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

Batch-effect detection, correction and characterisation in Illumina HumanMethylation450 and MethylationEPIC BeadChip array data

Background

Genomic technologies can be subject to significant batch-effects which are known to reduce experimental power and to potentially create false positive results. The Illumina Infinium Methylation BeadChip is a popular technology choice for epigenome-wide association studies (EWAS), but presently, little is known about the nature of batch-effects on these designs. Given the subtlety of biological phenotypes in many EWAS, control for batch-effects should be a consideration.

Results

Using the batch-effect removal approaches in the ComBat and Harman software, we examined two in-house datasets and compared results with three large publicly available datasets, (1214 HumanMethylation450 and 1094 MethylationEPIC BeadChips in total), and find that despite various forms of preprocessing, some batch-effects persist. This residual batch-effect is associated with the day of processing, the individual glass slide and the position of the array on the slide. Consistently across all datasets, 4649 probes required high amounts of correction. To understand the impact of this set to EWAS studies, we explored the literature and found three instances where persistently batch-effect prone probes have been reported in abstracts as key sites of differential methylation. As well as batch-effect susceptible probes, we also discover a set of probes which are erroneously corrected. We provide batch-effect workflows for Infinium Methylation data and provide reference matrices of batch-effect prone and erroneously corrected features across the five datasets spanning regionally diverse populations and three commonly collected biosamples (blood, buccal and saliva).

Conclusions

Batch-effects are ever present, even in high-quality data, and a strategy to deal with them should be part of experimental design, particularly for EWAS. Batch-effect removal tools are useful to reduce technical variance in Infinium Methylation data, but they need to be applied with care and make use of post hoc diagnostic measures.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-022-01277-9.

General Strategies for Glycan Microarray Data Processing and Analysis

Glycan microarrays provide a high-throughput technology for rapidly profiling interactions between carbohydrates and glycan-binding proteins (GBPs). Use of glycan microarrays involves several general steps, including construction of the microarray, carrying out the assay, detection of binding events, and analysis of the results. While multiple platforms have been developed to construct microarrays, most utilize fluorescence for detection of binding events. This chapter describes methods to acquire and process microarray images, including generating GAL files, imaging of the slide, aligning the grid, detecting problematic spots, and evaluating the quality of the data. The chapter focuses on processing our neoglycoprotein microarrays, but many of the lessons we have learned are applicable to other array formats.

TIGER: The gene expression regulatory variation landscape of human pancreatic islets

Genome-wide association studies (GWASs) identified hundreds of signals associated with type 2 diabetes (T2D). To gain insight into their underlying molecular mechanisms, we have created the translational human pancreatic islet genotype tissue-expression resource (TIGER), aggregating >500 human islet genomic datasets from five cohorts in the Horizon 2020 consortium T2DSystems. We impute genotypes using four reference panels and meta-analyze cohorts to improve the coverage of expression quantitative trait loci (eQTL) and develop a method to combine allele-specific expression across samples (cASE). We identify >1 million islet eQTLs, 53 of which colocalize with T2D signals. Among them, a low-frequency allele that reduces T2D risk by half increases CCND2 expression. We identify eight cASE colocalizations, among which we found a T2D-associated SLC30A8 variant. We make all data available through the TIGER portal (http://tiger.bsc.es), which represents a comprehensive human islet genomic data resource to elucidate how genetic variation affects islet function and translates into therapeutic insight and precision medicine for T2D.

Method to minimize ozone effect on Cy5 fluorescent intensity in DNA microarrays

Cyanine 5 (Cy5) is an established fluorescent dye in microarray analysis. It is degraded rapidly when exposed to atmospheric ozone during post-hybridization washes, which leads to loss of fluorescent intensity. To minimize this undesirable effect, we coated microarray slides with sodium dodecyl sulfate (SDS) solution at post-hybridization washes. The fluorescent intensities on coated slides were more stable than those on uncoated slide. We also performed the microarrays with SDS solution for a year to check the solution's effectiveness along with seasonal changes of atmospheric ozone level. Consistent results in microarray analysis were obtained using Cy5 dye under atmospheric ozone.

Hybridization of Environmental Microbial Community Nucleic Acids by GeoChip

Functional gene arrays, like the GeoChip, allow for the study of tens of thousands of genes in a single assay. The GeoChip array (5.0) contains probes for genes involved in geochemical cycling (N, C, S, and P), metal homeostasis, stress response, organic contaminant degradation, antibiotic resistance, secondary metabolism, and virulence factors as well as genes specific for fungi, protists, and viruses. Here, we briefly describe GeoChip design strategies (gene selection and probe design) and discuss minimum quantity and quality requirements for nucleic acids. We then provide detailed protocols for amplification, labeling, and hybridization of samples to the GeoChip.

