Improvement in the reproducibility and accuracy of DNA microarray quantification by optimizing hybridization conditions

Single-Step Printable Hydrogel Microarray Integrating Long-Chain DNA for the Discriminative and Size-Specific Sensing of Nucleic Acids

A simple approach to fabricating hydrogel-based DNA microarrays is reported by physically entrapping the rolling circle amplification (RCA) product inside printable in situ gelling hydrazone cross-linked poly(oligoethylene glycol methacrylate) hydrogels. The hydrogel-printed RCA microarray facilitates improved RCA immobilization (>65% even after vigorous washing) and resistance to denaturation relative to RCA-only printed microarrays in addition to size-discriminative sensing of DNA probes (herein, 27 or fewer nucleotides) depending on the internal porosity of the hydrogel. Furthermore, the high number of sequence repeats in the concatemeric RCA product enables high-sensitivity detection of complementary DNA probes without the need for signal amplification, with signal/noise ratios of 10 or more achieved over a short 30 min assay time followed by minimal washing. The inherent antifouling properties of the hydrogel enable discriminative hybridization in complex biological samples, particularly for short (∼10 nt) oligonucleotides whose hybridization in other assays tends to be transient and of low affinity. The scalable manufacturability and efficient performance of these hydrogel-printed RCA microarrays thus offer potential for rapid, parallel, and inexpensive sensing of short DNA/RNA biomarkers and ligands, a critical current challenge in diagnostic and affinity screening assays.

Current Technologies and Recent Developments for Screening of HPV-Associated Cervical and Oropharyngeal Cancers

Mucosal infection by the human papillomavirus (HPV) is responsible for a growing number of malignancies, predominantly represented by cervical cancer and oropharyngeal squamous cell carcinoma. Because of the prevalence of the virus, persistence of infection, and long latency period, novel and low-cost methods are needed for effective population level screening and monitoring. We review established methods for screening of cervical and oral cancer as well as commercially-available techniques for detection of HPV DNA. We then describe the ongoing development of microfluidic nucleic acid-based biosensors to evaluate circulating host microRNAs that are produced in response to an oncogenic HPV infection. The goal is to develop an ideal screening platform that is low-cost, portable, and easy to use, with appropriate signal stability, sensitivity and specificity. Advances in technologies for sample lysis, pre-treatment and concentration, and multiplexed nucleic acid detection are provided. Continued development of these devices provides opportunities for cancer screening in low resource settings, for point-of-care diagnostics and self-screening, and for monitoring response to vaccination or surgical treatment.

Ribosomal RNA depletion or exclusion has negligible effect on the detection of viruses in a pan viral microarray

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Ribosomal RNA depletion protocols were assessed to improve microarray performance.

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The outcome was compared with random amplification protocol.

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Ribosomal RNA depletion had little effect on the microarray performance.

Pan viral DNA microarrays, which can detect known, novel and multiple viral infections, are major laboratory assets contributing to the control of infectious diseases. The large quantity of ribosomal RNA (rRNA) found in tissue samples is thought to be a major factor contributing to the comparatively lower sensitivity of detecting RNA viruses, as a sequence-independent PCR is used to amplify unknown samples for microarray analysis. This study aimed to determine whether depletion or exclusion of rRNA can improve microarray detection and simplify its analysis. Therefore, two different rRNA depletion and exclusion protocols, RiboMinus™ technology and non-rRNA binding hexanucleotides, were applied to the microarray sample processing and the outcome was compared with those of the sequence-independent amplification protocol. This study concludes that the two procedures, described to deplete or exclude rRNA, have negligible effect on the microarrays detection and analysis and might only in combination with further techniques result in a significant enhancement of sensitivity. Currently, existing protocols of random amplification and background adjustment are pertinent for the purpose of sample processing for microarray analysis.

