Determination of strongly overlapping signaling activity from microarray data

Background

The Tissue Microarray (TMA) facilitates high-throughput analysis of hundreds of tissue specimens simultaneously. However, bottlenecks in the storage and manipulation of the data generated from TMA reviews have become apparent. A number of software applications have been developed to assist in image and data management; however no solution currently facilitates the easy online review, scoring and subsequent storage of images and data associated with TMA experimentation.

Results

This paper describes the design, development and validation of the Virtual Tissue Matrix (VTM). Through an intuitive HTML driven user interface, the VTM provides digital/virtual slide based images of each TMA core and a means to record observations on each TMA spot. Data generated from a TMA review is stored in an associated relational database, which facilitates the use of flexible scoring forms. The system allows multiple users to record their interpretation of each TMA spot for any parameters assessed. Images generated for the VTM were captured using a standard background lighting intensity and corrective algorithms were applied to each image to eliminate any background lighting hue inconsistencies or vignetting. Validation of the VTM involved examination of inter-and intra-observer variability between microscope and digital TMA reviews. Six bladder TMAs were immunohistochemically stained for E-Cadherin, β-Catenin and PhosphoMet and were assessed by two reviewers for the amount of core and tumour present, the amount and intensity of membrane, cytoplasmic and nuclear staining.

Conclusion

Results show that digital VTM images are representative of the original tissue viewed with a microscope. There were equivalent levels of inter-and intra-observer agreement for five out of the eight parameters assessed. Results also suggest that digital reviews may correct potential problems experienced when reviewing TMAs using a microscope, for example, removal of background lighting variance and tint, and potential disorientation of the reviewer, which may have resulted in the discrepancies evident in the remaining three parameters.     

DNA microarrays in parasitology: strengths and limitations

Genome sequencing efforts have provided a wealth of new biological information that promises to have a major impact on our understanding of parasites. Microarrays provide one of the major high-throughput platforms by which this information can be exploited in the laboratory. Many excellent reviews and technique articles have recently been published on applying microarrays to organisms for which fully annotated genomes are at hand. However, many parasitologists work on organisms whose genomes have been only partially sequenced and where little, if any, annotation is available. The focus of this review is on how to use and apply microarrays to these situations.     

Microarray Assistant clone organizer and array simulator

Microarrays are extensively used in molecular biology experiments. While several vendors offer microarrays on a variety of platforms, many researchers prefer to use custom microarrays with a selected list of clones for their experiments. Many research centers have established core facilities for the production of custom microarrays. Microarray production involves a number of steps, including maintaining a master list of stock clones, selecting required clones for custom microarrays, subculturing selected clones, amplifying inserts, recording results, and identifying the orientation of clones in the microarray. We have created a simple, user-friendly, and versatile Microsoft Excel spreadsheet-based software, Microarray Assistant, which can assist the user in all the steps of microarray design and synthesis. In addition, the program gives options to insert, delete, or interchange clones during various steps. The program also gives a visual picture of the locations of the clones in the plates, as well as in the microarray. The program can also be used to assist in the transfer of clones between plates of different configuration.     

Comparative microarray analysis

Microarrays enable high-throughput parallel gene expression analysis, and their use has grown exponentially during the past decade. We are now in a position where individual experiments could benefit from using the swelling public data repositories to allow microarrays to progress from being a hypothesis-generating tool to a powerful resource that can be used to test hypothesis about biology. Comparative microarray analysis could better distinguish phenotypes from associated phenotypes; identify valid differentially expressed genes by combining many studies; test new hypothesis; and discover fundamental patterns of gene regulation. This review aims to describe the additional methodology needed for such comparative microarray analysis, and we identify and discuss a number of problems such as loss of published data, lack of annotations, and variable array quality, which need to be solved before comparative microarray analysis can be used in a more systematic and powerful manner.     

A review of statistical methods for expression quantitative trait loci mapping

With high-throughput technologies now widely available, investigators can easily measure thousands of phenotypes for quantitative trait loci (QTL) mapping. Microarray measurements are particularly amenable to QTL mapping, as evidenced by a number of recent studies demonstrating utility across a broad range of biological endeavors. The early success stories have impelled a rapid increase in both the number and complexity of expression QTL (eQTL) experiments. Consequently, there is a need to consider the statistical principles involved in the design and analysis of these experiments and the methods currently being used. In this article we review these principles and methods and discuss the open questions most likely to yield significant progress toward increasing the amount of meaningful information obtained from eQTL mapping experiments.     

