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Microarray technology allows the rapid analysis of expression of thousands of genes in a sample. Gene expression profiles are likely characteristic of subtypes of sarcomas and may be useful in diagnosis and classification of this heterogeneous group of tumors. Gene expression may also be useful prognostically with respect to the natural history and response to therapy of these tumors.
We report the label-free detection of DNA hybridization using a metal-insulator-semiconductor (MIS) diode or capacitor. Upon immobilization of single-stranded DNA on the gold gate of a MIS capacitor, the capacitance versus voltage characteristics show a significant shift in the direction of negative voltages as expected from the immobilization of negative charges on the gate. The hybridization with the complementary strand gives rise to a further significant shift in the same direction as before, which is consistent with the increase of negative charges on the gate brought about by the hybridization. Fluorescence studies indicate that the immobilization and hybridization of DNA can be electrostatically promoted by electric fields externally applied to the MIS capacitors. The MIS diode detection method is applicable to all biomolecular interactions that affect the surface dipole at the interface between the metal gate and the electrolyte and can be extended to other chemical and biochemical systems such as proteins and cells.
A quartz crystal microbalance (QCM) DNA sensor, based on the nanoparticle amplification method, was developed for detection of Escherichia coli O157:H7. A thiolated single-stranded DNA (ssDNA) probe specific to E. coli O157:H7 eaeA gene was immobilized onto the QCM sensor surface through self-assembly. The hybridization was induced by exposing the ssDNA probe to the complementary target DNA, and resulted in the mass change and therefore frequency change of the QCM. Streptavidin conjugated Fe(3)O(4) nanoparticles (average diameter=145 nm) were used as "mass enhancers" to amplify the frequency change. Synthesized biotinylated oligonucleotides as well as E. coli O157:H7 eaeA gene fragments (151 bases) amplified using asymmetric PCR with biotin labeled primers were tested. As low as 10(-12)M synthesized oligonucleotides and 2.67 x 10(2) colony forming unit (CFU)/ml E. coli O157:H7 cells can be detected by the sensor. Linear correlation between frequency change and logarithmic number of bacterial cell concentration was found for E. coli O157:H7 from 2.67 x 10(2) to 2.67 x 10(6)CFU/ml.
Directed or exploratory drug development programs constantly seek robust screening platforms for the high fidelity identification and validation of potential targets. Small-molecule microarrays (SMMs) have risen to this call by elegantly forging the capability of combinatorial chemistry in producing myriad compounds with the powerful throughput afforded by microarrays. This synergism offers scientists a versatile tool for rapid compound analysis and discovery. Microarrays of small molecules have already been successfully applied in important areas ranging from protein profiling to the discovery of therapeutic leads. Recent interesting developments towards improved immobilization strategies and library creation methods, together with novel advances herein described, have set the stage for SMMs to take on wider and more routine applications in academia and industry. As a rapidly maturing technology, SMMs pave the way forward in high-throughput exploration, both in the identification of biologically significant natural and synthetic small molecules and in harnessing their vast potential towards medicinal and diagnostic applications.
During the last 5 years, the number of papers describing data obtained by microarray technology increased exponentially with about 3000 papers in 2003. Undoubtedly, cancer is by far the disease that received most of the attention as far as the amount of data generated. As array technology is rather new and highly dependent on bioinformatics, mathematics and statistics, a clear understanding of the knowledge and information derived from array-based experiments is not widely appreciated. We shall review herein some of the issues related to the construction of DNA arrays, quantities and heterogeneity of probes and targets, the consequences of the physical characteristics of the probes, data extraction and data analysis as well as the applications of array technology. Our goal is to bring to the general audience, some of the basics of array technology and its possible application in oncology. By discussing some of the basic aspects of the methodology, we hope to stimulate criticism concerning the conclusions proposed by authors, especially in the light of the very low degree of reproducibility already proven when commercially available platforms were compared . Regardless of its pitfalls, it is unquestionable that array technology will have a great impact in the management of cancer and its applications will range from the discovery of new drug targets, new molecular tools for diagnosis and prognosis as well as for a tailored treatment that will take into account the molecular determinants of a given tumor. Hence, we shall also highlight some of the already available and promising applications of array technology on the day-to-day practice of oncology.
DNA microarray technology has been widely used to simultaneously determine the expression levels of thousands of genes. A variety of approaches have been used, both in the implementation of this technology and in the analysis of the large amount of expression data. However, several practical issues still have not been resolved in a satisfactory manner, and among the most critical is the lack of agreement in the results obtained in different array platforms. In this study, we present a comparison of several microarray platforms [Affymetrix oligonucleotide arrays, custom complementary DNA (cDNA) arrays, and custom oligo arrays printed with oligonucleotides from three different sources] as well as analysis of various methods used for microarray target preparation and the reference design. The results indicate that the pairwise correlations of expression levels between platforms are relative low overall but that the log ratios of the highly expressed genes are strongly correlated, especially between Affymetrix and cDNA arrays. The microarray measurements were compared with quantitative real-time-polymerase chain reaction (QRT-PCR) results for 23 genes, and the varying degrees of agreement for each platform were characterized. We have also developed and tested a double amplification method which allows the use of smaller amounts of starting material. The added round of amplification produced reproducible results as compared to the arrays hybridized with single round amplified targets. Finally, the reliability of using a universal RNA reference for two-channel microarrays was tested and the results suggest that comparisons of multiple experimental conditions using the same control can be accurate.