Microarray experiments and factors which affect their reliability

Oligonucleotide microarrays belong to the basic tools of molecular biology and allow for simultaneous assessment of the expression level of thousands of genes. Analysis of microarray data is however very complex, requiring sophisticated methods to control for various factors that are inherent to the procedures used. In this article we describe the individual steps of a microarray experiment, highlighting important elements and factors that may affect the processes involved and that influence the interpretation of the results. Additionally, we describe methods that can be used to estimate the influence of these factors, and to control the way in which they affect the expression estimates. A comprehensive understanding of the experimental protocol used in a microarray experiment aids the interpretation of the obtained results. By describing known factors which affect expression estimates this article provides guidelines for appropriate quality control and pre-processing of the data, additionally applicable to other transcriptome analysis methods that utilize similar sample handling protocols.

Persistence and transgenerational effect of plant-mediated RNAi in aphids

Plant-mediated RNA interference (RNAi) has been successfully used as a tool to study gene function in aphids. The persistence and transgenerational effects of plant-mediated RNAi in the green peach aphid (GPA) Myzus persicae were investigated, with a focus on three genes with different functions in the aphid. Rack1 is a key component of various cellular processes inside aphids, while candidate effector genes MpC002 and MpPIntO2 (Mp2) modulate aphid-plant interactions. The gene sequences and functions did not affect RNAi-mediated down-regulation and persistence levels in the aphids. Maximal reduction of gene expression was ~70% and this was achieved at between 4 d and 8 d of exposure of the aphids to double-stranded RNA (dsRNA)-producing transgenic Arabidopsis thaliana. Moreover, gene expression levels returned to wild-type levels within ~6 d after removal of the aphids from the transgenic plants, indicating that a continuous supply of dsRNA is required to maintain the RNAi effect. Target genes were also down-regulated in nymphs born from mothers exposed to dsRNA-producing transgenic plants, and the RNAi effect lasted twice as long (12-14 d) in these nymphs. Investigations of the impact of RNAi over three generations of aphids revealed that aphids reared on dsMpC002 transgenic plants experienced a 60% decline in aphid reproduction levels compared with a 40% decline of aphids reared on dsRack1 and dsMpPIntO2 plants. In a field setting, a reduction of the aphid reproduction by 40-60% would dramatically decrease aphid population growth, contributing to a substantial reduction in agricultural losses.

Effectively identifying regulatory hotspots while capturing expression heterogeneity in gene expression studies

Expression quantitative trait loci (eQTL) mapping is a tool that can systematically identify genetic variation affecting gene expression. eQTL mapping studies have shown that certain genomic locations, referred to as regulatory hotspots, may affect the expression levels of many genes. Recently, studies have shown that various confounding factors may induce spurious regulatory hotspots. Here, we introduce a novel statistical method that effectively eliminates spurious hotspots while retaining genuine hotspots. Applied to simulated and real datasets, we validate that our method achieves greater sensitivity while retaining low false discovery rates compared to previous methods.

Proteomic approaches to identify cold-regulated soluble proteins

DIGE (differential in-gel electrophoresis) is a modified version of the widely used 2-D gel electrophoresis (2-DE) for separation of complex protein samples. Two extracts to be compared are differentially labeled using fluorescent cyanine dyes and then separated together by 2-DE. An internal standard labeled using a third dye is included. This approach avoids the pitfalls of gel distortions frequently observed in the standard procedure, which hamper the subsequent gel image analysis. Inclusion of an internal standard improves the quantitative evaluation of the protein patterns. Using the advantages of the DIGE approach, impact of minor temperature differences during cold stress treatment could be quantitatively monitored. We will describe the application of DIGE to monitor the impact of cold stress on the proteome pattern of Arabidopsis. In addition to the separation of proteins, we will also outline how plant growth is performed. Finally, we will also give protocols how proteins of interest can be identified by MALDI-TOF- as well as ESI-MS/MS.

