GP3: GenePix post-processing program for automated analysis of raw microarray data

FluoroCellTrack: An algorithm for automated analysis of high-throughput droplet microfluidic data

High-throughput droplet microfluidic devices with fluorescence detection systems provide several advantages over conventional end-point cytometric techniques due to their ability to isolate single cells and investigate complex intracellular dynamics. While there have been significant advances in the field of experimental droplet microfluidics, the development of complementary software tools has lagged. Existing quantification tools have limitations including interdependent hardware platforms or challenges analyzing a wide range of high-throughput droplet microfluidic data using a single algorithm. To address these issues, an all-in-one Python algorithm called FluoroCellTrack was developed and its wide-range utility was tested on three different applications including quantification of cellular response to drugs, droplet tracking, and intracellular fluorescence. The algorithm imports all images collected using bright field and fluorescence microscopy and analyzes them to extract useful information. Two parallel steps are performed where droplets are detected using a mathematical Circular Hough Transform (CHT) while single cells (or other contours) are detected by a series of steps defining respective color boundaries involving edge detection, dilation, and erosion. These feature detection steps are strengthened by segmentation and radius/area thresholding for precise detection and removal of false positives. Individually detected droplet and contour center maps are overlaid to obtain encapsulation information for further analyses. FluoroCellTrack demonstrates an average of a ~92-99% similarity with manual analysis and exhibits a significant reduction in analysis time of 30 min to analyze an entire cohort compared to 20 h required for manual quantification.

ArrayTrack: An FDA and Public Genomic Tool

A robust bioinformatics capability is widely acknowledged as central to realizing the promises of toxicogenomics. Successful application of toxicogenomic approaches, such as DNA microarrays, inextricably relies on appropriate data management, the ability to extract knowledge from massive amounts of data and the availability of functional information for data interpretation. At the FDA's National Center for Toxicological Research (NCTR), we are developing a public microarray data management and analysis software, called ArrayTrack that is also used in the routine review of genomic data submitted to the FDA. ArrayTrack stores a full range of information related to DNA microarrays and clinical and nonclinical studies as well as the digested data derived from proteomics and metabonomics experiments. In addition, ArrayTrack provides a rich collection of functional information about genes, proteins, and pathways drawn from various public biological databases for facilitating data interpretation. Many data analysis and visualization tools are available with ArrayTrack for individual platform data analysis, multiple omics data integration and integrated analysis of omics data with study data. Importantly, gene expression data, functional information, and analysis methods are fully integrated so that the data analysis and interpretation process is simplified and enhanced. Using ArrayTrack, users can select an analysis method from the ArrayTrack tool box, apply the method to selected microarray data and the analysis results can be directly linked to individual gene, pathway, and Gene Ontology analysis. ArrayTrack is publicly available online ( http://www.fda.gov/nctr/science/centers/toxicoinformatics/ArrayTrack/index.htm ), and the prospective user can also request a local installation version by contacting the authors.

BIOPHYSICAL PROPERTIES OF NUCLEIC ACIDS AT SURFACES RELEVANT TO MICROARRAY PERFORMANCE

Both clinical and analytical metrics produced by microarray-based assay technology have recognized problems in reproducibility, reliability and analytical sensitivity. These issues are often attributed to poor understanding and control of nucleic acid behaviors and properties at solid-liquid interfaces. Nucleic acid hybridization, central to DNA and RNA microarray formats, depends on the properties and behaviors of single strand (ss) nucleic acids (e.g., probe oligomeric DNA) bound to surfaces. ssDNA's persistence length, radius of gyration, electrostatics, conformations on different surfaces and under various assay conditions, its chain flexibility and curvature, charging effects in ionic solutions, and fluorescent labeling all influence its physical chemistry and hybridization under assay conditions. Nucleic acid (e.g., both RNA and DNA) target interactions with immobilized ssDNA strands are highly impacted by these biophysical states. Furthermore, the kinetics, thermodynamics, and enthalpic and entropic contributions to DNA hybridization reflect global probe/target structures and interaction dynamics. Here we review several biophysical issues relevant to oligomeric nucleic acid molecular behaviors at surfaces and their influences on duplex formation that influence microarray assay performance. Correlation of biophysical aspects of single and double-stranded nucleic acids with their complexes in bulk solution is common. Such analysis at surfaces is not commonly reported, despite its importance to microarray assays. We seek to provide further insight into nucleic acid-surface challenges facing microarray diagnostic formats that have hindered their clinical adoption and compromise their research quality and value as genomics tools.

