A simple strategy for the creation of a recombinant lectin microarray

Glycomics, i.e. the high-throughput analysis of carbohydrates, has yet to reach the level of ease and import of its counterparts, genomics and proteomics, due to the difficulties inherent in carbohydrate analysis. The advent of lectin microarray technology addresses many of these problems, providing a straightforward approach for glycomic analysis. However, current microarrays are limited to the available lectin set, which consists mainly of plant lectins isolated from natural sources. These lectins have inherent problems including inconsistent activity and availability. Also, many plant lectins are glycosylated, complicating glycomic evaluation of complex samples, which may contain carbohydrate-binding proteins. The creation of a recombinant, well-defined lectin set would resolve many of these issues. Herein, we describe an efficient strategy for the systematic creation of recombinant lectins for use in microarray technology. We present a small panel of simple-to-purify bacterially-derived lectins that show reliable activity and define their binding specificities by both carbohydrate microarray and ELISA. We utilize this panel to create a recombinant lectin microarray that is able to distinguish glycopatterns for both proteins and cell samples. This work opens the door to the establishment of a vast set of defined lectins via high-throughout approaches, advancing lectin microarray technology for glycomic analysis.     

利用基因表达谱挖掘差异表达功能类的稳健性

Gene Ontology广泛地应用于基于基因芯片数据的差异表达功能类分析。基因芯片技术存在检测缺失与检测误差等问题。本文探讨上述这二个因素对利用基因表达谱挖掘Gene Ontology中差异表达功能类的影响。结果显示,差异表达功能类对于检测缺失与检测误差干扰等有一定的稳健性。     

Current issues for DNA microarrays: platform comparison, double linear amplification, and universal RNA reference

DNA microarray technology has been widely used to simultaneously determine the expression levels of thousands of genes. A variety of approaches have been used, both in the implementation of this technology and in the analysis of the large amount of expression data. However, several practical issues still have not been resolved in a satisfactory manner, and among the most critical is the lack of agreement in the results obtained in different array platforms. In this study, we present a comparison of several microarray platforms [Affymetrix oligonucleotide arrays, custom complementary DNA (cDNA) arrays, and custom oligo arrays printed with oligonucleotides from three different sources] as well as analysis of various methods used for microarray target preparation and the reference design. The results indicate that the pairwise correlations of expression levels between platforms are relative low overall but that the log ratios of the highly expressed genes are strongly correlated, especially between Affymetrix and cDNA arrays. The microarray measurements were compared with quantitative real-time-polymerase chain reaction (QRT-PCR) results for 23 genes, and the varying degrees of agreement for each platform were characterized. We have also developed and tested a double amplification method which allows the use of smaller amounts of starting material. The added round of amplification produced reproducible results as compared to the arrays hybridized with single round amplified targets. Finally, the reliability of using a universal RNA reference for two-channel microarrays was tested and the results suggest that comparisons of multiple experimental conditions using the same control can be accurate.     

DNA microarray technology in toxicogenomics of aquatic models: methods and applications

Toxicogenomics represents the merging of toxicology with genomics and bioinformatics to investigate biological functions of genome in response to environmental contaminants. Aquatic species have traditionally been used as models in toxicology to characterize the actions of environmental stresses. Recent completion of the DNA sequencing for several fish species has spurred the development of DNA microarrays allowing investigators access to toxicogenomic approaches. However, since microarray technology is thus far limited to only a few aquatic species and derivation of biological meaning from microarray data is highly dependent on statistical arguments, the full potential of microarray in aquatic species research has yet to be realized. Herein we review some of the issues related to construction, probe design, statistical and bioinformatical data analyses, and current applications of DNA microarrays. As a model a recently developed medaka (Oryzias latipes) oligonucleotide microarray was described to highlight some of the issues related to array technology and its application in aquatic species exposed to hypoxia. Although there are known non-biological variations present in microarray data, it remains unquestionable that array technology will have a great impact on aquatic toxicology. Microarray applications in aquatic toxicogenomics will range from the discovery of diagnostic biomarkers, to establishment of stress-specific signatures and molecular pathways hallmarking the adaptation to new environmental conditions.     

