An improved single-cell cDNA amplification method for efficient high-density oligonucleotide microarray analysis

A systems-level understanding of a small but essential population of cells in development or adulthood (e.g. somatic stem cells) requires accurate quantitative monitoring of genome-wide gene expression, ideally from single cells. We report here a strategy to globally amplify mRNAs from single cells for highly quantitative high-density oligonucleotide microarray analysis that combines a small number of directional PCR cycles with subsequent linear amplification. Using this strategy, both the representation of gene expression profiles and reproducibility between individual experiments are unambiguously improved from the original method, along with high coverage and accuracy. The immediate application of this method to single cells in the undifferentiated inner cell masses of mouse blastocysts at embryonic day (E) 3.5 revealed the presence of two populations of cells, one with primitive endoderm (PE) expression and the other with pluripotent epiblast-like gene expression. The genes expressed differentially between these two populations were well preserved in morphologically differentiated PE and epiblast in the embryos one day later (E4.5), demonstrating that the method successfully detects subtle but essential differences in gene expression at the single-cell level among seemingly homogeneous cell populations. This study provides a strategy to analyze biophysical events in medicine as well as in neural, stem cell and developmental biology, where small numbers of distinctive or diseased cells play critical roles.     

Development of an oligonucleotide microarray method for Salmonella serotyping

Adequate identification of Salmonella enterica serovars is a prerequisite for any epidemiological investigation. This is traditionally obtained via a combination of biochemical and serological typing. However, primary strain isolation and traditional serotyping is time‐consuming and faster methods would be desirable. A microarray, based on two housekeeping and two virulence marker genes (atpD, gyrB, fliC and fljB), has been developed for the detection and identification of the two species of Salmonella (S. enterica and S. bongori), the five subspecies of S. enterica (II, IIIa, IIIb, IV, VI) and 43 S. enterica ssp. enterica serovars (covering the most prevalent ones in Austria and the UK). A comprehensive set of probes (n = 240), forming 119 probe units, was developed based on the corresponding sequences of 148 Salmonella strains, successfully validated with 57 Salmonella strains and subsequently evaluated with 35 blind samples including isolated serotypes and mixtures of different serotypes. Results demonstrated a strong discriminatory ability of the microarray among Salmonella serovars. Threshold for detection was 1 colony forming unit per 25 g of food sample following overnight (14 h) enrichment.     

Statistical analysis of oligonucleotide microarray data

Microchip arrays have become one of the most rapidly growing techniques for monitoring gene expression at the genomic level and thereby gaining valuable insight about various important biological mechanisms. Examples of such mechanisms are: identifying disease-causing genes, genes involved in the regulation of some aspect of the cell cycle, etc. In this article, we discuss the problem of estimating gene expression based on a proper statistical model. More precisely, we show how the model introduced by Li and Wong can be used in its full bivariate generality to provide a new measure of gene expression from high-density oligonucleotide arrays. We also present a second gene expression index based on a new way of reducing the model into a simpler univariate model. In both cases, the gene expression indices are shown to be unbiased and to have lower variance than the established ones. Moreover, we present a bootstrap method aiming at providing non-parametric confidence intervals for the expression index.     

Methylation detection oligonucleotide microarray analysis: a high-resolution method for detection of CpG island methylation

Methylation of CpG islands associated with genes can affect the expression of the proximal gene, and methylation of non-associated CpG islands correlates to genomic instability. This epigenetic modification has been shown to be important in many pathologies, from development and disease to cancer. We report the development of a novel high-resolution microarray that detects the methylation status of over 25 000 CpG islands in the human genome. Experiments were performed to demonstrate low system noise in the methodology and that the array probes have a high signal to noise ratio. Methylation measurements between different cell lines were validated demonstrating the accuracy of measurement. We then identified alterations in CpG islands, both those associated with gene promoters, as well as non-promoter-associated islands in a set of breast and ovarian tumors. We demonstrate that this methodology accurately identifies methylation profiles in cancer and in principle it can differentiate any CpG methylation alterations and can be adapted to analyze other species.     

