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7 pags., 1 fig., 2 tabs., Streptococcus pneumoniae is responsible for severe infections, causing millions of deaths yearly. Immunoglobulin G (IgG) antibodies against the capsular polysaccharide (CPS) offer S. pneumoniae serotype-specific protection. In this work, we examined the applicability of the microarray technology to detect CPS type-specific IgGs in serum, using a collection of 22 microarray-printed S. pneumoniae CPSs. First, printing of five CPSs onto nitrocellulose-coated glass slides was tested. Successful printing was only achieved for certain CPS types and concentrations. This behavior was tentatively related with diverse viscosities of the CPS solutions. Measurement of dynamic viscosities fully supported this assumption and helped to establish suitable CPS type-and concentration-dependent printing conditions. Next, the potential of CPS microarrays for detecting recognition by anti-CPS IgGs was examined using well-defined rabbit pneumococcal antisera. In all cases, the expected antiserum-CPS binding signals were detected, prompting a proof-of-concept analysis of human serum samples. Clearly distinct serum-and CPS-specific binding patterns and intensities were observed, evidencing selective detection of CPS type-specific IgGs. Compared to the ELISA assay commonly used to quantitate CPS type-specific IgGs in serum, the newly developed S. pneumoniae CPS microarrays offer the advantage of enabling the simultaneous analysis of multiple CPS-serum interactions using minute CPS amounts and significantly reduced serum volumes. Therefore, the approach could be particularly valuable for gauging the presence of CPS type-specific IgGs in human serum when sample volumes are limited and/or numerous serum samples are being examined., We gratefully acknowledge financial support from the Spanish Ministry of Economy, Industry, and Competitiveness (Grants BFU2015-70052-R and SAF2017-88664-R), the Spanish Ministry of Science, Innovation, and Universities, the Spanish State Research Agency, and the European Regional Development Fund (Grant RTI2018-099985-B-I00, MCIU/AEI/ FEDER, UE), and the CIBER of Respiratory Diseases (CIBERES), an initiative from the Spanish Institute of Health Carlos III (ISCIII).

Human activities have being responsible for the global environmental changes, resulting in an increase number of incident of vector- and rodent-borne diseases worldwide. Rodents and arthropods-borne viruses are important globally emerging and re-emerging viruses and most of them are RNA viruses. Efficient and early diagnosis of these infections are very important to prevent their spread, to improve clinical management of the patients, as wells to protect livestock and domestic animals. Currently, available diagnostic methods such as immunoassays, polymerase chain reaction and virus isolation can detect only one or few viruses in a single assay. The DNA microarray platform has emerged as diagnostic tool suitable for high throughput screening of pathogenic agents. The aim of this study was to develop a DNA microarray platform (RoboArboVirusChip) for detecting rodent- and arthropod-borne viruses, which belong to seven families: Bunyaviridae (genera Orthobunyavirus, Nairovirus and Phlebovirus), Flaviviridae (genus Flavivirus), Togaviridae (genus Alphavirus), Reoviridae (genera Orbivirus, Seadornavirus and Coltvivirus), Rhabdoviridae (genera Vesiculovirus and Ephemerovirus), and Asfarviridae (genus Asfarvirus). Specific oligonucleotide probes of 60-mer (n=4209) targeting 412 virus species and generic probes of 25-35-mer (n=87) targeting viruses at the genus level were designed. A total of 17 reference viruses belonging to the Bunyaviridae, Flaviviridae, Rhabdoviridae and Togaviridae families were used to standardize RoboArboVirusChip. All reference viruses were specifically detected without any cross hybridization; however, the generic probes were not able to identify the viruses at the genus level. The RoboArboVirusChip was able to specifically identify four viruses contained in three different mixtures: i) virus of different families, ii) virus of the Flavivirus genus, and iii) the Dengue virus (DENV) serotypes. The four DENV serotypes were use to evaluate the sensitivity of the RoboArboVirusChip, which was able to detect a minimum of 25 RNA copies/mL of the viruses, confirming its high sensitivity. The applicability of the RoboArboVirusChip to detect viruses in clinical samples was tested with serum samples obtained from dengue suspected cases (four positive cases and 40 negative cases). DENV was detected in the four positive serum samples, while in the 40 negative serum samples, it was not detected any virus. The results obtained in this study suggest that the RoboArboVirusChip platform could be a useful tool for early diagnosis of robovirus and arbovirus infections during epidemic outbreaks, helping in the rapid implementation of disease containment strategies As atividades humanas têm sido responsável por mudanças ambientais globais, resultando num aumento do número de casos de doenças transmitidas por vetores e roedores em todo o mundo. Os vírus transmitidos por roedores e artrópodes são vírus emergentes e re-emergentes de importância global, sendo que a maioria deles são vírus de RNA. O diagnóstico eficiente e precoce dessas infecções são muito importantes para evitar a sua propagação, para melhorar o manejo clínico dos pacientes e também, para proteger o gado e os animais domésticos. Atualmente, os métodos de diagnóstico disponíveis, tais como os imunoensaios, a reação em cadeia da polimerase e o isolamento viral podem detectar apenas um ou poucos vírus em um único ensaio. A plataforma de microarranjo de DNA tem surgido como uma ferramenta de diagnóstico apropriada para o monitoramento em larga escala de agentes patogênicos. O objetivo deste estudo foi desenvolver uma plataforma de microarranjo de DNA (RoboArboVirusChip) para a detecção de vírus transmitidos por roedores e artrópodes, os quais pertencem a sete famílias: Bunyaviridae (gêneros Orthobunyavirus, Nairovirus e Phlebovírus), Flaviviridae (gênero Flavivirus), Togaviridae (gênero Alphavirus), Reoviridae (gênero Orbivirus, Seadornavirus e Coltvivirus), Rhabdoviridae (géneros Vesiculovirus e Ephemerovirus), e Asfarviridae (gênero Asfarvirus). Sondas oligonucleotídicas de 60-mer (n=4209) específicas contra 412 espécies virais, e sondas genéricas de 25-35-mer (n=87) para detecção de vírus a nível do gênero foram desenhados. Um total de 17 vírus de referência, pertencentes às famílias Bunyaviridae, Flaviviridae, Rhabdoviridae e Togaviridae foram utilizados para padronizar o RoboArboVirusChip. Todos os vírus de referência foram detectados especificamente sem apresentação de hibridação cruzada, porem as sondas genéricas não foram capazes de detectar os vírus a nível do gênero. O RoboArboVirusChip foi capaz de identificar especificamente quatro vírus contidos em diferentes misturas: i) vírus de diferentes famílias, ii) vírus pertencentes ao gênero a Flavivirus, e iii) os sorotipos do vírus da dengue (DENV). Os quatro sorotipos do DENV foram utilizados para determinar a sensibilidade do RoboArboVirusChip, o qual foi capaz de detectar um mínimo de 25 copias de RNA/mL. A aplicabilidade do RoboArboVirusChip para detectar vírus em amostras clínicas foi avaliada testando amostras de soro de pacientes com suspeita de dengue (quatro casos positivos e 40 casos negativos). Os resultados obtidos neste estudo sugerem que o RoboArboVirusChip poderá ser uma ferramenta útil para o diagnóstico precoce da infecção causada por robovírus e arbovírus, auxiliando na rápida implementação de estratégias de contenção das doenças causadas por esses vírus

