Real-time DNA microarray analysis

We present a quantification method for affinity-based DNA microarrays which is based on the real-time measurements of hybridization kinetics. This method, i.e. real-time DNA microarrays, enhances the detection dynamic range of conventional systems by being impervious to probe saturation in the capturing spots, washing artifacts, microarray spot-to-spot variations, and other signal amplitude-affecting non-idealities. We demonstrate in both theory and practice that the time-constant of target capturing in microarrays, similar to all affinity-based biosensors, is inversely proportional to the concentration of the target analyte, which we subsequently use as the fundamental parameter to estimate the concentration of the analytes. Furthermore, to empirically validate the capabilities of this method in practical applications, we present a FRET-based assay which enables the real-time detection in gene expression DNA microarrays.     

Combinatorial image analysis of DNA microarray features

MOTIVATION: DNA and protein microarrays have become an established leading-edge technology for large-scale analysis of gene and protein content and activity. Contact-printed microarrays has emerged as a relatively simple and cost effective method of choice but its reliability is especially susceptible to quality of pixel information obtained from digital scans of spotted features in the microarray image. RESULTS: We address the statistical computation requirements for optimizing data acquisition and processing of digital scans. We consider the use of median filters to reduce noise levels in images and top-hat filters to correct for trends in background values. We also consider, as alternative estimators of spot intensity, discs of fixed radius, proportions of histograms and k-means clustering, either with or without a square-root intensity transformation and background subtraction. We identify, using combinatoric procedures, optimal filter and estimator parameters, in achieving consistency among the replicates of a gene on each microarray. Our results, using test data from microarrays of HCMV, indicate that a highly effective approach for improving reliability and quality of microarray data is to apply a 21 by 21 top-hat filter, then estimate spot intensity as the mean of the largest 20% of pixel values in the target region, after a square-root transformation, and corrected for background, by subtracting the mean of the smallest 70% of pixel values. AVAILABILITY: Fortran90 subroutines implementing these methods are available from the authors, or at http://www.bioss.ac.uk/~chris.     

Granulometric analysis of spots in DNA microarray images

As the topological properties of each spot in DNA microarray images may vary from one another, we employed granulometries to understand the shape-size content contributed due to a significant intensity value within a spot. Analysis was performed on the microarray image that consisted of 240 spots by using concepts from mathematical morphology. In order to find out indices for each spot and to further classify them, we adopted morphological multiscale openings, which provided microarrays at multiple scales. Successive opened microarrays were subtracted to identify the protrusions that were smaller than the size of structuring element. Spot-wise details, in terms of probability of these observed protrusions,were computed by placing a regularly spaced grid on microarray such that each spot was centered in each grid. Based on the probability of size distribution functions of these protrusions isolated at each level, we estimated the mean size and texture index for each spot. With these characteristics, we classified the spots in a microarray image into bright and dull categories through pattern spectrum and shape-size complexity measures. These segregated spots can be compared with those of hybridization levels.     

Differential analysis of DNA microarray gene expression data

Here, we review briefly the sources of experimental and biological variance that affect the interpretation of high-dimensional DNA microarray experiments. We discuss methods using a regularized t-test based on a Bayesian statistical framework that allow the identification of differentially regulated genes with a higher level of confidence than a simple t-test when only a few experimental replicates are available. We also describe a computational method for calculating the global false-positive and false-negative levels inherent in a DNA microarray data set. This method provides a probability of differential expression for each gene based on experiment-wide false-positive and -negative levels driven by experimental error and biological variance.     

GenePublisher: Automated analysis of DNA microarray data

GenePublisher, a system for automatic analysis of data from DNA microarray experiments, has been implemented with a web interface at http://www.cbs.dtu.dk/services/GenePublisher. Raw data are uploaded to the server together with a specification of the data. The server performs normalization, statistical analysis and visualization of the data. The results are run against databases of signal transduction pathways, metabolic pathways and promoter sequences in order to extract more information. The results of the entire analysis are summarized in report form and returned to the user.     