Hybridization kinetics analysis of an oligonucleotide microarray for microRNA detection

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

Portable system for microbial sample preparation and oligonucleotide microarray analysis

We have developed a three-component system for microbial identification that consists of (i) a universal syringe-operated silica minicolumn for successive DNA and RNA isolation, fractionation, fragmentation, fluorescent labeling, and removal of excess free label and short oligonucleotides; (ii) microarrays of immobilized oligonucleotide probes for 16S rRNA identification; and (iii) a portable battery-powered device for imaging the hybridization of fluorescently labeled RNA fragments with the arrays. The minicolumn combines a guanidine thiocyanate method of nucleic acid isolation with a newly developed hydroxyl radical-based technique for DNA and RNA labeling and fragmentation. DNA and RNA can also be fractionated through differential binding of double- and single-stranded forms of nucleic acids to the silica. The procedure involves sequential washing of the column with different solutions. No vacuum filtration steps, phenol extraction, or centrifugation is required. After hybridization, the overall fluorescence pattern is captured as a digital image or as a Polaroid photo. This three-component system was used to discriminate Escherichia coli, Bacillus subtilis, Bacillus thuringiensis, and human HL60 cells. The procedure is rapid: beginning with whole cells, it takes approximately 25 min to obtain labeled DNA and RNA samples and an additional 25 min to hybridize and acquire the microarray image using a stationary image analysis system or the portable imager.     

High-throughput identification of genetic markers using representational oligonucleotide microarray analysis

We describe a novel approach for high-throughput development of genetic markers using representational oligonucleotide microarray analysis. We test the performance of the method in sugar beet (Beta vulgaris L.) as a model for crop plants with little sequence information available. Genomic representations of both parents of a mapping population were hybridized on microarrays containing in total 146,554 custom made oligonucleotides based on sugar beet bacterial artificial chromosome (BAC) end sequences and expressed sequence tags (ESTs). Oligonucleotides showing a signal with one parental line only, were selected as potential marker candidates and placed onto an array, designed for genotyping of 184 F₂ individuals from the mapping population. Utilizing known co-dominant anchor markers we obtained 511 new dominant markers (392 derived from BAC end sequences, and 119 from ESTs) distributed over all nine sugar beet linkage groups and calculated genetic maps. Further improvements for large-scale application of the approach are discussed and its feasibility for the cost-effective and flexible generation of genetic markers is presented.     

Filtering genes to improve sensitivity in oligonucleotide microarray data analysis

Many recent microarrays hold an enormous number of probe sets, thus raising many practical and theoretical problems in controlling the false discovery rate (FDR). Biologically, it is likely that most probe sets are associated with un-expressed genes, so the measured values are simply noise due to non-specific binding; also many probe sets are associated with non-differentially-expressed (non-DE) genes. In an analysis to find DE genes, these probe sets contribute to the false discoveries, so it is desirable to filter out these probe sets prior to analysis. In the methodology proposed here, we first fit a robust linear model for probe-level Affymetrix data that accounts for probe and array effects. We then develop a novel procedure called FLUSH (Filtering Likely Uninformative Sets of Hybridizations), which excludes probe sets that have statistically small array-effects or large residual variance. This filtering procedure was evaluated on a publicly available data set from a controlled spiked-in experiment, as well as on a real experimental data set of a mouse model for retinal degeneration. In both cases, FLUSH filtering improves the sensitivity in the detection of DE genes compared to analyses using unfiltered, presence-filtered, intensity-filtered and variance-filtered data. A freely-available package called FLUSH implements the procedures and graphical displays described in the article.     

