基因表达谱芯片的数据挖掘

随着基因芯片技术的迅速发展,表达谱芯片分析及aCGH等方法已被广泛应用于生命科学各个研究领域,由此产生的数据也呈指数级增长。如何从海量数据中获取有生物学意义的结果成为摆在生物学工作者面前的难题。对表达谱芯片数据挖掘方法进行了综述。介绍了基本分析思路,当前重点分析方向,如GO分析、pathway与调控网络分析、聚类分析等计算法则和相关几款易用的分析软件。并介绍了几种科学自由计算软件在表达谱生物信息学分析中的应用。藉此为从事芯片分析的研究人员提供参考。     

Profiling of differential protein expression in angiogenin-induced HUVECs using antibody-arrayed ProteoChip

ProteoChip has been developed as a novel protein microarray technology. So far it has been applied in new lead screening and molecular diagnostics and we expect its role to grow in the field of biology. Here, we investigated the application of ProteoChip for the study of differential protein expression profiles in angiogenin-induced human umbilical vein endothelial cells (HUVECs). Antibody microarrays constructed by immobilizing 60 distinct antibodies against signal-transducing proteins on ProteoChip base plates were used to analyze the expression pattern of cell-signaling proteins in HUVECs treated with angiogenin. The antibody microarray approach showed that angiogenin induced the up- and down-regulation of several cellular regulators related with cell proliferation. Changes in the expression of signaling proteins determined by antibody microarray were validated by Western blot analysis. In this experiment, ten up-regulated proteins and six down-regulated proteins were identified and confirmed by immunoblot analysis. Taken together, these data suggest that antibody microarrays using ProteoChip technology can be a powerful tool for high-throughput analysis of proteomes in biological samples.     

Designing a microarray experiment to estimate dominance in maize (Zea mays L.)

Experiments using cDNA microarrays for the identification of genes with certain expression patterns require a thoughtfully planned design. This study was conducted to determine an optimal design for a microarray experiment to estimate differential gene expression between hybrids and their parental inbred lines in maize (i.e. dominance). It has two features: the contrasts of interest contain more than two genotypes and the procedure may be customised to other microarray experiments where different effects may influence hybridisation signals. A mixed model was used to include all important effects. Impacts during growth of the plant material were taken into consideration as well as those occurring during hybridisation. The results of a preliminary experiment were used to determine which effects were to be included in the model, and data from another microarray experiment were used to estimate variance components. In order to select good designs, an optimality criterion adapted to the problem of differential gene expression between hybrids and their parental inbred lines was defined. Two approaches were used to determine an optimal design: the first one simplifies the problem by dividing it into several subproblems, whereas the second is more sophisticated and uses a simulated annealing (SA) algorithm. We found that the first approach constitutes a useful means for designing microarray experiments to study this problem. Using the more sophisticated SA approach the design can be further improved.     

Effect of pooling samples on the efficiency of comparative studies using microarrays

MOTIVATION: Many biomedical experiments are carried out by pooling individual biological samples. However, pooling samples can potentially hide biological variance and give false confidence concerning the data significance. In the context of microarray experiments for detecting differentially expressed genes, recent publications have addressed the problem of the efficiency of sample pooling, and some approximate formulas were provided for the power and sample size calculations. It is desirable to have exact formulas for these calculations and have the approximate results checked against the exact ones. We show that the difference between the approximate and the exact results can be large. RESULTS: In this study, we have characterized quantitatively the effect of pooling samples on the efficiency of microarray experiments for the detection of differential gene expression between two classes. We present exact formulas for calculating the power of microarray experimental designs involving sample pooling and technical replications. The formulas can be used to determine the total number of arrays and biological subjects required in an experiment to achieve the desired power at a given significance level. The conditions under which pooled design becomes preferable to non-pooled design can then be derived given the unit cost associated with a microarray and that with a biological subject. This paper thus serves to provide guidance on sample pooling and cost-effectiveness. The formulation in this paper is outlined in the context of performing microarray comparative studies, but its applicability is not limited to microarray experiments. It is also applicable to a wide range of biomedical comparative studies where sample pooling may be involved.     

