Antibody microarray profiling of human prostate cancer sera: antibody screening and identification of potential biomarkers

We developed a practical strategy for serum protein profiling using antibody microarrays and applied the method to the identification of potential biomarkers in prostate cancer serum. Protein abundances from 33 prostate cancer and 20 control serum samples were compared to abundances from a common reference pool using a two-color fluorescence assay. Robotically spotted microarrays containing 184 unique antibodies were prepared on two different substrates: polyacrylamide based hydrogels on glass and poly-1-lysine coated glass with a photoreactive cross-linking layer. The hydrogel substrate yielded an average six-fold higher signal-to-noise ratio than the other substrate, and detection of protein binding was possible from a greater number of antibodies using the hydrogels. A statistical filter based on the correlation of data from "reverse-labeled" experiment sets accurately predicted the agreement between the microarray measurements and enzyme-linked immunosorbent assay measurements, showing that this parameter can serve to screen for antibodies that are functional on microarrays. Having defined a set of reliable microarray measurements, we identified five proteins (von Willebrand Factor, immunoglobulinM, Alpha1-antichymotrypsin, Villin and immunoglobulinG) that had significantly different levels between the prostate cancer samples and the controls. These developments enable the immediate use of high-density antibody and protein microarrays in biomarker discovery studies.     

Design of recombinant antibody microarrays for serum protein profiling: targeting of complement proteins

Antibody-based microarrays is a novel technology with great promise for high-throughput proteomics. The process of designing high-performing arrays has, however, turned out to be challenging. Here, we have designed the next generation of a human recombinant scFv antibody microarray platform for protein expression profiling of nonfractionated biotinylated human plasma and serum proteomes. The setup, based on black polymer Maxisorb slides interfaced with a fluorescent-based read-out system, was found to provide specific, sensitive (subpicomolar (pM) range) and reproducible means for protein profiling. Further, a chip-to-chip normalization protocol critical for comparing data generated on different chips was devised. Finally, the microarray data were found to correlate well with clinical laboratory data obtained using conventional methods, as demonstrated for a set of medium abundant (micromolar (microM) to nanomolar (nM) range) protein analytes in serum and plasma samples derived from healthy and complement-deficient individuals.     

Quality Assessment and Data Analysis for microRNA Expression Arrays

MicroRNAs are small (~22 nt) RNAs that regulate gene expression and play important roles in both normal and disease physiology. The use of microarrays for global characterization of microRNA expression is becoming increasingly popular and has the potential to be a widely used and valuable research tool. However, microarray profiling of microRNA expression raises a number of data analytic challenges that must be addressed in order to obtain reliable results. We introduce here a universal reference microRNA reagent set as well as a series of nonhuman spiked-in synthetic microRNA controls, and demonstrate their use for quality control and between-array normalization of microRNA expression data. We also introduce diagnostic plots designed to assess and compare various normalization methods. We anticipate that the reagents and analytic approach presented here will be useful for improving the reliability of microRNA microarray experiments.     

Quantitative serum proteomics from surface plasmon resonance imaging

The detection and quantification of specific proteins in complex mixtures is a major challenge for proteomics. For example, the development of disease-related biomarker panels will require fast and efficient methods for obtaining multiparameter protein profiles. We established a high throughput, label-free method for analyzing serum using surface plasmon resonance imaging of antibody microarrays. Microarrays were fabricated using standard pin spotting on bare gold substrates, and samples were applied for binding analysis using a camera-based surface plasmon resonance system. We validated the system by measuring the concentrations of four serum proteins using part of a 792-feature microarray. Transferrin concentrations were measured to be 2.1 mg/ml in human serum and 1.2 mg/ml in murine serum, which closely matched ELISA determinations of 2.6 and 1.2 mg/ml, respectively. In agreement with expected values, human and mouse albumin levels were measured to be 24.3 and 23.6 mg/ml, respectively. The lower limits of detection for the four measurements ranged from 14 to 58 ng/ml or 175 to 755 pm. Where purified target proteins are not available for calibration, the microarrays can be used for relative protein quantification. We used the antibody microarray to compare the serum protein profiles from three liver cancer patients and three non-liver cancer patients. Hierarchical clustering of the serum protein levels clearly distinguished two distinct profiles. Thirty-nine significant protein changes were detected (p < 0.05), 10 of which have been observed previously in serum. alpha-Fetoprotein, a known liver cancer marker, was observed to increase. These results demonstrate the feasibility of this high throughput approach for both absolute and relative protein expression profiling.     