Microarray-based RNA profiling of breast cancer: batch effect removal improves cross-platform consistency

Microarray is a powerful technique used extensively for gene expression analysis. Different technologies are available, but lack of standardization makes it challenging to compare and integrate data. Furthermore, batch-related biases within datasets are common but often not tackled. We have analyzed the same 234 breast cancers on two different microarray platforms. One dataset contained known batch-effects associated with the fabrication procedure used. The aim was to assess the significance of correcting for systematic batch-effects when integrating data from different platforms. We here demonstrate the importance of detecting batch-effects and how tools, such as ComBat, can be used to successfully overcome such systematic variations in order to unmask essential biological signals. Batch adjustment was found to be particularly valuable in the detection of more delicate differences in gene expression. Furthermore, our results show that prober adjustment is essential for integration of gene expression data obtained from multiple sources. We show that high-variance genes are highly reproducibly expressed across platforms making them particularly well suited as biomarkers and for building gene signatures, exemplified by prediction of estrogen-receptor status and molecular subtypes. In conclusion, the study emphasizes the importance of utilizing proper batch adjustment methods when integrating data across different batches and platforms.

Signal oscillation is another reason for variability in microarray-based gene expression quantification

Microarrays have been widely used for various biological applications, such as, gene expression profiling, determination of SNPs, and disease profiling. However, quantification and analysis of microarray data have been a challenge. Previously, by taking into account translational and rotational diffusion of the target DNA, we have shown that the rate of hybridization depends on its size. Here, by mathematical modeling of surface diffusion of transcript, we show that the dynamics of hybridization on DNA microarray surface is inherently oscillatory and the amplitude of oscillation depends on fluid velocity. We found that high fluid velocity enhances the signal without affecting the background, and reduces the oscillation, thereby reducing likelihood of inter- and intra-experiment variability. We further show that a strong probe reduces dependence of signal-to-noise ratio on probe strength, decreasing inter-microarray variability. On the other hand, weaker probes are required for SNP detection. Therefore, we recommend high fluid velocity and strong probes for all microarray applications except determination of SNPs. For SNP detection, we recommend high fluid velocity with weak probe on the spot. We also recommend a surface with high adsorption and desorption rates of transcripts.

Toxicogenomic approaches in developmental toxicology testing

The emergence of toxicogenomic applications provides new tools to characterize, classify, and potentially predict teratogens. However, due to the vast number of experimental and statistical procedural steps, toxicogenomic studies are challenging. Here, we guide researchers through the basic framework of conducting toxicogenomic investigations in the field of developmental toxicology, providing examples of biological and technical factors that may influence response and interpretation. Furthermore, we review current, diverse applications of toxicogenomic-based approaches in teratology testing, including exposure-response characterization (dose and duration), chemical classification studies, and cross-model comparisons study designs. This review is intended to guide scientists through the challenging and complex structure of conducting toxicogenomic analyses, while considering the many applications of using toxicogenomics in study designs and the future of these types of "omics" approaches in developmental toxicology.

Impact of the spotted microarray preprocessing method on fold-change compression and variance stability

Background

The standard approach for preprocessing spotted microarray data is to subtract the local background intensity from the spot foreground intensity, to perform a log2 transformation and to normalize the data with a global median or a lowess normalization. Although well motivated, standard approaches for background correction and for transformation have been widely criticized because they produce high variance at low intensities. Whereas various alternatives to the standard background correction methods and to log2 transformation were proposed, impacts of both successive preprocessing steps were not compared in an objective way.

Results

In this study, we assessed the impact of eight preprocessing methods combining four background correction methods and two transformations (the log2 and the glog), by using data from the MAQC study. The current results indicate that most preprocessing methods produce fold-change compression at low intensities. Fold-change compression was minimized using the Standard and the Edwards background correction methods coupled with a log2 transformation. The drawback of both methods is a high variance at low intensities which consequently produced poor estimations of the p-values. On the other hand, effective stabilization of the variance as well as better estimations of the p-values were observed after the glog transformation.