Functional DNA repair signature of cancer cell lines exposed to a set of cytotoxic anticancer drugs using a multiplexed enzymatic repair assay on biochip

The development of resistances to conventional anticancer drugs compromises the efficacy of cancer treatments. In the case of DNA-targeting chemotherapeutic agents, cancer cells may display tolerance to the drug-induced DNA lesions and/or enhanced DNA repair. However, the role of DNA damage response (DDR) and DNA repair in this chemoresistance has yet to be defined. To provide insights in this challenging area, we analyzed the DNA repair signature of 7 cancer cell lines treated by 5 cytotoxic drugs using a recently developed multiplexed functional DNA repair assay. This comprehensive approach considered the complexity and redundancy of the different DNA repair pathways. Data was analyzed using clustering methods and statistical tests. This DNA repair profiling method defined relevant groups based on similarities between different drugs, thus providing information relating to their dominant mechanism of action at the DNA level. Similarly, similarities between different cell lines presumably identified identical functional DDR despite a high level of genetic heterogeneity between cell lines. Our strategy has shed new light on the contribution of specific repair sub-pathways to drug-induced cytotoxicity. Although further molecular characterisations are needed to fully unravel the mechanisms underlying our findings, our approach proved to be very promising to interrogate the complexity of the DNA repair response. Indeed, it could be used to predict the efficacy of a given drug and the chemosensitivity of individual patients, and thus to choose the right treatment for individualised cancer care.

Use of functional gene arrays for elucidating in situ biodegradation

Microarrays have revolutionized the study of microbiology by providing a high-throughput method for examining thousands of genes with a single test and overcome the limitations of many culture-independent approaches. Functional gene arrays (FGA) probe a wide range of genes involved in a variety of functions of interest to microbial ecology (e.g., carbon degradation, N fixation, metal resistance) from many different microorganisms, cultured and uncultured. The most comprehensive FGA to date is the GeoChip array, which targets tens of thousands of genes involved in the geochemical cycling of carbon, nitrogen, phosphorus, and sulfur, metal resistance and reduction, energy processing, antibiotic resistance and contaminant degradation as well as phylogenetic information (gyrB). Since the development of GeoChips, many studies have been performed using this FGA and have shown it to be a powerful tool for rapid, sensitive, and specific examination of microbial communities in a high-throughput manner. As such, the GeoChip is well-suited for linking geochemical processes with microbial community function and structure. This technology has been used successfully to examine microbial communities before, during, and after in situ bioremediation at a variety of contaminated sites. These studies have expanded our understanding of biodegradation and bioremediation processes and the associated microorganisms and environmental conditions responsible. This review provides an overview of FGA development with a focus on the GeoChip and highlights specific GeoChip studies involving in situ bioremediation.

New Agilent platform DNA microarrays for transcriptome analysis of Plasmodium falciparum and Plasmodium berghei for the malaria research community

Background

DNA microarrays have been a valuable tool in malaria research for over a decade but remain in limited use in part due their relatively high cost, poor availability, and technical difficulty. With the aim of alleviating some of these factors next-generation DNA microarrays for genome-wide transcriptome analysis for both Plasmodium falciparum and Plasmodium berghei using the Agilent 8x15K platform were designed.

Methods

Probe design was adapted from previously published methods and based on the most current transcript predictions available at the time for P. falciparum or P. berghei. Array performance and transcriptome analysis was determined using dye-coupled, aminoallyl-labelled cDNA and streamlined methods for hybridization, washing, and array analysis were developed.

Results

The new array design marks a notable improvement in the number of transcripts covered and average number of probes per transcript. Array performance was excellent across a wide range of transcript abundance, with low inter-array and inter-probe variability for relative abundance measurements and it recapitulated previously observed transcriptional patterns. Additionally, improvements in sensitivity permitted a 20-fold reduction in necessary starting RNA amounts, further reducing experimental costs and widening the range of application.

Conclusions

DNA microarrays utilizing the Agilent 8x15K platform for genome-wide transcript analysis in P. falciparum and P. berghei mark an improvement in coverage and sensitivity, increased availability to the research community, and simplification of the experimental methods.

Target concentration dependence of DNA melting temperature on oligonucleotide microarrays

The design of microarrays is currently based on studies focusing on DNA hybridization reaction in bulk solution. However, the presence of a surface to which the probe strand is attached can make the solution-based approximations invalid, resulting in sub-optimum hybridization conditions. To determine the effect of surfaces on DNA duplex formation, the authors studied the dependence of DNA melting temperature (T(m)) on target concentration. An automated system was developed to capture the melting profiles of a 25-mer perfect-match probe-target pair initially hybridized at 23°C. Target concentrations ranged from 0.0165 to 15 nM with different probe amounts (0.03-0.82 pmol on a surface area of 10(18) Å(2)), a constant probe density (5 × 10(12) molecules/cm(2)) and spacer length (15 dT). The authors found that T(m) for duplexes anchored to a surface is lower than in-solution, and this difference increases with increasing target concentration. In a representative set, a target concentration increase from 0.5 to 15 nM with 0.82 pmol of probe on the surface resulted in a T(m) decrease of 6°C when compared with a 4°C increase in solution. At very low target concentrations, a multi-melting process was observed in low temperature domains of the curves. This was attributed to the presence of truncated or mismatch probes.