Semiparametric clustering method for microarray data analysis

Clustering is a major tool for microarray gene expression data analysis. The existing clustering methods fall mainly into two categories: parametric and nonparametric. The parametric methods generally assume a mixture of parametric subdistributions. When the mixture distribution approximately fits the true data generating mechanism, the parametric methods perform well, but not so when there is nonnegligible deviation between them. On the other hand, the nonparametric methods, which usually do not make distributional assumptions, are robust but pay the price for efficiency loss. In an attempt to utilize the known mixture form to increase efficiency, and to free assumptions about the unknown subdistributions to enhance robustness, we propose a semiparametric method for clustering. The proposed approach possesses the form of parametric mixture, with no assumptions to the subdistributions. The subdistributions are estimated nonparametrically, with constraints just being imposed on the modes. An expectation-maximization (EM) algorithm along with a classification step is invoked to cluster the data, and a modified Bayesian information criterion (BIC) is employed to guide the determination of the optimal number of clusters. Simulation studies are conducted to assess the performance and the robustness of the proposed method. The results show that the proposed method yields reasonable partition of the data. As an illustration, the proposed method is applied to a real microarray data set to cluster genes.     

An oligonucleotide microarray for mouse imprinted genes profiling

Genomic imprinting is an epigenetic phenomenon unique to mammals that causes some genes to be expressed according to their parental origin. It results in developmental asymmetry in the function of the parental genomes. We describe here a method for the profiling of imprinted genes based on the development of a mouse imprinting microchip containing oligonucleotides corresponding to 493 genes, including most of the known imprinted genes (IG = 63), genes involved in epigenetic processes (EPI = 15), in metabolism (= 147), in obesity (= 10) and in neurotransmission (= 256) and housekeeping reference genes (= 2). This custom oligonucleotide microarray has been constructed to make data analysis and handling more manageable than pangenomic microarrays. As a proof of concept we present the differential expression of these 493 genes in different tissues (liver, placenta, embryo) of C57BL6/J mice fed different diets. Appropriate experimental strategies and statistical tools were defined at each step of the data analysis process with regard to the different sources of constraints. Data were confirmed by expression analyses based on quantitative real-time PCR. These oligochips should make it possible to increase our understanding of the involvement of imprinted genes in the timing of expression programs, tissue by tissue, stage by stage, in response to nutrients, lifestyles and other as yet unknown critical environmental factors in a variety of physiopathological situations, and in animals of different strains, ages and sexes. The use of oligonucleotides makes it possible to expand this microchip to include the increasing number of imprinted genes discovered.     

Multipathogen oligonucleotide microarray for environmental and biodefense applications

Food-borne pathogens are a major health problem. The large and diverse number of microbial pathogens and their virulence factors has fueled interest in technologies capable of detecting multiple pathogens and multiple virulence factors simultaneously. Some of these pathogens and their toxins have potential use as bioweapons. DNA microarray technology allows the simultaneous analysis of thousands of sequences of DNA in a relatively short time, making it appropriate for biodefense and for public health uses. This paper describes methods for using DNA microarrays to detect and analyze microbial pathogens. The FDA-1 microarray was developed for the simultaneous detection of several food-borne pathogens and their virulence factors including Listeria spp., Campylobacter spp., Staphylococcus aureus enterotoxin genes and Clostridium perfringens toxin genes. Three elements were incorporated to increase confidence in the microarray detection system: redundancy of genes, redundancy of oligonucleotide probes (oligoprobes) for a specific gene, and quality control oligoprobes to monitor array spotting and target DNA hybridization. These elements enhance the reliability of detection and reduce the chance of erroneous results due to the genetic variability of microbes or technical problems with the microarray. The results presented demonstrate the potential of oligonucleotide microarrays for detection of environmental and biodefense relevant microbial pathogens.     

Evaluating oligonucleotide properties for DNA microarray probe design

Most current microarray oligonucleotide probe design strategies are based on probe design factors (PDFs), which include probe hybridization free energy (PHFE), probe minimum folding energy (PMFE), dimer score, hairpin score, homology score and complexity score. The impact of these PDFs on probe performance was evaluated using four sets of microarray comparative genome hybridization (aCGH) data, which included two array manufacturing methods and the genomes of two species. Since most of the hybridizing DNA is equimolar in CGH data, such data are ideal for testing the general hybridization properties of almost all candidate oligonucleotides. In all our data sets, PDFs related to probe secondary structure (PMFE, hairpin score and dimer score) are the most significant factors linearly correlated with probe hybridization intensities. PHFE, homology and complexity score are correlating significantly with probe specificities, but in a non-linear fashion. We developed a new PDF, pseudo probe binding energy (PPBE), by iteratively fitting dinucleotide positional weights and dinucleotide stacking energies until the average residue sum of squares for the model was minimized. PPBE showed a better correlation with probe sensitivity and a better specificity than all other PDFs, although training data are required to construct a PPBE model prior to designing new oligonucleotide probes. The physical properties that are measured by PPBE are as yet unknown but include a platform-dependent component. A practical way to use these PDFs for probe design is to set cutoff thresholds to filter out bad quality probes. Programs and correlation parameters from this study are freely available to facilitate the design of DNA microarray oligonucleotide probes.     

