Microarray gene expression analysis free of reverse transcription and dye labeling

A new microarray system has been developed for gene expression analysis using cationic gold nanoparticles with diameters of 250 nm as a target detection reagent. The approach utilizes nonlabeled target molecules hybridizing with complementary probes on the array, followed by incubation in a colloidal gold solution. The hybridization signal results from the precipitation of nanogold particles on the hybridized spots due to the electrostatic attraction of the cationic gold particles and the anionic phosphate groups in the target DNA backbone. In contrast to conventional fluorescent detection, this nanoparticle-based detection system eliminates the target labeling procedure. The visualization of hybridization signals can be accomplished with a flatbed scanner instead of a confocal laser scanner, which greatly simplifies the process and reduces the cost. The sensitivity is estimated to be less than 2 pg of DNA molecules captured on the array surface. The signal from hybridized spots quantitatively represents the amount of captured target DNA and therefore permits quantitative gene expression analysis. Cross-array reproducibility is adequate for detecting twofold or less signal changes across two microarray experiments.     

Spotting and validation of a genome wide oligonucleotide chip with duplicate measurement of each gene

The quality of DNA microarray based gene expression data relies on the reproducibility of several steps in a microarray experiment. We have developed a spotted genome wide microarray chip with oligonucleotides printed in duplicate in order to minimise undesirable biases, thereby optimising detection of true differential expression. The validation study design consisted of an assessment of the microarray chip performance using the MessageAmp and FairPlay labelling kits. Intraclass correlation coefficient (ICC) was used to demonstrate that MessageAmp was significantly more reproducible than FairPlay. Further examinations with MessageAmp revealed the applicability of the system. The linear range of the chips was three orders of magnitude, the precision was high, as 95% of measurements deviated less than 1.24-fold from the expected value, and the coefficient of variation for relative expression was 13.6%. Relative quantitation was more reproducible than absolute quantitation and substantial reduction of variance was attained with duplicate spotting. An analysis of variance (ANOVA) demonstrated no significant day-to-day variation.     

Microarrays: The Technology,Analysis and Application

DNA microarray analysis represents one of the major advances leading to the development of functional genomics and proteomics. It involves the fabrication of DNA either by in situ or on‐chip photolithographic synthesis or by inkjet or microjet deposition, as microspots immobilized on the surface of miniaturized substrates like glass or membranes. The immobilized DNA molecules are then allowed to hybridize with labeled complementary DNA. The hybrid DNA so formed is read through scanning devices, such as fluorescence and radioactivity. Further, computational approaches, for example, normalization and clustering allow thousands of genetic parameters in a single experiment to be simultaneously analyzed. This review discusses the fundamental principles and data analysis of the microarray technology, while focusing on its application in gene expression analysis, genotyping for point mutation and diseases diagnostics.     

Early BrdU-responsive genes constitute a novel class of senescence-associated genes in human cells

We identified genes that immediately respond to 5-bromodeoxyuridine (BrdU) in SUSM-1, an immortal fibroblastic line, with DNA microarray and Northern blot analysis. At least 29 genes were found to alter gene expression greater than twice more or less than controls within 36 h after addition of BrdU. They took several different expression patterns upon addition of BrdU, and the majority showed a significant alteration within 12 h. When compared among SUSM-1, HeLa, and TIG-7 normal human fibroblasts, 19 genes behaved similarly upon addition of BrdU. In addition, 14 genes, 9 of which are novel as regards senescence, behaved similarly in senescent TIG-7 cells. The genes do not seem to have a role in proliferation or cell cycle progression. These results suggest that the early BrdU-responsive genes represent early signs of cellular senescence and can be its new biomarkers.     

Evaluation of hybridization conditions for spotted oligonucleotide-based DNA microarrays

We compared different hybridization conditions of oligonucleotide-based DNA microarray to acquire optimized and reliable microarray data. Several parameters were evaluated at different hybridization conditions, including signal-to-background (S:B) ratios, signal dynamic range, usable spots, and reproducibility. Statistical analysis showed that better results were obtained when spotted, presynthesized long oligonucleotide arrays were blocked with succinic anhydride and hybridized at 42°C in the presence of 50% formamide.     