Molecular and cellular approaches for the detection of protein-protein interactions: latest techniques and current limitations

Homotypic and heterotypic protein interactions are crucial for all levels of cellular function, including architecture, regulation, metabolism, and signaling. Therefore, protein interaction maps represent essential components of post-genomic toolkits needed for understanding biological processes at a systems level. Over the past decade, a wide variety of methods have been developed to detect, analyze, and quantify protein interactions, including surface plasmon resonance spectroscopy, NMR, yeast two-hybrid screens, peptide tagging combined with mass spectrometry and fluorescence-based technologies. Fluorescence techniques range from co-localization of tags, which may be limited by the optical resolution of the microscope, to fluorescence resonance energy transfer-based methods that have molecular resolution and can also report on the dynamics and localization of the interactions within a cell. Proteins interact via highly evolved complementary surfaces with affinities that can vary over many orders of magnitude. Some of the techniques described in this review, such as surface plasmon resonance, provide detailed information on physical properties of these interactions, while others, such as two-hybrid techniques and mass spectrometry, are amenable to high-throughput analysis using robotics. In addition to providing an overview of these methods, this review emphasizes techniques that can be applied to determine interactions involving membrane proteins, including the split ubiquitin system and fluorescence-based technologies for characterizing hits obtained with high-throughput approaches. Mass spectrometry-based methods are covered by a review by Miernyk and Thelen (2008; this issue, pp. 597–609 ). In addition, we discuss the use of interaction data to construct interaction networks and as the basis for the exciting possibility of using to predict interaction surfaces.     

Loss of annexin A1 expression in human breast cancer detected by multiple high-throughput analyses

To test the efficacy of combined high-throughput analyses (HTA) in target gene identification, screening criteria were set using >fivefold difference by microarray and statistically significant changes (p<0.01) in SAGE and EST. Microarray analysis of two normal and seven breast cancer samples found 129 genes with >fivefold changes. Further SAGE and EST analyses of these genes identified four qualified genes, ERBB2, GATA3, AGR2, and ANXA1. Their expression pattern was validated by RT-PCR in both breast cell lines and tissue samples. Loss of ANXA1 in breast cancer was further confirmed at mRNA level by Human Breast Cancer Tissue Profiling Array and at protein level by immunohistochemical staining. This study demonstrated that combined HTA effectively narrowed the number of genes for further study, while retaining the sensitivity in identifying biologically important genes such as ERBB2 and ANXA1. A distinctive loss of ANXA1 in breast cancer suggests its involvement in maintaining normal breast biology.     

Assessment of differential gene expression in human peripheral nerve injury

Background  

Microarray technology is a powerful methodology for identifying differentially expressed genes. However, when thousands of genes in a microarray data set are evaluated simultaneously by fold changes and significance tests, the probability of detecting false positives rises sharply. In this first microarray study of brachial plexus injury, we applied and compared the performance of two recently proposed algorithms for tackling this multiple testing problem, Significance Analysis of Microarrays (SAM) and Westfall and Young step down adjusted p values, as well as t-statistics and Welch statistics, in specifying differential gene expression under different biological States.     

Microarray probe expression measures, data normalization and statistical validation

DNA microarray technology is a high-throughput method for gaining information on gene function. Microarray technology is based on deposition/synthesis, in an ordered manner, on a solid surface, of thousands of EST sequences/genes/oligonucleotides. Due to the high number of generated datapoints, computational tools are essential in microarray data analysis and mining to grasp knowledge from experimental results. In this review, we will focus on some of the methodologies actually available to define gene expression intensity measures, microarray data normalization, and statistical validation of differential expression.     

Automated expression and solubility screening of His-tagged proteins in 96-well format

A growing need for sensitive and high-throughput methods for screening the expression and solubility of recombinant proteins exists in structural genomics. Originally, the emergency solution was to use immediately available techniques such as manual lysis of expression cells followed by analysis of protein expression by gel electrophoresis. However, these handmade methods quickly proved to be unfit for the high-throughput demand of postgenomics, and it is now generally accepted that the long-term solution to this problem will be based on automation, on industrial standard-formatted experiments, and on downsizing samples and consumables. In agreement with this consensus, we have set up a fully automated method based on a dot-blot technology and using 96-well format consumables for assessing by immunodetection the amount of total and soluble recombinant histidine (His)-tagged proteins expressed in Escherichia coli. The method starts with the harvest of expression cells and ends with the display of solubility/expression results in milligrams of recombinant protein per liter of culture using a three-color code to assist analysis. The program autonomously processes 160 independent cultures at a time.     

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.     

Mining mouse microarray data

Microarrays of mouse genes are now available from several sources, and they have so far given new insights into gene expression in embryonic development, regions of the brain and during apoptosis. Microarray data posted on the internet can be reanalyzed to study a range of questions.