Using DNA microarrays to assay part function

In recent years, the capability of synthetic biology to design large genetic circuits has dramatically increased due to rapid advances in DNA synthesis technology and development of tools for large-scale assembly of DNA fragments. Large genetic circuits require more components (parts), especially regulators such as transcription factors, sigma factors, and viral RNA polymerases to provide increased regulatory capability, and also devices such as sensors, receivers, and signaling molecules. All these parts may have a potential impact upon the host that needs to be considered when designing and fabricating circuits. DNA microarrays are a well-established technique for global monitoring of gene expression and therefore are an ideal tool for systematically assessing the impact of expressing parts of genetic circuits in host cells. Knowledge of part impact on the host enables the user to design circuits from libraries of parts taking into account their potential impact and also to possibly modify the host to better tolerate stresses induced by the engineered circuit. In this chapter, we present the complete methodology of performing microarrays from choice of array platform, experimental design, preparing samples for array hybridization, and associated data analysis including preprocessing, normalization, clustering, identifying significantly differentially expressed genes, and interpreting the data based on known biology. With these methodologies, we also include lists of bioinformatic resources and tools for performing data analysis. The aim of this chapter is to provide the reader with the information necessary to be able to systematically catalog the impact of genetic parts on the host and also to optimize the operation of fully engineered genetic circuits.

Nucleic Acid programmable protein arrays: versatile tools for array-based functional protein studies

Protein microarrays offer a global perspective on the function of expressed gene products. However, technical issues related to the stability and dynamic range of microarrays printed with purified protein have hampered their widespread adoption. Taking an alternate approach, the Nucleic Acid Programmable Protein Array (NAPPA) is constructed by spotting protein-encoding plasmid DNA at high density, in addressable fashion, on an array surface. Proteins are subsequently generated in situ just prior to experimentation using cell-free expression systems. As such, the NAPPA platform offers a unique and viable alternative that circumvents many of the inherent limitations of spotted protein arrays, enabling diverse functional protein studies including protein-small molecule, protein-protein, antigen-antibody, and protein-nucleic acid interactions. It further offers a versatile and adaptable platform amenable to a variety of capture modalities and expression systems, and, most importantly, construction of the array is accessible to any lab with an array printer and laser slide scanner. This unit is intended to provide a reference for investigators wishing to generate arrays based on this platform, and details (1) the basic construction of cDNA-based protein microarrays from DNA isolation to printing and development, (2) quality-control efforts taken to ensure the usefulness and integrity of microarray data, and (3) a particular example of the application of self-assembling protein arrays to screen for blood-borne antibody biomarkers.

Comparison of microarray and quantitative real-time PCR methods for measuring MicroRNA levels in MSC cultures

The capacity for self-renewal and the multilineage potential of mesenchymal stromal cells (MSC) offer a therapeutic promise for regenerative medicine. MicroRNAs (miRNAs) are small noncoding RNAs that play a key regulatory role during differentiation both at the level of posttranslational modulation and epigenetic control. Studies on MSCs have just begun to identify miRNA profiles in MSC and differentiated MSC. While several methods are available for miRNA exploration, microarrays and quantitative real-time PCR (qPCR) are the most common. Since there are several microarray and qPCR platforms available for miRNA detection, it is valuable to explore how these methods compare. We used the NCode Multi-Species miRNA microarray (Invitrogen) and the TaqMan Human microRNA array (Applied Biosystems) to compare microRNA expression in undifferentiated MSCs and MSCs differentiated into early osteoblasts. We show that while there is a somewhat low correlation between these two methods, there is a subset of miRNA measurements that did correlate.

Current Clinical and Pharmaceutical Applications of Microarrays: From Disease Biomarkers Discovery to Automated Diagnostics

Microarrays used for measuring chromosomal aberrations in genomic DNA and for defining gene expression patterns have become almost routine. A microarray consists of an arrayed series of microscopic spots each containing either DNA or protein molecules known as feature reporters. Advances in microarray fabrication and in feature detection systems, such as high-resolution scanners and their associated software, lead to high-throughput screening of the genome or the transcriptome of a cell or a group of cells in only few days. Despite the potential of high-density microarrays, several problems about data interpretation are still to be solved. In addition, targeted microarrays are shown to be useful tools for rapid and accurate diagnosis of diseases. The aim of this review was to discuss the impact of microarrays on different application levels from the definition of disease biomarkers to pharmaceutical and clinical diagnostics.