High-resolution epifluorescence and time-of-flight secondary ion mass spectrometry chemical imaging comparisons of single DNA microarray spots

DNA microarray assay performance is commonly compromised by spot-spot probe and signal variations as well as heterogeneity within printed microspots. Accurate metrics for captured DNA target signal rely upon uniform spot distribution of both probe and target DNA to yield reliable hybridized signal. While often presumed, this is neither easily achieved nor often proven experimentally. High-resolution imaging techniques were used to determine spot heterogeneity in identical DNA array microspots comprising varied ratios of unlabeled and dye-labeled DNA probes contact-printed onto commercial arraying surfaces. Epifluorescence imaging data for individual array microspots were correlated with time-of-flight secondary ion mass spectrometry (TOF-SIMS) chemical state imaging of the same spots. Epifluorescence imaging intensity distinguished varying DNA density distributed both within a given spot and from spot to spot. TOF-SIMS chemical analysis confirmed these heterogeneous printed DNA distributions by tracking bound Cy3 dye, DNA base, and phosphate specific ion fragments often correlating to fluorescence patterns within identical spots. TOF-SIMS ion fragments originating from probe DNA and Cy3 dye are enriched in microspot centers, correlating with high fluorescence intensity regions. Both TOF-SIMS and epifluorescence support Marangoni flow effects on spot drying, with high-density DNA-Cy3 located in spot centers and nonhomogeneous DNA distribution within printed spots. Microspot image dimensional analysis results for DNA droplet spreading show differing DNA densities across printed spots. The study directly supports different DNA probe chemical and spatial microenvironments within spots that yield spot-spot signal variations known to affect DNA target hybridization efficiencies and kinetics. These variations critically affect probe-target duplex formation and DNA array signal generation.

Spot addressing for microarray images structured in hexagonal grids

In this work, an efficient method for spot addressing in images, which are generated by the scanning of hexagonal structured microarrays, is proposed. Initially, the blocks of the image are separated using the projections of the image. Next, all the blocks of the image are processed separately for the detection of each spot. The spot addressing procedure begins with the detection of the high intensity objects, which are probably the spots of the image. Next, the Growing Concentric Hexagon algorithm, which uses the properties of the hexagonal grid, is introduced for the detection of the non-hybridized spots. Finally, the Voronoi diagram is applied to the centers of the detected spots for the gridding of the image. The method is evaluated using spots generated from the scanning of the Beadchip of Illumina, which is used for the detection of Single Nucleotide Polymorphisms in the human genome, and uses hexagonal structure for the location of the spots. For the evaluation, the detected centers for each of the spot in the image are compared to the centers of the annotation, obtaining up to 98% accuracy for the spot addressing procedure.

Computational Strategies for Analyzing Data in Gene Expression Microarray Experiments

Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.

ArrayTrack: an FDA and public genomic tool

A robust bioinformatics capability is widely acknowledged as central to realizing the promises of toxicogenomics. Successful application of toxicogenomic approaches, such as DNA microarrays, inextricably relies on appropriate data management, the ability to extract knowledge from massive amounts of data, and the availability of functional information for data interpretation. At the FDA's National Center for Toxicological Research (NCTR), we are developing a public microarray data management and analysis software, called ArrayTrack, that is also used in the routine review of genomic data submitted to the FDA. ArrayTrack stores a full range of information related to DNA microarrays and clinical and non-clinical studies as well as the digested data derived from proteomics and metabonomics experiments. In addition, ArrayTrack provides a rich collection of functional information about genes, proteins, and pathways drawn from various public biological databases for facilitating data interpretation. Many data analysis and visualization tools are available with ArrayTrack for individual platform data analysis, multiple omics data integration, and integrated analysis of omics data with study data. Importantly, gene expression data, functional information, and analysis methods are fully integrated so that the data analysis and interpretation process is simplified and enhanced. Using ArrayTrack, users can select an analysis method from the ArrayTrack tool box, apply the method to selected microarray data, and the analysis of results can be directly linked to individual gene, pathway, and Gene Ontology analysis. ArrayTrack is publicly available online ( http://www.fda.gov/nctr/science/centers/toxicoinformatics/ArrayTrack/index.htm ) and the prospective user can also request a local installation version by contacting the authors.