Conclusion

As both fold-change magnitudes and p-values are important in the context of microarray class comparison studies, we therefore recommend to combine the Edwards correction with a hybrid transformation method that uses the log2 transformation to estimate fold-change magnitudes and the glog transformation to estimate p-values.

The impact of quantitative optimization of hybridization conditions on gene expression analysis

Background

With the growing availability of entire genome sequences, an increasing number of scientists can exploit oligonucleotide microarrays for genome-scale expression studies. While probe-design is a major research area, relatively little work has been reported on the optimization of microarray protocols.

Results

As shown in this study, suboptimal conditions can have considerable impact on biologically relevant observations. For example, deviation from the optimal temperature by one degree Celsius lead to a loss of up to 44% of differentially expressed genes identified. While genes from thousands of Gene Ontology categories were affected, transcription factors and other low-copy-number regulators were disproportionately lost. Calibrated protocols are thus required in order to take full advantage of the large dynamic range of microarrays.

For an objective optimization of protocols we introduce an approach that maximizes the amount of information obtained per experiment. A comparison of two typical samples is sufficient for this calibration. We can ensure, however, that optimization results are independent of the samples and the specific measures used for calibration. Both simulations and spike-in experiments confirmed an unbiased determination of generally optimal experimental conditions.

Conclusions

Well calibrated hybridization conditions are thus easily achieved and necessary for the efficient detection of differential expression. They are essential for the sensitive pro filing of low-copy-number molecules. This is particularly critical for studies of transcription factor expression, or the inference and study of regulatory networks.

Microarray meta-analysis database (M(2)DB): a uniformly pre-processed, quality controlled, and manually curated human clinical microarray database

Background

Over the past decade, gene expression microarray studies have greatly expanded our knowledge of genetic mechanisms of human diseases. Meta-analysis of substantial amounts of accumulated data, by integrating valuable information from multiple studies, is becoming more important in microarray research. However, collecting data of special interest from public microarray repositories often present major practical problems. Moreover, including low-quality data may significantly reduce meta-analysis efficiency.

Results

M²DB is a human curated microarray database designed for easy querying, based on clinical information and for interactive retrieval of either raw or uniformly pre-processed data, along with a set of quality-control metrics. The database contains more than 10,000 previously published Affymetrix GeneChip arrays, performed using human clinical specimens. M²DB allows online querying according to a flexible combination of five clinical annotations describing disease state and sampling location. These annotations were manually curated by controlled vocabularies, based on information obtained from GEO, ArrayExpress, and published papers. For array-based assessment control, the online query provides sets of QC metrics, generated using three available QC algorithms. Arrays with poor data quality can easily be excluded from the query interface. The query provides values from two algorithms for gene-based filtering, and raw data and three kinds of pre-processed data for downloading.

Conclusion

M²DB utilizes a user-friendly interface for QC parameters, sample clinical annotations, and data formats to help users obtain clinical metadata. This database provides a lower entry threshold and an integrated process of meta-analysis. We hope that this research will promote further evolution of microarray meta-analysis.

Washing scaling of GeneChip microarray expression

BACKGROUND

Post-hybridization washing is an essential part of microarray experiments. Both the quality of the experimental washing protocol and adequate consideration of washing in intensity calibration ultimately affect the quality of the expression estimates extracted from the microarray intensities.

RESULTS

We conducted experiments on GeneChip microarrays with altered protocols for washing, scanning and staining to study the probe-level intensity changes as a function of the number of washing cycles. For calibration and analysis of the intensity data we make use of the 'hook' method which allows intensity contributions due to non-specific and specific hybridization of perfect match (PM) and mismatch (MM) probes to be disentangled in a sequence specific manner. On average, washing according to the standard protocol removes about 90% of the non-specific background and about 30-50% and less than 10% of the specific targets from the MM and PM, respectively. Analysis of the washing kinetics shows that the signal-to-noise ratio doubles roughly every ten stringent washing cycles. Washing can be characterized by time-dependent rate constants which reflect the heterogeneous character of target binding to microarray probes. We propose an empirical washing function which estimates the survival of probe bound targets. It depends on the intensity contribution due to specific and non-specific hybridization per probe which can be estimated for each probe using existing methods. The washing function allows probe intensities to be calibrated for the effect of washing. On a relative scale, proper calibration for washing markedly increases expression measures, especially in the limit of small and large values.