The performance indicator called limit of detection for microarray platform (LODP) was defined in ISO 16578:2013. The methods to determine practical LODP were explored. In general, + 3 SD of the background is used as the signal strength of limit of detection and criteria for dividing positive and negative results. Since the negative signal had been defined differently for each microarray platform, signals obtained from Non-Probe Spots (NPS) installed on the microarrays were defined as the “background” of microarrays. LODP was determined as the lowest concentration of which the average signal exceeded Avg. + 3 SD of the background (NPS) and the signal was significantly different from those of the lower and higher adjacent concentration points measured with a diluted series of reference materials. For reliable qualitative analysis, the positive results can be defined as signals higher than those corresponding to LODP and negative results as lower signals, without determining limit of detection for all target probes. The use of LODP also enables comparisons of platform performances without checking sequence dependencies, and assists to select reliable and fitting platforms for experimental purposes.

Background DNA copy number profiles from microarray and sequencing experiments sometimes contain wave artefacts which may be introduced during sample preparation and cannot be removed completely by existing preprocessing methods. Besides, large derivative log ratio spread (DLRS) of the probes correlating with poor DNA quality is sometimes observed in genome screening experiments and may lead to unreliable copy number profiles. Depending on the extent of these artefacts and the resulting misidentification of copy number alterations/variations (CNA/CNV), it may be desirable to exclude such samples from analyses or to adapt the downstream data analysis strategy accordingly. Results Here, we propose a method to distinguish reliable genomic copy number profiles from those containing heavy wave artefacts and/or large DLRS. We define four features that adequately summarize the copy number profiles for reliability assessment, and train a classifier on a dataset of 1522 copy number profiles from various microarray platforms. The method can be applied to predict the reliability of copy number profiles irrespective of the underlying microarray platform and may be adapted for those sequencing platforms from which copy number estimates could be computed as a piecewise constant signal. Further details can be found at https://github.com/baudisgroup/CNARA. Conclusions We have developed a method for the assessment of genomic copy number profiling data, and suggest to apply the method in addition to and after other state-of-the-art noise correction and quality control procedures. CNARA could be instrumental in improving the assessment of data used for genomic data mining experiments and support the reliable functional attribution of copy number aberrations especially in cancer research. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3074-7) contains supplementary material, which is available to authorized users.

Diatoms are major contributors to global carbon fixation and constitute a significant portion of biofilms found in lotic ecosystems. Despite their widespread abundance and the fact that extensive studies have been performed on morphological features of frustules, molecular tools for the identification of diatoms are not commonly available. This study focuses on the development of oligonucleotide probes for the detection of diatom species relevant to water quality assessment. The selected panel of diatoms covers all the species found in water of varying quality from the rivers of central-East Apennine (Italy). Small subunit rRNA-targeted probes were applied to a microarray platform as well as to a new technique termed Primer–Probe, with the aim of obtaining a molecular tool suitable for accurate identification of both single and mixed species diatom populations. The Primer–Probe technique together with dot-blot assays proved to be ideal for the preliminary screening of a large set of DNA oligonucleotides designed by ARB software. It was shown that microarrays, as a promising technology for rapid and simultaneous detection of a wide range of species-specific genetic markers, can be adapted to monitor changes within a diatom community. It is suggested that microarrays will provide a molecular basis for microbial identification to support standard microscopy techniques used by ecologists and environmental scientists for monitoring water quality.

MicroRNA (miRNA) microarrays have been successfully used for profiling miRNA expression in many physiological processes such as development, differentiation, oncogenesis, and other disease processes. Detecting miRNA by miRNA microarray is actually based on nucleic acid hybridization between target molecules and their corresponding complementary probes. Due to the small size and high degree of similarity among miRNA sequences, the hybridization condition must be carefully optimized to get specific and reliable signals. Previously, we reported a microarray platform to detect miRNA expression. In this study, we evaluated the sensitivity and specificity of our microarray platform. After systematic analysis, we determined an optimized hybridization condition with high sensitivity and specificity for miRNA detection. Our results would be helpful for other hybridization-based miRNA detection methods, such as northern blot and nuclease protection assay.

The quantitative nuclease protection assay (qNPA) is a very simple and highly sensitive method for measuring mRNA transcripts, can be used on a variety of sample types, and is amenable to high-throughput sample processing. We have combined the power of the qNPA assay with the density of a DNA microarray to create a qNPA Microarray platform. This platform is compatible with common laboratory equipment: it uses fluorescence-based detection, can be analyzed with common microarray scanners, and is in an SBS footprint with 96-well layout for high-throughput applications. Here, we demonstrate the characteristics of a qNPA Microarray slide that contains up to 1700 gene elements per well. We show that the new platform can reliably detect transcripts at levels as low as 10 fM with median CVs below 12%. On a standardized set of samples, the qNPA Microarray detected the same trends in gene expression as the original qNPA technology, real time qPCR, and Affymetrix GeneChip DNA Microarrays. Given its ease of use, compatibility with multiple sample types, high-throughput capabilities, and its integration with standard laboratory equipment, the qNPA Microarray is a powerful new platform for gene expression research.

