cancer and the feasibility of identifying the antigenic targets using a combination of protein microarrays and mass spectrometry.     

Evidence for a role of autoantibodies to heat shock protein 60, 70, and 90 in patients with dermatitis herpetiformis

Heat shock proteins (Hsp) are highly conserved immunomodulatory molecules upregulated when cells are exposed to stressful stimuli, such as inflammation. Their involvement in various autoimmune diseases, including autoimmune bullous diseases and celiac disease, has been increasingly recognized. To further study the role of Hsp in autoimmune bullous diseases, we have investigated for the first time the humoral autoimmune response to Hsp40, Hsp60, Hsp70, and Hsp90 in patients with dermatitis herpetiformis (DH; n = 26), bullous pemphigoid (BP; n = 23), and pemphigus vulgaris (PV; n = 16), the first representing a cutaneous manifestation of celiac disease. While in patients with active BP and PV, serum levels of autoantibodies against these Hsp did not differ from the corresponding age- and gender-matched healthy controls (n = 9–14); circulating autoantibodies against Hsp60, Hsp70, and Hsp90 were found to be increased at the active disease stage of DH. Further analysis of this latter patient subgroup showed that these anti-Hsp autoantibodies decreased in parallel with serum autoantibodies against epidermal and tissue transglutaminase during remission of skin lesions following a gluten-free diet, revealing significantly positive correlations. Although further studies on larger groups of patients will be needed to confirm the present data, our results support the notion that autoantibodies against Hsp60, Hsp70, and Hsp90 deserve attention in the study of the mechanisms that promote the development and maintenance of DH and possibly also the underlying celiac disease as well as potential novel disease biomarkers.     

Antibody microarrays: current status and key technological advances

Antibody-based microarrays are among the novel classes of rapidly evolving proteomic technologies that holds great promise in biomedicine. Miniaturized microarrays (< 1 cm2) can be printed with thousands of individual antibodies carrying the desired specificities, and with biological sample (e.g., an entire proteome) added, virtually any specifically bound analytes can be detected. While consuming only minute amounts (< microL scale) of reagents, ultra- sensitive assays (zeptomol range) can readily be performed in a highly multiplexed manner. The microarray patterns generated can then be transformed into proteomic maps, or detailed molecular fingerprints, revealing the composition of the proteome. Thus, protein expression profiling and global proteome analysis using this tool will offer new opportunities for drug target and biomarker discovery, disease diagnostics, and insights into disease biology. Adopting the antibody microarray technology platform, several biomedical applications, ranging from focused assays to proteome-scale analysis will be rapidly emerging in the coming years. This review will discuss the current status of the antibody microarray technology focusing on recent technological advances and key issues in the process of evolving the methodology into a high-performing proteomic research tool.     

Protein microarrays discover angiotensinogen and PRKRIP1 as novel targets for autoantibodies in chronic renal disease

Biomarkers for early detection of chronic kidney disease are needed, as millions of patients suffer from chronic diseases predisposing them to kidney failure. Protein microarrays may also hold utility in the discovery of auto-antibodies in other conditions not commonly considered auto-immune diseases. We hypothesized that proteins are released as a consequence of damage at a cellular level during end-organ damage from renal injury, not otherwise recognized as self-antigens, and an adaptive humoral immune response to these proteins might be detected in the blood, as a noninvasive tracker of this injury. The resultant antibodies (Ab) detected in the blood would serve as effective biomarkers for occult renal injury, enabling earlier clinical detection of chronic kidney disease than currently possible, because of the redundancy of the serum creatinine as a biomarker for early kidney injury. To screen for novel autoantibodies in chronic kidney disease, 24 protein microarrays were used to compare serum Ab from patients with chronic kidney disease against matched controls. From a panel of 38 antigens with increased Ab binding, four were validated in 71 individuals, with (n=50) and without (n=21) renal insufficiency. Significant elevations in the titer of novel auto-Ab were noted against angiotensinogen and PRKRIP1 in renal insufficiency. Current validation is underway to evaluate if these auto-Ab can provide means to follow the evolution of chronic kidney disease in patients with early stages of renal insufficiency, and if these rising titers of these auto-Ab correlate with the rate of progression of chronic kidney disease.     

Autoantibody Profiling on Human Proteome Microarray for Biomarker Discovery in Cerebrospinal Fluid and Sera of Neuropsychiatric Lupus

Autoantibodies in cerebrospinal fluid (CSF) from patients with neuropsychiatric systemic lupus erythematosus (NPSLE) may be potential biomarkers for prediction, diagnosis, or prognosis of NPSLE. We used a human proteome microarray with~17,000 unique full-length human proteins to investigate autoantibodies associated with NPSLE. Twenty-nine CSF specimens from 12 NPSLE, 7 non-NPSLE, and 10 control (non-systemic lupus erythematosus)patients were screened for NPSLE-associated autoantibodies with proteome microarrays. A focused autoantigen microarray of candidate NPSLE autoantigens was applied to profile a larger cohort of CSF with patient-matched sera. We identified 137 autoantigens associated with NPSLE. Ingenuity Pathway Analysis revealed that these autoantigens were enriched for functions involved in neurological diseases (score = 43).Anti-proliferating cell nuclear antigen (PCNA) was found in the CSF of NPSLE and non-NPSLE patients. The positive rates of 4 autoantibodies in CSF specimens were significantly different between the SLE (i.e., NPSLE and non-NPSLE) and control groups: anti-ribosomal protein RPLP0, anti-RPLP1, anti-RPLP2, and anti-TROVE2 (also known as anti-Ro/SS-A). The positive rate for anti-SS-A associated with NPSLE was higher than that for non-NPSLE (31.11% cf. 10.71%; P = 0.045).Further analysis showed that anti-SS-A in CSF specimens was related to neuropsychiatric syndromes of the central nervous system in SLE (P = 0.009). Analysis with Spearman’s rank correlation coefficient indicated that the titers of anti-RPLP2 and anti-SS-A in paired CSF and serum specimens significantly correlated. Human proteome microarrays offer a powerful platform to discover novel autoantibodies in CSF samples. Anti-SS-A autoantibodies may be potential CSF markers for NPSLE.