A conceptual and practical overview of cDNA microarray technology: implications for basic and clinical sciences

cDNA microarray is an innovative technology that facilitates the analysis of the expression of thousands of genes simultaneously. The utilization of this methodology, which is rapidly evolving, requires a combination of expertise from the biological, mathematical and statistical sciences. In this review, we attempt to provide an overview of the principles of cDNA microarray technology, the practical concerns of the analytical processing of the data obtained, the correlation of this methodology with other data analysis methods such as immunohistochemistry in tissue microarrays, and the cDNA microarray application in distinct areas of the basic and clinical sciences.

Pipeline for macro- and microarray analyses

The pipeline for macro- and microarray analyses (PMmA) is a set of scripts with a web interface developed to analyze DNA array data generated by array image quantification software. PMmA is designed for use with single- or double-color array data and to work as a pipeline in five classes (data format, normalization, data analysis, clustering, and array maps). It can also be used as a plugin in the BioArray Software Environment, an open-source database for array analysis, or used in a local version of the web service. All scripts in PMmA were developed in the PERL programming language and statistical analysis functions were implemented in the R statistical language. Consequently, our package is a platform-independent software. Our algorithms can correctly select almost 90% of the differentially expressed genes, showing a superior performance compared to other methods of analysis. The pipeline software has been applied to 1536 expressed sequence tags macroarray public data of sugarcane exposed to cold for 3 to 48 h. PMmA identified thirty cold-responsive genes previously unidentified in this public dataset. Fourteen genes were up-regulated, two had a variable expression and the other fourteen were down-regulated in the treatments. These new findings certainly were a consequence of using a superior statistical analysis approach, since the original study did not take into account the dependence of data variability on the average signal intensity of each gene. The web interface, supplementary information, and the package source code are available, free, to non-commercial users at http://ipe.cbmeg.unicamp.br/pub/PMmA.

Ropinirole alters gene expression profiles in SH-SY5Y cells: a whole genome microarray study

Ropinirole (ROP) is a dopamine agonist that has been used as therapy for Parkinson's disease. In the present study, we aimed to detect whether gene expression was modulated by ROP in SH-SY5Y cells. SH-SY5Y cell lines were treated with 10 µM ROP for 2 h, after which total RNA was extracted for whole genome analysis. Gene expression profiling revealed that 113 genes were differentially expressed after ROP treatment compared with control cells. Further pathway analysis revealed modulation of the phosphatidylinositol 3-kinase (PI3K) signaling pathway, with prominent upregulation of PIK3C2B. Moreover, batches of regulated genes, including PIK3C2B, were found to be located on chromosome 1. These findings were validated by quantitative RT-PCR and Western blot analysis. Our study, therefore, revealed that ROP altered gene expression in SH-SY5Y cells, and future investigation of PIK3C2B and other loci on chromosome 1 may provide long-term implications for identifying novel target genes of Parkinson's disease.

Data analysis tools and methods for DNA microarray and high-throughput sequencing data

The recent rapid development of biotechnological approaches has enabled the production of large whole genome level biological data sets. In order to handle thesedata sets, reliable and efficient automated tools and methods for data processingand result interpretation are required. Bioinformatics, as the field of studying andprocessing biological data, tries to answer this need by combining methods and approaches across computer science, statistics, mathematics and engineering to studyand process biological data. The need is also increasing for tools that can be used by the biological researchers themselves who may not have a strong statistical or computational background, which requires creating tools and pipelines with intuitive user interfaces, robust analysis workflows and strong emphasis on result reportingand visualization. Within this thesis, several data analysis tools and methods have been developed for analyzing high-throughput biological data sets. These approaches, coveringseveral aspects of high-throughput data analysis, are specifically aimed for gene expression and genotyping data although in principle they are suitable for analyzing other data types as well. Coherent handling of the data across the various data analysis steps is highly important in order to ensure robust and reliable results. Thus,robust data analysis workflows are also described, putting the developed tools andmethods into a wider context. The choice of the correct analysis method may also depend on the properties of the specific data setandthereforeguidelinesforchoosing an optimal method are given. The data analysis tools, methods and workflows developed within this thesis have been applied to several research studies, of which two representative examplesare included in the thesis. The first study focuses on spermatogenesis in murinetestis and the second one examines cell lineage specification in mouse embryonicstem cells.