Two-color,rolling-circle amplification on antibody microarrays for sensitive,multiplexed serum-protein measurements

The ability to conveniently and rapidly profile a diverse set of proteins has valuable applications. In a step toward further enabling such a capability, we developed the use of rolling-circle amplification (RCA) to measure the relative levels of proteins from two serum samples, labeled with biotin and digoxigenin, respectively, that have been captured on antibody microarrays. Two-color RCA produced fluorescence up to 30-fold higher than direct-labeling and indirect-detection methods using antibody microarrays prepared on both polyacrylamide-based hydrogels and nitrocellulose. Replicate RCA measurements of multiple proteins from sets of 24 serum samples were highly reproducible and accurate. In addition, RCA enabled reproducible measurements of distinct expression profiles from lower-abundance proteins that were not measurable using the other detection methods. Two-color RCA on antibody microarrays should allow the convenient acquisition of expression profiles from a great diversity of proteins for a variety of applications.     

A novel antibody microarray format using non-covalent antibody immobilization with chemiluminescent detection

To date, protein and antibody microarrays have been used in reverse-phase and sandwich-based methods in order to detect known proteins such as biomarkers in samples. Our group developed "libraries" of antibodies against unknown proteins, referred to as mKIAA proteins, and we attempted to discover candidate novel biomarkers by protein expression profiling.To profile mKIAA protein expression using these antibodies, we established an antibody microarray system using chemiluminescent detection. A number of techniques for protein-antibody microarrays have been reported; however, no entirely suitable protocol for crude protein samples has been established. To address this issue, we immobilized purified antibodies on hydrophilic surface polymer slides (Maxisorp, Nunc). Although our system is based on the direct labeling of crude protein samples, we achieved sufficient sensitivity (detection limit: 50 pg mL(-1)) and low backgrounds. This sensitivity is on a level with the sandwich immunoassay-based antibody array system. Using our protocol, we developed an antibody microarray spotted with 960 anti-mKIAA antibodies (total: 3888 spots for quadruplicate assessments), and we carried out protein expression profiling of mKIAA proteins. In this study, we generated an expression profile of 960 mKIAA proteins and compared the present results with those obtained via cDNA microarray.     

Validation of serum protein profiles by a dual antibody array approach

In recent years, affinity-based technologies have become important tools for serum profiling to uncover protein expression patterns linked to disease state or therapeutic effects. In this study, we describe a path towards the production of an antibody microarray to allow protein profiling of biotinylated human serum samples with reproducible sensitivity in the picomolar range. With the availability of growing numbers of affinity reagents, protein profiles are to be validated in efficient manners and we describe a cross-platform strategy based on data concordance with a suspension bead array to interrogate the identical set of antibodies with the same cohort of serum samples. Comparative analysis enabled to screen for high-performing antibodies, which were displaying consistent results across the two platforms and targeting known serum components. Moreover, data processing methods such as sample referencing and normalization were evaluated for their effects on inter-platform agreement. Our work suggests that mutual validation of protein expression profiles using alternative microarray platforms holds great potential in becoming an important and valuable component in affinity-based high-throughput proteomic screenings as it allows to narrow down the number of discovered targets prior to orthogonal, uniplexed validation approaches.     

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.     

Comparison of algorithms for the analysis of Affymetrix microarray data as evaluated by co-expression of genes in known operons

Oligonucleotide microarrays are an informative tool to elucidate gene regulatory networks. In order for gene expression levels to be comparable across microarrays, normalization procedures have to be invoked. A large number of methods have been described to correct for systematic biases in microarray experiments. The performance of these methods has been tested only to a limited extend. Here, we evaluate two different types of microarray analyses: (i) the same gene in replicate samples and (ii) different, but co-expressed genes in the same sample. The reliability of the latter analysis needs to be determined for the analysis of regulatory networks and our report is the first attempt to evaluate for the accuracy of different microarray normalization methods in this respect. Consistent with previous results we observed a large effect of the normalization method on the outcome of the expression analyses. Our analyses indicate that different normalization methods should be performed depending on whether a study is aiming to detect differential gene expression between independent samples or whether co-expressed genes should be identified. We make recommendations about the most appropriate method to use.     

Comparison of normalization methods for CodeLink Bioarray data

Background  

The quality of microarray data can seriously affect the accuracy of downstream analyses. In order to reduce variability and enhance signal reproducibility in these data, many normalization methods have been proposed and evaluated, most of which are for data obtained from cDNA microarrays and Affymetrix GeneChips. CodeLink Bioarrays are a newly emerged, single-color oligonucleotide microarray platform. To date, there are no reported studies that evaluate normalization methods for CodeLink Bioarrays.     

