Microarrays for identifying binding sites and probing structure of RNAs

Oligonucleotide microarrays are widely used in various biological studies. In this review, application of oligonucleotide microarrays for identifying binding sites and probing structure of RNAs is described. Deep sequencing allows fast determination of DNA and RNA sequence. High-throughput methods for determination of secondary structures of RNAs have also been developed. Those methods, however, do not reveal binding sites for oligonucleotides. In contrast, microarrays directly determine binding sites while also providing structural insights. Microarray mapping can be used over a wide range of experimental conditions, including temperature, pH, various cations at different concentrations and the presence of other molecules. Moreover, it is possible to make universal microarrays suitable for investigations of many different RNAs, and readout of results is rapid. Thus, microarrays are used to provide insight into oligonucleotide sequences potentially able to interfere with biological function. Better understanding of structure–function relationships of RNA can be facilitated by using microarrays to find RNA regions capable to bind oligonucleotides. That information is extremely important to design optimal sequences for antisense oligonucleotides and siRNA because both bind to single-stranded regions of target RNAs.     

Functional RNA microarrays for high-throughput screening of antiprotein aptamers

High-throughput methods for generating aptamer microarrays are described. As a proof-of-principle, the microarrays were used to screen the affinity and specificity of a pool of robotically selected antilysozyme RNA aptamers. Aptamers were transcribed in vitro in reactions supplemented with biotinyl-guanosine 5'-monophosphate, which led to the specific addition of a 5' biotin moiety, and then spotted on streptavidin-coated microarray slides. The aptamers captured target protein in a dose-dependent manner, with linear signal response ranges that covered seven orders of magnitude and a lower limit of detection of 1 pg/mL (70 fM). Aptamers on the microarray retained their specificity for target protein in the presence of a 10,000-fold (w/w) excess of T-4 cell lysate protein. The RNA aptamer microarrays performed comparably to current antibody microarrays and within the clinically relevant ranges of many disease biomarkers. These methods should also prove useful for generating other functional RNA microarrays, including arrays for genomic noncoding RNAs that bind proteins. Integrating RNA aptamer microarray production with the maturing technology for automated in vitro selection of antiprotein aptamers should result in the high-throughput production of proteome chips.     

ProCAT: a data analysis approach for protein microarrays

Protein microarrays provide a versatile method for the analysis of many protein biochemical activities. Existing DNA microarray analytical methods do not translate to protein microarrays due to differences between the technologies. Here we report a new approach, ProCAT, which corrects for background bias and spatial artifacts, identifies significant signals, filters nonspecific spots, and normalizes the resulting signal to protein abundance. ProCAT provides a powerful and flexible new approach for analyzing many types of protein microarrays.     

New normalization methods for cDNA microarray data

MOTIVATION: The focus of this paper is on two new normalization methods for cDNA microarrays. After the image analysis has been performed on a microarray and before differentially expressed genes can be detected, some form of normalization must be applied to the microarrays. Normalization removes biases towards one or other of the fluorescent dyes used to label each mRNA sample allowing for proper evaluation of differential gene expression. RESULTS: The two normalization methods that we present here build on previously described non-linear normalization techniques. We extend these techniques by firstly introducing a normalization method that deals with smooth spatial trends in intensity across microarrays, an important issue that must be dealt with. Secondly we deal with normalization of a new type of cDNA microarray experiment that is coming into prevalence, the small scale specialty or 'boutique' array, where large proportions of the genes on the microarrays are expected to be highly differentially expressed. AVAILABILITY: The normalization methods described in this paper are available via http://www.pi.csiro.au/gena/ in a software suite called tRMA: tools for R Microarray Analysis upon request of the authors. Images and data used in this paper are also available via the same link.     

Fabrication of high quality microarrays

Fabrication of DNA microarray demands that between ten (diagnostic microarrays) and many hundred thousands of probes (research or screening microarrays) are efficiently immobilised to a glass or plastic surface using a suitable chemistry. DNA microarray performance is measured by parameters like array geometry, spot density, spot characteristics (morphology, probe density and hybridised density), background, specificity and sensitivity. At least 13 factors affect these parameters and factors affecting fabrication of microarrays are used in this review to compare different fabrication methods (spotted microarrays and in situ synthesis of microarrays) and immobilisation chemistries.     

