An antibody-based microarray assay for the simultaneous detection of aflatoxin B<Subscript>1</Subscript> and fumonisin B<Subscript>1</Subscript>

Advances in microsystem technology have enabled protein and nucleic acid-based microarrays to be used in various applications, including the study of diseases, drug discovery, genetic screening, and clinical and food diagnostics. Analytical methods for the detection of mycotoxins, however, remain largely based on thin layer chromatography (TLC), high pressure liquid chromatography (HPLC), or enzyme-linked Immunosorbent assay (ELISA) . The aim of our work, therefore, was to transfer an immunological assay from microtitrr plates into microarray format, in order to develop a multiparametric, rapid, sensitive and inexpensive method for the detection of mycotoxins for use in food safety applications. Microarray technology enables the fast and parallel analysis of a multitude of biologically relevant parameters. Not only nucleic acid-based tests but also peptide, antigen, and antibody assays, using different formats of microarrays, have evolved within the last decade. Antibody-based microarrays provide a powerful tool that can be used to generate rapid and detailed expression profiles of a defined set of analytes in complex samples and are potentially useful for generating rapid immunological assays of food contaminants. In this paper, we report a feasibility study of the application of antibody microarrays for the simultaneous (or independent) detection of two common mycotoxins, Aflatoxin B₁ and Fumonisin B₁. We present the development of microarray detection of aflatoxin B1 and fumonisin B1 in standard solutions with detection limits of 3 ng/ml of AFB1 and 43 ng/ml for FB1, and have developed a competitive immunoassay in microarray format for simultaneous analyses. The quality of the microarray data is comparable to data generated by microplate-based immunoassay (ELISA), but further investigations are needed in order to characterise our method more fully. We hope that these preliminary results might suggest that further research is warranted in order to develop hapten microarrays for the immunochemical simultaneous analysis of mycotoxins, as well as for other small molecules (e.g. bacterial toxins or biological warfare agents).     

Correlation analysis of external RNA controls reveals its utility for assessment of microarray assay

Quality control of a microarray experiment has become an important issue for both research and regulation. External RNA controls (ERCs), which can be either added to the total RNA level (tERCs) or introduced right before hybridization (cERCs), are designed and recommended by commercial microarray platforms for assessment of performance of a microarray experiment. However, the utility of ERCs has not been fully realized mainly due to the lack of sufficient data resources. The US Food and Drug Administration (FDA)-led community-wide Microarray Quality Control (MAQC) study generates a large amount of microarray data with implementation of ERCs across several commercial microarray platforms. The utility of ERCs in quality control by assessing the ERCs’ concentration-response behavior was investigated in the MAQC study. In this work, an ERC-based correlation analysis was conducted to assess the quality of a microarray experiment. We found that the pairwise correlations of tERCs are sample independent, indicating that the array data obtained from different biological samples can be treated as technical replicates in analysis of tERCs. Consequently, the commonly used quality control method of applying correlation analysis on technical replicates can be adopted for assessing array performance based on different biological samples using tERCs. The proposed approach is sensitive to identifying outlying assays and is not dependent on the choice of normalization method.     

Enzymatic analysis of WWP2 E3 ubiquitin ligase using protein microarrays identifies autophagy-related substrates

WWP2 is a HECT E3 ligase that targets protein Lys residues for ubiquitination and is comprised of an N-terminal C2 domain, four central WW domains, and a C-terminal catalytic HECT domain. The peptide segment between the middle WW domains, the 2,3-linker, is known to autoinhibit the catalytic domain, and this autoinhibition can be relieved by phosphorylation at Tyr369. Several protein substrates of WWP2 have been identified, including the tumor suppressor lipid phosphatase PTEN, but the full substrate landscape and biological functions of WWP2 remain to be elucidated. Here, we used protein microarray technology and the activated enzyme phosphomimetic mutant WWP2^Y369E to identify potential WWP2 substrates. We identified 31 substrate hits for WWP2^Y369E using protein microarrays, of which three were known autophagy receptors (NDP52, OPTN, and SQSTM1). These three hits were validated with in vitro and cell-based transfection assays and the Lys ubiquitination sites on these proteins were mapped by mass spectrometry. Among the mapped ubiquitin sites on these autophagy receptors, many had been previously identified in the endogenous proteins. Finally, we observed that WWP2 KO SH-SH5Y neuroblastoma cells using CRISPR-Cas9 showed a defect in mitophagy, which could be rescued by WWP2^Y369E transfection. These studies suggest that WWP2-mediated ubiquitination of the autophagy receptors NDP52, OPTN, and SQSTM1 may positively contribute to the regulation of autophagy     

