Comparison of different probe-level analysis techniques for oligonucleotide microarrays

Three different software packages for the probe-level analysis of high-density oligonucleotide microarray data were compared using an experiment-derived data set that was validated using real-time PCR. The efficiency with which these three programs could identify true positives in this data set was assessed. In addition, estimates of false-positive and false-negative rates were determined. The performance of the programs using very small data sets was also compared, and recommendations for use are suggested.     

Improved background correction for spotted DNA microarrays.

Most microarray scanning software for glass spotted arrays provides estimates for the intensity for the "foreground" and "background" of two channels for every spot. The common approach in further analyzing such data is to first subtract the background from the foreground for each channel and to use the ratio of these two results as the estimate of the expression level. The resulting ratios are, after possible averaging over replicates, the usual inputs for further data analysis, such as clustering. If, with this background correction procedure, the foreground intensity was smaller than the background intensity for a channel, that spot (on that array) yields no usable data. In this paper it is argued that this preprocessing leads to estimates of the expression that have a much larger variance than needed when the expression levels are low.     

A comparison of background correction methods for two-colour microarrays

MOTIVATION: Microarray data must be background corrected to remove the effects of non-specific binding or spatial heterogeneity across the array, but this practice typically causes other problems such as negative corrected intensities and high variability of low intensity log-ratios. Different estimators of background, and various model-based processing methods, are compared in this study in search of the best option for differential expression analyses of small microarray experiments. RESULTS: Using data where some independent truth in gene expression is known, eight different background correction alternatives are compared, in terms of precision and bias of the resulting gene expression measures, and in terms of their ability to detect differentially expressed genes as judged by two popular algorithms, SAM and limma eBayes. A new background processing method (normexp) is introduced which is based on a convolution model. The model-based correction methods are shown to be markedly superior to the usual practice of subtracting local background estimates. Methods which stabilize the variances of the log-ratios along the intensity range perform the best. The normexp+offset method is found to give the lowest false discovery rate overall, followed by morph and vsn. Like vsn, normexp is applicable to most types of two-colour microarray data. AVAILABILITY: The background correction methods compared in this article are available in the R package limma (Smyth, 2005) from http://www.bioconductor.org. SUPPLEMENTARY INFORMATION: Supplementary data are available from http://bioinf.wehi.edu.au/resources/webReferences.html.     

Detection and discovery of RNA modifications using microarrays

Using a microarray that tiles all known yeast non-coding RNAs, we compared RNA from wild-type cells with RNA from mutants encoding known and putative RNA modifying enzymes. We show that at least five types of RNA modification (dihydrouridine, m1G, m2(2)G, m1A and m6(2)A) catalyzed by 10 different enzymes (Trm1p, Trm5, Trm10p, Dus1p-Dus4p, Dim1p, Gcd10p and Gcd14p) can be detected by virtue of differential hybridization to oligonucleotides on the array that are complementary to the modified sites. Using this approach, we identified a previously undetected m1A modification in GlnCTG tRNA, the formation of which is catalyzed by the Gcd10/Gcd14 complex. complex.     

Multiplicative background correction for spotted microarrays to improve reproducibility

We propose a simple approach, the multiplicative background correction, to solve a perplexing problem in spotted microarray data analysis: correcting the foreground intensities for the background noise, especially for spots with genes that are weakly expressed or not at all. The conventional approach, the additive background correction, directly subtracts the background intensities from foreground intensities. When the foreground intensities marginally dominate the background intensities, the additive background correction provides unreliable estimates of the differential gene expression levels and usually presents M-A plots with fishtails or fans. Unreliable additive background correction makes it preferable to ignore the background noise, which may increase the number of false positives. Based on the more realistic multiplicative assumption instead of the conventional additive assumption, we propose to logarithmically transform the intensity readings before the background correction, with the logarithmic transformation symmetrizing the skewed intensity readings. This approach not only precludes the fishtails and fans in the M-A plots, but provides highly reproducible background-corrected intensities for both strongly and weakly expressed genes. The superiority of the multiplicative background correction to the additive one as well as the no background correction is justified by publicly available self-hybridization datasets.     