CONCLUSIONS

Washing is among the factors which potentially distort expression measures. The proposed first-order correction method allows direct implementation in existing calibration algorithms for microarray data. We provide an experimental 'washing data set' which might be used by the community for developing amendments of the washing correction.

Flexible biochips for detection of biomolecules

Miniaturization of biosensors is envisaged by the development of biochips consisting of parallel microarray patterns of binding sites on rigid substrates, such as glass or silicon. Thin plastic substrates are promising flexible alternatives because of the possibility for large-area roll-to-roll manufacturing of disposable chips at lower costs. Mature optical lithography technology faces many challenges when used to pattern flexible foils as a result of the substrate instabilities, especially at higher temperatures. In this work, flexible biochips with gold electrode patterns were fabricated on thin polyethylene naphthalate (PEN) foils using photolithography. The gold electrode structures of the chips were manufactured by direct metal patterning and by lift-off processing. Both methodologies resulted in well-defined electrode patterns as concluded from optical microscopy and scanning electron microscopy (SEM) characterization and resistance measurements. The biochips were successfully employed for the electrical and optical detection of DNA molecules. The DNA detection was based on the immobilization of capture DNA between electrode gaps, hybridization with biotin-labeled target DNA, and enzymatic silver enhancement.

Use of diagnostic accuracy as a metric for evaluating laboratory proficiency with microarray assays using mixed-tissue RNA reference samples

Effective use of microarray technology in clinical and regulatory settings is contingent on the adoption of standard methods for assessing performance. The MicroArray Quality Control project evaluated the repeatability and comparability of microarray data on the major commercial platforms and laid the groundwork for the application of microarray technology to regulatory assessments. However, methods for assessing performance that are commonly applied to diagnostic assays used in laboratory medicine remain to be developed for microarray assays. A reference system for microarray performance evaluation and process improvement was developed that includes reference samples, metrics and reference datasets. The reference material is composed of two mixes of four different rat tissue RNAs that allow defined target ratios to be assayed using a set of tissue-selective analytes that are distributed along the dynamic range of measurement. The diagnostic accuracy of detected changes in expression ratios, measured as the area under the curve from receiver operating characteristic plots, provides a single commutable value for comparing assay specificity and sensitivity. The utility of this system for assessing overall performance was evaluated for relevant applications like multi-laboratory proficiency testing programs and single-laboratory process drift monitoring. The diagnostic accuracy of detection of a 1.5-fold change in signal level was found to be a sensitive metric for comparing overall performance. This test approaches the technical limit for reliable discrimination of differences between two samples using this technology. We describe a reference system that provides a mechanism for internal and external assessment of laboratory proficiency with microarray technology and is translatable to performance assessments on other whole-genome expression arrays used for basic and clinical research.

Assessment of repeated microarray experiments using mixed tissue RNA reference samples

Genome-scale gene expression technologies are increasingly being applied for biological research as a whole and toxicological screening in particular. In order to monitor data quality and process drift, we adopted the use of two rat-tissue mixtures (brain, liver, kidney, and testis) previously introduced as RNA reference samples. These samples were processed every time a microarray experiment was hybridized, thereby verifying the comparability of the resulting expression data for cross-study comparison. This study presents the analysis of 21 technical replicates of these two mixed-tissue samples using Affymetrix RAE230_2 GeneChip over a period of 12 months. The results show that detection sensitivity, measured by the number of present and absent sequences, is robust, and data correlation, indicated by scatter plots, varies little over time. Receiver operating characteristic (ROC) curves show the sensitivity and specificity of the current measurements are consistent with arrays previously classified as well performing. Overall, this paper shows that the inclusion of standard samples during microarray labeling and hybridization experiments is useful to benchmark the performance of microarray experiments over time and allows discovery of any process drift that, if it occurs, may confound the comparison of these datasets.