Differentially Expressed RNA from Public Microarray Data Identifies Serum Protein Biomarkers for Cross-Organ Transplant Rejection and Other Conditions

Serum proteins are routinely used to diagnose diseases, but are hard to find due to low sensitivity in screening the serum proteome. Public repositories of microarray data, such as the Gene Expression Omnibus (GEO), contain RNA expression profiles for more than 16,000 biological conditions, covering more than 30% of United States mortality. We hypothesized that genes coding for serum- and urine-detectable proteins, and showing differential expression of RNA in disease-damaged tissues would make ideal diagnostic protein biomarkers for those diseases. We showed that predicted protein biomarkers are significantly enriched for known diagnostic protein biomarkers in 22 diseases, with enrichment significantly higher in diseases for which at least three datasets are available. We then used this strategy to search for new biomarkers indicating acute rejection (AR) across different types of transplanted solid organs. We integrated three biopsy-based microarray studies of AR from pediatric renal, adult renal and adult cardiac transplantation and identified 45 genes upregulated in all three. From this set, we chose 10 proteins for serum ELISA assays in 39 renal transplant patients, and discovered three that were significantly higher in AR. Interestingly, all three proteins were also significantly higher during AR in the 63 cardiac transplant recipients studied. Our best marker, serum PECAM1, identified renal AR with 89% sensitivity and 75% specificity, and also showed increased expression in AR by immunohistochemistry in renal, hepatic and cardiac transplant biopsies. Our results demonstrate that integrating gene expression microarray measurements from disease samples and even publicly-available data sets can be a powerful, fast, and cost-effective strategy for the discovery of new diagnostic serum protein biomarkers.     

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.     

Development of a protein microarray using sequence-specific DNA binding domain on DNA chip surface

A protein microarray based on DNA microarray platform was developed to identify protein-protein interactions in vitro. The conventional DNA chip surface by 156-bp PCR product was prepared for a substrate of protein microarray. High-affinity sequence-specific DNA binding domain, GAL4 DNA binding domain, was introduced to the protein microarray as fusion partner of a target model protein, enhanced green fluorescent protein. The target protein was oriented immobilized directly on the DNA chip surface. Finally, monoclonal antibody of the target protein was used to identify the immobilized protein on the surface. This study shows that the conventional DNA chip can be used to make a protein microarray directly, and this novel protein microarray can be applicable as a tool for identifying protein-protein interactions.     

Reverse-phase protein lysate microarrays for cell signaling analysis

'Reverse-phase' protein lysate microarray (RPA) assays use micro-scale, cell lysate dot blots that are printed to a substrate, followed by quantitative immunochemical protein detection, known to be particularly effective across many samples. Large-scale sample collection is a labor-intensive and time-consuming process; the information yielded from RPA assays, however, provides unique opportunities to experimentally interpret theoretical protein networks quantitatively. When specific antibodies are used, RPA can generate 1,000 times more data points using 10,000 times less sample volume than an ordinary western blot, enabling researchers to monitor quantitative proteomic responses for various time-scale and input-dose gradients simultaneously. Hence, the RPA system can be an excellent method for experimental validation of theoretical protein network models. Besides the initial screening of primary antibodies, collection of several hundreds of sample lysates from 1- to 8-h periods can be completed in approximately 10 d; subsequent RPA printing and signal detection steps require an additional 2-3 d.     