Detection of antigen-specific T cells on p/MHC microarrays

The development of high-throughput protein microarrays for rapidly determining antigen-specific T-cell receptor repertoires of diverse T-cell populations can enable comprehensive, broad-based analyses of T-cell responses. Promising applications include medical diagnostics, vaccine development, treatment of autoimmune diseases and detection of potential agents of bioterrorism. In this study, we examined the feasibility of using peptide/major histocompatibility complex (p/MHC) microarrays to selectively capture and enumerate antigen-specific T cells. Results are presented for p/MHC microarrays consisting of a dimeric MHC-immunoglobulin complex, K(b)-Ig, loaded with either a cognate or non-cognate peptide for binding CD8(+) T cells. We quantified the sensitivity of these K(b)-Ig microarrays by measuring a lower detection limit of 0.05% antigen-specific CD8(+) T cells mixed with splenocytes from C57BL/6J mouse. A fivefold increase in this lower detection limit (0.01%) was achieved using a secondary capture anti-Ig antibody to coat the microarray surface. This higher sensitivity is comparable to that obtained using standard state-of-the-art fluorescence activated cell sorting (FACS) instruments. We also found that contacting the T-cell suspension with the K(b)-Ig microarrays under mild shear flow conditions produced more uniform distributions of captured T cells on the individual spots and better spot-to-spot reproducibility across the entire microarray.     

Detection and measurement of alternative splicing using splicing-sensitive microarrays

Splicing and alternative splicing are major processes in the interpretation and expression of genetic information for metazoan organisms. The study of splicing is moving from focused attention on the regulatory mechanisms of a selected set of paradigmatic alternative splicing events to questions of global integration of splicing regulation with genome and cell function. For this reason, parallel methods for detecting and measuring alternative splicing are necessary. We have adapted the splicing-sensitive oligonucleotide microarrays used to estimate splicing efficiency in yeast to the study of alternative splicing in vertebrate cells and tissues. We use gene models incorporating knowledge about splicing to design oligonucleotides specific for discriminating alternatively spliced mRNAs from each other. Here we present the main strategies for design, application, and analysis of spotted oligonucleotide arrays for detection and measurement of alternative splicing. We demonstrate these strategies using a two-intron yeast gene that has been altered to produce different amounts of alternatively spliced RNAs, as well as by profiling alternative splicing in NCI 60 cancer cell lines.     

Detection of CFTR mutations using ARMS and low-density microarrays

The amplification refractory mutation system (ARMS) is routinely used for the identification of specific mutations within genomes. This PCR-based assay, although simple, is performed at a low-throughput scale, usually requiring gel-electrophoresis for the identification of specific mutations. We have applied the ARMS technology to a low-density microarray system to facilitate the needs of the medical clinic; high-throughput capabilities and ease-of-use. Mutations within the cystic fibrosis transmembrane regulator (CFTR) gene (DeltaF508, 1717-1G>A, G542X, 621+1G>T, and N1303K) were detected by multiplex-ARMS-PCR, and fragments were post-PCR labeled with Cy5. Amine-modified probes specific for both the wild-type and mutant forms of each mutation site were attached to glass substrates. Following hybridization of the PCR fragments to the attached probes (in a low-density microarray format), confirmation of the presence of specific sequences was achieved using a commercial scanner, as well as a fabricated low-cost fluorescent detector and applicable software. The novel combination of the ARMS and low-density microarray technologies allows for a high-throughput, simple means to rapidly identify multiple known mutations for many genetic diseases including cystic fibrosis.     

