Optimizing microarray-based in situ transcription-translation of proteins for matrix-assisted laser desorption ionization mass spectrometry

The analysis of self-assembled protein microarrays, using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry, combines two high-throughput platforms for investigation of the proteome. In this article, we describe the fabrication in situ of protein arrays optimized for MALDI characterization. Using the green fluorescent protein (GFP) both as an epitope for immobilization and as a gauge for relative protein expression, we were able to generate amounts of protein on the array slides sufficient for MALDI identification. In addition, expression of N-terminal protein constructs fused to GFP demonstrated mass shifts consistent with that of the full-length protein. We envision this technology to be important for the functional screening of protein interactions.     

Data merging for integrated microarray and proteomic analysis.

The functioning of even a simple biological system is much more complicated than the sum of its genes, proteins and metabolites. A premise of systems biology is that molecular profiling will facilitate the discovery and characterization of important disease pathways. However, as multiple levels of effector pathway regulation appear to be the norm rather than the exception, a significant challenge presented by high-throughput genomics and proteomics technologies is the extraction of the biological implications of complex data. Thus, integration of heterogeneous types of data generated from diverse global technology platforms represents the first challenge in developing the necessary foundational databases needed for predictive modelling of cell and tissue responses. Given the apparent difficulty in defining the correspondence between gene expression and protein abundance measured in several systems to date, how do we make sense of these data and design the next experiment? In this review, we highlight current approaches and challenges associated with integration and analysis of heterogeneous data sets, focusing on global analysis obtained from high-throughput technologies.     

Accuracy of genotyping for single nucleotide polymorphisms by a microarray-based single nucleotide polymorphism typing method involving hybridization of short allele-specific oligonucleotides.

Advances in technologies for identifying genetic polymorphisms rapidly and accurately will dramatically accelerate the discovery of disease-related genes. Among a variety of newly described methods for rapid typing of single-nucleotide polymorphisms (SNPs), gene detection using DNA microarrays is gradually achieving widespread use. This method involves the use of short (11- to 13-mer) allele-specific oligonucleotides. This method allows simultaneous analysis of many SNPs in DNAs from a large number of individuals, in a single experiment. In this work, we evaluated the accuracy of a new microarray-based short allele-specific oligonucleotide (ASO) hybridization method. There is a 96-well formatted array on a single plate, in which up to 256 spots are included in each well. Fluorescent probes for our experiments were produced by multiplex PCR amplification often target SNP-containing regions. We genotyped 192 individuals across a panel of ten single base variations, which included an insertion/deletion polymorphism. For comparison, we genotyped the same individuals for the same SNPs by the method of single-base extension with fluorescence detection. The typing accuracies of the microarray-based PCR-ASO and single-base extension methods were calculated as 99.9% and 99.1%, respectively, on the basis of genotyping results determined by direct sequencing. We conclude that the microarray-based hybridization method using short ASO probes represents a potential breakthrough technology for typing large numbers of SNPs rapidly and efficiently.     

Resource and energy recovery options for fermentation industry residuals

Over the last 40 years, the fermentation industry has provided facility planners, plant operators and environmental engineers with a wide range of residuals management challenges and resource/energy recovery opportunities. In response, the industry has helped pioneer the use of a number of innovative resource and energy recovery technologies. Production of animal feed supplements, composts, fertilizers, soil amendments, commercial baking additives and microbial protein materials have all been detailed in the literature. In many such cases, recovery of by-products significantly reduces the need for treatment and disposal facilities. Stable, reliable anaerobic biological treatment processes have also been developed to recovery significant amounts of energy in the form of methane gas. Alternatively, dewatered or condensed organic fermentation industry residuals have been used as fuels for incineration-based energy recovery systems. The sale or use of recovered by-products and/or energy can be used to offset required processing costs and provide a technically and environmentally viable alternative to traditional treatment and disposal strategies. This review examines resource recovery options currently used or proposed for fermentation industry residuals and the conditions necessary for their successful application.     