Microarrays are widely used in gene expression studies, and custom expression microarrays are popular to monitor expression changes of a customer-defined set of genes. However, the complexity of transcriptomes uncovered recently make custom expression microarray design a non-trivial task. Pervasive transcription and alternative processing of transcripts generate a wealth of interweaved transcripts that requires well-considered probe design strategies and is largely neglected in existing approaches. We developed the web server CEM-Designer that facilitates microarray platform independent design of custom expression microarrays for complex transcriptomes. CEM-Designer covers (i) the collection and generation of a set of unique target sequences from different sources and (ii) the selection of a set of sensitive and specific probes that optimally represents the target sequences. Probe design itself is left to third party software to ensure that probes meet provider-specific constraints. CEM-Designer is available at http://designpipeline.bioinf.uni-leipzig.de.

High throughput methods deliver large amount of data serving to describe the physiological treatment that is being studied. In the case of microarrays, there would be a clear benefit to integrate the published data sets. However, the numerous methodological discrepancies between microarray platforms make this comparison impossible. This incompatibility is magnified when considering the peculiar context of transcript management in early embryogenesis. The total RNA content is known to profoundly fluctuate during development. In addition, the mRNA population is subjected to poly(A) tail shortening and elongating events, a characteristic of stored and recruited messengers. These intrinsic factors need to be considered when interpreting any transcript abundance profiles during early development. As a consequence, many methodological details affect microarray platform performances and prevent compatibility. In an effort to maximize our microarray platform performance, we determined the various sources of variation for every one of the main steps leading to the production of microarray data. The five main steps involved in sample preparation were evaluated, as well as conditions for post-hybridization validation by qRT-PCR. These determinations were essential for the implementation of standardized procedures for our Research Network but they can also provide insight into the compatibility issues that the microarray community is now facing.

Evaluation of: Tissari P, Zumla A, Tarkka E et al. Accurate and rapid identification of bacterial species from positive blood cultures with a DNA-based microarray platform: an observational study. Lancet 375(9710), 224–230 (2010).Rapid and accurate detection of life-threatening microbial pathogens with high morbidity and mortality is important for the treatment of suitable antibiotics to patients with infectious diseases. Recently, DNA microarray-based identification of pathogenic microorganisms has attracted much attention from clinical researchers owing to its efficiency, specificity and reproducible high-throughput capacities, compared with conventional culture-based diagnosis, which require a long period of time for cultivation and labor-intensive assay steps. In this article, we review recently published microarray-based diagnostic studies for accurate and rapid identification of pathogens infected in bloodstream and discuss the advantages and limitations over conventional diagnostic methods.

Autoantigen microarrays are being used increasingly to study autoimmunity. Significant variation has been observed when comparing microarray surfaces, printing methods, and probing conditions. In the present study, 24 surfaces and several arraying parameters were analyzed using >500 feature autoantigen microarrays printed with quill pins. A small subset of slides, including FAST®, PATH®, and SuperEpoxy2, performed well while maintaining the sensitivity and specificity of autoantigen microarrays previously demonstrated by our laboratory. By optimizing the major variables in our autoantigen microarray platform, subtle differences in serum samples can be identified that will shed light on disease pathogenesis.

The interest towards extracellular vesicles (EVs) has grown exponentially over the last few years; being involved in intercellular communication and serving as reservoirs for biomarkers for tumors, they have a great potential for liquid biopsy development, possibly replacing many costly and invasive tissue biopsies. Here we propose, for the first time, the use of a Si/SiO2 interferometric, microarray platform for multiparametric intact EVs analysis combining label-free EVs mass quantitation and high sensitivity fluorescence based phenotyping. Label free interferometric measurement allows to quantify the amount of vesicles captured by printed antibodies while, on the same chip, EVs are also detected by fluorescence in a sandwich immunoassay. The proposed method simultaneously detects, quantify and phenotype intact EVs in a microarray format.

The DNA microarray technology is currently a useful biomedical tool which has been developed for a variety of diagnostic applications. However, the development pathway has not been smooth and the technology has faced some challenges. The reliability of the microarray data and also the clinical utility of the results in the early days were criticized. These criticisms added to the severe competition from other techniques, such as next-generation sequencing (NGS), impacting the growth of microarray-based tests in the molecular diagnostic market.Thanks to the advances in the underlying technologies as well as the tremendous effort offered by the research community and commercial vendors, these challenges have mostly been addressed. Nowadays, the microarray platform has achieved sufficient standardization and method validation as well as efficient probe printing, liquid handling and signal visualization. Integration of various steps of the microarray assay into a harmonized and miniaturized handheld lab-on-a-chip (LOC) device has been a goal for the microarray community. In this respect, notable progress has been achieved in coupling the DNA microarray with the liquid manipulation microsystem as well as the supporting subsystem that will generate the stand-alone LOC device.In this chapter, we discuss the major challenges that microarray technology has faced in its almost two decades of development and also describe the solutions to overcome the challenges. In addition, we review the advancements of the technology, especially the progress toward developing the LOC devices for DNA diagnostic applications.

The use of cross-species hybridization (CSH) to DNA microarrays, in which the target RNA and microarray probe are from different species, has increased in the past few years. CSH is used in comparative, evolutionary and ecological studies of closely related species, and for gene-expression profiling of many species that lack a representative microarray platform. However, unlike species-specific hybridization, CSH is still considered a non-standard use of microarrays. Here, we present the recent developments in the field of CSH for cDNA and oligomer microarray platforms. We discuss issues that influence the quality of CSH results, including platform choice, experiment design and data analysis, and suggest strategies that can lead to improvement of CSH studies to investigate species diversity.