Development of miniaturized immunoassay: Influence of surface chemistry and comparison with enzyme-linked immunosorbent assay and Western blot

Protein microarray technology provides a useful approach for the simultaneous serodetection of various antibodies in low sample volumes. To implement functional protein microarrays, appropriate surface chemistry must be designed so that both the protein structure and the biological activity can be retained. In the current study, two surface chemistries for protein microarrays and immunofluorescent assays were developed. Glass slides were functionalized with N-hydroxysuccinimide (NHS) ester via a monofunctional silane or maleic anhydride-alt-methyl vinyl ether (MAMVE) copolymer to allow covalent grafting of histone proteins. Analytical performance of these microarrays was then evaluated for the detection of anti-histone autoantibodies present in the sera of patients suffering from a systemic autoimmune disease, namely systemic lupus erythematosus (SLE), and the results were compared with those of the classical enzyme-linked immunosorbent assay (ELISA) and Western blot. The detection limit of our MAMVE copolymer microarrays was 50-fold lower than that of the classical ELISA. Furthermore, 100-fold less volume of biological samples was required with these miniaturized immunoassays.     

Development of protein microarray technology to monitor biomarkers of rheumatoid arthritis disease

Most biological processes are mediated by complex networks of molecular interactions involving proteins. The analysis of protein expression in biological samples is especially important in the identification and monitoring of biomarkers for disease progression and therapeutic endpoints. In this paper, the development of a protein microarray format for multiplexed quantitative analysis of several potential markers for rheumatoid arthritis (RA) is described. Development of a high-performance protein microarray system depends on several key parameters such as surface chemistry, capture agents, immobilization technology, and methods used for signal detection and quantification. Several technical possibilities were investigated and compared: poly-L-lysine versus self-assembled monolayer of octadecyl phosphoric acid ester for surface chemistries; noncontact piezoelectric versus contact printing technology for antibody deposition; CCD camera capture versus fluorescent scanning for image detection; and the concentration of coating antibody. On the basis of reproducibility, signal-to-noise ratio, and sensitivity we have selected self-assembled monolayer, noncontact piezoelectric printer, and high-read-out fluorescence scanning for our microarray format. This format was used to perform multiplexed quantitative analysis of several potential markers of disease progression of rheumatoid arthritis: IL-1, IL-6, IL-8, MCP-1, and SAA. Some assays, such as MCP-1, provided a working range that covered physiologically relevant concentrations. Other assays, such as IL-6 and SAA, lacked sensitivity or were too sensitive for measuring biological concentrations, respectively. The results described demonstrate the applicability of protein microarrays to monitor RA markers; however, sandwich assay methodologies need to be further optimized to measure the appropriate biological ranges of these markers on one chip.     

Identification of the atypical MAPK Erk3 as a novel substrate for p21-activated kinase (Pak) activity

The class I p21-activated kinases (Pak1-3) regulate many essential biological processes, including cytoskeletal rearrangement, cell cycle progression, apoptosis, and cellular transformation. Although many Pak substrates, including elements of MAPK signaling cascades, have been identified, it is likely that additional substrates remain to be discovered. Identification of such substrates, and determination of the consequences of their phosphorylation, is essential for a better understanding of class I Pak activity. To identify novel class I Pak substrates, we used recombinant Pak2 to screen high density protein microarrays. This approach identified the atypical MAPK Erk3 as a potential Pak2 substrate. Solution-based in vitro kinase assays using recombinant Erk3 confirmed the protein microarray results, and phospho-specific antisera identified serine 189, within the Erk3 activation loop, as a site directly phosphorylated by Pak2 in vitro. Erk3 protein is known to shuttle between the cytoplasm and the nucleus, and we showed that selective inhibition of class I Pak kinase activity in cells promoted increased nuclear accumulation of Erk3. Pak inhibition in cells additionally reduced the extent of Ser(189) phosphorylation and inhibited the formation of Erk3-Prak complexes. Collectively, our results identify the Erk3 protein as a novel class I Pak substrate and further suggest a role for Pak kinase activity in atypical MAPK signaling.     

Immunostaining with dissociable antibody microarrays

The availability of a large number of biological materials such as cDNA, antibodies, recombinant proteins, and tissues has promoted the development of microarray technologies that make use of these materials in high-throughput screening assays. However, because microarray technologies have been less successful in examining proteins than DNA and mRNA, there is a need for improved protein microarray systems. To address this need, we developed an antibody microarray-based immunostaining method that can analyze the properties of a large number of proteins simultaneously. In this method, antibodies are arrayed and immobilized on a solid support and cells bearing antigens of interest are attached to a second support. Apposition of the two supports allows the antibodies to dissociate from the array support and bind to the cellular antigens. After separation of the supports, antigen-bound antibodies can be detected by standard secondary antibody techniques. These "dissociable" antibody arrays were used to detect both the expression and subcellular localization of a large number of specific proteins in various cultured cell types.     