Chasing the dream: plant EST microarrays

DNA microarray technology is poised to make an important contribution to the field of plant biology. Stimulated by recent funding programs, expressed sequence tag sequencing and microarray production either has begun or is being contemplated for most economically important plant species. Although the DNA microarray technology is still being refined, the basic methods are well established. The real challenges lie in data analysis and data management. To fully realize the value of this technology, centralized databases that are capable of storing microarray expression data and managing information from a variety of sources will be needed. These information resources are under development and will help usher in a new era in plant functional genomics.     

Impact of DNA chips on haematological oncology

As the Human Genome Project hurtles towards completion, DNA microarray technology offers the potential to open wide new windows into the study of genome complexity. DNA chips can be used for many different purposes, most prominently to measure levels of gene expression (messenger RNA abundance) for tens of thousands of genes simultaneously. But how much of this data is useful and is some superfluous? Can array data be used to identify a handful of critical genes that will lead to a more detailed taxonomy of haematological malignancies and can this or similar array data be used to predict clinical outcome? It is still too early to predict what the ultimate impact of DNA chips will be on our understanding of cancer biology. There are many critically important questions about this new field that are yet unaddressed. By the publication of this article, it is hoped that the technology of DNA chips will be opened up and demystified, and that additional opportunities for creative exploration will be catalysed.     

基因表达谱芯片的数据挖掘

随着基因芯片技术的迅速发展,表达谱芯片分析及aCGH等方法已被广泛应用于生命科学各个研究领域,由此产生的数据也呈指数级增长。如何从海量数据中获取有生物学意义的结果成为摆在生物学工作者面前的难题。对表达谱芯片数据挖掘方法进行了综述。介绍了基本分析思路,当前重点分析方向,如GO分析、pathway与调控网络分析、聚类分析等计算法则和相关几款易用的分析软件。并介绍了几种科学自由计算软件在表达谱生物信息学分析中的应用。藉此为从事芯片分析的研究人员提供参考。     

A perspective on microarrays: current applications,pitfalls, and potential uses

With advances in robotics, computational capabilities, and the fabrication of high quality glass slides coinciding with increased genomic information being available on public databases, microarray technology is increasingly being used in laboratories around the world. In fact, fields as varied as: toxicology, evolutionary biology, drug development and production, disease characterization, diagnostics development, cellular physiology and stress responses, and forensics have benefiting from its use. However, for many researchers not familiar with microarrays, current articles and reviews often address neither the fundamental principles behind the technology nor the proper designing of experiments. Although, microarray technology is relatively simple, conceptually, its practice does require careful planning and detailed understanding of the limitations inherently present. Without these considerations, it can be exceedingly difficult to ascertain valuable information from microarray data. Therefore, this text aims to outline key features in microarray technology, paying particular attention to current applications as outlined in recent publications, experimental design, statistical methods, and potential uses. Furthermore, this review is not meant to be comprehensive, but rather substantive; highlighting important concepts and detailing steps necessary to conduct and interpret microarray experiments. Collectively, the information included in this text will highlight the versatility of microarray technology and provide a glimpse of what the future may hold.     