Identifying Fishes through DNA Barcodes and Microarrays

Background

International fish trade reached an import value of 62.8 billion Euro in 2006, of which 44.6% are covered by the European Union. Species identification is a key problem throughout the life cycle of fishes: from eggs and larvae to adults in fisheries research and control, as well as processed fish products in consumer protection.

Methodology/Principal Findings

This study aims to evaluate the applicability of the three mitochondrial genes 16S rRNA (16S), cytochrome b (cyt b), and cytochrome oxidase subunit I (COI) for the identification of 50 European marine fish species by combining techniques of “DNA barcoding” and microarrays. In a DNA barcoding approach, neighbour Joining (NJ) phylogenetic trees of 369 16S, 212 cyt b, and 447 COI sequences indicated that cyt b and COI are suitable for unambiguous identification, whereas 16S failed to discriminate closely related flatfish and gurnard species. In course of probe design for DNA microarray development, each of the markers yielded a high number of potentially species-specific probes in silico, although many of them were rejected based on microarray hybridisation experiments. None of the markers provided probes to discriminate the sibling flatfish and gurnard species. However, since 16S-probes were less negatively influenced by the “position of label” effect and showed the lowest rejection rate and the highest mean signal intensity, 16S is more suitable for DNA microarray probe design than cty b and COI. The large portion of rejected COI-probes after hybridisation experiments (>90%) renders the DNA barcoding marker as rather unsuitable for this high-throughput technology.

Conclusions/Significance

Based on these data, a DNA microarray containing 64 functional oligonucleotide probes for the identification of 30 out of the 50 fish species investigated was developed. It represents the next step towards an automated and easy-to-handle method to identify fish, ichthyoplankton, and fish products.

Stimulated gene expression profiles as a blood marker of major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a moderately heritable disorder with a high lifetime prevalence. At present, laboratory blood tests to support MDD diagnosis are not available.

METHODS

We used a classifier approach on blood gene expression profiles of a unique set of unmedicated subjects (MDD patients and control subjects) to select genes with expression predictive for disease status. To reveal blood gene expression changes related to major depressive disorder-disease, we applied a powerful ex vivo stimulus to the blood: incubation with lipopolysaccharide (LPS; 10 ng/mL blood).

RESULTS

Based on LPS-stimulated blood gene expression using whole-genome microarrays (primary cohort; 21 MDD patients, 21 healthy control subjects), we identified a set of genes (CAPRIN1, CLEC4A, KRT23, MLC1, PLSCR1, PROK2, ZBTB16) that serves as a molecular signature of MDD. These findings were validated using an independent quantitative polymerase chain reaction method (primary cohort, p = .007). The difference between depressive patients and control subjects was confirmed (p = .019) in a replication cohort of 13 MDD patients and 14 control subjects. The MDD signature score comprised expression levels of seven genes could discriminate depressive patients from control subjects with sensitivity of 76.9% and specificity of 71.8%.

CONCLUSIONS

We have shown for the first time that molecular analysis of stimulated blood cells can be used as an endophenotype for MDD diagnosis, which is a milestone in establishing biomarkers for neuropsychiatric disorders with moderate heritability in general. Our results may provide a new entry point for following and predicting treatment outcome, as well as prediction of severity and recurrence of major depressive disorder.

Tourette syndrome: gene expression as a tool to discover drug targets

Gene expression microarrays are a high-throughput, cost-effective method for measuring the expression of all genes in a sample. By comparing the expression patterns of healthy controls to diseased subjects, the genetic regulatory pathways underlying and affected by the disease can be elucidated. Furthermore, dysregulated genes are possible candidates for pharmaceutical therapy. Here, we consider the possibility of applying this approach to Tourette syndrome. We also review current theories of Tourette syndrome etiology and pharmacology.

Challenges in defining predictive markers for response to endocrine therapy in breast cancer

Endocrine therapy is a major treatment modality for hormone-dependent breast cancer. It has a relatively low morbidity, and there is evidence that antihormonal treatments have had a significant effect in reducing mortality for breast cancer. Despite this, resistance to endocrine therapy, either primary or acquired during treatment, occurs in the majority of patients, and is a major obstacle to optimal clinical management. There is therefore an urgent need to identify, on an individual basis, those tumors that are most likely to respond to endocrine therapy (so sparing patients with resistant tumors the needless side effects of ineffective therapy), and the mechanisms of resistance in tumors that are nonresponsive to treatment (so these can be bypassed). These needs are the focus of this review, which discusses the particular issues encountered when investigating the potential of multigene expression signatures as predictive factors for response to aromatase inhibitors, which have recently become front-line endocrine therapies for postmenopausal patients with breast cancer.