基于遗传算法的基因表达数据的K-均值聚类分析

聚类算法在基因表达数据的分析处理过程中得到日益广泛的应用。本文通过把K-均值聚类算法引入到遗传算法中,结合基因微阵列的特点,来讨论一种基于遗传算法的K-均值聚类模型,目的是利用遗传算法的全局性来提高聚类算法找到全局最优的可能性,实验结果证明,该算法可以很好地解决某些基因表达数据的聚类分析问题。     

A probabilistic framework for microarray data analysis: Fundamental probability models and statistical inference

Gene expression studies generate large quantities of data with the defining characteristic that the number of genes (whose expression profiles are to be determined) exceed the number of available replicates by several orders of magnitude. Standard spot-by-spot analysis still seeks to extract useful information for each gene on the basis of the number of available replicates, and thus plays to the weakness of microarrays. On the other hand, because of the data volume, treating the entire data set as an ensemble, and developing theoretical distributions for these ensembles provides a framework that plays instead to the strength of microarrays. We present theoretical results that under reasonable assumptions, the distribution of microarray intensities follows the Gamma model, with the biological interpretations of the model parameters emerging naturally. We subsequently establish that for each microarray data set, the fractional intensities can be represented as a mixture of Beta densities, and develop a procedure for using these results to draw statistical inference regarding differential gene expression. We illustrate the results with experimental data from gene expression studies on Deinococcus radiodurans following DNA damage using cDNA microarrays.     

Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation

Background

Although numerous investigations have compared gene expression microarray platforms, preprocessing methods and batch correction algorithms using constructed spike-in or dilution datasets, there remains a paucity of studies examining the properties of microarray data using diverse biological samples. Most microarray experiments seek to identify subtle differences between samples with variable background noise, a scenario poorly represented by constructed datasets. Thus, microarray users lack important information regarding the complexities introduced in real-world experimental settings. The recent development of a multiplexed, digital technology for nucleic acid measurement enables counting of individual RNA molecules without amplification and, for the first time, permits such a study.

Results

Using a set of human leukocyte subset RNA samples, we compared previously acquired microarray expression values with RNA molecule counts determined by the nCounter Analysis System (NanoString Technologies) in selected genes. We found that gene measurements across samples correlated well between the two platforms, particularly for high-variance genes, while genes deemed unexpressed by the nCounter generally had both low expression and low variance on the microarray. Confirming previous findings from spike-in and dilution datasets, this “gold-standard” comparison demonstrated signal compression that varied dramatically by expression level and, to a lesser extent, by dataset. Most importantly, examination of three different cell types revealed that noise levels differed across tissues.

Conclusions

Microarray measurements generally correlate with relative RNA molecule counts within optimal ranges but suffer from expression-dependent accuracy bias and precision that varies across datasets. We urge microarray users to consider expression-level effects in signal interpretation and to evaluate noise properties in each dataset independently.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-649) contains supplementary material, which is available to authorized users.     

几种基因芯片技术的比较

基因芯片技术因其自身的优越性将成为基因组序列分析和基因表达研究的主要工具。但是由于不同的实验室使用不同的技术平台 ,从 2 5到 80个碱基的寡核苷酸芯片和长度从数百到几千个碱基的cDNA阵列。不同技术平台的存在 ,为芯片资料的利用和芯片领域的标准化分析带来了困难。通过对不同技术平台的研究比较 ,短的和长的寡核苷酸芯片技术具有明显的优势 ,可望成为将来主要的技术方法。     

A simple and robust algorithm for microarray data clustering based on gene population-variance ratio metric

With the advent of the microarray technology, the field of life science has been greatly revolutionized, since this technique allows the simultaneous monitoring of the expression levels of thousands of genes in a particular organism. However, the statistical analysis of expression data has its own challenges, primarily because of the huge amount of data that is to be dealt with, and also because of the presence of noise, which is almost an inherent characteristic of microarray data. Clustering is one tool used to mine meaningful patterns from microarray data. In this paper, we present a novel method of clustering yeast microarray data, which is robust and yet simple to implement. It identifies the best clusters from a given dataset on the basis of the population of the clusters as well as the variance of the feature values of the members from the cluster-center. It has been found to yield satisfactory results even in the presence of noisy data.     