Protein microarrays and novel detection platforms

The field of proteomics has undergone rapid advancements over the last decade and protein microarrays have emerged as a promising technological platform for the challenging task of studying complex proteomes. This gel-free approach has found an increasing number of applications due to its ability to rapidly and efficiently study thousands of proteins simultaneously. Different protein microarrays, including capture arrays, reverse-phase arrays, tissue microarrays, lectin microarrays and cell-free expression microarrays, have emerged, which have demonstrated numerous applications for proteomics studies including biomarker discovery, protein interaction studies, enzyme-substrate profiling, immunological profiling and vaccine development, among many others. The need to detect extremely low-abundance proteins in complex mixtures has provided motivation for the development of sensitive, real-time and multiplexed detection platforms. Conventional label-based approaches like fluorescence, chemiluminescence and use of radioactive isotopes have witnessed substantial advancements, with techniques like quantum dots, gold nanoparticles, dye-doped nanoparticles and several bead-based methods now being employed for protein microarray studies. In order to overcome the limitations posed by label-based technologies, several label-free approaches like surface plasmon resonance, carbon nanotubes and nanowires, and microcantilevers, among others, have also advanced in recent years, and these methods detect the query molecule itself. The scope of this article is to outline the protein microarray techniques that are currently being used for analytical and function-based proteomics and to provide a detailed analysis of the key technological advances and applications of various detection systems that are commonly used with microarrays.     

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.     

From combinatorial chemistry to chemical microarray

Combinatorial chemistry was first applied to the generation of peptide arrays in 1984. Since then, the field of combinatorial chemistry has evolved rapidly into a new discipline. There is a great need for the development of methods to examine the proteome functionally at a global level. Using many of the techniques and instruments developed for DNA microarrays, chemical microarray methods have advanced significantly in the past three years. High-density chemical microarrays can now be synthesized in situ on glass slides or be printed through covalent linkage or non-specific adsorption to the surface of the solid-support with fully automatic arrayers. Microfabrication methods enable one to generate arrays of microsensors at the end of optical fibers or arrays of microwells on a flat surface. In conjunction with the one-bead one-compound combinatorial library method, chemical microarrays have proven to be very useful in lead identification and optimization. High-throughput protein expression systems, robust high-density protein, peptide and small-molecule microarray systems, and automatic mass spectrometers are critical tools for the field of functional proteomics.     

A model of base-call resolution on broad-spectrum pathogen detection resequencing DNA microarrays

Oligonucleotide microarrays offer the potential to efficiently test for multiple organisms, an excellent feature for surveillance applications. Among these, resequencing microarrays are of particular interest, as they possess additional unique capabilities to track pathogens’ genetic variations and perform detailed discrimination of closely related organisms. However, this potential can only be realized if the costs of developing the detection microarray are kept at a manageable level. Selection and verification of the probes are key factors affecting microarray design costs that can be reduced through the development and use of in silico modeling. Models created for other types of microarrays do not meet all the required criteria for this type of microarray. We describe here in silico methods for designing resequencing microarrays targeted for multiple organism detection. The model development presented here has focused on accurate base-call prediction in regions that are applicable to resequencing microarrays designed for multiple organism detection, a variation from other uses of a predictive model in which perfect prediction of all hybridization events is necessary. The model will assist in simplifying the design of resequencing microarrays and in reduction of the time and costs required for their development for new applications.     

Tissue microarrays: emerging standard for biomarker validation

With the widespread use of DNA microarrays, hundreds of biomarkers are in need of validation in cohorts of well-annotated clinical samples. Tissue microarrays are emerging as the tool par excellence to rapidly perform DNA, RNA, and especially protein expression analyses on large numbers of clinical samples. Although still somewhat limited by the subjectivity of scoring methods and tissue sample representativeness, TMAs represent an increasingly validated means of understanding the clinical impact of diagnostic-related, prognostic-related, and therapy-related markers. Automated methods are being developed for TMA analysis and cell microarrays and frozen tissue TMAs have been better optimized. More and more biomarker studies are availing themselves of the high-throughput nature of TMAs, recognizing that they are becoming indispensable for rapid translation of laboratory data to the clinic.     

