Automated evaluation and normalization of immunohistochemistry on tissue microarrays with a DNA microarray scanner

Hundreds of tissue samples may be assembled in a tissue microarray format for simultaneous immunostaining assessment of protein expression profiling. A DNA microarray two-color laser scanner was used for automated analysis of tissue microarray indirect immunofluorescence. On sections from both a human lung adenocarcinoma and a squamous cell carcinoma tissue microarray, fluorescence intensity for two epidermal growth factor receptors (EGFR and c-erbB2) correlates with diagnostic pathologic assessment, indicating that immunohistochemistry quantitation can be achieved. Importantly, double-label indirect immunofluorescence detection with the cDNA scanner demonstrates that one reference antigen can normalize tumor marker immunosignal for the cellular content of tissue microarray tissue cores. Therefore, DNA microarray scanners and associated image analysis software provide general and efficient analysis of tissue microarray immunostaining, including estimation of specific protein expression levels.     

Exploratory methods for checking quality of microarray data

In microarray experiments many undesirable systematic variations are commonly observed. Often investigators analyzing microarray data need to make subjective decisions about the quality of the experiment, by examining its chip image and a simple scatter plot. Thus, a more rigorous but simple method is desirable to determine the quality of microarray data. We propose two exploratory methods to investigate the quality of microarray experiments with replicated chips. The first method is based on correlations among chips and the second on the actual intensity values for each gene. The proposed methods are illustrated using a real microarray data set. The methods provide an initial estimation for determining the quality of microarray experiments.     

A suite of Perl modules for handling microarray data

We describe PerlMAT, a Perl microarray toolkit providing easy to use object-oriented methods for the simplified manipulation, management and analysis of microarray data. The toolkit provides objects for the encapsulation of microarray spots and reporters, several common microarray data file formats and GAL files. In addition, an analysis object provides methods for data processing, and an image object enables the visualisation of microarray data. This important addition to the Perl developer's library will facilitate more widespread use of Perl for microarray application development within the bioinformatics community. The coherent interface and well-documented code enables rapid analysis by even inexperienced Perl developers. AVAILABILITY: Software is available at http://sourceforge.net/projects/perlmat     

蛋白微阵列研究进展

蛋白微阵列是随着基因微阵列技术发展起来的,用于基因微阵列的制备方法、信号的检测及分析系统,也可用于蛋白微阵列。各种蛋白微阵列基质的发展,提高了蛋白的固定效率。放射性同位数、化学发光、激光共聚焦荧光扫描等技术都已用于微阵列的检测。重组蛋白技术的发展,提高了蛋白微阵列检测的通量和灵敏度。蛋白微阵列具有通量高、使用样品少、重复性好、可定量的特点,使其在生物医药科学研究中得到了广泛应用。本综述了蛋白微阵列的制备及其在免疫检测、医学诊断及蛋白组研究中的应用。     

Adjustment of systematic microarray data biases

MOTIVATION: Systematic differences due to experimental features of microarray experiments are present in most large microarray data sets. Many different experimental features can cause biases including different sources of RNA, different production lots of microarrays or different microarray platforms. These systematic effects present a substantial hurdle to the analysis of microarray data. RESULTS: We present here a new method for the identification and adjustment of systematic biases that are present within microarray data sets. Our approach is based on modern statistical discrimination methods and is shown to be very effective in removing systematic biases present in a previously published breast tumor cDNA microarray data set. The new method of 'Distance Weighted Discrimination (DWD)' is shown to be better than Support Vector Machines and Singular Value Decomposition for the adjustment of systematic microarray effects. In addition, it is shown to be of general use as a tool for the discrimination of systematic problems present in microarray data sets, including the merging of two breast tumor data sets completed on different microarray platforms. AVAILABILITY: Matlab software to perform DWD can be retrieved from https://genome.unc.edu/pubsup/dwd/     

EzArray: A web-based highly automated Affymetrix expression array data management and analysis system

Background  

Though microarray experiments are very popular in life science research, managing and analyzing microarray data are still challenging tasks for many biologists. Most microarray programs require users to have sophisticated knowledge of mathematics, statistics and computer skills for usage. With accumulating microarray data deposited in public databases, easy-to-use programs to re-analyze previously published microarray data are in high demand.     

基因芯片技术及其在肿瘤基因组学中的应用

基因芯片又称DNA微阵列,分为cDNA微阵列和寡聚核苷酸微阵列。DNA微阵列技术是探索基因组功能的一种强有力工具。扼要介绍基因芯片、表达谱芯片技术和原理,以及基因芯片技术在肿瘤基因组学中的应用。     

Development of a protein microarray using sequence-specific DNA binding domain on DNA chip surface

A protein microarray based on DNA microarray platform was developed to identify protein-protein interactions in vitro. The conventional DNA chip surface by 156-bp PCR product was prepared for a substrate of protein microarray. High-affinity sequence-specific DNA binding domain, GAL4 DNA binding domain, was introduced to the protein microarray as fusion partner of a target model protein, enhanced green fluorescent protein. The target protein was oriented immobilized directly on the DNA chip surface. Finally, monoclonal antibody of the target protein was used to identify the immobilized protein on the surface. This study shows that the conventional DNA chip can be used to make a protein microarray directly, and this novel protein microarray can be applicable as a tool for identifying protein-protein interactions.     

Importing MAGE-ML format microarray data into BioConductor

The microarray gene expression markup language (MAGE-ML) is a widely used XML (eXtensible Markup Language) standard for describing and exchanging information about microarray experiments. It can describe microarray designs, microarray experiment designs, gene expression data and data analysis results. We describe RMAGEML, a new Bioconductor package that provides a link between cDNA microarray data stored in MAGE-ML format and the Bioconductor framework for preprocessing, visualization and analysis of microarray experiments. AVAILABILITY: http://www.bioconductor.org. Open Source.     