Introduction to the development and validation of predictive biomarker models from high-throughput data sets

High-throughput technologies can routinely assay biological or clinical samples and produce wide data sets where each sample is associated with tens of thousands of measurements. Such data sets can be mined to discover biomarkers and develop statistical models capable of predicting an endpoint of interest from data measured in the samples. The field of biomarker model development combines methods from statistics and machine learning to develop and evaluate predictive biomarker models. In this chapter, we discuss the computational steps involved in the development of biomarker models designed to predict information about individual samples and review approaches often used to implement each step. A practical example of biomarker model development in a large gene expression data set is presented. This example leverages BDVal, a suite of biomarker model development programs developed as an open-source project (see http://bdval.org /).

Effects of ozone exposure during microarray posthybridization washes and scanning

The increasing prevalence of array-based comparative genomic hybridization in the clinical laboratory necessitates the implementation of quality control measures to attain accurate results with a high level of confidence. Environmental ozone is present in all industrialized cities and has been found to be detrimental to array data even at levels considered acceptable by US Environmental Protection Agency standards. In this study, we characterized the effect of ozone on microarray data on three different labeling platforms that use different fluorescent dyes (Cy3 and Cy5, Alexa Fluor 555 and Alexa Fluor 647, and Alexa Fluor 3 and Alexa Fluor 5) that are commonly used in array-based comparative genomic hybridization. We investigated the effects of ozone on microarray data by washing the array in variable ozone environments. In addition, we observed the effects of prolonged exposure to ozone on the microarray after washing in an ozone-free environment. Our results demonstrate the necessity of minimizing ozone exposure when washing and drying the microarray. We also found that washed microarrays produce the best results when immediately scanned; however, if a low-ozone environment is maintained, there will be little compromise in the data collected.

Comparative genomic hybridization on BAC arrays

Alterations in genomic DNA are a key feature of many constitutional disorders and cancer. The discovery of the underlying regions of gene dosage has thus been essential in dissecting complex disease phenotypes and identifying targets for therapeutic intervention and diagnostic testing. The development of array comparative genomic hybridization (aCGH) using bacterial artificial chromosomes (BACs) as hybridization targets has facilitated the discovery and fine mapping of novel genomic alterations allowing rapid identification of target genes. In BAC aCGH, DNA samples are first labeled with fluorescent dyes through a random priming reaction with 100-400 ng of genomic DNA. This probe is then co-hybridized to an array consisting of BAC clones, either tiling the genome (approximately 50 kbp resolution) or spaced at intervals (e.g., 1 Mbp resolution). The resulting arrays are then imaged and the signal at each locus is compared between a reference and test sample to determine the copy number status. The DNA samples to be analyzed may be derived from either fresh, frozen, or formalin-fixed paraffin-embedded material, and sample requirements are currently significantly lower than those for oligonucleotide platforms due to the high probe-binding capacity of BAC clone targets (approximately 150 kbp) compared to oligonucleotides (25-80 bp). In this chapter, we describe in detail the technical procedure required to perform copy number analysis of genomes with BAC aCGH.

Methylation analysis by DNA immunoprecipitation (MeDIP)

Alteration in epigenetic regulation of gene expression is a common event in human cancer and developmental disease. CpG island hypermethylation and consequent gene silencing is observed for many genes involved in a diverse range of functions and pathways that become deregulated in the disease state. Comparative profiling of the methylome is therefore useful in disease gene discovery. The ability to identify epigenetic alterations on a global scale is imperative to understanding the patterns of gene silencing that parallel disease progression. Methylated DNA immunoprecipitation (MeDIP) is a technique that isolates methylated DNA fragments by immunoprecipitating with 5'-methylcytosine-specific antibodies. The enriched methylated DNA can then be analyzed in a locus-specific manner using PCR assay or in a genome-wide fashion by comparative genomic hybridization against a sample without MeDIP enrichment. This article describes the detailed protocol for MeDIP and hybridization of MeDIP DNA to a whole-genome tiling path BAC array.

