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.     

Identifying protein interactions with metal-modified DNA using microarray technology

Protein microarrays have been used extensively to identify protein–protein interactions; however, this technology has not been widely applied to protein–DNA interactions. In particular, this work demonstrates the utility of this technique for rapidly identifying interactions of proteins with metal-modified DNA. Protein macroarray experiments were carried out with high mobility group protein 1 (HMG-1) and cisplatin- and chromium-modified 50-mer oligonucleotides to demonstrate “proof of principle.” Commercially available protein microarrays containing many different classes of human proteins were then employed to search for additional interactions with cisplatin-modified DNA. The results of the microarray experiments confirmed some known interactions and, more importantly, identified many novel protein interactions, demonstrating the utility of this method as a rapid, high-throughput technique to discover proteins that interact with metal-modified DNA. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Duodenal ulcer-related antigens from Helicobacter pylori: immunoproteome and protein microarray approaches

Helicobacter pylori is an important risk factor of duodenal ulcer (DU). Although many virulence factors of H. pylori have been identified, few have been reported to show an association with the pathogenesis of DU. The aims of this study were to identify H. pylori antigens showing a high seropositivity in DU and to develop a platform for rapid and easy diagnosis for DU. Because DU and gastric cancer (GC) are considered clinical divergent gastroduodenal diseases, we compared two-dimensional immunoblots of an acid-glycine extract of an H. pylori strain from a patient with DU probed with serum samples from 10 patients with DU and 10 with GC to identify DU-related antigens. Of the 11 proteins that were strongly recognized by serum IgG from DU patients, translation elongation factor EF-G (FusA), catalase (KatA), and urease alpha subunit (UreA) were identified as DU-related antigens, showing a higher seropositivity in DU samples (n = 124) than in GC samples (n = 95) (FusA, 70.2 versus 45.3%; KatA, 50.8 versus 41.1%; UreA, 44.4 versus 27.4%). In addition, we found that the use of multiple antigens improved the discrimination between patients with DU and those with GC as the odds ratios increased from 1.82 (95% confidence interval (CI), 0.79-4.21; p = 0.1607) for seropositivity for FusA, KatA, or UreA alone to 4.95 (95% CI, 2.05-12.0; p = 0.0004) for two of the three antigens and to 5.71 (95% CI, 1.86-17.6; p = 0.0024) for all three antigens. Moreover a protein array containing the three DU-related antigens was developed to test the idea of using multiple biomarkers in diagnosis. We conclude that FusA, KatA, and UreA are DU-related antigens of H. pylori, and the combination of these on a protein array provided a rapid and convenient method for detecting serum antibody patterns of DU patients.     

Profiling of alopecia areata autoantigens based on protein microarray technology

Protein biochips have a great potential in future parallel processing of complex samples as a research tool and in diagnostics. For the generation of protein biochips, highly automated technologies have been developed for cDNA expression library production, high throughput protein expression, large scale analysis of proteins, and protein microarray generation. Using this technology, we present here a strategy to identify potential autoantigens involved in the pathogenesis of alopecia areata, an often chronic disease leading to the rapid loss of scalp hair. Only little is known about the putative autoantigen(s) involved in this process. By combining protein microarray technology with the use of large cDNA expression libraries, we profiled the autoantibody repertoire of sera from alopecia areata patients against a human protein array consisting of 37,200 redundant, recombinant human proteins. The data sets obtained from incubations with patient sera were compared with control sera from clinically healthy persons and to background incubations with anti-human IgG antibodies. From these results, a smaller protein subset was generated and subjected to qualitative and quantitative validation on highly sensitive protein microarrays to identify novel alopecia areata-associated autoantigens. Eight autoantigens were identified by protein chip technology and were successfully confirmed by Western blot analysis. These autoantigens were arrayed on protein microarrays to generate a disease-associated protein chip. To confirm the specificity of the results obtained, sera from patients with psoriasis or hand and foot eczema as well as skin allergy were additionally examined on the disease-associated protein chip. By using alopecia areata as a model for an autoimmune disease, our investigations show that the protein microarray technology has potential for the identification and evaluation of autoantigens as well as in diagnosis such as to differentiate alopecia areata from other skin diseases.     