Transcriptome for photobiological hydrogen production induced by sulfur deprivation in the green alga Chlamydomonas reinhardtii

Photobiological hydrogen production using microalgae is being developed into a promising clean fuel stream for the future. In this study, microarray analyses were used to obtain global expression profiles of mRNA abundance in the green alga Chlamydomonas reinhardtii at different time points before the onset and during the course of sulfur-depleted hydrogen production. These studies were followed by real-time quantitative reverse transcription-PCR and protein analyses. The present work provides new insights into photosynthesis, sulfur acquisition strategies, and carbon metabolism-related gene expression during sulfur-induced hydrogen production. A general trend toward repression of transcripts encoding photosynthetic genes was observed. In contrast to all other LHCBM genes, the abundance of the LHCBM9 transcript (encoding a major light-harvesting polypeptide) and its protein was strongly elevated throughout the experiment. This suggests a major remodeling of the photosystem II light-harvesting complex as well as an important function of LHCBM9 under sulfur starvation and photobiological hydrogen production. This paper presents the first global transcriptional analysis of C. reinhardtii before, during, and after photobiological hydrogen production under sulfur deprivation.

Poly(dG) spacers lead to increased surface coverage of DNA probes: an XPS study of oligonucleotide binding to zirconium phosphonate modified surfaces

A spacer is often employed between the surface linking group and the probe sequence to improve the performance of DNA microarrays. Previous work demonstrated that a consecutive stretch of guanines as a spacer increased target capture during hybridization relative to probes with either no spacer or a similar stretch of one of the other nucleotides. Using zirconium phosphonate modified surfaces with 5'-phosphorylated ssDNA probes, the present study compares the surface coverage of ssDNA probes containing either a poly(dG) spacer or a poly(dA) spacer. Surface coverages are quantified by XPS using a modified overlayer model. The results show that after treatment to mimic conditions of the passivation and hybridization steps the probe with the poly(dG) spacer has about twice the surface coverage as the probe with the poly(dA) spacer, indicating that increased target capture is due to higher probe coverage. When monitoring the surface coverage after each rinsing step, it is observed that the probe with the poly(dA) spacer is more susceptible to rinsing, suggesting the interaction with the surface is different for the two probes. It is suggested that the formation of G quadruplexes causes an increased avidity of the probe for the zirconium phosphonate surface.

Isostable DNA

The high fidelity detection of multiple DNA sequences in multiplex assays calls for duplexes whose stability is independent of sequence (isostable DNA), forming under universally stringent conditions. Nature did not evolve DNA to form isostable duplexes. Here we report how probe strands can be modified so that an all-A/T target strand is bound with the same or slightly higher affinity than the corresponding all-G/C strand with the same sequence of purines and pyrimidines. We refer to these probes that feature covalently attached ligands as "decorated nucleic acids". Caps, intercalators, and locks were used to stabilize A/T duplexes, and N4-ethylcytosine residues were employed to tune down the stability of G/C duplexes without significantly affecting base pairing selectivity. Near-isostability was demonstrated in solution and on microarrays of high and low density. Further, it is shown that hybridization results involving decorated probes on microarrays can be predicted on the basis of thermodynamic data for duplex formation in solution. Predictable formation of isostable DNA not only benefits microarrays for gene expression analysis and genotyping, but may also improve the sequence-specificity of other applications that rely on the massively parallel formation of Watson-Crick duplexes.

Optimization of cDNA microarrays procedures using criteria that do not rely on external standards

Background

The measurement of gene expression using microarray technology is a complicated process in which a large number of factors can be varied. Due to the lack of standard calibration samples such as are used in traditional chemical analysis it may be a problem to evaluate whether changes done to the microarray procedure actually improve the identification of truly differentially expressed genes. The purpose of the present work is to report the optimization of several steps in the microarray process both in laboratory practices and in data processing using criteria that do not rely on external standards.