Gene expression profiles of endothelial progenitor cells by oligonucleotide microarray analysis

Among the many tissue stem or progenitor cells recently being unveiled, endothelial progenitor cells (EPCs) have attracted particular attention, not only because of their cardinal role in vascular biology and embryology but also because of their potential use in the therapeutic development of a variety of postnatal diseases, including cardiovascular and peripheral vascular disorders and cancer. The aim of this study is to provide some basic and comprehensive information on gene expression of EPCs to characterize the cells in molecular terms. Here, we focus on EPCs derived from CD34-positive mononuclear cells of human umbilical cord blood. The EPCs were purified and expanded in culture and analyzed by a high-density oligonucleotide microarray and real-time RT-PCR analysis. We identified 169 up-regulated and 107 down-regulated genes in the EPCs compared with three differentiated endothelial cells of human umbilical vein endothelial cells (HUVEC), human lung microvascular endothelial cells (LMEC) and human aortic endothelial cells (AoEC). It is expected that the obtained list include key genes which are critical for EPC function and survival and thus potential targets of EPC recognition in vivo and therapeutic modulation of vasculogenesis in cancer as well as other diseases, in which de novo vasculogenesis plays a crucial role. For instance, the list includes Syk and galectin-3, which encode protein tyrosine kinase and β-galactoside-binding protein, respectively, and are expressed higher in EPCs than the three control endothelial cells. In situ hybridization showed that the genes were expressed in isolated cells in the fetal liver at E11.5 and E14.5 of mouse development.     

Quantitative oligonucleotide microarray data analysis with an artificial standard probe strategy

Quantitative data analysis is an important element in several applications of DNA microarray, including mRNA expression profiling and estimation of infectious doses for pathogens. Here, we introduce an artificial standard probe strategy for quantitative pathogen detection using an oligonucleotide chip as a model system. The standard capture probe sequence was artificially designed to prevent non-specific hybridization with bacterial targets. Based on the fluorescence intensities of artificial standard spots, the raw fluorescence intensity data for specific spots could be corrected to generate linear correlations with target concentrations. Therefore, our novel artificial standard probe may be effectively applied for the correction of chip-to-chip variations and quantitative data analysis of a one-color labeled DNA microarray system.     

Global single-cell cDNA amplification to provide a template for representative high-density oligonucleotide microarray analysis

We describe here a protocol for the representative amplification of global mRNAs from typical single mammalian cells to provide a template for high-density oligonucleotide microarray analysis. A single cell is lysed in a tube without purification and first-strand cDNAs are synthesized using a poly(dT)-tailed primer. Unreacted primer is specifically eliminated by exonuclease treatment and second strands are generated with a second poly(dT)-tailed primer after poly(dA) tailing of the first-strand cDNAs. The cDNAs are split into four tubes, which are independently directionally amplified by PCR, and then recombined. The amplified products (approximately 100 ng) show superior representation and reproducibility of original gene expression, especially for genes expressed in more than 20 copies per cell, compared with those obtained by a conventional PCR protocol, and can effectively be used for quantitative PCR and EST analyses. The cDNAs are then subjected to another PCR amplification with primers bearing the T7 promoter sequence. The resultant cDNA products are gel purified, amplified by one final cycle and used for isothermal linear amplification by T7 RNA polymerase to synthesize cRNAs for microarray hybridization. This protocol yields cDNA templates sufficient for more than 80 microarray hybridizations from a single cell, and can be completed in 5-6 days.