MicroRNA expression in human airway smooth muscle cells: role of miR-25 in regulation of airway smooth muscle phenotype

Defining mechanisms by which differentiated, contractile smooth muscle cells become proliferative and secretory in response to mechanical and environmental stress is crucial for determining the contribution of airway smooth muscle (ASM) to inflammatory responses that result in airway disease. Regulation by microRNAs (miRNAs) has emerged as an important post-transcriptional mechanism regulating gene expression that may modulate ASM phenotype, but little is known about the expression and functions of miRNA in smooth muscle. In the present study we used microarrays to determine whether miRNAs in human ASM cells are altered by a proinflammatory stimulus. In ASM cells exposed to IL-1beta, TNF-alpha, and IFN-gamma, we found 11 miRNAs to be significantly down-regulated. We verified decreased expression of miR-25, miR-140*, mir-188, and miR-320 by quantitative PCR. Analysis of miR-25 expression indicates that it has a broad role in regulating ASM phenotype by modulating expression of inflammatory mediators such as RANTES, eotaxin, and TNF-alpha; genes involved in extracellular matrix turnover; and contractile proteins, most notably myosin heavy chain. miRNA binding algorithms predict that miR-25 targets Krüppel-like factor 4 (KLF4), a potent inhibitor of smooth muscle-specific gene expression and mediator of inflammation. Our study demonstrates that inhibition of miR-25 in cytokine-stimulated ASM cells up-regulates KLF4 expression via a post-transcriptional mechanism. This provides novel evidence that miR-25 targets KLF4 in ASM cells and proposes that miR-25 may be an important mediator of ASM phenotype.

Diagnostic devices as biomaterials: a review of nucleic acid and protein microarray surface performance issues

This review of current DNA and protein microarray diagnostic and bio-analytical technologies focuses on the different surface chemistries used in these miniaturized surface-capture formats. Description of current strategies in bio-immobilization and coupling to create multiplexed affinity bioassays in micrometer-sized printed spots, problems with current formats and review of some detection methods are included. Recommendations for improving long-standing challenges in DNA- and protein-based arrays are forwarded. The biomaterials community can contribute relevant expertise to these formidable bio-interfacial problems that represent significant barriers to clinical implementation of microarray assays.

Molecular classification of nodal metastasis in primary larynx squamous cell carcinoma

Classification and prognosis of larynx squamous cell carcinoma (LSCC) depends on clinical and histopathological examination. Currently, expression profiling harbors the potential to investigate, classify, and better manage cancer. Gene expression profiles of 22 primary LSCCs were analyzed by microarrays containing 19,200 cDNAs. GOAL functionally classified differentially expressed genes, and a novel "in silico" procedure identified physical gene clusters differentially transcribed. A signature of 158 genes differentiated tumors with nodal metastasis. A novel statistical method allowed categorization of metastatic tumors into 2 distinct subgroups of differential gene expression patterns. Among genes correlated to nodal metastatic progression, we verified in vitro that NM23-H3 reduced cell motility and TRIM8 were a growth suppressor. Six chromosomal regions were specifically downregulated in metastatic tumors. This large-scale gene expression analysis in LSCC provides information on changes in genomic activity associated with lymphonodal metastasis and identifies molecules that might prove useful as novel therapeutic targets.

Genes and proteins differentially expressed during in vitro malignant transformation of bovine pancreatic duct cells

Pancreatic carcinoma has an extremely bad prognosis due to lack of early diagnostic markers and lack of effective therapeutic strategies. Recently, we have established an in vitro model recapitulating the first steps in the carcinogenesis of the pancreas. SV40 large T antigen-immortalized bovine pancreatic duct cells formed intrapancreatic adenocarcinoma tumors on k-ras(mut) transfection after orthotopic injection in the nude mouse pancreas. Here we identified genes and proteins differentially expressed in the course of malignant transformation using reciprocal suppression subtractive hybridization and 2D gel electrophoresis and mass spectrometry, respectively. We identified 34 differentially expressed genes, expressed sequence tags, and 15 unique proteins. Differential expression was verified for some of the genes or proteins in samples from pancreatic carcinoma. Among these genes and proteins, the majority had already been described either to be influenced by a mutated ras or to be differentially expressed in pancreatic adenocarcinoma, thus proving the feasibility of our model. Other genes and proteins (e.g., BBC1, GLTSCR2, and rhoGDIalpha), up to now, have not been implicated in pancreatic tumor development. Thus, we were able to establish an in vitro model of pancreatic carcinogenesis, which enabled us to identify genes and proteins differentially expressed during the early steps of malignant transformation.