Results

We performed a cDNA microarry experiment including RNA from samples with high expected differential gene expression termed "high contrasts" (rat cell lines AR42J and NRK52E) compared to self-self hybridization, and optimized a pipeline to maximize the number of genes found to be differentially expressed in the "high contrasts" RNA samples by estimating the false discovery rate (FDR) using a null distribution obtained from the self-self experiment. The proposed high-contrast versus self-self method (HCSSM) requires only four microarrays per evaluation. The effects of blocking reagent dose, filtering, and background corrections methodologies were investigated. In our experiments a dose of 250 ng LNA (locked nucleic acid) dT blocker, no background correction and weight based filtering gave the largest number of differentially expressed genes. The choice of background correction method had a stronger impact on the estimated number of differentially expressed genes than the choice of filtering method. Cross platform microarray (Illumina) analysis was used to validate that the increase in the number of differentially expressed genes found by HCSSM was real.

Conclusion

The results show that HCSSM can be a useful and simple approach to optimize microarray procedures without including external standards. Our optimizing method is highly applicable to both long oligo-probe microarrays which have become commonly used for well characterized organisms such as man, mouse and rat, as well as to cDNA microarrays which are still of importance for organisms with incomplete genome sequence information such as many bacteria, plants and fish.

Review of the literature examining the correlation among DNA microarray technologies

DNA microarray technologies are used in a variety of biological disciplines. The diversity of platforms and analytical methods employed has raised concerns over the reliability, reproducibility and correlation of data produced across the different approaches. Initial investigations (years 2000-2003) found discrepancies in the gene expression measures produced by different microarray technologies. Increasing knowledge and control of the factors that result in poor correlation among the technologies has led to much higher levels of correlation among more recent publications (years 2004 to present). Here, we review the studies examining the correlation among microarray technologies. We find that with improvements in the technology (optimization and standardization of methods, including data analysis) and annotation, analysis across platforms yields highly correlated and reproducible results. We suggest several key factors that should be controlled in comparing across technologies, and are good microarray practice in general.

Interferon-mediated immunopathological events are associated with atypical innate and adaptive immune responses in patients with severe acute respiratory syndrome

It is not understood how immune inflammation influences the pathogenesis of severe acute respiratory syndrome (SARS). One area of strong controversy is the role of interferon (IFN) responses in the natural history of SARS. The fact that the majority of SARS patients recover after relatively moderate illness suggests that the prevailing notion of deficient type I IFN-mediated immunity, with hypercytokinemia driving a poor clinical course, is oversimplified. We used proteomic and genomic technology to systematically analyze host innate and adaptive immune responses of 40 clinically well-described patients with SARS during discrete phases of illness from the onset of symptoms to discharge or a fatal outcome. A novel signature of high IFN-alpha, IFN-gamma, and IFN-stimulated chemokine levels, plus robust antiviral IFN-stimulated gene (ISG) expression, accompanied early SARS sequelae. As acute illness progressed, SARS patients entered a crisis phase linked to oxygen saturation profiles. The majority of SARS patients resolved IFN responses at crisis and expressed adaptive immune genes. In contrast, patients with poor outcomes showed deviated ISG and immunoglobulin gene expression levels, persistent chemokine levels, and deficient anti-SARS spike antibody production. We contend that unregulated IFN responses during acute-phase SARS may culminate in a malfunction of the switch from innate immunity to adaptive immunity. The potential for the use of the gene signatures we describe in this study to better assess the immunopathology and clinical management of severe viral infections, such as SARS and avian influenza (H5N1), is therefore worth careful examination.