蛋白微阵列研究进展

蛋白微阵列是随着基因微阵列技术发展起来的,用于基因微阵列的制备方法、信号的检测及分析系统,也可用于蛋白微阵列。各种蛋白微阵列基质的发展,提高了蛋白的固定效率。放射性同位数、化学发光、激光共聚焦荧光扫描等技术都已用于微阵列的检测。重组蛋白技术的发展,提高了蛋白微阵列检测的通量和灵敏度。蛋白微阵列具有通量高、使用样品少、重复性好、可定量的特点,使其在生物医药科学研究中得到了广泛应用。本综述了蛋白微阵列的制备及其在免疫检测、医学诊断及蛋白组研究中的应用。     

Protein microarray technology

Microarray technology allows the simultaneous analysis of thousands of parameters within a single experiment. Microspots of capture molecules are immobilized in rows and columns onto a solid support and exposed to samples containing the corresponding binding molecules. Readout systems based on fluorescence, chemiluminescence, mass spectrometry, radioactivity or electrochemistry can be used to detect complex formation within each microspot. Such miniaturized and parallelized binding assays can be highly sensitive, and the extraordinary power of the method is exemplified by array-based gene expression analysis. In these systems, arrays containing immobilized DNA probes are exposed to complementary targets and the degree of hybridization is measured. Recent developments in the field of protein microarrays show applications for enzyme-substrate, DNA-protein and different types of protein-protein interactions. Here, we discuss theoretical advantages and limitations of any miniaturized capture-molecule-ligand assay system and discusses how the use of protein microarrays will change diagnostic methods and genome and proteome research.     

Robust estimation of protein expression ratios with lysate microarray technology

MOTIVATION: The protein lysate microarray is a developing proteomic technology for measuring protein expression levels in a large number of biological samples simultaneously. A challenge for accurate quantification is the relatively narrow dynamic range associated with the commonly used chromogenic signal detection system. To facilitate accurate measurement of the relative expression levels, each sample is serially diluted and each diluted version is spotted on a nitrocellulose-coated slide in triplicate. Thus, each sample yields multiple measurements in different dynamic ranges of the detection system. This study aims to develop suitable algorithms that yield accurate representations of the relative expression levels in different samples from multiple data points. RESULTS: We evaluated two algorithms for estimating relative protein expression in different samples on the lysate microarray by means of a cross-validation procedure. For this purpose as well as for quality control we designed a 1440-spot lysate microarray containing 80 identical samples of purified bovine serum albumin, printed in triplicate with six 2-fold dilutions. Our analysis showed that the algorithm based on a robust least squares estimator provided the most accurate quantification of the protein lysate microarray data. We also demonstrated our methods by estimating relative expression levels of p53 and p21 in either p53(+/+) or p53(-/-) HCT116 colon cancer cells after two drug treatments and their combinations on another lysate microarray. AVAILABILITY: http://www.cs.tut.fi/~mirceanc/lysate_array_bioinformatics.htm     

Protein microarray technology

Emerging drug resistance thwarts progress in chemotherapy, resulting in increased morbidity, mortality and healthcare costs. Understanding the mechanisms by which drug resistance phenotypes emerge is important to prolong the useful life of existing drugs but may also highlight pathways that play a role in the acquisition of resistance and which may themselves present resistance-proof drug targets. Comparative proteomic approaches have demonstrated potential to link drug resistance phenotypes to molecular changes but will also prove powerful in the elucidation of the mechanisms by which drug resistance arises.     

New developments in microarray technology

Microarrays have emerged as indispensable research tools for gene expression profiling and mutation analysis. New classification of cancer subtypes, dissecting the yeast metabolism and large-scale genotyping of human single nucleotide polymorphisms are important results being obtained with this technique. Realizing the microsphere-based massively parallel signature sequencing technique as fluid microarrays, building new types of protein arrays and constructing miniaturized flow-through systems, which can potentially take this technology from the research bench into industrial, clinical and other routine applications, exemplify the intense developments that are now ongoing in this field.     

Biomarker discovery using protein microarray technology platforms: antibody-antigen complex profiling

Protein microarrays represent an important new tool in proteomic systems biology. This review focuses on the contributions of protein microarrays to the discovery of novel disease biomarkers through antibody-based assays. Of particular interest is the use of protein microarrays for immune response profiling, through which a disease-specific antibody repertoire may be defined. The antigens and antibodies revealed by these studies are useful for clinical assay development, with enormous potential to aid in diagnosis, prognosis, disease staging and treatment selection. The discovery and characterization of novel biomarkers specifically tailored to disease type and stage are expected to enable personalized medicine by facilitating preventative medicine, predictive diagnostics and individualized curative therapies.     