Plant database resources at The Institute for Genomic Research

With the completion of the genome sequences of the model plants Arabidopsis and rice, and the continuing sequencing efforts of other economically important crop plants, an unprecedented amount of genome sequence data is now available for large-scale genomics studies and analyses, such as the identification and discovery of novel genes, comparative genomics, and functional genomics. Efficient utilization of these large data sets is critically dependent on the ease of access and organization of the data. The plant databases at The Institute for Genomic Research (TIGR) have been set up to maintain various data types including genomic sequence, annotation and analyses, expressed transcript assemblies and analyses, and gene expression profiles from microarray studies. We present here an overview of the TIGR database resources for plant genomics and describe methods to access the data.

AccuTyping: new algorithms for automated analysis of data from high-throughput genotyping with oligonucleotide microarrays

Microarray-based analysis of single nucleotide polymorphisms (SNPs) has many applications in large-scale genetic studies. To minimize the influence of experimental variation, microarray data usually need to be processed in different aspects including background subtraction, normalization and low-signal filtering before genotype determination. Although many algorithms are sophisticated for these purposes, biases are still present. In the present paper, new algorithms for SNP microarray data analysis and the software, AccuTyping, developed based on these algorithms are described. The algorithms take advantage of a large number of SNPs included in each assay, and the fact that the top and bottom 20% of SNPs can be safely treated as homozygous after sorting based on their ratios between the signal intensities. These SNPs are then used as controls for color channel normalization and background subtraction. Genotype calls are made based on the logarithms of signal intensity ratios using two cutoff values, which were determined after training the program with a dataset of ∼160 000 genotypes and validated by non-microarray methods. AccuTyping was used to determine >300 000 genotypes of DNA and sperm samples. The accuracy was shown to be >99%. AccuTyping can be downloaded from .

Evidence of genetic underexpression in chorionic villi samples of euploid fetuses with increased nuchal translucency at 10–11 weeks' gestation

OBJECTIVE

To retrospectively investigate whether the genetic profile from chorionic villous sampling (CVS) found in euploid fetuses with increased NT differs from matched controls.

STUDY DESIGN

We employed cDNA microarray technology to characterize and compare the gene expression profile of chorionic villous tissues (which encompass the trophoblast and inner mesenchymal core) belonging to four singleton male fetuses with increased NT at 10-11 weeks' gestation. A pool of four normal chorionic villous tissues belonging to four respective fetuses, matched for gestational age and gender, was used as controls.

RESULTS

In euploid fetuses, we found several underexpressed genes, possibly involved in mechanisms associated with the abnormal NT thickness. All these genes are likely to belong to the mesenchymal core of the villus that originates from the extraembryonic mesoderm, and thus might be closely representative of the embryonic genetic profile. They include: (1) genes of embryonic development and differentiation such as Endothelin 3 (EDN3) and secreted frizzled-related protein 4 (SFRP4); (2) genes of the extracellular matrix (ECM) metabolism such as tissue inhibitor of metalloproteinase1 (TIMP1), and disintegrin-like and matrix metalloproteinase (MMP) (reprolysin type) with thrombospondin type 1 Motif or ADAMTS2, exostoses (multiple)-like 1 (EXTL1), heparan sulfate (HS) 6-O-sulfotransferase 1 or HS6ST1, fibronectin 1 (FN1) and Integrin Alpha 10 (ITGA10) involved in HS and proteoglycan bio-synthesis, ECM synthesis and cell-matrix adhesion; (3) genes involved in vessel formation and differentiation such as angiogenic factor (VG5Q), and in blood pressure control and muscle contraction, like Endothelin 3 or EDN3 and sarcolemma associated protein (SLMAP). Such lower expressions of the villous tissues might be related to an immature genetic profile of the embryo development as well as abnormal regulation of ECM bio-synthesis and/or improper vessel growth and blood pressure control. Also, the results partially support the theories proposed for NT enlargement such as altered composition of ECM and abnormal/delayed development of the circulatory system.

CONCLUSIONS

Abnormal extraembryonic genetic expression is found at 10-11 weeks' gestation in euploid fetuses with increased NT. If both extra- and intraembryonic mesoderms express the same genetic alterations, then microarray analyses on CVS could be used to screen several mesoderm-derivate anomalies.