Summary With the global expansion of harmful algal blooms (HABs), several measures, including molecular approaches, have been undertaken to monitor its occurrence. Many reports have indicated the signifi- cant roles of bacteria in controlling algal bloom dynamics. Attempts have been made to utilize the bacteria/harmful algae relationship in HAB monitoring. In this study, bacterial assemblages monitored during coastal HABs and bacterial commu- nities in induced microcosm blooms were investigated. Samples were analysed using denatur- ing gradient gel electrophoresis (DGGE) of the 16S rRNA gene. DGGE bands with peculiar patterns before, during, and after algal blooms were isolated and identified. Probes for six ribotypes representing organisms associated with Chatonella spp., Hetero- capsa circularisquama ,o rHeterosigma akashiwo were used for analysis on NanoChip electronic microarray. In addition, a new approach using cul- tured bacteria species was developed to detect longer (533 bp) polymerase chain reaction-amplified prod- ucts on the electronic microarray. The use of fluores- cently labelled primers allowed the detection of individual species in single or mixed DNA conditions. The developed approach enabled the detection of the presence or absence and relative abundance of the HAB-related ribotypes in coastal and microcosm blooms. This study indicates the ability of electronic microarray platform to detect or monitor bacteria in natural and induced environments.

Dye-specific bias effects, commonly observed in the two-color microarray platform, are normally corrected using the dye swap design. This design, however, is relatively expensive and labor-intensive. We propose a self-self hybridization design as an alternative to the dye swap design. In this design, the treated and control samples are labeled with Cy5 and Cy3 (or Cy3 and Cy5), respectively, without dye swap, along with a set of self-self hybridizations on the control sample. We compare this design with the dye swap design through investigation of mouse primary hepatocytes treated with three peroxisome proliferator-activated receptor-alpha (PPARalpha) agonists at three dose levels. Using Agilent's Whole Mouse Genome microarray, differentially expressed genes (DEG) were determined for both the self-self hybridization and dye swap designs. The DEG concordance between the two designs was over 80% across each dose treatment and chemical. Furthermore, 90% of DEG-associated biological pathways were in common between the designs, indicating that biological interpretations would be consistent. The reduced labor and expense for the self-self hybridization design make it an efficient substitute for the dye swap design. For example, in larger toxicogenomic studies, only about half the chips are required for the self-self hybridization design compared to that needed in the dye swap design.

https://nottingham-repository.worktribe.com/output/980858 The demand for diagnostic tools that allow simultaneous screening of samples for multiple pathogens is increasing because they overcome the limitations of other methods, which can only screen for a single or a few pathogens at a time. Microarrays offer the advantages of being capable to test a large number of samples simultaneously, screening for multiple pathogen types per sample and having comparable sensitivity to existing methods such as PCR. Array design is often considered the most important process in any microarray experiment and can be the deciding factor in the success of a study. There are currently no microarrays for simultaneous detection of rodent-borne pathogens. The aim of this report is to explicate the design, development and evaluation of a microarray platform for use as a screening tool that combines ease of use and rapid identification of a number of rodent-borne pathogens of zoonotic importance. Nucleic acid was amplified by multiplex biotinylation PCR prior to hybridisation onto microarrays. The array sensitivity was comparable to standard PCR, though less sensitive than real-time PCR. The array presented here is a prototype microarray identification system for zoonotic pathogens that can infect rodent species.

Microarrays have been developed for analysis of transcriptional profiles in many organisms. For experimental simplicity and systems for which microarrays do not exist, it would be desirable to use a standard microarray platform for the analysis of multiple systems. The molecular barcode (MB) Affymetrix GeneChip could serve as such a platform. The reproducibility and quantitative capacity of the MB GeneChip were examined. Using mixed PCR templates of defined template quantity, the individual concentration responses of 384 array features were measured and shown to be highly reproducible. Moreover, the binding behaviors of the mismatched array features mirror those of the matched features, at reduced intensity. Additional analysis defined the importance of particular sequence motifs in the prediction of high-affinity and low-affinity target hybridization. The data support the future application of MB GeneChips for quantitative applications. It is proposed that at least seven orders-of-magnitude in accurate concentration sensitivity could be achieved.

We have assessed the utility of RNA titration samples for evaluating microarray platform performance and the impact of different normalization methods on the results obtained. As part of the MicroArray Quality Control project, we investigated the performance of five commercial microarray platforms using two independent RNA samples and two titration mixtures of these samples. Focusing on 12,091 genes common across all platforms, we determined the ability of each platform to detect the correct titration response across the samples. Global deviations from the response predicted by the titration ratios were observed. These differences could be explained by variations in relative amounts of messenger RNA as a fraction of total RNA between the two independent samples. Overall, both the qualitative and quantitative correspondence across platforms was high. In summary, titration samples may be regarded as a valuable tool, not only for assessing microarray platform performance and different analysis methods, but also for determining some underlying biological features of the samples.

SummaryHerein we report the expanded functional group compatibility of small-molecule microarrays to include immobilization of primary alcohols, secondary alcohols, phenols, carboxylic acids, hydroxamic acids, thiols, and amines on a single slide surface. Small-molecule “diversity microarrays” containing nearly 10,000 known bioactive small molecules, natural products, and small molecules originating from several diversity-oriented syntheses were produced by using an isocyanate-mediated covalent capture strategy. Selected printed bioactive compounds were detected with antibodies against compounds of interest. The new surface of the diversity microarrays is highly compatible with approaches involving cellular lysates. This feature has enabled a robust, optimized screening methodology using cellular lysates, allowing the detection of specific interactions with a broad range of binding affinity by using epitope-tagged or chimeric fluorescent proteins without prior purification. We believe that this expanded research capability has considerable promise in biology and medicine.

Diagnostic microarrays are a useful tool for the simultaneous detection of multiple targets. In this chapter we describe the use of a simple tube-based microarray platform for the detection of plant infecting viruses and viroids.