A statistically driven approach for image segmentation and signal extraction in cDNA microarrays.

The increasing use of cDNA microarrays necessitates the development of methods for extracting quality data. Here, we set forth hurdles to overcome in image analysis of microarrays. We emphasize the importance of objective data extraction methods resulting in reliable signal estimates. Based on statistical principles, we describe a method for automated grid alignment, spot detection, background estimation, flagging, and signal extraction. A software application that we call SignalViewer has been implemented for this method. We identify areas where we improved upon current methods used for array image analysis at each step in the process. Finally, we give examples to illustrate the performance of our algorithms on raw data.     

Assessment of methylation level prediction accuracy in methyl-DNA immunoprecipitation and sodium bisulfite based microarray platforms

In this study, we verified the accuracy of two array methods—methylated DNA immunoprecipitation coupled with CpG island microarrays (MeDIP-CGI-arrays) and sodium bisulfite conversion based microarrays (BC-arrays)—in predicting regional methylation levels as measured by pyrosequencing of bisulfite converted DNA (BC-pyrosequencing). To test the accuracy of these methods we used the Agilent Human CpG island and the Illumina HumanMethylation27 microarrays respectively, and compared microarray outputs to the data from targeted BC-pyrosequencing assays from several genomic regions of corresponding samples. We observed relatively high correlation with BC-pyrosequencing data for both array platforms, R = 0.87 for BC-Array and R = 0.79 for MeDIP-CGI array. However, MeDIP-CGI array were less reliable in predicting intermediate levels of DNA methylation. Several bioinformatics strategies, to ameliorate the performance of the MeDIP-CGI-Arrays did not improve the correlation with BC-pyrosequencing data. The high scalability, low cost and simpler analysis of BC-arrays, together with the recent extended coverage may make them a more versatile methylation analysis tool.     

Reproducible and inexpensive probe preparation for oligonucleotide arrays

We present a new protocol for the preparation of nucleic acids for microarray hybridization. DNA is fragmented quantitatively and reproducibly by using a hydroxyl radical-based reaction, which is initiated by hydrogen peroxide, iron(II)-EDTA and ascorbic acid. Following fragmentation, the nucleic acid fragments are densely biotinylated using a biotinylated psoralen analog plus UVA light and hybridized on microarrays. This non-enzymatic protocol circumvents several practical difficulties associated with DNA preparation for microarrays: the lack of reproducible fragmentation patterns associated with enzymatic methods; the large amount of labeled nucleic acids required by some array designs, which is often combined with a limited amount of starting material; and the high cost associated with currently used biotinylation methods. The method is applicable to any form of nucleic acid, but is particularly useful when applying double-stranded DNA on oligonucleotide arrays. Validation of this protocol is demonstrated by hybridizing PCR products with oligonucleotide-coated microspheres and PCR amplified cDNA with Affymetrix Cancer GeneChip microarrays.     

Oligosaccharide microarrays fabricated on aminooxyacetyl functionalized glass surface for characterization of carbohydrate-protein interaction

Carbohydrate-protein interactions play important biological roles in biological processes. But there is a lack of high-throughput methods to elucidate recognition events between carbohydrates and proteins. This paper reported a convenient and efficient method for preparing oligosaccharide microarrays, wherein the underivatized oligosaccharide probes were efficiently immobilized on aminooxyacetyl functionalized glass surface by formation of oxime bonding with the carbonyl group at the reducing end of the suitable carbohydrates via irreversible condensation. Prototypes of carbohydrate microarrays containing 10 oligosaccharides were fabricated on aminooxyacetyl functionalized glass by robotic arrayer. Utilization of the prepared carbohydrate microarrays for the characterization of carbohydrate-protein interaction reveals that carbohydrates with different structural features selectively bound to the corresponding lectins with relative binding affinities that correlated with those obtained from solution-based assays. The limit of detection (LOD) for lectin ConA on the fabricated carbohydrate microarrays was determined to be approximately 0.008 microg/mL. Inhibition experiment with soluble carbohydrates also demonstrated that the binding affinities of lectins to different carbohydrates could be analyzed quantitatively by determining IC(50) values of the soluble carbohydrates with the carbohydrate microarrays. This work provides a simple procedure to prepare carbohydrate microarray for high-throughput parallel characterization of carbohydrate-protein interaction.     