Mantle cell lymphoma (MCL) is characterized by over-expression of cyclin Dl as a result of the characteristic t(11;14)(q13;q32). However, this translocation alone has proven not to be sufficient for lymphomagenesis, suggesting the involvement of additional alterations. We have characterized 35 cases of MCL by array comparative genomic hybridization with an average resolution of 0.97 Mb distributed over the complete human genome. The most common alterations were losses in 1p13.2-p31.1, 6q16.2-q27, 8p21.3, 9p13.2-p24.3, 9q13-q31.3, 11q14.3-q23.3, 13q14.13-q21.31, 13q33.1-q34, and 22q11.23-q13.33 and gains involving 3q21.2-q29, 7p12.1-p22.3, 8q24.13-q24.23, and 18q21.33-q22.3. Four homozygous deletions were identified in totally three patients; two overlapping at 1p32.3, and two adjacent at 13q32.3. The homozygous deletions at 1p32.3 cover the CDKN2C locus (coding for p18), while the region at 13q32.3 does not encompass any known tumor suppressor genes. A gain in 3q was significantly associated with shorter survival (P=0.047).
Even a simple, small-scale, microarray experiment generates thousands to millions of data points. Clearly, spreadsheets or plotting programs do not suffice for analysis of such large volumes of data, and comprehensive analysis requires systematic methods for selection and organization of data. This chapter focuses on the concepts and algorithms of hierarchical clustering and the most commonly employed methods of partitioning or organizing microarray data, and freely available software that implements these algorithms.
DNA microarrays and, more recently, protein microarrays, have become important tools for high-throughput genomic and proteomic studies. Transfected cell microarrays are a complementary technique in which array features comprise clusters of cells overexpressing defined cDNAs. Complementary DNAs cloned in expression vectors are printed on microscope slides, which become living arrays after the addition of a lipid transfection reagent and adherent mammalian cells. This article discusses two potential uses of cell microarrays in drug discovery: as a method of screening for gene products involved in biological processes of pharmaceutical interest and as in situ protein microarrays for the development and assessment of leads.
Extracting a comprehensive overview from the huge amount of information arising from whole-genome analyses is a significant challenge. This review critically surveys the state of the art methods that are used to connect information from functional genomic studies to biological function. Cluster analysis methods for inferring the correlation between genes are discussed, as are the methods for integrating gene expression information with existing information on biological pathways and the methods that combine cluster analysis with biological information to reconstruct novel biological networks.
In just a few years, microarrays have gone from obscurity to being almost ubiquitous in biological research. At the same time, the statistical methodology for microarray analysis has progressed from simple visual assessments of results to a weekly deluge of papers that describe purportedly novel algorithms for analysing changes in gene expression. Although the many procedures that are available might be bewildering to biologists who wish to apply them, statistical geneticists are recognizing commonalities among the different methods. Many are special cases of more general models, and points of consensus are emerging about the general approaches that warrant use and elaboration.
High-density microarrays are useful tools to study gene expression for the purpose of characterizing functional tissue changes in response to the action of drugs and chemicals. To test whether high-density expression data can identify mechanisms of toxicity and to identify an unknown sample through its RNA expression pattern, groups of male Wistar rats were administered 6 hepatotoxicants. The compounds chosen for this study were microcystin-LR (MLR), phenobarbital (PB), lipopolysaccharide (LPS), carbon tetrachloride (CT), thioacetamide (THA), and cyproterone acetate (CPA). These hepatotoxicants are known to induce adverse liver effects through different mechanisms. Liver mRNA was isolated and used to generate biotinylated cRNA for hybridization to a custom 1,600-rat gene DNA microarray. Treatment correlation matrices analyzed hybridization data from a hepatotoxicant-blinded sample, with gene expression coefficients (GEC) evaluated by means of hierarchical cluster analysis and visual representation as dendrograms. The experimental liver toxicity from the different treatments was confirmed by means of concurrent histopathology, liver enzymes, and bilirubin assays. This toxico genomic analysis identified multiple genes and groups of genes that were affected by the hepatotoxicants on study, indicating that high-density microarray expression data are useful to identify groups of genes involved in toxicity. In addition, the mRNA expression profile of an unidentified sample can be accurately identified when compared with the expression profiles resident in the data set. This study supports the use of gene expression-profiling technology to determine or to predict toxic liver effects.