We aim at understanding the genomic influence on milk quality and synthesis by comparing two sheep breeds using sheep-specific microarray technology. From sheep ESTs deposited at NCBI we generated a chip carrying about 22,000 non-redundant features in quadruplicate, achieving very good technical outcomes. Oligos were in situ generated on chip using the Combimatrix equipment. We analysed the mammary transcriptome in individuals of two sheep breeds at two lactation stages, to identify genes controlling milk production and metabolic pathways in which these genes are involved. With |FC|>1.4, and p-value≤0.05, 142 and 14 genes resulted differentially expressed in stages 01 and 02, respectively.

The aim of this study was to explore in 30 healthy volunteers the impact of whole Kamut® Khorasan on the fecal microbiota by the phylogenetic microarray platform High Taxonomic Fingerprint (HTF)-Microbi.Array, and metabolic profiles by using solid-phase microextraction–gas chromatography–mass spectrometry (SPME–GC–MS). With regard to the fecal microbiota, a tendency towards a reduction in Bacteroides/Prevotella and an enrichment in Clostridium cluster XIVa was observed after whole Kamut® Khorasan intake, compared to whole durum wheat adopted as control diet. The metabolic profile of subjects administered with the whole Kamut® Khorasan-based diet, in comparison to the control, was mainly characterized by phenol, nonanol and short chain fatty acids (SCFA), whereas alcohols, such as oleyl alcohol and isopropyl alcohol, better discriminated the whole durum wheat intake. Co-abundance analysis of microbiota and metabolome data evidenced the presence of a potentially health-promoting co-abundance group (CAG), which was more abundant in the whole Kamut® Khorasan-based diet group. These results may contribute to support our previous in vivo findings related to the anti-inflammatory activity and counteraction of oxidative stress by Kamut® Khorasan-based cereal foods.

Background Global gene expression profiling by DNA microarrays is an invaluable tool in biological research. However, existing labeling methods are time consuming and costly and therefore often limit the scale of microarray experiments and sample throughput. Here we introduce a new, fast, inexpensive method for direct random-primed fluorescent labeling of eukaryotic cDNA for gene expression analysis and compare the results obtained on the NimbleGen microarray platform with two other widely-used labeling methods, namely the NimbleGen-recommended double-stranded cDNA protocol and the indirect (aminoallyl) method. Results Two total RNA samples were labeled with each method and hybridized to NimbleGen expression arrays. Although all methods tested here provided similar global results and biological conclusions, the new direct random-primed cDNA labeling method provided slightly better correlation between replicates compared to the other methods and thus increased ability to find statistically significant differentially expressed genes. Conclusion The new direct random-primed cDNA labeling method introduced here is suitable for gene expression microarrays and provides a rapid, inexpensive alternative to existing methods. Using NimbleGen microarrays, the method produced excellent results comparable to those obtained with other methods. However, the simplicity and cost-effectiveness of the new method allows for increased sample throughput in microarray experiments and makes the process amenable to automation with a relatively simple liquid handling system.

The nanoparticle-amplified surface plasmon resonance imaging (SPRI) method is a miRNA profiling technique that employs a combination of surface poly(A) enzyme chemistry and nanoparticle-amplified SPRI measurements on a microarray platform. The surface reaction of poly(A) polymerase creates poly(A) tails on miRNAs hybridized onto locked nucleic acid (LNA) microarrays. DNA-modified nanoparticles are then adsorbed onto poly(A) tails and are detected using SPRI. This technique was initially developed by Corn’s laboratory in the Department of Chemistry, University of California (J Am Chem Soc 128:14044–14046, 2006). It is an ultrasensitive method for the detection of multiple miRNAs at attomole levels.

Background Affymetrix GeneChips utilize 25-mer oligonucleotides probes linked to a silica surface to detect targets in solution. Mismatches due to single nucleotide polymorphisms (SNPs) can affect the hybridization between probes and targets. Previous research has indicated that binding between probes and targets strongly depends on the positions of these mismatches. However, there has been substantial variability in the effect of mismatch type across studies. Methods By taking advantage of naturally occurring mismatches between rhesus macaque transcripts and human probes from the Affymetrix U133 Plus 2 GeneChip, we collected the largest 25-mer probes dataset with single-base mismatches at each of the 25 positions on the probe ever used in this type of analysis. Results A mismatch at the center of a probe led to a greater loss in signal intensity than a mismatch at the ends of the probe, regardless of the mismatch type. There was a slight asymmetry between the ends of a probe: effects of mismatches at the 3' end of a probe were greater than those at the 5' end. A cross study comparison of the effect of mismatch types revealed that results were not in good agreement among different reports. However, if the mismatch types were consolidated to purine or pyrimidine mismatches, cross study conclusions could be generated. Conclusion The comprehensive assessment of the effects of single-base mismatches on microarrays provided in this report can be useful for improving future versions of microarray platform design and the corresponding data analysis algorithms.

Background Several different microarray platforms are available for measuring gene expression. There are disagreements within the microarray scientific community for intra- and inter-platform consistency of these platforms. Both high and low consistencies were demonstrated across different platforms in terms of genes with significantly differential expression. Array studies for gene expression are used to explore biological causes and effects. Therefore, consistency should eventually be evaluated in a biological setting to reveal the functional differences between the examined samples, not just a list of differentially expressed genes (DEG). In this study, we investigated whether different platforms had a high consistency from the biologically functional perspective. Results DEG data without filtering the different probes in microarrays from different platforms generated from kidney samples of rats treated with the kidney carcinogen, aristolochic acid, in five test sites using microarrays from Affymetrix, Applied Biosystems, Agilent, and GE health platforms (two sites using Affymetrix for intra-platform comparison) were input into the Ingenuity Pathway Analysis (IPA) system for functional analysis. The functions of the DEG lists determined by IPA were compared across the four different platforms and two test sites for Affymetrix platform. Analysis results showed that there is a very high level of consistency between the two test sites using the same platform or among different platforms. The top functions determined by the different platforms were very similar and reflected carcinogenicity and toxicity of aristolochic acid in the rat kidney. Conclusion Our results demonstrate that highly consistent biological information can be generated from different microarray platforms.