Proteome microarray technology and application: higher,wider, and deeper

ABSTRACT

Introduction: Protein microarray is a powerful tool for both biological study and clinical research. The most useful features of protein microarrays are their miniaturized size (low reagent and sample consumption), high sensitivity and their capability for parallel/high-throughput analysis. The major focus of this review is functional proteome microarray.

Areas covered: For proteome microarray, this review will discuss some recently constructed proteome microarrays and new concepts that have been used for constructing proteome microarrays and data interpretation in past few years, such as PAGES, M-NAPPA strategy, VirD technology, and the first protein microarray database. this review will summarize recent proteomic scale applications and address the limitations and future directions of proteome microarray technology.

Expert opinion: Proteome microarray is a powerful tool for basic biological and clinical research. It is expected to see improvements in the currently used proteome microarrays and the construction of more proteome microarrays for other species by using traditional strategies or novel concepts. It is anticipated that the maximum number of features on a single microarray and the number of possible applications will be increased, and the information that can be obtained from proteome microarray experiments will more in-depth in the future.     

Cell adhesion profiling using extracellular matrix protein microarrays

We have developed a microarray-based system for cell adhesion profiling of large panels of cell-adhesive proteins to increase the throughput of in vitro cell adhesion assays, which are currently primarily performed in multiwell plates. Miniaturizing cell adhesion assays to an array format required the development of protocols for the reproducible microspotting of extracellular matrix (ECM) protein solutions and for the handling of cell suspensions during the assay. We generated ECM protein microarrays with high reproducibility in microspot protein content using nitrocellulose-coated glass microslides, combined with piezoelectric microspotting of protein solutions. Protocols were developed that allowed us to use 5000 cells or fewer on an array of 4 x 4 mm consisting of 64 microspots. Using this microarray system, we identified differences of adhesive properties of three cell lines to 14 different ECM proteins. Furthermore, the sensitivity and accuracy of the assays were increased using microarrays with ranges of ECM protein amounts. This microarray system will be particularly useful for extensive comparative cell adhesion profiling studies when only low amounts of adhesive substrate and cells, such as stem cells or cells from biopsies, are available.     

Identification of a Highly Conserved Epitope on Avian Influenza Virus Non-Structural Protein 1 Using a Peptide Microarray

Avian influenza virus (AIV) non-structural protein 1 (NS1) is a multifunctional protein. It is present at high levels in infected cells and can be used for AIV detection and diagnosis. In this study, we generated monoclonal antibody (MAb) D7 against AIV NS1 protein by immunization of BALB/c mice with purified recombinant NS1 protein expressed in Escherichia coli. Isotype determination revealed that the MAb was IgG₁/κ-type subclass. To identify the epitope of the MAb D7, the NS1 protein was truncated into a total of 225 15-mer peptides with 14 amino acid overlaps, which were spotted for a peptide microarray. The results revealed that the MAb D7 recognized the consensus DAPF motif. Furthermore, the AIV NS1 protein with the DAPF motif deletion was transiently expressed in 293T cells and failed to react with MAb D7. Subsequently, the DAPF motif was synthesized with an elongated GSGS linker at both the C- and N-termini. The MAb D7 reacted with the synthesized peptide both in enzyme-linked immunosorbent assay (ELISA) and dot-blot assays. From these results, we concluded that DAPF motif is the epitope of MAb D7. To our knowledge, this is the first report of a 4-mer epitope on the NS1 protein of AIV that can be recognized by MAb using a peptide microarray, which is able to simplify epitope identification, and that could serve as the basis for immune responses against avian influenza.     