Statistical approaches for the analysis of DNA methylation microarray data

Following the rapid development and adoption in DNA methylation microarray assays, we are now experiencing a growth in the number of statistical tools to analyze the resulting large-scale data sets. As is the case for other microarray applications, biases caused by technical issues are of concern. Some of these issues are old (e.g., two-color dye bias and probe- and array-specific effects), while others are new (e.g., fragment length bias and bisulfite conversion efficiency). Here, I highlight characteristics of DNA methylation that suggest standard statistical tools developed for other data types may not be directly suitable. I then describe the microarray technologies most commonly in use, along with the methods used for preprocessing and obtaining a summary measure. I finish with a section describing downstream analyses of the data, focusing on methods that model percentage DNA methylation as the outcome, and methods for integrating DNA methylation with gene expression or genotype data.     

DNA microarray analysis for human congenital heart disease

Right ventricular hypertrophy and failure are prominent features in cyanotic congenital heart disease, tetralogy of Fallot (TF). Patients with TF require primary cardiac surgery at a very young age. To gain insight into the underlying molecular mechanisms of right ventricular hypertrophy and to identify gene(s) involved in TF, differential gene expression profile was assessed using expression-based microarray technology on right ventricular biopsies from young TF patients who underwent primary correction. By using quantitative immunohistochemistry, expression of vascular endothelial growth factor (VEGF), flk-1, and extracellular matrix (ECM) proteins (collagens and fibronectin) as well as vessel counts and myocyte cell size was evaluated in TF patients in relation to age-matched controls. Among 236 genes showing altered expression pattern in TF patients, VEGF (1.8-fold) and ECM markers were clearly upregulated (fibronectin, 2.4-fold; collagen Iα, 7.5-fold; and collagen III, 4.4-fold); flk-1 and most matrix metalloproteinases (MMPs) remained unchanged, except the levels of MMP-13 and-17 declined. Tissue inhibitors of metalloproteinases showed a downregulated pattern. Staining of VEGF in cardiomyocytes and of ECM proteins (fibronectin, collagen I and III) in interstitial as well as in perivascular area was increased (p<0.01) in TF patients. Morphometric analysis revealed enhanced vascular density (p<0.05) with unchanged wall thickness and enlarged myocyte cross-sectional areas (p<0.01) with linear correlation (p<0.01) with the age in TF-1 patients. We conclude that the upregulation of genes encoding VEGF and ECM proteins are the key events contributing to right ventricular hypertrophy and stunted angiogenesis in patients with TF.     

Automated high-throughput probe production for DNA microarray analysis

DNA microarrays have become an established tool for gene expression profiling. Construction of these microarrays using immobilized cDNAs is a common experimental strategy. However, this is extremely laborious, requiring the preparation of hundreds or thousands of cDNA probes. To minimize this initial bottleneck, we developed a comprehensive high-throughput robotic system to prepare DNA probes suitable for microarray analysis with minimal user intervention. We describe an automated system using the MultiPROBE Nucleic Acid Purification Workstation to provide the liquid handling and other functions needed to optimize this process. We were able to carry out fully automated plasmid cDNA isolation, PCR assay setup, and PCR purification and also to direct the characterization and tracking of DNA probes during processing. Protocols began with the initial preparation of a plasmid DNA archive of bacterial stocks in parallel 96-well plates (192 samples/run) and continued through to the dilution and reformatting of chip-ready DNA probes in 384-well format. These and other probe production procedures and additional instrument systems were used to process fully a set of mouse cDNA clones that were then validated by differential gene expression analysis.     