CGcgh: a tool for molecular karyotyping using DNA microarray-based comparative genomic hybridization (array-CGH)

Microarray-based comparative genomic hybridization (array-CGH) is a technique by which variations in copy numbers between two genomes can be analyzed using DNA microarrays. Array CGH has been used to survey chromosomal amplifications and deletions in fetal aneuploidies or cancer tissues. Herein we report a user-friendly, MATLAB-based, array CGH analyzing program, Chang Gung comparative genomic hybridization (CGcgh), as a standalone PC version. The analyzed chromosomal data are displayed in a graphic interface, and CGcgh allows users to launch a corresponding G-banding ideogram. The abnormal DNA copy numbers (gains and losses) can be identified automatically using a user defined window size (default value is 50 probes) and sequential student t-tests with sliding windows along with chromosomes. CGcgh has been tested in multiple karyotype-confirmed human samples, including five published cases and trisomies 13, 18, 21 and X from our laboratories, and 18 cases of which microarray data are available publicly. CGcgh can be used to detect the copy number changes in small genomic regions, which are commonly encountered by clinical geneticists. CGcgh works well for the data from cDNA microarray, spotted oligonucleotide microarrays, and Affymetrix Human Mapping Arrays (10K, 100K, 500K Array Sets). The program can be freely downloaded from . Y. S. Lee and A. Chao contributed equally to this work.     

基于cDNA芯片的梨品种S基因型鉴定及新S-RNase基因进化分析

梨品种S基因型鉴定对梨栽培中授粉品种选择和遗传育种都具有重要意义。本研究利用梨S-RNase基因荧光标记的特异引物PCR扩增获得梨品种荧光标记的cDNA特异产物;进一步完善梨S-RNase基因cDNA芯片,以被检测梨品种cDNA特异序列与梨S-RNase基因cDNA芯片杂交检测不同梨品种S基因型,并发现新的S-RNase基因。结果表明:利用梨S-RNase基因cDNA芯片鉴定了泸定王皮梨、兴山24号、弥渡百合等35个未知S基因型梨品种,确定了各品种的S基因型。结合PCRRFLP及DNA克隆和测序等技术,发现了7个新的S-RNase基因资源,获得了新S-RNase基因序列。序列分析表明各新S-RNase基因均具有S-RNase基因特异区域序列的典型特征;进化分析显示7个新S-RNase基因主要属于蔷薇科苹果亚科S-RNase类群,且存在种间和属间比种内和属内进化关系更近的现象。7个新的S基因分别命名为:PpS_(53)(Pyrus pyrifolia S53)、PpS_(54)、PpS_(55)、PpS_(56)、PpS_(57)、PpS_(58)和PpS_(59),GenBank登录号分别为:KX581753、KX581754、KX581755、KX581756、KX581757、KX581751和KX581752。     

Development of microarray-based diagnostics of voles and shrews for use in biodiversity monitoring studies, and evaluation of mitochondrial cytochrome oxidase I vs. cytochrome b as genetic markers

Molecular methods are widely used for species identification of mammals. In particular, the mitochondrial cytochrome b gene sequence has proven helpful for this purpose. Microarray technology can now open up new perspectives for biodiversity monitoring. With microarrays, many thousands of genetically based characteristics can be tested on one microscopic glass slide called a 'chip'. A 'Mammalia-Chip', for example, could include redundant diagnostic markers to unambiguously identify all European mammal species. Of broader use, and therefore economically more relevant, could be a 'Biodiversity-Chip', containing diagnostic features to distinguish key species in the taxa of bacteria, lichen, molluscs, insects, fungi, mammals, etc. An important prerequisite for any mixed-phyla chip is a standardization of methods. One of the most promising genes as a universal marker for all eukaryotes is cytochrome oxidase I. We show that cytochrome oxidase I is adequate for the discrimination of different species of voles and shrews with cluster analysis. Based on these results we present a diagnostic microarray-chip using cytochrome oxidase I sequences for the identification of three species of Sorex (Soricidae, Insectivora) and four species of Microtus (Arvicolinae, Rodentia). We conclude that cytochrome oxidase I can be used as an alternative marker to cytochrome b in a mixed-phyla chip, or both genes can be used in combination to enhance redundance and thus robustness of a specific chip including small mammals.     

In-lab three-dimensional printing: An inexpensive tool for experimentation and visualization for the field of organogenesis

The development of the microscope in 1590 by Zacharias Janssenby and Hans Lippershey gave the world a new way of visualizing details of morphogenesis and development. More recent improvements in this technology including confocal microscopy, scanning electron microscopy (SEM) and optical projection tomography (OPT) have enhanced the quality of the resultant image. These technologies also allow a representation to be made of a developing tissue's three-dimensional (3-D) form. With all these techniques however, the image is delivered on a flat two-dimensional (2-D) screen. 3-D printing represents an exciting potential to reproduce the image not simply on a flat screen, but in a physical, palpable three-dimensional structure. Here we explore the scope that this holds for exploring and interacting with the structure of a developing organ in an entirely novel way. As well as being useful for visualization, 3-D printers are capable of rapidly and cost-effectively producing custom-made structures for use within the laboratory. We here describe the advantages of producing hardware for a tissue culture system using an inexpensive in-lab printer.     