Detection and characterization of cellular immune responses using peptide-MHC microarrays

The detection and characterization of antigen-specific T cell populations is critical for understanding the development and physiology of the immune system and its responses in health and disease. We have developed and tested a method that uses arrays of peptide–MHC complexes for the rapid identification, isolation, activation, and characterization of multiple antigen-specific populations of T cells. CD4⁺ or CD8⁺ lymphocytes can be captured in accordance with their ligand specificity using an array of peptide–MHC complexes printed on a film-coated glass surface. We have characterized the specificity and sensitivity of a peptide–MHC array using labeled lymphocytes from T cell receptor transgenic mice. In addition, we were able to use the array to detect a rare population of antigen-specific T cells following vaccination of a normal mouse. This approach should be useful for epitope discovery, as well as for characterization and analysis of multiple epitope-specific T cell populations during immune responses associated with viral and bacterial infection, cancer, autoimmunity, and vaccination.     

Model-based analysis of non-specific binding for background correction of high-density oligonucleotide microarrays

Motivation: High-density DNA microarrays provide us with usefultools for analyzing DNA and RNA comprehensively. However, thebackground signal caused by the non-specific binding (NSB) betweenprobe and target makes it difficult to obtain accurate measurements.To remove the background signal, there is a set of backgroundprobes on Affymetrix Exon arrays to represent the amount ofnon-specific signals, and an accurate estimation of non-specificsignals using these background probes is desirable for improvementof microarray analyses. Results: We developed a thermodynamic model of NSB on shortnucleotide microarrays in which the NSBs are modeled by duplexformation of probes and multiple hypothetical targets. We fittedthe observed signal intensities of the background probes withthose expected by the model to obtain the model parameters.As a result, we found that the presented model can improve theaccuracy of prediction of non-specific signals in comparisonwith previously proposed methods. This result will provide auseful method to correct for the background signal in oligonucleotidemicroarray analysis. Availability: The software is implemented in the R languageand can be downloaded from our website (http://www-shimizu.ist.osaka-u.ac.jp/shimizu_lab/MSNS/). Contact: furusawa{at}ist.osaka-u.ac.jp Supplementary information: Supplementary data are availableat Bioinformatics online. The authors wish it to be known that, in their opinion, thefirst two authors should be regarded as joint First Authors. Associate Editor: Trey Ideker     

Diagnosis of rheumatoid arthritis using light: correction of motion artefacts and color visualization of multispectral images

State‐of‐the‐art image‐processing methods offer new possibilities for diagnosing diseases using scattered light. The optical diagnosis of rheumatism is taken as an example to show that the diagnostic sensitivity can be improved using overlapped pseudocolored images of different wavelengths, provided that multispectral images are recorded to compensate for any motion‐related artefacts that occur during examination. (© 2010 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Detection of tmRNA molecules on microarrays at low temperatures using helper oligonucleotides

Background  

The hybridization of synthetic Streptococcus pneumoniae tmRNA on a detection microarray is slow at 34°C resulting in low signal intensities.     

The correction of eye blink artefacts in the EEG: a comparison of two prominent methods

Background

The study investigated the residual impact of eyeblinks on the electroencephalogram (EEG) after application of different correction procedures, namely a regression method (eye movement correction procedure, EMCP) and a component based method (Independent Component Analysis, ICA).

Methodology/Principle Findings

Real and simulated data were investigated with respect to blink-related potentials and the residual mutual information of uncorrected vertical electrooculogram (EOG) and corrected EEG, which is a measure of residual EOG contribution to the EEG. The results reveal an occipital positivity that peaks at about 250ms after the maximum blink excursion following application of either correction procedure. This positivity was not observable in the simulated data. Mutual information of vertical EOG and EEG depended on the applied regression procedure. In addition, different correction results were obtained for real and simulated data. ICA yielded almost perfect correction in all conditions. However, under certain conditions EMCP yielded comparable results to the ICA approach.

Conclusion

In conclusion, for EMCP the quality of correction depended on the EMCP variant used and the structure of the data, whereas ICA always yielded almost perfect correction. However, its disadvantage is the much more complex data processing, and that it requires a suitable amount of data.     