Évaluation du caryotype moléculaire en tant qu’outil diagnostique chez les enfants avec déficience intellectuelle et/ou malformations congénitales

Le caryotype moléculaire permet d’identifier un CNV chez 10-14% des individus atteints de déficience intellectuelle et/ou de malformations congénitales. C’est pourquoi il s’agit maintenant de l’analyse de première intention chez ces patients. Toutefois, le rendement diagnostique n’est pas aussi bien défini en contexte prénatal et l’identification de CNVs de signification clinique incertaine y est particulièrement problématique à cause du risque d’interruption de grossesse. Nous avons donc testé 49 fœtus avec malformations majeures et un caryotype conventionnel normal avec une micropuce CGH pangénomique, et obtenu un diagnostic dans 8,2% des cas. Par ailleurs, des micropuces à très haute résolution combinant le caryotype moléculaire et le génotypage de SNPs ont récemment été introduites sur le marché. En plus d’identifier les CNVs, ces plateformes détectent les LOHs, qui peuvent indiquer la présence d’une mutation homozygote ou de disomie uniparentale. Ces anomalies pouvant être associées à la déficience intellectuelle ou à des malformations, leur détection est particulièrement intéressante pour les patients dont le phénotype reste inexpliqué. Cependant, le rendement diagnostique de ces plateformes n’est pas confirmé, et l’utilité clinique réelle des LOHs n’est toujours pas établie. Nous avons donc testé 21 enfants atteints de déficience intellectuelle pour qui les méthodes standards d’analyse génétique n’avaient pas résulté en un diagnostic, et avons pu faire passer le rendement diagnostique de 14,3% à 28,6% grâce à l’information fournie par les LOHs. Cette étude démontre l’utilité clinique d’une micropuce CGH pangénomique chez des fœtus avec malformations, de même que celle d’une micropuce SNP chez des enfants avec déficience intellectuelle.

Development of a Fully Automated Image Analysis Method for High Density cDNA and array CGH Microarray Based Genomic Studies

In this thesis, different techniques for image analysis of high density microarrays have been investigated. Most of the existing image analysis techniques require prior knowledge of image specific parameters and direct user intervention for microarray image quantification. The objective of this research work was to develop of a fully automated image analysis method capable of accurately quantifying the intensity information from high density microarrays images. The method should be robust against noise and contaminations that commonly occur in different stages of microarray development.

A New Gridding Technique for High Density Microarray Images Using Intensity Projection Profile of Best Sub Image

As the technologies for the fabrication of high quality microarray advances rapidly, quantification of microarray data becomes a major task. Gridding is the first step in the analysis of microarray images for locating the subarrays and individual spots within each subarray. For accurate gridding of high-density microarray images, in the presence of contamination and background noise, precise calculation of parameters is essential. This paper presents an accurate fully automatic gridding method for locating suarrays and individual spots using the intensity projection profile of the most suitable subimage. The method is capable of processing the image without any user intervention and does not demand any input parameters as many other commercial and academic packages. According to results obtained, the accuracy of our algorithm is between 95-100% for microarray images with coefficient of variation less than two. Experimental results show that the method is capable of gridding microarray images with irregular spots, varying surface intensity distribution and with more than 50% contamination

Protein Microarray: "Theory" to "Real Practice"

Fueled by ever-growing genomic information and rapid developments of proteomics–the large scale analysis of proteins and mapping its functional role has become one of the most important disciplines for characterizing complex cell function. For building functional linkages between the biomolecules, and for providing insight into the mechanisms of biological processes, last decade witnessed the exploration of combinatorial and chip technology for the detection of bimolecules in a high throughput and spatially addressable fashion. Among the various techniques developed, the protein chip technology has been rapid. Recently we demonstrated a new platform called “Spacially addressable protein array” (SAPA) to profile the ligand receptor interactions. To optimize the platform, the present study investigated various parameters such as the surface chemistry and role of additives for achieving high density and high-throughput detection with minimal nonspecific protein adsorption. In summary the present poster will address some of the critical challenges in protein micro array technology and the process of fine tuning to achieve the optimum system for solving real biological problems.

Uso de la Hibridación Genómica Comparativa-Array (HGC-a) en pacientes con retraso global del desarrollo y fenotipo particular. Revisión sistemática de la literatura

Introducción: la hibridación genómica comparativa en una técnica que permite la exploración de las anormalidades cromosómicas. Su utilidad en la aproximación de los pacientes con retraso global del desarrollo o fenotipo dismórfico, sin embargo, no ha sido explorada mediante una revisión sistemática de la literatura. Metodología: realizó una revisión sistemática de la literatura. Se incluyeron estudios controlados, cuasi-experimentales, de cohortes, de casos y controles, transversales y descriptivos publicados en idiomas inglés y español entre los años 2000 y 2013. Se realizó un análisis de la evidencia con un enfoque cualitativo y cuantitativo. Se realizó un análisis del riesgo de sesgo de los estudios incluidos. Resultados: se incluyeron 4 estudios que cumplieron con los criterios de inclusión. La prevalencia de alteraciones cromosómicas en los niños con retraso global del desarrollo fue de entre el 6 y 13%. El uso de la técnica permitió identificar alteraciones que no fueron detectadas mediante el cariotipo. Conclusiones: la hibridación genómica comparativa es una técnica útil en la aproximación diagnóstica de los niños con retraso global del desarrollo y del fenotipo dismórfico y permite una mayor detección de alteraciones comparada con el cariotipo.