Cancer gene profiling for response prediction

Preoperative treatment strategies are now recommended for a variety of human cancers. Unfortunately, the response of individual tumors to a preoperative treatment is not uniform, and ranges from complete regression to resistance. This poses a considerable clinical dilemma, because patients with a priori resistant tumors could either be spared exposure to radiation or DNA-damaging drugs, i.e., they could be referred to primary surgery or dose-intensified protocols could be pursued. Because the response of an individual tumor as well as therapy-induced side effects represent the major limiting factors of current treatment strategies, identifying molecular markers of response or for treatment toxicity have become exceedingly important. However, complex phenotypes such as tumor responsiveness to multimodal treatments probably do not depend on the expression levels of just one or a few genes and proteins. Therefore, methods that allow comprehensive interrogation of genetic pathways and networks hold great promise in delivering such tumor-specific signatures, because expression levels of tens of thousands of genes can be monitored simultaneously. During the past few years, microarray technology has emerged as a central tool in addressing pertinent clinical questions, the answers to which are critical for the realization of a personalized genomic medicine, in which patients will be treated based on the biology of their tumor and their genetic profile (1-4).

Diverse RNA-binding proteins interact with functionally related sets of RNAs, suggesting an extensive regulatory system

RNA-binding proteins (RBPs) have roles in the regulation of many post-transcriptional steps in gene expression, but relatively few RBPs have been systematically studied. We searched for the RNA targets of 40 proteins in the yeast Saccharomyces cerevisiae: a selective sample of the approximately 600 annotated and predicted RBPs, as well as several proteins not annotated as RBPs. At least 33 of these 40 proteins, including three of the four proteins that were not previously known or predicted to be RBPs, were reproducibly associated with specific sets of a few to several hundred RNAs. Remarkably, many of the RBPs we studied bound mRNAs whose protein products share identifiable functional or cytotopic features. We identified specific sequences or predicted structures significantly enriched in target mRNAs of 16 RBPs. These potential RNA-recognition elements were diverse in sequence, structure, and location: some were found predominantly in 3′-untranslated regions, others in 5′-untranslated regions, some in coding sequences, and many in two or more of these features. Although this study only examined a small fraction of the universe of yeast RBPs, 70% of the mRNA transcriptome had significant associations with at least one of these RBPs, and on average, each distinct yeast mRNA interacted with three of the RBPs, suggesting the potential for a rich, multidimensional network of regulation. These results strongly suggest that combinatorial binding of RBPs to specific recognition elements in mRNAs is a pervasive mechanism for multi-dimensional regulation of their post-transcriptional fate.

Regulation of gene transcription has been extensively studied, but much less is known about how the fates of the resulting mRNA transcripts are regulated. We were intrigued by the fact that while most eukaryotic genomes encode hundreds of RNA-binding proteins (RBPs), the targets and regulatory roles of only a small fraction of these proteins have been characterized. In this study, we systematically identified the RNAs associated with a select sample of 40 of the approximately 600 predicted RBPs in the budding yeast, Saccharomyces cerevisiae. We found that most of these RBPs bound specific sets of mRNAs whose protein products share physiological themes or similar locations within the cell. For 16 of the 40 RBPs, we identified sequence motifs significantly enriched in their RNA targets that presumably mediate recognition of the target by the RBP. The intricate, overlapping patterns of mRNAs associated with RBPs suggest an extensive combinatorial system for post-transcriptional regulation, involving dozens or even hundreds of RBPs. The organization and molecular mechanisms involved in this regulatory system, including how RBP–mRNA interactions are integrated with signal transduction systems and how they affect the fates of their RNA targets, provide abundant opportunities for investigation and discovery.

A systematic study of the RNA targets of 40 known or predicted RNA-binding proteins in yeast suggests that an extensive system of dozens or hundreds of specific RNA-binding proteins may act to regulate the post-transcriptional fate of most or all RNAs in the yeast cell.

Development of a novel ozone- and photo-stable HyPer5 red fluorescent dye for array CGH and microarray gene expression analysis with consistent performance irrespective of environmental conditions

Background

Array-based comparative genomic hybridization (CGH) and gene expression profiling have become vital techniques for identifying molecular defects underlying genetic diseases. Regardless of the microarray platform, cyanine dyes (Cy3 and Cy5) are one of the most widely used fluorescent dye pairs for microarray analysis owing to their brightness and ease of incorporation, enabling high level of assay sensitivity. However, combining both dyes on arrays can become problematic during summer months when ozone levels rise to near 25 parts per billion (ppb). Under such conditions, Cy5 is known to rapidly degrade leading to loss of signal from either "homebrew" or commercial arrays. Cy5 can also suffer disproportionately from dye photobleaching resulting in distortion of (Cy5/Cy3) ratios used in copy number analysis. Our laboratory has been active in fluorescent dye research to find a suitable alternative to Cy5 that is stable to ozone and resistant to photo-bleaching. Here, we report on the development of such a dye, called HyPer5, and describe its' exceptional ozone and photostable properties on microarrays.