Novel and simple transformation algorithm for combining microarray data sets

Background  

With microarray technology, variability in experimental environments such as RNA sources, microarray production, or the use of different platforms, can cause bias. Such systematic differences present a substantial obstacle to the analysis of microarray data, resulting in inconsistent and unreliable information. Therefore, one of the most pressing challenges in the field of microarray technology is how to integrate results from different microarray experiments or combine data sets prior to the specific analysis.     

Intra-Platform Repeatability and Inter-Platform Comparability of MicroRNA Microarray Technology

Over the last decade, DNA microarray technology has provided a great contribution to the life sciences. The MicroArray Quality Control (MAQC) project demonstrated the way to analyze the expression microarray. Recently, microarray technology has been utilized to analyze a comprehensive microRNA expression profiling. Currently, several platforms of microRNA microarray chips are commercially available. Thus, we compared repeatability and comparability of five different microRNA microarray platforms (Agilent, Ambion, Exiqon, Invitrogen and Toray) using 309 microRNAs probes, and the Taqman microRNA system using 142 microRNA probes. This study demonstrated that microRNA microarray has high intra-platform repeatability and comparability to quantitative RT-PCR of microRNA. Among the five platforms, Agilent and Toray array showed relatively better performances than the others. However, the current lineup of commercially available microRNA microarray systems fails to show good inter-platform concordance, probably because of lack of an adequate normalization method and severe divergence in stringency of detection call criteria between different platforms. This study provided the basic information about the performance and the problems specific to the current microRNA microarray systems.     

Signal stability of Cy3 and Cy5 on antibody microarrays

Background  

The antibody microarray technique is a newly emerging proteomics tool for differential protein expression analyses that uses fluorescent dyes Cy 3 and Cy 5. Environmental factors, such as light exposure, can affect the signal intensity of fluorescent dyes on microarray slides thus, it is logical to scan microarray slides immediately after the final wash and drying processes. However, no research data are available concerning time-dependent changes of fluorescent signals on antibody microarray slides to this date. In the present study, microarray slides were preserved at -20°C after regular microarray experiments and were rescanned at day 10, 20 and 30 to evaluate change in signal intensity.     

Ultrasonic approach to enhance signal-to-noise in three dimensional microarray

Three-dimensional polyacrylamide gel microarray (3-D gel microarray) has a strong adsorption for hybridzation probes with fluorescent label, resulting in high fluorescent background noise. The background noise has hampered the broad application of the microarray in scientific research. A low-frequency ultrasound of 40 kHz has been successfully employed to efficiently remove the nonspecifically bound fluorescent probes from the 3-D gel microarray. The method can significantly enhance the signal to noise ratio of the 3-D gel microarray and make it work better in single-nucleotide polymorphism genotyping.     

MADE4: an R package for multivariate analysis of gene expression data

SUMMARY: MADE4, microarray ade4, is a software package that facilitates multivariate analysis of microarray gene-expression data. MADE4 accepts a wide variety of gene-expression data formats. MADE4 takes advantage of the extensive multivariate statistical and graphical functions in the R package ade4, extending these for application to microarray data. In addition, MADE4 provides new graphical and visualization tools that aid in interpretation of multivariate analysis of microarray data.     

Assessing sources of variability in microarray gene expression data

Experiments using microarrays abound in genomic research, yet one factor remains in question. Without replication, how much stock can we put into the findings of microarray experiments? In addition, there is a growing desire to integrate microarray data with other molecular databases. To accomplish this in a scientifically acceptable manner, we must be able to measure the validity and quality of microarray data. Otherwise, it would be the weakest link in any integration process. Validating and evaluating the quality of data requires the ability to determine the reproducibility of results. Data obtained from a microarray experiment designed as a feasibility test provided a unique opportunity to partition and quantify several sources of variation that are likely to be present in most microarray experiments. We use this opportunity to discuss the origins of variability observed in microarray experiments and provide some suggestions for how to minimize or avoid them when designing an experiment.     

Identification of HCV-1b by Low-Density cDNA Microarray-Based Assay

A rapid and sensitive microarray assay for the detection of HCV-1b was developed in our laboratory and a cDNA fragment library for HCV-1b cDNA microarray probes was constructed. The full-length cDNAs of HCV-1b were digested with restriction endonuclease Sau3A I and the fragments were cloned with the pMD18-T vectors. Positive clones were isolated and identified by sequencing. The cDNA microarray was prepared by spotting the gene fragment on the surface of an amido-modified glass slide using the robotics system and samples were fluorescent labeled by the restriction display PCR (RD-PCR) technique, In the present study, modified protocols were used for probe selection and hybridization temperature. The detection of a microarray was validated by the hybridization and the sequence analysis. A total of 22 different specific gene fragments of HCV-1b ranging from 250 to 750 bp were isolated and sequenced, and these fragments were further used as probes in the microarray preparation. The diagnostic validity of the microarray method was evaluated after the washing and scanning process. The results of hybridization and sequence data analysis showed a significant specificity and sensitivity in the detection of HCV-1b RNA. The method of preparing microarray probes by construction of cDNA fragments library was effective, rapid, and simple; the optimized microarray was sensitive in the clinical detection of HCV-1b. The RD-PCR technique for the sample labeling was useful for significantly increasing the sensitivity of the assay. The cDNA microarray assay can be widely used in the clinical diagnosis of HCV-1b.