Biomarker discovery using protein microarray technology platforms: antibody-antigen complex profiling

Protein microarrays represent an important new tool in proteomic systems biology. This review focuses on the contributions of protein microarrays to the discovery of novel disease biomarkers through antibody-based assays. Of particular interest is the use of protein microarrays for immune response profiling, through which a disease-specific antibody repertoire may be defined. The antigens and antibodies revealed by these studies are useful for clinical assay development, with enormous potential to aid in diagnosis, prognosis, disease staging and treatment selection. The discovery and characterization of novel biomarkers specifically tailored to disease type and stage are expected to enable personalized medicine by facilitating preventative medicine, predictive diagnostics and individualized curative therapies.     

Multiplex SNP genotyping in pooled DNA samples by a four-colour microarray system

We selected 125 candidate single nucleotide polymorphisms (SNPs) in genes belonging to the human type 1 interferon (IFN) gene family and the genes coding for proteins in the main type 1 IFN signalling pathway by screening databases and by in silico comparison of DNA sequences. Using quantitative analysis of pooled DNA samples by solid-phase mini-sequencing, we found that only 20% of the candidate SNPs were polymorphic in the Finnish and Swedish populations. To allow more effective validation of candidate SNPs, we developed a four-colour microarray-based mini-sequencing assay for multiplex, quantitative allele frequency determination in pooled DNA samples. We used cyclic mini-sequencing reactions with primers carrying 5′-tag sequences, followed by capture of the products on microarrays by hybridisation to complementary tag oligonucleotides. Standard curves prepared from mixtures of known amounts of SNP alleles demonstrate the applicability of the system to quantitative analysis, and showed that for about half of the tested SNPs the limit of detection for the minority allele was below 5%. The microarray-based genotyping system established here is universally applicable for genotyping and quantification of any SNP, and the validated system for SNPs in type 1 IFN-related genes should find many applications in genetic studies of this important immunoregulatory pathway.     

Regression approaches for microarray data analysis.

A variety of new procedures have been devised to handle the two-sample comparison (e.g., tumor versus normal tissue) of gene expression values as measured with microarrays. Such new methods are required in part because of some defining characteristics of microarray-based studies: (i) the very large number of genes contributing expression measures which far exceeds the number of samples (observations) available and (ii) the fact that by virtue of pathway/network relationships, the gene expression measures tend to be highly correlated. These concerns are exacerbated in the regression setting, where the objective is to relate gene expression, simultaneously for multiple genes, to some external outcome or phenotype. Correspondingly, several methods have been recently proposed for addressing these issues. We briefly critique some of these methods prior to a detailed evaluation of gene harvesting. This reveals that gene harvesting, without additional constraints, can yield artifactual solutions. Results obtained employing such constraints motivate the use of regularized regression procedures such as the lasso, least angle regression, and support vector machines. Model selection and solution multiplicity issues are also discussed. The methods are evaluated using a microarray-based study of cardiomyopathy in transgenic mice.     

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.     

Antibody colocalization microarray: a scalable technology for multiplex protein analysis in complex samples

DNA microarrays were rapidly scaled up from 256 to 6.5 million targets, and although antibody microarrays were proposed earlier, sensitive multiplex sandwich assays have only been scaled up to a few tens of targets. Cross-reactivity, arising because detection antibodies are mixed, is a known weakness of multiplex sandwich assays that is mitigated by lengthy optimization. Here, we introduce (1) vulnerability as a metric for assays. The vulnerability of multiplex sandwich assays to cross-reactivity increases quadratically with the number of targets, and together with experimental results, substantiates that scaling up of multiplex sandwich assays is unfeasible. We propose (2) a novel concept for multiplexing without mixing named antibody colocalization microarray (ACM). In ACMs, both capture and detection antibodies are physically colocalized by spotting to the same two-dimensional coordinate. Following spotting of the capture antibodies, the chip is removed from the arrayer, incubated with the sample, placed back onto the arrayer and then spotted with the detection antibodies. ACMs with up to 50 targets were produced, along with a binding curve for each protein. The ACM was validated by comparing it to ELISA and to a small-scale, conventional multiplex sandwich assay (MSA). Using ACMs, proteins in the serum of breast cancer patients and healthy controls were quantified, and six candidate biomarkers identified. Our results indicate that ACMs are sensitive, robust, and scalable.     

Protein and antibody microarray technology

Following the age of genomics having sequenced the human genome, interest is shifted towards the function of genes. This new age of proteomics brings about a change of methods to study the properties of gene products on a large scale. Protein separation technologies are now applied to allow high-throughput purification and characterisation of proteins. Two-dimensional-gel electrophoresis (2DE) and mass spectrometry (MS) have become widely used tools in the field of proteomics. At the same time, protein and antibody microarrays have been developed as successor of DNA microarrays to soon allow the proteome-wide screening of protein function in parallel. This review is aimed to introduce this new technology and to highlight its current prospects and limitations.