A practical question based on cross-platform microarray data normalization: are BOEC more like large vessel or microvascular endothelial cells or neither of them?

Since the available microarray data of BOEC (human blood outgrowth endothelial cells), large vessel, and microvascular endothelial cells were from two different platforms, a working cross-platform normalization method was needed to make these data comparable. With six HUVEC (human umbilical vein endothelial cells) samples hybridized on two-channel cDNA arrays and six HUVEC samples on Affymetrix arrays, 64 possible combinations of a three-step normalization procedure were investigated to search for the best normalization method, which was selected, based on two criteria measuring the extent to which expression profiles of biological samples of the same cell type arrayed on two platforms were indistinguishable. Next, three discriminative gene lists between the large vessel and the microvascular endothelial cells were achieved by SAM (significant analysis of microarrays), PAM (prediction analysis for microarrays), and a combination of SAM and PAM lists. The final discriminative gene list was selected by SVM (support vector machine). Based on this discriminative gene list, SVM classification analysis with best tuning parameters and 10,000 times of validations showed that BOEC were far from large vessel cells, they either formed their own class, or fell into the microvascular class. Based on all the common genes between the two platforms, SVM analysis further confirmed this conclusion.     

Normalization strategies for microRNA profiling experiments: a ‘normal’ way to a hidden layer of complexity?

MicroRNA (miRNA) profiling is a first important step in elucidating miRNA functions. Real time quantitative PCR (RT-qPCR) and microarray hybridization approaches as well as ultra high throughput sequencing of miRNAs (small RNA-seq) are popular and widely used profiling methods. All of these profiling approaches face significant introduction of bias. Normalization, often an underestimated aspect of data processing, can minimize systematic technical or experimental variation and thus has significant impact on the detection of differentially expressed miRNAs. At present, there is no consensus normalization method for any of the three miRNA profiling approach. Several normalization techniques are currently in use, of which some are similar to mRNA profiling normalization methods, while others are specifically modified or developed for miRNA data. The characteristic nature of miRNA molecules, their composition and the resulting data distribution of profiling experiments challenges the selection of adequate normalization techniques. Based on miRNA profiling studies and comparative studies on normalization methods and their performances, this review provides a critical overview of commonly used and newly developed normalization methods for miRNA RT-qPCR, miRNA hybridization microarray, and small RNA-seq datasets. Emphasis is laid on the complexity, the importance and the potential for further optimization of normalization techniques for miRNA profiling datasets.     

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.     

Methods and applications of antibody microarrays in cancer research

Antibody microarrays have great potential for significant value in biological research. Cancer research in particular could benefit from the unique experimental capabilities of this technology. This article examines the current state of antibody microarray technological developments and assay formats, along with a review of the demonstrated applications to cancer research. Work is ongoing in the refinement of various aspects of the protocols and the development of robust methods for routine use. Antibody microarray experimental formats can be broadly categorized into two classes: (1) direct labeling experiments, and (2) dual antibody sandwich assays. In the direct labeling method, the covalent labeling of all proteins in a complex mixture provides a means for detecting bound proteins after incubation on an antibody microarray. If proteins are labeled with a tag, such as biotin, the signal from bound proteins can be amplified. In the sandwich assay, proteins captured on an antibody microarray are detected by a cocktail of detection antibodies, each antibody matched to one of the spotted antibodies. Each format has distinct advantages and disadvantages. Several applications of antibody arrays to cancer research have been reported, including the analysis of proteins in blood serum, resected frozen tumors, cell lines, and on membranes of blood cells. These demonstrations clearly show the utility of antibody microarrays for cancer research and signal the imminent expansion of this platform to many areas of biological research.     

Advances in functional protein microarray technology

Numerous innovations in high-throughput protein production and microarray surface technologies have enabled the development of addressable formats for proteins ordered at high spatial density. Protein array implementations have largely focused on antibody arrays for high-throughput protein profiling. However, it is also possible to construct arrays of full-length, functional proteins from a library of expression clones. The advent of protein-based microarrays allows the global observation of biochemical activities on an unprecedented scale, where hundreds or thousands of proteins can be simultaneously screened for protein-protein, protein-nucleic acid, and small molecule interactions. This technology holds great potential for basic molecular biology research, disease marker identification, toxicological response profiling and pharmaceutical target screening.     