Molecular analyses of biological samples have traditionally been pursued in parallel, with those researchers studying genetic diversity having few technical approaches in common with those studying gene expression. Increasingly, scientists recognize the importance of integrating analytical technologies to further research, particularly into emerging fields such as epigenetics and the genetics of gene expression. In this chapter, we describe a suite of applications that take advantage of the Illumina® bead-based microarrays, all of which are read out on a single analytical instrument. The integration of whole genome genotyping, high throughput focused genotyping, whole transcriptome expression profiling, focused expression profiling of fresh or preserved tissues, allele-specific expression profiling and DNA methylation assays on the BeadArray™ Reader allows researchers to expand their perspectives, from whole genomes to single bases, from genetics to expression and on to epigenetics.

Background The number of gene expression studies in the public domain is rapidly increasing, representing a highly valuable resource. However, dataset-specific bias precludes meta-analysis at the raw transcript level, even when the RNA is from comparable sources and has been processed on the same microarray platform using similar protocols. Here, we demonstrate, using Affymetrix data, that much of this bias can be removed, allowing multiple datasets to be legitimately combined for meaningful meta-analyses. Results A series of validation datasets comparing breast cancer and normal breast cell lines (MCF7 and MCF10A) were generated to examine the variability between datasets generated using different amounts of starting RNA, alternative protocols, different generations of Affymetrix GeneChip or scanning hardware. We demonstrate that systematic, multiplicative biases are introduced at the RNA, hybridization and image-capture stages of a microarray experiment. Simple batch mean-centering was found to significantly reduce the level of inter-experimental variation, allowing raw transcript levels to be compared across datasets with confidence. By accounting for dataset-specific bias, we were able to assemble the largest gene expression dataset of primary breast tumours to-date (1107), from six previously published studies. Using this meta-dataset, we demonstrate that combining greater numbers of datasets or tumours leads to a greater overlap in differentially expressed genes and more accurate prognostic predictions. However, this is highly dependent upon the composition of the datasets and patient characteristics. Conclusion Multiplicative, systematic biases are introduced at many stages of microarray experiments. When these are reconciled, raw data can be directly integrated from different gene expression datasets leading to new biological findings with increased statistical power., BMC Medical Genomics, 1 (42), ISSN:1755-8794

Microarray-hybridization specificity is one of the main effectors of microarray result quality. In the present review, we suggest a definition for specificity that spans four hybridization levels, from the single probe to the microarray platform. For increased hybridization specificity, it is important to quantify the extent of the specificity at each of these levels, and correct the data accordingly. We outline possible effects of low hybridization specificity on the obtained results and list possible effectors of hybridization specificity. In addition, we discuss several studies in which theoretical approaches, empirical means or data filtration were used to identify specificity effectors, and increase the specificity of the hybridization results. However, these various approaches may not yet provide an ultimate solution; rather, further tool development is needed to enhance microarray-hybridization specificity.

In this manuscript, we report how transfection efficiencies vary as a function of the substrate upon which cells adhere using a polymer microarray platform to allow rapid analysis of a large number of substrates. During these studies, traditional transfection protocols were nonsatisfactory, and thus we developed an approach in which an ultrasonic nebulizer was used to dispense lipoplexes onto cell-based microarrays in the absence of liquid. Under these conditions, droplets were directly deposited onto the cells thereby enhancing transfection. This approach was successfully applied to the transfection of various cell lines immobilized on a library of polyacrylates and permitted the identification of highly efficient transfection/polymer combinations, while showing that specific polymer–cell interactions may promote the efficacy of chemical transfection.

Carbohydrate microarrays can map out key interactions of carbohydrates and proteins in a high-throughput manner, but require the inclusion of a range of sugars for their optimal use. Here we present the synthesis and use of a new hydroxylamine-modified fluorous tag that allows the facile incorporation of reducing sugars into a noncovalent fluorous-based microarray after simple purification by fluorous solid-phase extraction (FSPE). The microarray supports quantitative screening against carbohydrate-binding proteins.

This chapter will give a short introduction into current technology related and biology related problems in the microarray technology, approaches undertaken to overcome such problems, and, finally, it will present some key features of most commercial platforms which have emerged over the past few years and may help to solve some technical problems at least related to the instrumentation side. Standard operating protocols, guidelines, as well as careful and routine checking of the instrumentation, such as spotting or hybridization devices as well as microarray reader (laser/charge-coupled device (CCD) camera), are necessary for running a microarray platform successfully. Furthermore, standardized reference material, databases, and analysis software will be necessary for generating confidential data across all platforms and laboratories and will additionally support the full power of DNA microarray technology as a research and diagnostic tool.

BackgroundThe comparability of gene expression data generated with different microarray platforms is still a matter of concern. Here we address the performance and the overlap in the detection of differentially expressed genes for five different microarray platforms in a challenging biological context where differences in gene expression are few and subtle.ResultsGene expression profiles in the hippocampus of five wild-type and five transgenic δC-doublecortin-like kinase mice were evaluated with five microarray platforms: Applied Biosystems, Affymetrix, Agilent, Illumina, LGTC home-spotted arrays. Using a fixed false discovery rate of 10% we detected surprising differences between the number of differentially expressed genes per platform. Four genes were selected by ABI, 130 by Affymetrix, 3,051 by Agilent, 54 by Illumina, and 13 by LGTC. Two genes were found significantly differentially expressed by all platforms and the four genes identified by the ABI platform were found by at least three other platforms. Quantitative RT-PCR analysis confirmed 20 out of 28 of the genes detected by two or more platforms and 8 out of 15 of the genes detected by Agilent only. We observed improved correlations between platforms when ranking the genes based on the significance level than with a fixed statistical cut-off. We demonstrate significant overlap in the affected gene sets identified by the different platforms, although biological processes were represented by only partially overlapping sets of genes. Aberrances in GABA-ergic signalling in the transgenic mice were consistently found by all platforms.ConclusionThe different microarray platforms give partially complementary views on biological processes affected. Our data indicate that when analyzing samples with only subtle differences in gene expression the use of two different platforms might be more attractive than increasing the number of replicates. Commercial two-color platforms seem to have higher power for finding differentially expressed genes between groups with small differences in expression.