Automatic analysis of DNA microarray images using mathematical morphology

MOTIVATION: DNA microarrays are an experimental technology which consists in arrays of thousands of discrete DNA sequences that are printed on glass microscope slides. Image analysis is an important aspect of microarray experiments. The aim of this step is to reduce an image of spots into a table with a measure of the intensity for each spot. Efficient, accurate and automatic analysis of DNA spot images is essential in order to use this technology in laboratory routines. RESULTS: We present an automatic non-supervised set of algorithms for a fast and accurate spot data extraction from DNA microarrays using morphological operators which are robust to both intensity variation and artefacts. The approach can be summarised as follows. Initially, a gridding algorithm yields the automatic segmentation of the microarray image into spot quadrants which are later individually analysed. Then the analysis of the spot quadrant images is achieved in five steps. First, a pre-quantification, the spot size distribution law is calculated. Second, the background noise extraction is performed using a morphological filtering by area. Third, an orthogonal grid provides the first approach to the spot locus. Fourth, the spot segmentation or spot boundaries definition is carried out using the watershed transformation. And fifth, the outline of detected spots allows the signal quantification or spot intensities extraction; in this respect, a noise model has been investigated. The performance of the algorithm has been compared with two packages: ScanAlyze and Genepix, showing its robustness and precision.     

Objective method of comparing DNA microarray image analysis systems

Many image analysis systems are available for processing the images produced by laser scanning of DNA microarrays. The image processing system takes pixel-level intensity data and converts it to a set of gene-level expression or copy number summaries that will be used in further analyses. Image analysis systems currently in use differ with regard to the specific algorithms they implement, ease of use, and cost. Thus, it would be desirable to have an objective means of comparing systems. Here we describe a systematic method of comparing image processing results produced by different image analysis systems using a series of replicate microarray experiments. We demonstrate the method with a comparison of cDNA microarray data generated by the UCSF Spot and the GenePix image processing systems.     

DNA microarray data and contextual analysis of correlation graphs

Background  

DNA microarrays are used to produce large sets of expression measurements from which specific biological information is sought. Their analysis requires efficient and reliable algorithms for dimensional reduction, classification and annotation.     

DNA microarray analysis of region-specific gene expression in the mouse epididymis

Microarray analysis was carried out to identify genes with enriched expression in the initial segment region of the mouse epididymis. A set of approximately 15 000 clones developed at the National Institutes for Aging and consisting of expressed sequence tags (ESTs) derived from pre- and peri-implantation embryos, Embryonic Day 12.5 female gonad/mesonephros, and newborn ovary were hybridized with probes generated against the initial segment (epididymal region 1) and the remainder of the epididymis (epididymal regions 2-5). The median values for the normalized ratios of region 1 to regions 2-5 from three independent experiments were averaged for each gene/EST using Genespring 5.0 software. The majority of clones showed a ratio of 1.0, suggesting they were expressed at similar levels in all epididymal regions. In addition, 123 clones exhibited 2-fold or higher expression in the initial segment, including Cres3, prostein, lipocalin 2, ALEX3, synaptotagmin-like 4, erm, and milk fat globule factor, whereas 216 clones, including elafin-like 1, lactotransferrin, Sin3B, zinc-finger protein 91, and membrane-type frizzled-related protein, showed 2-fold or higher expression in epididymal regions 2-5. Northern blot analyses of 12 clones predicted by microarray analysis to be either enriched in the initial segment (n = 8), enriched in epididymal regions 2-5 (n = 2), or similar in all regions (n = 2) were carried out. All clones exhibited the expected region-specific expression, thus confirming the microarray results. The studies presented here show a global survey of region-specific gene expression in the epididymis, identifying 15287 sequences, the majority of which have not previously been shown to be expressed in this organ.     

Methylation of repetitive DNA sequences in the brain during aging of the rat

Methylation of repetitive DNA sequences (RDS) of the genomic DNA of the brain of 15- and 88-week old rats was analysed by digestion with HpaII, MspI, EcoRI + HpaII and EcoRI + MspI followed by end-labelling. mCpG doublets are present in two RDS of approximately 5 and 0.4 kb, and are also randomly distributed throughout the genome. Hemimethylated mCpC doublets also occur. Both mCpG and mCpC doublets are found more in the old than in the young. This age-related increase in DNA methylation occurs both at CCGG sites of the RDS and in the entire genome. Such increase in DNA methylation may alter chromatin conformation and gene expression in the brain as the rat ages.