The Botany Array Resource: e-Northerns, Expression Angling, and promoter analyses

The Botany Array Resource provides the means for obtaining and archiving microarray data for Arabidopsis thaliana as well as biologist-friendly tools for viewing and mining both our own and other's data, for example, from the AtGenExpress Consortium. All the data produced are publicly available through the web interface of the database at http://bbc.botany.utoronto.ca. The database has been designed in accordance with the Minimum Information About a Microarray Experiment convention -- all expression data are associated with the corresponding experimental details. The database is searchable and it also provides a set of useful and easy-to-use web-based data-mining tools for researchers with sophisticated yet understandable output graphics. These include Expression Browser for performing 'electronic Northerns', Expression Angler for identifying genes that are co-regulated with a gene of interest, and Promomer for identifying potential cis-elements in the promoters of individual or co-regulated genes.     

A microarray-based genotyping and genetic mapping approach for highly heterozygous outcrossing species enables localization of a large fraction of the unassembled Populus trichocarpa genome sequence

Microarrays have demonstrated significant power for genome-wide analyses of gene expression, and recently have also revolutionized the genetic analysis of segregating populations by genotyping thousands of loci in a single assay. Although microarray-based genotyping approaches have been successfully applied in yeast and several inbred plant species, their power has not been proven in an outcrossing species with extensive genetic diversity. Here we have developed methods for high-throughput microarray-based genotyping in such species using a pseudo-backcross progeny of 154 individuals of Populus trichocarpa and P. deltoides analyzed with long-oligonucleotide in situ- synthesized microarray probes . Our analysis resulted in high-confidence genotypes for 719 single-feature polymorphism (SFP) and 1014 gene expression marker (GEM) candidates. Using these genotypes and an established microsatellite (SSR) framework map, we produced a high-density genetic map comprising over 600 SFPs, GEMs and SSRs. The abundance of gene-based markers allowed us to localize over 35 million base pairs of previously unplaced whole-genome shotgun (WGS) scaffold sequence to putative locations in the genome of P. trichocarpa . A high proportion of sampled scaffolds could be verified for their placement with independently mapped SSRs, demonstrating the previously un-utilized power that high-density genotyping can provide in the context of map-based WGS sequence reassembly. Our results provide a substantial contribution to the continued improvement of the Populus genome assembly, while demonstrating the feasibility of microarray-based genotyping in a highly heterozygous population. The strategies presented are applicable to genetic mapping efforts in all plant species with similarly high levels of genetic diversity.     

There is no silver bullet--a guide to low-level data transforms and normalisation methods for microarray data

To overcome random experimental variation, even for simple screens, data from multiple microarrays have to be combined. There are, however, systematic differences between arrays, and any bias remaining after experimental measures to ensure consistency needs to be controlled for. It is often difficult to make the right choice of data transformation and normalisation methods to achieve this end. In this tutorial paper we review the problem and a selection of solutions, explaining the basic principles behind normalisation procedures and providing guidance for their application.     

基因芯片数据分析与处理

基因芯片技术在基因表达分析等应用过程中产生大量的数据,如何处理和分析这些数据并从中提取出有价值的生物学信息是一个极为重要的问题.其过程包括原始数据的获取及处理、标准化数据的统计学分析、以及数据的存储和交流等.     

基因芯片数据分析及在植物基因组研究中的应用

基因芯片作为一种新兴的技术手段已经在植物学、动物学、医学和农学等多个研究领域中发挥了重要作用。本文就基因芯片数据分析的各个环节,包括芯片数据的预处理、归一化、差异基因的判断、聚类分析以及基因芯片在植物功能基因组研究中的应用进行了综述。     

Signal and sensitivity enhancement through optical interference coating for DNA and protein microarray applications.