In the context of cancer diagnosis and treatment, we consider the problem of constructing an accurate prediction rule on the basis of a relatively small number of tumor tissue samples of known type containing the expression data on very many (possibly thousands) genes. Recently, results have been presented in the literature suggesting that it is possible to construct a prediction rule from only a few genes such that it has a negligible prediction error rate. However, in these results the test error or the leave-one-out cross-validated error is calculated without allowance for the selection bias. There is no allowance because the rule is either tested on tissue samples that were used in the first instance to select the genes being used in the rule or because the cross-validation of the rule is not external to the selection process; that is, gene selection is not performed in training the rule at each stage of the cross-validation process. We describe how in practice the selection bias can be assessed and corrected for by either performing a cross-validation or applying the bootstrap external to the selection process. We recommend using 10-fold rather than leave-one-out cross-validation, and concerning the bootstrap, we suggest using the so-called .632+ bootstrap error estimate designed to handle overfitted prediction rules. Using two published data sets, we demonstrate that when correction is made for the selection bias, the cross-validated error is no longer zero for a subset of only a few genes.
In microarray data there are a number of biological samples, each assessed for the level of gene expression for a typically large number of genes. There is a need to examine these data with statistical techniques to help discern possible patterns in the data. Our technique applies a combination of mathematical and statistical methods to progressively take the data set apart so that different aspects can be examined for both general patterns and very specific effects. Unfortunately, these data tables are often corrupted with extreme values (outliers), missing values, and non-normal distributions that preclude standard analysis. We develop a robust analysis method to address these problems. The benefits of this robust analysis will be both the understanding of large-scale shifts in gene effects and the isolation of particular sample-by-gene effects that might be either unusual interactions or the result of experimental flaws. Our method requires a single pass and does not resort to complex "cleaning" or imputation of the data table before analysis. We illustrate the method with a commercial data set.
We present a coupled two-way clustering approach to gene microarray data analysis. The main idea is to identify subsets of the genes and samples, such that when one of these is used to cluster the other, stable and significant partitions emerge. The search for such subsets is a computationally complex task. We present an algorithm, based on iterative clustering, that performs such a search. This analysis is especially suitable for gene microarray data, where the contributions of a variety of biological mechanisms to the gene expression levels are entangled in a large body of experimental data. The method was applied to two gene microarray data sets, on colon cancer and leukemia. By identifying relevant subsets of the data and focusing on them we were able to discover partitions and correlations that were masked and hidden when the full dataset was used in the analysis. Some of these partitions have clear biological interpretation; others can serve to identify possible directions for future research.
Papillary thyroid carcinoma (PTC) is clinically heterogeneous. Apart from an association with ionizing radiation, the etiology and molecular biology of PTC is poorly understood. We used oligo-based DNA arrays to study the expression profiles of eight matched pairs of normal thyroid and PTC tissues. Additional PTC tumors and other tissues were studied by reverse transcriptase-PCR and immunohistochemistry. The PTCs showed concordant expression of many genes and distinct clustered profiles. Genes with increased expression in PTC included many encoding adhesion and extracellular matrix proteins. Expression was increased in 8/8 tumors for 24 genes and in 7/8 tumors for 22 genes. Among these genes were several previously known to be overexpressed in PTC, such as MET, LGALS3, KRT19, DPP4, MDK, TIMP1, and FN1. The numerous additional genes include CITED1, CHI3L1, ODZ1, N33, SFTPB, and SCEL. Reverse transcriptase-PCR showed high expression of CITED1, CHI3L1, ODZ1, and SCEL in 6/6 additional PTCs. Immunohistochemical analysis detected CITED1 and SFTPB in 49/52 and 39/52 PTCs, respectively, but not in follicular thyroid carcinoma and normal thyroid tissue. Genes underexpressed in PTC included tumor suppressors, thyroid function-related proteins, and fatty acid binding proteins. Expression was decreased in 7/8 tumors for eight genes and decreased in 6/8 tumors for 19 genes. We conclude that, despite its clinical heterogeneity, PTC is characterized by consistent and specific molecular changes. These findings reveal clues to the molecular pathways involved in PTC and may provide biomarkers for clinical use.
This paper presents an overview of how microsystem technology tools can be applied to the development of rapid, out-of-laboratory measurement capabilities for the determinations of toxigenic fungi and mycotoxins in foodstuffs. Most of the topics discussed are all under investigation within the European Commission-sponsored project Good-Food (FP6-IST). These are DNA arrays, electronic noses and electronic tongues for the detection of fungal contaminants in feed, and biosensors and chemical sensors based on microfabricated electrode systems, antibodies and novel synthetic receptors for the detection of specific mycotoxins. The approach to resolution of these difficult measurement problems in real matrices requires a multidisciplinary approach. The technology tools discussed can provide a route to the rapid, on-site generation of data that can aid the safe production of high-quality foodstuffs.