Computational systems analysis of developmental toxicity: design, development and implementation of a Birth Defects Systems Manager (BDSM)

Birth defects and developmental disabilities remain an important public health issue worldwide. With the availability of genomic sequences from a growing number of human and model organisms and the rapid expansion of the public repositories holding large-scale gene expression datasets, a computational systems analysis of developmental toxicology can incorporate this vast digital information toward the realization of predictive models for complex disease. Here we describe the initial design, development and implementation of a Birth Defects Systems Manager (BDSM). The project was motivated by the need for a computational-bioinformatics infrastructure to manage vast digital information from functional genomics and for a new knowledge environment specifically engineered for the analysis of developmental processes and toxicities. Proof-of-concept tested BDSM using meta-analysis of gene expression data collected from different laboratories, technology platforms, and study models. The composite dataset incorporated 232 microarray comparisons of RNA samples by single or dual microarray platforms, cDNA or oligonucleotide based probes, and human or mouse sequence information. Preliminary results identified system-level features in the embryonic transcriptome as it reacted to various developmental-teratological stimuli. BDSM is open access through the worldwide web (http://systemsanalysis.louisville.edu/) and can be integrated with other bioinformatics tools and resources to advance the pace of discovery in birth defects research.

Rapid and reliable detection and identification of GM events using multiplex PCR coupled with oligonucleotide microarray

To devise a rapid and reliable method for the detection and identification of genetically modified (GM) events, we developed a multiplex polymerase chain reaction (PCR) coupled with a DNA microarray system simultaneously aiming at many targets in a single reaction. The system included probes for screening gene, species reference gene, specific gene, construct-specific gene, event-specific gene, and internal and negative control genes. 18S rRNA was combined with species reference genes as internal controls to assess the efficiency of all reactions and to eliminate false negatives. Two sets of the multiplex PCR system were used to amplify four and five targets, respectively. Eight different structure genes could be detected and identified simultaneously for Roundup Ready soybean in a single microarray. The microarray specificity was validated by its ability to discriminate two GM maizes Bt176 and Bt11. The advantages of this method are its high specificity and greatly reduced false-positives and -negatives. The multiplex PCR coupled with microarray technology presented here is a rapid and reliable tool for the simultaneous detection of GM organism ingredients.

Reliability analysis of microarray data using fuzzy c-means and normal mixture modeling based classification methods

MOTIVATION

A serious limitation in microarray analysis is the unreliability of the data generated from low signal intensities. Such data may produce erroneous gene expression ratios and cause unnecessary validation or post-analysis follow-up tasks. Therefore, the elimination of unreliable signal intensities will enhance reproducibility and reliability of gene expression ratios produced from microarray data. In this study, we applied fuzzy c-means (FCM) and normal mixture modeling (NMM) based classification methods to separate microarray data into reliable and unreliable signal intensity populations.

RESULTS

We compared the results of FCM classification with those of classification based on NMM. Both approaches were validated against reference sets of biological data consisting of only true positives and true negatives. We observed that both methods performed equally well in terms of sensitivity and specificity. Although a comparison of the computation times indicated that the fuzzy approach is computationally more efficient, other considerations support the use of NMM for the reliability analysis of microarray data.

AVAILABILITY

The classification approaches described in this paper and sample microarray data are available as Matlab( TM ) (The MathWorks Inc., Natick, MA) programs (mfiles) and text files, respectively, at http://rc.kfshrc.edu.sa/bssc/staff/MusaAsyali/Downloads.asp. The programs can be run/tested on many different computer platforms where Matlab is available.

CONTACT

asyali@kfshrc.edu.sa.

2,3,7,8-Tetrachlorodibenzo-p-dioxin induces suppressor of cytokine signaling 2 in murine B cells