Optical inteference (OI) coated slides with unique optical properties were utilized in microarray analyses, demonstrating their enhanced detection sensitivity over traditional microarray substrates. The OI coating is comprised of a proprietary multilayered, dielectric, thin-film interference coating located beneath the functional coating (aminosilane or epoxysilane). It is designed to enhance the fluorescence in the Cy3 and Cy5 channel by increasing the light absorption of the dyes by about 6-fold and by redirecting emitted fluorescence into the detector during scanning, resulting in a theoretical limit of about 12-fold signal amplification. Two-color DNA microarray experiments conducted on the OI slides showed over 8-fold signal amplification, conservation of gene expression ratios, and increased signal-to-noise ratio when compared to control slides, indicating enhanced detection sensitivity. Protein microarray assays also exhibited over 8-fold signal amplification at three different target concentrations, demonstrating the versatility of the OI slides for different microarray applications. Further, the DNA and protein assays performed on the OI slides exhibited excellent detection sensitivity even at the low target amounts essential for diagnostic applications. The OI slides are compatible with commonly used protocols, printers, scanners and other microarray equipment. Therefore, the OI slides offer an attractive alternative to traditional microarray substrates, where enhanced detection sensitivity is desired.     

Developing integrated workflows for the digitisation of herbarium specimens using a modular and scalable approach

Digitisation programmes in many institutes frequently involve disparate and irregular funding, diverse selection criteria and scope, with different members of staff managing and operating the processes. These factors have influenced the decision at the Royal Botanic Garden Edinburgh to develop an integrated workflow for the digitisation of herbarium specimens which is modular and scalable to enable a single overall workflow to be used for all digitisation projects. This integrated workflow is comprised of three principal elements: a specimen workflow, a data workflow and an image workflow.The specimen workflow is strongly linked to curatorial processes which will impact on the prioritisation, selection and preparation of the specimens. The importance of including a conservation element within the digitisation workflow is highlighted. The data workflow includes the concept of three main categories of collection data: label data, curatorial data and supplementary data. It is shown that each category of data has its own properties which influence the timing of data capture within the workflow. Development of software has been carried out for the rapid capture of curatorial data, and optical character recognition (OCR) software is being used to increase the efficiency of capturing label data and supplementary data. The large number and size of the images has necessitated the inclusion of automated systems within the image workflow.     

Random forest for gene selection and microarray data classification

A random forest method has been selected to perform both gene selection and classification of the microarray data. In this embedded method, the selection of smallest possible sets of genes with lowest error rates is the key factor in achieving highest classification accuracy. Hence, improved gene selection method using random forest has been proposed to obtain the smallest subset of genes as well as biggest subset of genes prior to classification. The option for biggest subset selection is done to assist researchers who intend to use the informative genes for further research. Enhanced random forest gene selection has performed better in terms of selecting the smallest subset as well as biggest subset of informative genes with lowest out of bag error rates through gene selection. Furthermore, the classification performed on the selected subset of genes using random forest has lead to lower prediction error rates compared to existing method and other similar available methods.     

Tissue microarray-based immunohistochemical study can significantly underestimate the expression of HER2 and progesterone receptor in ductal carcinoma in situ of the breast

Abstract

The accuracy of immunohistochemical (IHC) analysis on tissue microarray (TMA)-based studies largely depends on the uniformity of the staining pattern for a given antibody and minimal intratumor heterogeneity of a given tumor. Our study was designed to investigate the concordance of expression in TMA and whole sections of estrogen receptor (ER), progesterone receptor (PR) and HER2 using IHC analysis for ductal carcinoma in situ (DCIS) of the breast. Seventy-five consecutive cases of DCIS were retrieved, reviewed and used to construct the TMA. IHC analysis of the expression of ER, PR, and HER2 were performed on TMA and whole sections of the corresponding cases, and the results were compared. The specificity and sensitivity for TMA-based assays were 87.0, 75.9, 90.6 and 90.4%, and 76.1, 27.3 for ER, PR and HER2, respectively. The concordance and discordance were 89.3, 76.0 and 72.0%, and 6.7, 13.3 and 16.0% for ER, PR, HER2, respectively. The kappa values were 0.83, 0.89 and 0.42 for ER, PR and HER2, respectively. The non-concordance rates were inversely related to core number, with 46.67, 22.67 and 11.56% for one core, two cores, and three cores, respectively, per marker per case (p < 0.001), but not associated with tumor size. Our results showed that the intratumor heterogeneity and the number of cores have a great impact on the results of TMA-based studies. Increasing the number of tissue cores per case may help improve the accuracy and concordance with whole section results. Although TMA remains an effective tool for translational research, we should be cautious in our interpretation of these results.