Antibody microarrays are an emerging technology that promises to be a powerful tool for the detection of disease biomarkers. The current technology for protein microarrays has been derived primarily from DNA microarrays and is not fully characterized for use with proteins. For example, there are a myriad of surface chemistries that are commercially available for antibody microarrays, but there are no rigorous studies that compare these different surfaces. Therefore, we have used a sandwich enzyme-linked immunosorbent assay (ELISA) microarray platform to analyze 17 different commercially available slide types. Full standard curves were generated for 23 different assays. We found that this approach provides a rigorous and quantitative system for comparing the different slide types based on spot size and morphology, slide noise, spot background, lower limit of detection, and reproducibility. These studies demonstrate that the properties of the slide surface affect the activity of immobilized antibodies and the quality of data produced. Although many slide types produce useful data, glass slides coated with aldehyde silane, poly-l-lysine, or aminosilane (with or without activation with a crosslinker) consistently produce superior results in the sandwich ELISA microarray analyses we performed.

DNA microarrays have rapidly evolved toward a platform for massively paralleled gene expression analysis. Despite its widespread use, the technology has been criticized to be vulnerable to technical variability. Addressing this issue, recent comparative, interplatform, and interlaboratory studies have revealed that, given defined procedures for "wet lab" experiments and data processing, a satisfactory reproducibility and little experimental variability can be achieved. In view of these advances in standardization, the requirement for uniform sample preparation becomes evident, especially if a microarray platform is used as a facility, i.e., by different users working in the laboratory. While one option to reduce technical variability is to dedicate one laboratory technician to all microarray studies, we have decided to automate the entire RNA sample preparation implementing a liquid handling system coupled to a thermocycler and a microtiter plate reader. Indeed, automated RNA sample preparation prior to chip analysis enables (1) the reduction of experimentally caused result variability, (2) the separation of (important) biological variability from (undesired) experimental variation, and (3) interstudy comparison of gene expression results. Our robotic platform can process up to 24 samples in parallel, using an automated sample preparation method that produces high-quality biotin-labeled cRNA ready to be hybridized on Affymetrix GeneChips. The results show that the technical interexperiment variation is less pronounced than with manually prepared samples. Moreover, experiments using the same starting material showed that the automated process yields a good reproducibility between samples.

Microarrays have enabled the determination of how thousands of genes are expressed to coordinate function within single organisms. Yet applications to natural or engineered communities where different organisms interact to produce complex properties are hampered by theoretical and technological limitations. Here we describe a general method to accurately identify low-abundant targets in systems containing complex mixtures of homologous targets. We combined an analytical predictor of nonspecific probe–target interactions (cross-hybridization) with an optimization algorithm that iteratively deconvolutes true probe–target signal from raw signal affected by spurious contributions (cross-hybridization, noise, background, and unequal specific hybridization response). The method was capable of quantifying, with unprecedented specificity and accuracy, ribosomal RNA (rRNA) sequences in artificial and natural communities. Controlled experiments with spiked rRNA into artificial and natural communities demonstrated the accuracy of identification and quantitative behavior over different concentration ranges. Finally, we illustrated the power of this methodology for accurate detection of low-abundant targets in natural communities. We accurately identified Vibrio taxa in coastal marine samples at their natural concentrations (

This review summarizes, in a balanced and comprehensive manner, the various components of microarrays and their types, substrate architecture, platforms for microarray probe implementation, standardizations and confounders. The review is intended to familiarize the beginner with the principles of experimental design and the selection of an appropriate microarray platform. This parallel technology has revolutionized transcriptomic approaches to data profiling and has a major role in the identification of expressed genes, classification and diagnosis studies. The technology is still evolving and guidelines for standardization and reporting have been developed and are being improved.

Publisher Summary This chapter focuses on DNA microarray-based large-scale gene expression studies in the nervous system concentrating on the choice of microarray platform, data analysis, and interpretation. Microarray experiments start with array design and synthesis. The purity and quality of the starting RNA has a significant effect on the results of microarray experiments, so it is essential that all the steps of RNA isolation be carried out with maximum care and speed. In addition, choosing the appropriate array slides is essential. The chapter proposes several data analysis and interpretation steps found prerequisite for most microarray experiments. Several bioinformatic approaches are available that facilitate the data interpretation and further hypothesis building. The potential presence of false positives and false negatives in a microarray experiment requires validating a subset of the information (genes) identified by microarray analysis. The analytical tools are becoming more standardized and statistical thresholds are more accepted. Bioinformatic mining of the data and functional confirmation remains an important challenge and is one of the exciting areas of progress.

Solution reactions using photogenerated reagents (Gao, X.; Yu, P.; LeProust, E.; Sonigo, L.; Pellois, J. P.; Zhang, H. J. Am. Chem. Soc. 1998, 120, 12698) are a potentially powerful means for combinatorial parallel synthesis of addressable molecular microarrays. In this report, we demonstrate that this chemistry permits combinatorial screening of reaction conditions on a microarray platform. Using this method of optimization and our reaction apparatus, efficient photogenerated acids and reaction conditions suitable for removal of the acid labile protection group on 5'-O of nucleotides are identified in a short period of time. The chemistry platform demonstrated opens new avenues for rapid, simultaneous investigation of multiple reactions using different reagents and reaction parameters directly on a solid support (e.g., a glass plate). The combinatorial screening method described may be extended to include general organic reactions employing photogenerated and conventional reagents as well as a microarray reaction device. This should be especially valuable for efficient synthesis of addressable organic compound libraries.