Cancer classification from the gene expression profiles by Discriminant Kernel-PLS

Cancer diagnosis depending on microarray technology has drawn more and more attention in the past few years. Accurate and fast diagnosis results make gene expression profiling produced from microarray widely used by a large range of researchers. Much research work highlights the importance of gene selection and gains good results. However, the minimum sets of genes derived from different methods are seldom overlapping and often inconsistent even for the same set of data, partially because of the complexity of cancer disease. In this paper, cancer classification was attempted in an alternative way of the whole gene expression profile for all samples instead of partial gene sets. Here, the three common sets of data were tested by NIPALS-KPLS method for acute leukemia, prostate cancer and lung cancer respectively. Compared to other conventional methods, the results showed wide improvement in classification accuracy. This paper indicates that sample profile of gene expression may be explored as a better indicator for cancer classification, which deserves further investigation.     

A high-performance, small-scale microarray for expression profiling of many samples in Arabidopsis-pathogen studies

Studies of the behavior of biological systems often require monitoring of the expression of many genes in a large number of samples. While whole-genome arrays provide high-quality gene-expression profiles, their high cost generally limits the number of samples that can be studied. Although inexpensive small-scale arrays representing genes of interest could be used for many applications, it is challenging to obtain accurate measurements with conventional small-scale microarrays. We have developed a small-scale microarray system that yields highly accurate and reproducible expression measurements. This was achieved by implementing a stable gene-based quantile normalization method for array-to-array normalization, and a probe-printing design that allows use of a statistical model to correct for effects of print tips and uneven hybridization. The array measures expression values in a single sample, rather than ratios between two samples. This allows accurate comparisons among many samples. The array typically yielded correlation coefficients higher than 0.99 between technically duplicated samples. Accuracy was demonstrated by a correlation coefficient of 0.88 between expression ratios determined from this array and an Affymetrix GeneChip, by quantitative RT-PCR, and by spiking known amounts of specific RNAs into the RNA samples used for profiling. The array was used to compare the responses of wild-type, rps2 and ndr1 mutant plants to infection by a Pseudomonas syringae strain expressing avrRpt2. The results suggest that ndr1 affects a defense-signaling pathway(s) in addition to the RPS2-dependent pathway, and indicate that the microarray is a powerful tool for systems analyses of the Arabidopsis disease-signaling network.     

Development of a "reverse capture" autoantibody microarray for studies of antigen-autoantibody profiling

Diagnosing cancers based on serum profiling is a particularly attractive concept. However, the technical challenges to analysis of the serum proteome arise from the dynamic range of protein amounts. Cancer sera contain antibodies that react with a unique group of autologous cellular antigens, which affords a dramatic amplification of signal in the form of antibodies relative to the amount of the corresponding antigens. The serum autoantibody repertoire from cancer patients might, therefore, be exploited for antigen-antibody profiling. To date, studies of antigen-antibody reactivity using microarrays have relied on recombinant proteins or synthetic peptides as arrayed features. However, recombinant proteins and/or synthetic peptides may fail to accurately detect autoantibody binding due to the lack of proper PTMs. Here we describe the development and use of a "reverse capture" autoantibody microarray. Our "reverse capture" autoantibody microarray is based on the dual-antibody sandwich immunoassay platform of ELISA, which allows the antigens to be immobilized in their native configuration. As "proof-of-principle", we demonstrate its use for antigen-autoantibody profiling with sera from patients with prostate cancer and benign prostate hyperplasia.     

Protein microarrays for gene expression and antibody screening.

Proteins translate genomic sequence information into function, enabling biological processes. As a complementary approach to gene expression profiling on cDNA microarrays, we have developed a technique for high-throughput gene expression and antibody screening on chip-size protein microarrays. Using a picking/spotting robot equipped with a new transfer stamp, protein solutions were gridded onto polyvinylidene difluoride filters at high density. Specific purified protein was detected on the filters with high sensitivity (250 amol or 10 pg of a test protein). On a microarray made from bacterial lysates of 92 human cDNA clones expressed in a microtiter plate, putative protein expressors could be reliably identified. The rate of false-positive clones, expressing proteins in incorrect reading frames, was low. Product specificity of selected clones was confirmed on identical microarrays using monoclonal antibodies. Cross-reactivities of some antibodies with unrelated proteins imply the use of protein microarrays for antibody specificity screening against whole libraries of proteins. Because this application would not be restricted to antigen-antibody systems, protein microarrays should provide a general resource for high-throughput screens of gene expression and receptor-ligand interactions.