Gene Expression Analysis Platform (GEAP): A highly customizable, fast,versatile and ready-to-use microarray analysis platform

There are still numerous challenges to be overcome in microarray data analysis because advanced, state-of-the-art analyses are restricted to programming users. Here we present the Gene Expression Analysis Platform, a versatile, customizable, optimized, and portable software developed for microarray analysis. GEAP was developed in C# for the graphical user interface, data querying, storage, results filtering and dynamic plotting, and R for data processing, quality analysis, and differential expression. Through a new automated system that identifies microarray file formats, retrieves contents, detects file corruption, and solves dependencies, GEAP deals with datasets independently of platform. GEAP covers 32 statistical options, supports quality assessment, differential expression from single and dual-channel experiments, and gene ontology. Users can explore results by different plots and filtering options. Finally, the entire data can be saved and organized through storage features, optimized for memory and data retrieval, with faster performance than R. These features, along with other new options, are not yet present in any microarray analysis software. GEAP accomplishes data analysis in a faster, straightforward, and friendlier way than other similar software, while keeping the flexibility for sophisticated procedures. By developing optimizations, unique customizations and new features, GEAP is destined for both advanced and non-programming users.     

Subnanoliter enzymatic assays on microarrays

Many areas of research today are based on enzymatic assays most of which are still performed as enzyme-linked immunosorbent assays in microtiter plates. The demand for highly parallel screening of thousands of samples eventually led to a miniaturization and automation of these assays. However, the final transfer of enzymatic assays from a microtiter-based technology to microarrays has proven to be difficult for various reasons, such as the inability to maintain unbound reaction products on the spot of reaction or the missing capability of multiplexing. Here, we have conducted multiplex enzymatic assays in subnanoliter volumes on a single microarray using the multiple spotting technology. We were able to measure enzymatic activity with a sensitivity down to 35 enzyme molecules, applying only conventional flat microarray surfaces and standard microarray hardware. We have performed assays of inhibition and applied this format for the detection of prognostic markers, such as cathepsin D. The new approach allows the rapid and multiplex screening of thousands of samples on a single microarray with applications in drug screening, metagenomics, and high-throughput enzyme assays.     

An XML message broker framework for exchange and integration of microarray data

MOTIVATION: Microarrays are an important research tool for the advancement of basic biological sciences. However this technology has yet to be integrated with clinical decision making. We have implemented an information framework based on the Microarray Gene Expression Markup Language (MAGE-ML) specification. We are using this framework to develop a test-bed integrated database application to identify genomic and imaging markers for diagnosis of breast cancer. RESULTS: We developed extensible software architecture for retrieving data from different microarray databases using MAGE-ML and for combining microarray data with breast cancer image analysis and clinical data for correlation studies. The framework we developed will provide the necessary data integration to move microarray research from basic biological sciences to clinical applications. AVAILABILITY: Open source software will be available from SourceForge (http://sourceforge.net/projects/microsoap/).     

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.     

MicroRNA Profiling in Human Colon Cancer Cells during 5-Fluorouracil-Induced Autophagy

Autophagy modulation is now recognized as a potential therapeutic approach for cancer (including colorectal cancer), yet the molecular mechanisms regulating autophagy in response to cellular stress are still not well understood. MicroRNAs (miRNAs) have been found to play important roles in controlling many cellular functions, including growth, metabolism and stress response. The physiological importance of the miRNA-autophagy interconnection is only beginning to be elucidated. MiRNA microarray technology facilitates analysis of global miRNA expression in certain situations. In this study, we explored the expression profile of miRNAs during the response of human colon cancer cells (HT29s) to 5-FU treatment and nutrient starvation using miRNA microarray analysis. The alteration of miRNA expression showed the same pattern under both conditions was further testified by qRT-PCR in three human colon cancer cell lines. In addition, bioinformatic prediction of target genes, pathway analysis and gene network analysis were performed to better understand the roles of these miRNAs in the regulation of autophagy. We identified and selected four downregulated miRNAs including hsa-miR-302a-3p and 27 upregulated miRNAs under these two conditions as having the potential to target genes involved in the regulation of autophagy in human colon cancer cells. They have the potential to modulate autophagy in 5-FU-based chemotherapy in colorectal cancer.     