The tagged microarray marker (TAM) method for high-throughput detection of single nucleotide and indel polymorphisms

The tagged microarray marker (TAM) method allows high-throughput differentiation between predicted alternative PCR products. Typically, the method is used as a molecular marker approach to determining the allelic states of single nucleotide polymorphisms (SNPs) or insertion-deletion (indel) alleles at genomic loci in multiple individuals. Biotin-labeled PCR products are spotted, unpurified, onto a streptavidin-coated glass slide and the alternative products are differentiated by hybridization to fluorescent detector oligonucleotides that recognize corresponding allele-specific tags on the PCR primers. The main attractions of this method are its high throughput (thousands of PCRs are analyzed per slide), flexibility of scoring (any combination, from a single marker in thousands of samples to thousands of markers in a single sample, can be analyzed) and flexibility of scale (any experimental scale, from a small lab setting up to a large project). This protocol describes an experiment involving 3,072 PCRs scored on a slide. The whole process from the start of PCR setup to receiving the data spreadsheet takes 2 d.

GeneMCL in microarray analysis

Accurately and reliably identifying the actual number of clusters present with a dataset of gene expression profiles, when no additional information on cluster structure is available, is a problem addressed by few algorithms. GeneMCL transforms microarray analysis data into a graph consisting of nodes connected by edges, where the nodes represent genes, and the edges represent the similarity in expression of those genes, as given by a proximity measurement. This measurement is taken to be the Pearson correlation coefficient combined with a local non-linear rescaling step. The resulting graph is input to the Markov Cluster (MCL) algorithm, which is an elegant, deterministic, non-specific and scalable method, which models stochastic flow through the graph. The algorithm is inherently affected by any cluster structure present, and rapidly decomposes a graph into cohesive clusters. The potential of the GeneMCL algorithm is demonstrated with a 5730 gene subset (IGS) of the Van't Veer breast cancer database, for which the clusterings are shown to reflect underlying biological mechanisms. (c) 2005 Elsevier Ltd. All rights reserved.

Linkage disequilibrium assessment via log-linear modeling of SNP haplotype frequencies

Analyses of high-density single-nucleotide polymorphism (SNP) data, such as genetic mapping and linkage disequilibrium (LD) studies, require phase-known haplotypes to allow for the correlation between tightly linked loci. However, current SNP genotyping technology cannot determine phase, which must be inferred statistically. In this paper, we present a new Bayesian Markov chain Monte Carlo (MCMC) algorithm for population haplotype frequency estimation, particulary in the context of LD assessment. The novel feature of the method is the incorporation of a log-linear prior model for population haplotype frequencies. We present simulations to suggest that 1) the log-linear prior model is more appropriate than the standard coalescent process in the presence of recombination (>0.02cM between adjacent loci), and 2) there is substantial inflation in measures of LD obtained by a "two-stage" approach to the analysis by treating the "best" haplotype configuration as correct, without regard to uncertainty in the recombination process. Genet Epidemiol 25:106-114, 2003. (C) 2003 Wiley-Liss, Inc.

Neoglycolipid probes prepared via oxime ligation for microarray analysis of oligosaccharide-protein interactions

Neoglycolipid technology is the basis of a microarray platform for assigning oligosaccharide ligands for carbohydrate-binding proteins. The strategy for generating the neoglycolipid probes by reductive amination results in ring opening of the core monosaccharides. This often limits applicability to short-chain saccharides, although the majority of recognition motifs are satisfactorily presented with neoglycolipids of longer oligosaccharides. Here, we describe neoglycolipids prepared by oxime ligation. We provide evidence from NMR studies that a significant proportion of the oxime-linked core monosaccharide is in the ring-closed form, and this form selectively interacts with a carbohydrate-binding protein. By microarray analyses we demonstrate the effective presentation with oxime-linked neoglycolipids of (1) Lewis(x) trisaccharide to antibodies to Lewisx, (2) sialyllactose analogs to the sialic acid-binding receptors, siglecs, and (3) N-glycans to a plant lectin that requires an intact N-acetylglucosamine core.

Phenotype MicroArray (TM) in the metabolic characterisation of Salmonella serotypes Agona, Enteritidis, Give, Hvittingfoss, Infantis, Newport and Typhimurium

The Phenotype MicroArray (TM) (PM) technology was used to study the metabolic characteristics of 29 Salmonella strains belonging to seven serotypes of S. enterica spp. enterica. Strains of serotypes Typhimurium (six strains among definite phage types DTs 1, 40 and 104) and Agona (two strains) were tested for 949 substrates, Enteritidis (six strains of phage type PT1), Give, Hvittingfoss, Infantis and Newport strains (two of each) were tested for 190 substrates and seven other Agona strains for 95 substrates. The strains represented 18 genotypes in pulsed-field gel electrophoresis (PFGE). Among 949 substrates, 18 were identified that could be used to differentiate between the strains of those seven serotypes or within a single serotype. Unique metabolic differences between the Finnish endemic Typhimurium DT1 and Agona strains were detected, for example, in the metabolism of d-tagatose, d-galactonic acid gamma-lactone and l-proline as a carbon source. Thus, the PM technique is a useful tool for identifying potential differential markers on a metabolic basis that could be used for epidemiological surveillance.