Gene expression profiling reveals two separate mechanisms regulating apoptosis in rectal carcinomas in vivo

The level of apoptosis in rectal carcinomas of patients treated by surgery only predicts local failure; patients with intrinsically high-apoptotic tumors develop less local recurrences than patients with low levels of apoptosis. To identify genes involved in this intrinsic apoptotic process in vivo, 47 rectal tumors with known apoptotic phenotype (24 low- and 23 high-apoptotic) were analyzed by oligonucleotide microarray technology. We identified several genes differentially expressed between low- and high-apoptotic tumors. Unsupervised clustering of the tumors based on expression levels of these genes separated the low-apoptotic from the high-apoptotic tumors, indicating a gene expression-dependent regulation. In addition, this clustering revealed two subgroups of high-apoptotic tumors. One high-apoptotic subgroup showed subtle differences in mRNA and protein expression of the known apoptotic regulators BAX, cIAP2 and ARC compared to the low-apoptotic tumors. The other subgroup of high-apoptotic tumors showed high expression of immune-related genes; predominantly HLA class II and chemokines, but also HLA class I and interferon-inducible genes were highly expressed. Immunohistochemistry revealed HLA-DR expression in epithelial tumor cells in 70% of these high-apoptotic tumors. The expression data suggest that high levels of apoptosis in rectal carcinoma patients can be the result of either slightly altered expression of known pro- and anti-apoptotic genes or high expression of immune-related genes.     

基因表达差异显示分析技术在创伤研究中的应用

基因是细胞增殖,分化,成熟等各项生命活动的调控中心,也是许多痢疾发生,发展和转归的决定性因素。基因表达的变化必然导致细胞,组织,器官乃至整个机体的各种异常。包括创伤在内的各种内外刺激,都可不同程度地引起基因表达的变化,最终妨碍机体健康。随着生物信息学的逐渐兴起和分子生物学的不断发展并向其他学科的逐渐渗透,业已建立起一系列研究基因表达变化的切实可行的技术手段（即“基因表达差异分析技术”,如DNA微阵列）,对捕获基因表达的种种变化具有重要价值。这些技术已经在肿瘤及其他疾病的研究中得到广泛应用,近几年也逐渐进入创伤研究领域,在一定程度上推动了创伤研究的发展。     

Alteration of apoptotic signaling molecules as a function of time after radiation in human neuroblastoma cells

Ascertaining the time-dependent regulation of induced apoptosis and radioresistance is important to understand the relationship between the level of spontaneous apoptosis in cells and their radiosensitivity. Accordingly, we investigated the time-dependent expression of apoptosis related genes and radioresistance in neuroblastoma cells. Serum-starved human SK-N-MC cells were exposed to low linear energy transfer (LET) radiation (2 Gy) and incubated for 15, 30, 45 min, and 48 h. Radioresistance was investigated by examining the NFκB DNA-binding activity, cellular toxicity, DNA fragmentation, and expression of apoptotic signal transduction molecules. NFκB DNA binding activity was analyzed using electrophoretic mobility shift assay (EMSA). Cellular toxicity was measured using MTT assay. DNA fragmentation was quantified by labeling with fluorescein-conjugated deoxynucleotides. Microarray analysis was performed using cDNA microarray and relative gene expression was measured as % GAPDH and, subsequently validated using Q-PCR. Induction of NFκB analyzed using EMSA showed an increased DNA-binding activity at all time points investigated. Induced DNA fragmentation was observed after 15, 30, and 45 min post-radiation. Relatively, induced fragmentation was reduced after 48 h. Compared to the untreated controls cellular toxicity was induced with low LET radiation after 15, 30, and 45 min. Conversely, cytotoxicity was relatively less at 48 h after low LET radiation. Microarray analysis after low LET radiation revealed time-dependent modulation of apoptosis-related genes that are involved in radio-adaptation, spontaneous apoptosis-related early-responsive genes and late response genes. These results suggest that the time-dependent regulation of apoptotic response may determine the relationship between the level of spontaneous apoptosis in cells and their radiosensitivity.