Single-Stranded DNA Catalyzes Hybridization of PCR-Products to Microarray Capture Probes

Since its development, microarray technology has evolved to a standard method in the biotechnological and medical field with a broad range of applications. Nevertheless, the underlying mechanism of the hybridization process of PCR-products to microarray capture probes is still not completely understood, and several observed phenomena cannot be explained with current models. We investigated the influence of several parameters on the hybridization reaction and identified ssDNA to play a major role in the process. An increase of the ssDNA content in a hybridization reaction strongly enhanced resulting signal intensities. A strong influence could also be observed when unlabeled ssDNA was added to the hybridization reaction. A reduction of the ssDNA content resulted in a massive decrease of the hybridization efficiency. According to these data, we developed a novel model for the hybridization mechanism. This model is based on the assumption that single stranded DNA is necessary as catalyst to induce the hybridization of dsDNA. The developed hybridization model is capable of giving explanations for several yet unresolved questions regarding the functionality of microarrays. Our findings not only deepen the understanding of the hybridization process, but also have immediate practical use in data interpretation and the development of new microarrays.     

MASQOT: a method for cDNA microarray spot quality control

Background  

cDNA microarray technology has emerged as a major player in the parallel detection of biomolecules, but still suffers from fundamental technical problems. Identifying and removing unreliable data is crucial to prevent the risk of receiving illusive analysis results. Visual assessment of spot quality is still a common procedure, despite the time-consuming work of manually inspecting spots in the range of hundreds of thousands or more.     

Gene expression profiling: methodological challenges,results, and prospects for addiction research

This review describes the current methods used to profile gene expression. These methods include microarrays, spotted arrays, serial analysis of gene expression (SAGE), and massive parallel signature sequencing (MPSS). Methodological and statistical problems in interpreting microarray and spotted array experiments are also discussed. Methods and formats such as minimum information about microarray experiments (MIAME) needed to share gene expression data are described. The last part of the review provides an overview of the application of gene-expression profiling technology to substance abuse research and discusses future directions.     

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。     

Microarrays in biology and medicine

The remarkable speed with which biotechnology has become critical to the practice of life sciences owes much to a series of technological revolutions. Microarray is the latest invention in this ongoing technological revolution. This technology holds the promise to revolutionize the future of biology and medicine unlike any other technology that preceded it. Development of microarray technology has significantly changed the way questions about diseases and/or biological phenomena are addressed. This is because microarrays facilitate monitoring the expression of thousands of genes or proteins in a single experiment. This enormous power of microarrays has enabled scientists to monitor thousands of genes and their products in a given living organism in one experiment, and to understand how these genes function in an orchestrated manner. Obtaining such a global view of life at the molecular level was impossible using conventional molecular biological techniques. However, despite all the progress made in developing this technology, microarray is yet to reach a point where all data are obtained, analyzed, and shared in a standardized fashion. The present article is a brief overview of microarray technologies and their applications with an emphasis on DNA microarray.     

A simple, rapid, and sensitive integrated protein microarray for simultaneous detection of multiple antigens and antibodies of five human hepatitis viruses (HBV, HCV, HDV, HEV, and HGV)

Protein microarrays for parallel detection of multiple viral antigens and antibodies have not yet been described in the field of human hepatitis virus infections. Here, we describe a simple, rapid and sensitive integrated protein microarray with three different reaction models. The integrated protein microarray could simultaneously determine in human sera two viral antigens (HBsAg, HBeAg) and seven viral antibodies (HBsAb, HBcAb, HBeAb, HCVAb, HDVAb, HEVAb, HGVAb) of human hepatitis viruses within 20 min. The results of the protein microarray were assessed directly by the naked eye but can also be analyzed by a quantitative detector. The detection limit of this protein microarray was 0.1 ng/ml for HBsAg. Overall, >85% concordance was observed between the integrated protein microarrays and an enzyme-linked immunosorbent assay for above hepatitis viral antigen and antibody detections in human sera. This integrated protein microarray can be easily optimized for clinical use and epidemiological screening for multiple hepatitis virus infections.