The B cell, a major component of humoral immunity, is a sensitive target for the immunotoxic effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), possibly by rendering cells less responsive to antigenic or mitogenic stimulation. Potential mechanisms of TCDD action on B cells were examined in murine B cell lymphoma cells (CH12.LX) treated with 3 nM TCDD or dimethyl sulfoxide vehicle using sequence-verified cDNA microarrays. One transcript that was significantly induced by TCDD was suppressor of cytokine signaling 2 (Socs2). Changes in Socs2 mRNA levels paralleled that of Cyp1a1 with a maximal 3-fold induction observed at 4 h, as determined by quantitative real-time polymerase chain reaction. Socs2 induction seems B cell-specific, because no induction was observed in TCDD-responsive mouse hepatoma cells or human breast cancer cells. TCDD-mediated induction of Socs2 mRNA was dose-dependent and exhibited the characteristic structure-activity relationships observed for the aryl hydrocarbon receptor (AhR) ligands 3,3',4,4',5-pentachlorobiphenyl (PCB-126), indolo[3,2-b]-carbazole, and beta-naphthoflavone. Experiments with cycloheximide and AhR-deficient B cells indicated that Socs2 mRNA induction is a primary effect that is AhR-dependent. Western blot analysis confirmed that Socs2 and Cyp1a1 protein levels were also induced in CH12.LX cells. Promoter analysis revealed the presence of four dioxin-response elements within 1000 base pairs upstream of the Socs2 transcriptional start site, and a reporter gene regulated by the Socs2 promoter was inducible by TCDD. Promoter activity was also dependent on a functional AhR signaling pathway. These results indicate that Socs2 is a primary TCDD-inducible gene that may represent a novel mechanism by which TCDD elicits its immunosuppressive effects.

Global survey of chromatin accessibility using DNA microarrays

An increasing number of studies indicate a central role for chromatin remodeling in the regulation of gene expression. Current methods for high-resolution studies of the relationship between chromatin accessibility and transcription are low throughput, making a genome-wide study impractical. To enable the simultaneous measurement of the global chromatin accessibility state at the resolution of single genes, we developed the Chromatin Array technique, in which chromatin is separated by its condensation state using either the solubility differences of mono- and oligonucleosomes in specific buffers or controlled DNase I digestion and selection of the large refractory (condensed) DNA fragments. By probing with a comparative genomic hybridization style microarray, we can determine the condensation state of thousands of individual loci and correlate this with transcriptional activity. Applying this technique to the breast tumor model cell line, MCF7, we found that when the condensation is homogeneous in the population of cells, expression is inversely proportional to the level of accessibility and the two methods of accessibility-based target selection correlate well. Using functional annotation and comparative genomic hybridization data, we have begun to decipher the possible biological implications of the relationship between chromatin accessibility and expression.

Development of public toxicogenomics software for microarray data management and analysis

A robust bioinformatics capability is widely acknowledged as central to realizing the promises of toxicogenomics. Successful application of toxicogenomic approaches, such as DNA microarray, inextricably relies on appropriate data management, the ability to extract knowledge from massive amounts of data and the availability of functional information for data interpretation. At the FDA's National Center for Toxicological Research (NCTR), we are developing a public microarray data management and analysis software, called ArrayTrack. ArrayTrack is Minimum Information About a Microarray Experiment (MIAME) supportive for storing both microarray data and experiment parameters associated with a toxicogenomics study. A quality control mechanism is implemented to assure the fidelity of entered expression data. ArrayTrack also provides a rich collection of functional information about genes, proteins and pathways drawn from various public biological databases for facilitating data interpretation. In addition, several data analysis and visualization tools are available with ArrayTrack, and more tools will be available in the next released version. Importantly, gene expression data, functional information and analysis methods are fully integrated so that the data analysis and interpretation process is simplified and enhanced. ArrayTrack is publicly available online and the prospective user can also request a local installation version by contacting the authors.

Assessment of reliability of microarray data and estimation of signal thresholds using mixture modeling

DNA microarray is an important tool for the study of gene activities but the resultant data consisting of thousands of points are error-prone. A serious limitation in microarray analysis is the unreliability of the data generated from low signal intensities. Such data may produce erroneous gene expression ratios and cause unnecessary validation or post-analysis follow-up tasks. In this study, we describe an approach based on normal mixture modeling for determining optimal signal intensity thresholds to identify reliable measurements of the microarray elements and subsequently eliminate false expression ratios. We used univariate and bivariate mixture modeling to segregate the microarray data into two classes, low signal intensity and reliable signal intensity populations, and applied Bayesian decision theory to find the optimal signal thresholds. The bivariate analysis approach was found to be more accurate than the univariate approach; both approaches were superior to a conventional method when validated against a reference set of biological data that consisted of true and false gene expression data. Elimination of unreliable signal intensities in microarray data should contribute to the quality of microarray data including reproducibility and reliability of gene expression ratios.