Formalin fixed paraffin-embedded (FFPE) tumor specimens are the conventionally archived material in clinical practice, representing an invaluable tissue source for biomarkers development, validation and routine implementation. For many prospective clinical trials, this material has been collected allowing for a prospective-retrospective study design which represents a successful strategy to define clinical utility for candidate markers. Gene expression data can be obtained even from FFPE specimens with the broadly used Affymetrix HG-U133 Plus 2.0 microarray platform. Nevertheless, important major discrepancies remain in expression data obtained from FFPE compared to fresh-frozen samples, prompting the need for appropriate data processing which could help to obtain more consistent results in downstream analyses. In a publicly available dataset of matched frozen and FFPE expression data, the performances of different normalization methods and specifically designed Chip Description Files (CDFs) were compared. The use of an alternative CDFs together with fRMA normalization significantly improved frozen-FFPE sample correlations, frozen-FFPE probeset correlations and agreement of differential analysis between different tumor subtypes. The relevance of our optimized data processing was assessed and validated using two independent datasets. In this study we demonstrated that an appropriate data processing can significantly improve the reliability of gene expression data derived from FFPE tissues using the standard Affymetrix platform. Tools for the implementation of our data processing algorithm are made publicly available at http://www.biocut.unito.it/cdf-ffpe/.

Activity-based protein profiling (ABPP) is a chemical method that utilizes active-site-directed probes to determine the functional state of enzymes in complex proteomes. Probe-labeled enzymes are typically detected by in-gel fluorescence scanning, a robust technique that nonetheless exhibits some key deficiencies, including limited sensitivity and resolution, as well as ambiguity regarding the molecular identity of the enzymes under investigation. Here, we report a microarray platform for ABPP that addresses these limitations. In this platform, proteomes are treated with ABPP probes in solution, after which labeled enzymes are captured and visualized on glass slides displaying an array of anti-enzyme antibodies. We show that ABPP microarrays exhibit superior sensitivity and resolution compared to gel-based methods, permitting the parallel analysis of several enzyme activities in proteomes, including cancer-associated proteases such as urokinase, matrix metalloproteinase-9, and prostate-specific antigen.

Studies by Tsibris et al. (1) and Wang et al. (2) used microarray analysis to identify differentially expressed genes in leiomyoma compared with normal myometrium. Both groups used the same microarray platform (the HUGeneFL, 6,800 gene array), for four patients in the case of Tsibris and colleagues and seven patients in the case of Wang et al. To minimize the risk of inaccurate results, both groups used specific thresholds to define differentially expressed genes. Tsibris et al. used literature support to justify a two-fold difference or greater as being biologically relevant, while Wang and colleagues used an unpaired t-test to evaluate differences in fibroid-myometrium pairs. Furthermore, each group only reported genes that were consistently different among the studied tissue pairs from multiple patients. Based on these efforts, one would expect the likelihood of a type I error to be less than 1 gene in 6,800 for each of these studies.

Background We report the development of a microarray platform for rapid and cost-effective genetic mapping, and its evaluation using rice as a model. In contrast to methods employing whole-genome tiling microarrays for genotyping, our method is based on low-cost spotted microarray production, focusing only on known polymorphic features. Results We have produced a genotyping microarray for rice, comprising 880 single feature polymorphism (SFP) elements derived from insertions/deletions identified by aligning genomic sequences of the japonica cultivar Nipponbare and the indica cultivar 93-11. The SFPs were experimentally verified by hybridization with labeled genomic DNA prepared from the two cultivars. Using the genotyping microarrays, we found high levels of polymorphism across diverse rice accessions, and were able to classify all five subpopulations of rice with high bootstrap support. The microarrays were used for mapping of a gene conferring resistance to Magnaporthe grisea, the causative organism of rice blast disease, by quantitative genotyping of samples from a recombinant inbred line population pooled by phenotype. Conclusion We anticipate this microarray-based genotyping platform, based on its low cost-per-sample, to be particularly useful in applications requiring whole-genome molecular marker coverage across large numbers of individuals.

Background DNA microarrays are now routinely used to monitor the transcript levels of thousands of genes simultaneously. However, the array fabrication method, hybridization conditions, and oligodeoxyribonucleotide probe length can impact the performance of a DNA microarray platform.

DNA microarrays represent a technological intersection between biology and computers that enables gene expression analysis in human tissues on a genome-wide scale. This application can be expected to prove extremely valuable for the study of the genetic basis of complex diseases. Despite the enormous promise of this revolutionary technology, there are several issues and possible pitfalls that may undermine the authority of the microarray platform. We discuss some of the conceptual, practical, statistical, and logistical issues surrounding the use of microarrays for gene expression profiling. These issues include the imprecise definition of normal in expression comparisons; the cellular and subcellular heterogeneity of the tissues being studied; the difficulty in establishing the statistically valid comparability of arrays; the logistical logjam in analysis, presentation, and archiving of the vast quantities of data generated; and the need for confirmational studies that address the functional relevance of findings. Although several complicated issues must be resolved, the potential payoff remains large.

Background Illumina Sentrix-6 Whole-Genome Expression BeadChips are relatively new microarray platforms which have been used in many microarray studies in the past few years. These Chips have a unique design in which each Chip contains six microarrays and each microarray consists of two separate physical strips, posing special challenges for precise between-array normalization of expression values. Results None of the normalization strategies proposed so far for this microarray platform allow for the possibility of systematic variation between the two strips comprising each array. That this variation can be substantial is illustrated by a data example. We demonstrate that normalizing at the strip-level rather than at the array-level can effectively remove this between-strip variation, improve the precision of gene expression measurements and discover more differentially expressed genes. The gain is substantial, yielding a 20% increase in statistical information and doubling the number of genes detected at a 5% false discovery rate. Functional analysis reveals that the extra genes found tend to have interesting biological meanings, dramatically strengthening the biological conclusions from the experiment. Strip-level normalization still outperforms array-level normalization when non-expressed probes are filtered out. Conclusion Plots are proposed which demonstrate how the need for strip-level normalization relates to inconsistent intensity range variation between the strips. Strip-level normalization is recommended for the preprocessing of Illumina Sentrix-6 BeadChips whenever the intensity range is seen to be inconsistent between the strips. R code is provided to implement the recommended plots and normalization algorithms.