Limited agreement among three global gene expression methods highlights the requirement for non-global validation

MOTIVATION: DNA microarrays have revolutionized biological research, but their reliability and accuracy have not been extensively evaluated. Thorough testing of microarrays through comparison to dissimilar gene expression methods is necessary in order to determine their accuracy. RESULTS: We have systematically compared three global gene expression methods on all available histologically normal samples from five human organ types. The data included 25 Affymetrix high-density oligonucleotide array experiments, 23 expressed sequence tag based expression (EBE) experiments and 5 SAGE experiments. The reported gene-by-gene expression patterns showed a wide range of correlations between pairs of methods. This level of agreement was sufficient for accurate clustering of datasets from the same tissue and dissimilar methods, but highlights the need for thorough validation of individual gene expression measurements by alternate, non-global methods. Furthermore, analyses of mRNA abundance distributions indicate limitations in the EBE and SAGE methods at both high- and low-expression levels.     

Immobilization strategies for small molecule,peptide and protein microarrays

Protein, peptide and small molecule microarrays are valuable tools in biological research. In the last decade, substantial progress has been achieved to make these powerful technologies more reliable and available for researchers. This review describes chemical preparation methods for these microarrays with focus on site‐selective and bioorthogonal immobilization reactions, particularly the Staudinger ligation and the thiol‐ene reaction. In addition, the application of peptide microarrays, which were prepared by Staudinger ligation, to substrate specificity mapping is illustrated. Copyright © 2009 European Peptide Society and John Wiley & Sons, Ltd.     

Assessment and optimisation of normalisation methods for dual-colour antibody microarrays

Background  

Recent advances in antibody microarray technology have made it possible to measure the expression of hundreds of proteins simultaneously in a competitive dual-colour approach similar to dual-colour gene expression microarrays. Thus, the established normalisation methods for gene expression microarrays, e.g. loess regression, can in principle be applied to protein microarrays. However, the typical assumptions of such normalisation methods might be violated due to a bias in the selection of the proteins to be measured. Due to high costs and limited availability of high quality antibodies, the current arrays usually focus on a high proportion of regulated targets. Housekeeping features could be used to circumvent this problem, but they are typically underrepresented on protein arrays. Therefore, it might be beneficial to select invariant features among the features already represented on available arrays for normalisation by a dedicated selection algorithm.     

糖芯片的制备和检测应用研究进展

近数十年来,糖芯片逐渐成为分析糖介导的识别和结合作用的强有力工具,具有样品检测用量少、特异性强和高通量等优点,可以大大提高糖生物学研究的效率。主要介绍了通过糖库的构建、共价结合和非共价吸附法等方法制备糖芯片的过程,糖芯片的检测方法及其在生物学研究和生物医学领域的应用,以期为糖芯片相关研究提供参考。     

Shrinkage‐based Diagonal Discriminant Analysis and Its Applications in High‐Dimensional Data

Summary High‐dimensional data such as microarrays have brought us new statistical challenges. For example, using a large number of genes to classify samples based on a small number of microarrays remains a difficult problem. Diagonal discriminant analysis, support vector machines, and k‐nearest neighbor have been suggested as among the best methods for small sample size situations, but none was found to be superior to others. In this article, we propose an improved diagonal discriminant approach through shrinkage and regularization of the variances. The performance of our new approach along with the existing methods is studied through simulations and applications to real data. These studies show that the proposed shrinkage‐based and regularization diagonal discriminant methods have lower misclassification rates than existing methods in many cases.