Genetic expression profiling of six odontogenic tumors

Odontogenic tumors are rare neoplasms arising from the odontogenic apparatus. We aimed to identify molecular characteristics associated with odontogenic tumorigenesis and malignancy. To this end, we investigated the expression level of human genes by using, for the first time in odontogenic tumors, the technique of expression profiling. Gene expression alterations common to all six odontogenic tumors were identified by the use of cDNA microarrays containing 19,000 human cDNAs. Statistical analysis on a subset of 4974 cDNAs present in the biopsies identified 506 distinct genes associated with the tumors (p-value < 0.01). Gene ontology analysis of the cellular processes which were differentially regulated in odontogenic tumors was accomplished by the use of a subset of 1409 annotated genes. Finally, 43 cDNAs differentiated the three malignant odontogenic tumors (ameloblastic carcinoma, clear cell odontogenic tumor, granular cell odontogenic tumor) from the three benign ameloblastoma biopsies (p < 0.01). The identified genes might help us better classify borderline odontogenic tumors.

Gene expression profiling of the hypoxia signaling pathway in hypoxia-inducible factor 1alpha null mouse embryonic fibroblasts

Hypoxia is defined as a deficiency of oxygen reaching the tissues of the body, and it plays a critical role in development and pathological conditions, such as cancer. Once tumors outgrow their blood supply, their central portion becomes hypoxic and the tumor stimulates angiogenesis through the activation of the hypoxia-inducible factors (HIFs). HIFs are transcription factors that are regulated in an oxygen-dependent manner by a group of prolyl hydroxylases (known as PHDs or HPHs). Our understanding of hypoxia signaling is limited by our incomplete knowledge of HIF target genes. cDNA microarrays and a cell line lacking a principal HIF protein, HIF1alpha, were used to identify a more complete set of hypoxia-regulated genes. The microarrays identified a group of 286 clones that were significantly influenced by hypoxia and 54 of these were coordinately regulated by cobalt chloride. The expression profile of HIF1alpha -/- cells also identified a group of downregulated genes encoding enzymes involved in protecting cells from oxidative stress, offering an explanation for the increased sensitivity of HIF1alpha -/- cells to agents that promote this type of response. The microarray studies confirmed the hypoxia-induced expression of the HIF regulating prolyl hydroxylase, PHD2. An analysis of the members of the PHD family revealed that they are differentially regulated by cobalt chloride and hypoxia. These results suggest that HIF1alpha is the predominant isoform in fibroblasts and that it regulates a wide battery of genes critical for normal cellular function and survival under various stresses.

Options available--from start to finish--for obtaining data from DNA microarrays II

Microarray technology has undergone a rapid evolution. With widespread interest in large-scale genomic research, an abundance of equipment and reagents have now become available and affordable to a large cross section of the scientific community. As protocols become more refined, careful investigators are able to obtain good quality microarray data quickly. In most recent times, however, perhaps one of the biggest obstacles researchers face is not the manufacture and use of microarrays at the bench, but storage and analysis of the array data. This review discusses the most recent equipment, reagents and protocols available to the researcher, as well as describing data analysis and storage options available from the evolving field of microarray informatics.

Gestational and lactational exposure of male mice to diethylstilbestrol causes long-term effects on the testis, sperm fertilizing ability in vitro, and testicular gene expression

The objective of the study was to determine the long-term effects of gestational and lactational exposure to diethylstilbestrol (DES; 0, 0.1, 1, and 10 microg/kg maternal body weight) on mouse testicular growth, epididymal sperm count, in vitro fertilizing ability, and testicular gene expression using cDNA microarrays and real-time PCR in mice on postnatal day (PND) 21, 105, and 315. In the high dose group there was a persistent decrease in the number of Sertoli cells, and sperm count was decreased on PND315 (P < 0.05). Sperm motion was unaffected; however, the in vitro fertilizing ability of epididymal sperm was decreased in the high dose group on both PND105 (P < 0.001) and PND315 (P < 0.05). Early and latent alterations in the expression of genes involved in estrogen signaling (estrogen receptor alpha), steroidogenesis (steroidogenic factor 1, 17alpha-hydroxylase/C17,20-lyase, P450 side chain cleavage, steroidogenic acute regulatory protein, and scavenger receptor class B1), lysosomal function (LGP85 and prosaposin), and regulation of testicular development (testicular receptor 2, inhibin/activin beta C, and Hoxa10) were confirmed by real-time PCR. The results demonstrate that early exposure to DES causes long-term adverse effects on testicular development and sperm function, and these effects are associated with changes in testicular gene expression, even long after the cessation of DES exposure.