Detection of bacterial pathogens in environmental samples using DNA microarrays

Polymerase chain reaction (PCR) is an important tool for pathogen detection, but historically, it has not been possible to accurately identify PCR products without sequencing, Southern blots, or dot-blots. Microarrays can be coupled with PCR where they serve as a set of parallel dot-blots to enhance product detection and identification. Microarrays are composed of many discretely located probes on a solid substrate such as glass. Each probe is composed of a sequence that is complimentary to a pathogen-specific gene sequence. PCR is used to amplify one or more genes and the products are then hybridized to the array to identify species-specific polymorphism within one or more genes. We illustrate this type of array using 16S rDNA probes suitable for distinguishing between several salmonid pathogens. We also describe the use of microarrays for direct detection of either RNA or DNA without the aid of PCR, although the sensitivity of these systems currently limits their application for pathogen detection. Finally, microarrays can also be used to "fingerprint" bacterial isolates and they can be used to identify diagnostic markers suitable for developing new PCR-based detection assays. We illustrate this type of array for subtyping an important food-borne pathogen, Listeria monocytogenes.     

Detection of allergen specific immunoglobulins by microarrays coupled to microfluidics

Allergen microarrays are under development for a component‐resolved diagnosis of Type I (IgE‐mediated) allergies. Here we report an improved microarray coupled to microfluidics for the detection of allergen specific immunoglobulin E (IgE). The signal intensity for IgE detection in serum has been improved by using glass slides coated with a novel poly[DMA‐co‐NAS] brush copolymer which is able to immobilize allergens in their native conformation and by carrying out the incubation step in dynamic conditions. The assay, fully automated, was performed in a microcell, using a software‐controlled fluidic processor, to bring assay reagents on the surface of the array. Microfluidics turns the binding between serum immunoglobulins and immobilized allergens from a diffusion‐limited to a kinetic‐limited process by ensuring an efficient mixing of serum samples on the surface of the microarray. As a result of this, the binding of high affinity IgE antibodies is enhanced whereas that of low affinity IgG antibodies, which are present at higher concentration, is impaired paving the way to more accurate and sensitive results.     

Integrating probe-level expression changes across generations of Affymetrix arrays

There is an urgent need for bioinformatic methods that allow integrative analysis of multiple microarray data sets. While previous studies have mainly concentrated on reproducibility of gene expression levels within or between different platforms, we propose a novel meta-analytic method that takes into account the vast amount of available probe-level information to combine the expression changes across different studies. We first show that the comparability of relative expression changes and the consistency of differentially expressed genes between different Affymetrix array generations can be considerably improved by determining the expression changes at the probe-level and by considering the latest information on probe-level sequence matching instead of the probe annotations provided by the manufacturer. With the improved probe-level expression change estimates, data from different generations of Affymetrix arrays can be combined more effectively. This will allow for the full exploitation of existing results when designing and analyzing new experiments.     

Detection of mitochondrial single nucleotide polymorphisms using a primer elongation reaction on oligonucleotide microarrays

We have developed a novel allele-specific primer elongation protocol using a DNA polymerase on oligonucleotide chips. Oligonucleotide primers carrying polymorphic sites at their free 3'end were covalently bound to glass slides. The generation of single-stranded targets of genomic DNA containing single nuclotide polymorphisms (SNPs) to be typed was achieved by an asymmetric PCR reaction or exonuclease treatment of phosphothioate (PTO)-modified PCR products. In the presence of DNA polymerase and all four dNTPs, with Cy3-dUTP replacing dTTP, allele-specific extension of the immobilized primers took place along a stretch of target DNA sequence. The yield of elongated products was increased by repeated reaction cycles. We performed multiplexed assays with many small DNA targets, or used single targets of up to 4.4 kb mitochondrial DNA (mtDNA) sequence to detect multiple SNPs in one reaction. The latter approach greatly simplifies preamplification of SNP-containing regions, thereby providing a framework for typing hundreds of mtDNA polymorphisms.     

A comparison of probe-level and probeset models for small-sample gene expression data

Background  

Statistical methods to tentatively identify differentially expressed genes in microarray studies typically assume larger sample sizes than are practical or even possible in some settings.