Results

Our results show HyPer5 signal to be stable to high ozone levels. Repeated exposure of mouse arrays hybridized with HyPer5-labeled cDNA to 300 ppb ozone at 5, 10 and 15 minute intervals resulted in no signal loss from the dye. In comparison, Cy5 arrays showed a dramatic 80% decrease in total signal during the same interval. Photobleaching experiments show HyPer5 to be resistant to light induced damage with 3- fold improvement in dye stability over Cy5. In high resolution array CGH experiments, HyPer5 is demonstrated to detect chromosomal aberrations at loci 2p21-16.3 and 15q26.3-26.2 from three patient sample using bacterial artificial chromosome (BAC) arrays. The photostability of HyPer5 is further documented by repeat array scanning without loss of detection. Additionally, HyPer5 arrays are shown to preserve sensitivity and data quality from gene expression experiments.

Conclusion

HyPer5 is a red fluorescent dye that behaves functionally similar to Cy5 except in stability to ozone and light. HyPer5 is demonstrated to be resistant to ozone at up to 300 ppb, levels significantly higher than commonly observed during summer months. Consequently, HyPer5 dye can be used in parallel with Cy3 under any environmental conditions in array experiments.

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.

Technical considerations in using DNA microarrays to define regulons

Transcription is the major regulatory target of gene expression in bacteria, and is controlled by many regulatory proteins and RNAs. Microarrays are a powerful tool to study the regulation of transcription on a genomic scale. Here we describe the use of transcription profiling and ChIP-chip to study transcriptional regulation in bacteria. Transcription profiling determines the outcome of regulatory events whereas ChIP-chip identifies the protein-DNA interactions that determine these events. Together they can provide detailed information on transcriptional regulatory systems.

Optimization of experimental design parameters for high-throughput chromatin immunoprecipitation studies

High-throughput, microarray-based chromatin immunoprecipitation (ChIP-chip) technology allows in vivo elucidation of transcriptional networks. However this complex is not yet readily accessible, in part because its many parameters have not been systematically evaluated and optimized. We address this gap by systematically assessing experimental-design parameters including antibody purity, dye-bias, array-batch, inter-day hybridization bias, amplification method and choice of hybridization control. The combined performance of these optimized parameters shows a 90% validation rate in ChIP-chip analysis of Myc genomic binding in HL60 cells using two different microarray platforms. Increased sensitivity and decreased noise in ChIP-chip assays will enable wider use of this methodology to accurately and affordably elucidate transcriptional networks.

Accurate discovery of expression quantitative trait loci under confounding from spurious and genuine regulatory hotspots

In genomewide mapping of expression quantitative trait loci (eQTL), it is widely believed that thousands of genes are trans-regulated by a small number of genomic regions called "regulatory hotspots," resulting in "trans-regulatory bands" in an eQTL map. As several recent studies have demonstrated, technical confounding factors such as batch effects can complicate eQTL analysis by causing many spurious associations including spurious regulatory hotspots. Yet little is understood about how these technical confounding factors affect eQTL analyses and how to correct for these factors. Our analysis of data sets with biological replicates suggests that it is this intersample correlation structure inherent in expression data that leads to spurious associations between genetic loci and a large number of transcripts inducing spurious regulatory hotspots. We propose a statistical method that corrects for the spurious associations caused by complex intersample correlation of expression measurements in eQTL mapping. Applying our intersample correlation emended (ICE) eQTL mapping method to mouse, yeast, and human identifies many more cis associations while eliminating most of the spurious trans associations. The concordances of cis and trans associations have consistently increased between different replicates, tissues, and populations, demonstrating the higher accuracy of our method to identify real genetic effects.

Comparison of the diagnostic performance of human whole genome microarrays using mixed-tissue RNA reference samples

Universal approaches for assessing the diagnostic performance of microarray assays are essential for the application of microarray technology to clinical and regulatory settings. Reference systems for diagnostic assays in laboratory medicine typically involve the utilization of reference samples, metrics, and reference datasets to ensure that measurements are comparable and true. For microarray performance evaluation and process improvement, reference samples can be composed of mixes of different tissue or cell line RNAs that contain tissue-selective analytes at defined target ratios. The diagnostic accuracy of detected changes in expression, measured as the area under the curve from receiver-operating characteristic plots, can provide a single commutable value for comparing assay specificity and sensitivity. Examples of applying this method for assessing overall performance are provided using public datasets generated on five commercial human whole genome microarray platforms for the MicroArray Quality Control project, a community-wide effort to address issues surrounding microarray data reliability.