Peptide microarrays for detailed, high-throughput substrate identification, kinetic characterization, and inhibition studies on protein kinase A

A microarray-based mix-and-measure, nonradioactive multiplex method with real-time detection was used for substrate identification, assay development, assay optimisation, and kinetic characterization of protein kinase A (PKA). The peptide arrays included either up to 140 serine/threonine-containing peptides or a concentration series of a smaller number of peptides. In comparison with existing singleplex assays, data quality was high, variation in assay conditions and reagent consumption were reduced considerably, and assay development could be accelerated because phosphorylation kinetics were monitored simultaneously on 4, 12, or 96 arrays. PKA was shown to phosphorylate many peptides containing known PKA phosphorylation sites as well as some new substrates. The kinetic behavior of the enzyme and the mechanism of inhibition by AMP-PNP, staurosporin, and PKA inhibitor peptide on the peptide microarray correlated well with data from homogeneous assays. Using this multiplex setup, we showed that the kinetic parameters of PKA and the potency of PKA inhibitors can be affected by the sequence of the peptide substrate. The technology enables kinetic monitoring of kinase activity in a multiplex setting such as a cell or tissue lysate. Finally, this high-throughput method allows fast identification of peptide substrates for serine/threonine kinases that are still uncharacterized.     

Label-free high-throughput microRNA expression profiling from total RNA

MicroRNAs (miRNAs) are key biological regulators and promising disease markers whose detection technologies hold great potentials in advancing fundamental research and medical diagnostics. Currently, miRNAs in biological samples have to be labeled before being applied to most high-throughput assays. Although effective, these labeling-based approaches are usually labor-intensive, time-consuming and liable to bias. Besides, the cross-hybridization of co-existing miRNA precursors (pre-miRNAs) is not adequately addressed in most assays that use total RNA as input. Here, we present a hybridization-triggered fluorescence strategy for label-free, microarray-based high-throughput miRNA expression profiling. The total RNA is directly applied to the microarray with a short fluorophore-linked oligonucleotide Universal Tag which can be selectively captured by the target-bound probes via base-stacking effects. This Stacking-Hybridized Universal Tag (SHUT) assay has been successfully used to analyze as little as 100 ng total RNA from human tissues, and found to be highly specific to homogenous miRNAs. Superb discrimination toward single-base mismatch at the 5' or 3' end has been demonstrated. Importantly, the pre-miRNAs generated negligible signals, validating the direct use of total RNA.     

Functional studies on transfected cell microarray analysed by linear regression modelling

Transfected cell microarray is a promising method for accelerating the functional exploration of the genome, giving information about protein function in the living cell. The microarrays consist of clusters of cells (spots) overexpressing or silencing a particular gene product. The subsequent analysis of the phenotypic consequences of such perturbations can then be detected using cell-based assays. The focus in the present study was to establish an experimental design and a robust analysis approach for fluorescence intensity data, and to address the use of replicates for studying regulation of gene expression with varying complexity and effect size. Our analysis pipeline includes measurement of fluorescence intensities, normalization strategies using negative control spots and internal control plasmids, and linear regression (ANOVA) modelling for estimating biological effects and calculating P-values for comparisons of interests. Our results show the potential of transfected cell microarrays in studying complex regulation of gene expression by enabling measurement of biological responses in cells with overexpression and downregulation of specific gene products, combined with the possibility of assaying the effects of external stimuli. Simulation experiments show that transfected cell microarrays can be used to reliably detect even quantitatively minor biological effects by including several technical and experimental replicates.     

A scalable method for integration and functional analysis of multiple microarray datasets

MOTIVATION: The diverse microarray datasets that have become available over the past several years represent a rich opportunity and challenge for biological data mining. Many supervised and unsupervised methods have been developed for the analysis of individual microarray datasets. However, integrated analysis of multiple datasets can provide a broader insight into genetic regulation of specific biological pathways under a variety of conditions. RESULTS: To aid in the analysis of such large compendia of microarray experiments, we present Microarray Experiment Functional Integration Technology (MEFIT), a scalable Bayesian framework for predicting functional relationships from integrated microarray datasets. Furthermore, MEFIT predicts these functional relationships within the context of specific biological processes. All results are provided in the context of one or more specific biological functions, which can be provided by a biologist or drawn automatically from catalogs such as the Gene Ontology (GO). Using MEFIT, we integrated 40 Saccharomyces cerevisiae microarray datasets spanning 712 unique conditions. In tests based on 110 biological functions drawn from the GO biological process ontology, MEFIT provided a 5% or greater performance increase for 54 functions, with a 5% or more decrease in performance in only two functions.