Revision of the nonequilibrium thermal dissociation and stringent washing approaches for identification of mixed nucleic acid targets by microarrays

Microarray experiments typically involve washing steps that remove hybridized nonspecific targets with the purpose of improving the signal-to-noise ratio. The quality of washing ultimately affects downstream analysis of the microarray and interpretation. The paucity of fundamental studies directed towards understanding the dissociation of mixed targets from microarrays makes the development of meaningful washing/dissociation protocols difficult. To fill the void, we examined activation energies and preexponential coefficients of 47 perfect match (PM) and double-mismatch (MM) duplex pairs to discover that there was no statistical difference between the kinetics of the PM and MM duplexes. Based on these findings, we evaluated the nonequilibrium thermal dissociation (NTD) approach, which has been used to identify specific microbial targets in mixed target samples. We found that the major premises for various washing protocols and the NTD approach might be seriously compromised because: (i) nonspecific duplexes do not always dissociate before specific ones, and (ii) the relationship between dissociation rates of the PM and MM duplexes depends on temperature and duplex sequence. Specifically for the NTD, we show that previously suggested use of reference curves, indices of curves and temperature ramps lead to erroneous conclusions.

Elimination of laboratory ozone leads to a dramatic improvement in the reproducibility of microarray gene expression measurements

Background

Environmental ozone can rapidly degrade cyanine 5 (Cy5), a fluorescent dye commonly used in microarray gene expression studies. Cyanine 3 (Cy3) is much less affected by atmospheric ozone. Degradation of the Cy5 signal relative to the Cy3 signal in 2-color microarrays will adversely reduce the Cy5/Cy3 ratio resulting in unreliable microarray data.

Results

Ozone in central Arkansas typically ranges between ~22 ppb to ~46 ppb and can be as high as 60–100 ppb depending upon season, meteorological conditions, and time of day. These levels of ozone are common in many areas of the country during the summer. A carbon filter was installed in the laboratory air handling system to reduce ozone levels in the microarray laboratory. In addition, the airflow was balanced to prevent non-filtered air from entering the laboratory. These modifications reduced the ozone within the microarray laboratory to ~2–4 ppb. Data presented here document reductions in Cy5 signal on both in-house produced microarrays and commercial microarrays as a result of exposure to unfiltered air. Comparisons of identically hybridized microarrays exposed to either carbon-filtered or unfiltered air demonstrated the protective effect of carbon-filtration on microarray data as indicated by Cy5 and Cy3 intensities. LOWESS normalization of the data was not able to completely overcome the effect of ozone-induced reduction of Cy5 signal. Experiments were also conducted to examine the effects of high humidity on microarray quality. Modest, but significant, increases in Cy5 and Cy3 signal intensities were observed after 2 or 4 hours at 98–99% humidity compared to 42% humidity.

Conclusion

Simple installation of carbon filters in the laboratory air handling system resulted in low and consistent ozone levels. This allowed the accurate determination of gene expression by microarray using Cy5 and Cy3 fluorescent dyes.

Microarrays for rapid identification of plant viruses

Many factors affect the development and application of diagnostic techniques. Plant viruses are an inherently diverse group that, unlike cellular pathogens, possess no nucleotide sequence type (e.g., ribosomal RNA sequences) in common. Detection of plant viruses is becoming more challenging as globalization of trade, particularly in ornamentals, and the potential effects of climate change enhance the movement of viruses and their vectors, transforming the diagnostic landscape. Techniques for assessing seed, other propagation materials and field samples for the presence of specific viruses include biological indexing, electron microscopy, antibody-based detection, including enzyme-linked immunosorbent assay (ELISA), polymerase chain reaction (PCR), and microarray detection. Of these, microarray detection provides the greatest capability for parallel yet specific testing, and can be used to detect individual, or combinations of viruses and, using current approaches, to do so with a sensitivity comparable to ELISA. Methods based on PCR provide the greatest sensitivity among the listed techniques but are limited in parallel detection capability even in "multiplexed" applications. Various aspects of microarray technology, including probe development, array fabrication, assay target preparation, hybridization, washing, scanning, and interpretation are presented and discussed, for both current and developing technology.