凝集素芯片对体液细胞进行检测方法的建立和初步应用

建立对体液细胞进行自动捕获的凝集素芯片体系,利用凝集素对糖链的特异亲和作用捕获细胞,提取白血病患者外周血、肺癌胸水和肝腹水中细胞进行荧光标记,凝集素芯片捕获,激光扫描仪检测捕获细胞的荧光信号,常规HE染色后光学显微镜下观察细胞的形态并进行免疫化学反应,流式细胞仪验证凝集素芯片的特异性．结果表明：凝集素芯片可以对体液中的癌细胞进行自动捕获,对癌细胞膜表面糖链进行识别．芯片检测的细胞浓度最少可达每mL10^4个左右．芯片有较好的重复性和特异性．这种凝集素芯片可用于临床体液中癌细胞的检测分析,对癌细胞膜表面凝集素亲和位点进行即时、高通量的检测,为了解细胞膜表面聚糖在癌变过程中的变化提供了一个技术平台．     

Microarrays in biology and medicine

The remarkable speed with which biotechnology has become critical to the practice of life sciences owes much to a series of technological revolutions. Microarray is the latest invention in this ongoing technological revolution. This technology holds the promise to revolutionize the future of biology and medicine unlike any other technology that preceded it. Development of microarray technology has significantly changed the way questions about diseases and/or biological phenomena are addressed. This is because microarrays facilitate monitoring the expression of thousands of genes or proteins in a single experiment. This enormous power of microarrays has enabled scientists to monitor thousands of genes and their products in a given living organism in one experiment, and to understand how these genes function in an orchestrated manner. Obtaining such a global view of life at the molecular level was impossible using conventional molecular biological techniques. However, despite all the progress made in developing this technology, microarray is yet to reach a point where all data are obtained, analyzed, and shared in a standardized fashion. The present article is a brief overview of microarray technologies and their applications with an emphasis on DNA microarray.     

Consolidated strategy for the analysis of microarray spike-in data

As the number of users of microarray technology continues to grow, so does the importance of platform assessments and comparisons. Spike-in experiments have been successfully used for internal technology assessments by microarray manufacturers and for comparisons of competing data analysis approaches. The microarray literature is saturated with statistical assessments based on spike-in experiment data. Unfortunately, the statistical assessments vary widely and are applicable only in specific cases. This has introduced confusion into the debate over best practices with regards to which platform, protocols and data analysis tools are best. Furthermore, cross-platform comparisons have proven difficult because reported concentrations are not comparable. In this article, we introduce two new spike-in experiments, present a novel statistical solution that enables cross-platform comparisons, and propose a comprehensive procedure for assessments based on spike-in experiments. The ideas are implemented in a user friendly Bioconductor package: spkTools. We demonstrated the utility of our tools by presenting the first spike-in-based comparison of the three major platforms–Affymetrix, Agilent and Illumina.     

乳酸菌基因芯片应用研究进展

基因芯片技术是上世纪90年代兴起的一种对成百上千甚至上万个基因同时进行检测的新技术,具有高通量、并行化的特点,广泛应用于基因表达谱测定、基因功能预测、基因突变检测和多态性分析等方面。多种乳酸菌基因组全序列以及其大量EST、16S rDNA、16S-23S基因间区和功能基因序列测定的完成,有力地推动了基因芯片技术在乳酸菌研究中的应用。介绍了基因芯片的基本原理及乳酸菌基因芯片在基因表达、种属鉴定等研究中的应用进展,以期更好地利用和开发乳酸菌基因芯片。     

改性聚二甲基硅氧烷 (iPDMS) 蛋白芯片在肿瘤相关抗原自身抗体筛查中的应用

肿瘤标记的快速筛查是临床早期诊断的难题。利用化学发光蛋白质芯片技术,对低丰度的肿瘤相关抗原的自身抗体进行高灵敏度的筛选,是一种有益的尝试。本研究首先将带烯烃末端的、引发聚合反应的表面引发剂加入到常规聚二甲基硅氧烷材料中,再通过热交联反应固定到聚二甲基硅氧烷的三维结构中,形成改性聚二甲基硅氧烷 (iPDMS)。为了使iPDMS材料具有抗蛋白质非特异性吸附的特性,在活性引发位点处通过表面引发的原子转移自由基聚合反应合成poly(OEGMA) 高分子刷。最后将20种肿瘤相关的抗原利用高通量喷点打印技术打印到芯片的特定区域,并组装成iPDMS芯片的48孔检测微孔板。对临床上常见的8种肿瘤患者血清进行分析,发现VEGFR1和VEGF121自身抗体对常见的8种肿瘤具有检测价值,有望成为肿瘤快速筛查的检测指标。     

Quantitative quality control in microarray experiments and the application in data filtering,normalization and false positive rate prediction

Data preprocessing including proper normalization and adequate quality control before complex data mining is crucial for studies using the cDNA microarray technology. We have developed a simple procedure that integrates data filtering and normalization with quantitative quality control of microarray experiments. Previously we have shown that data variability in a microarray experiment can be very well captured by a quality score q(com) that is defined for every spot, and the ratio distribution depends on q(com). Utilizing this knowledge, our data-filtering scheme allows the investigator to decide on the filtering stringency according to desired data variability, and our normalization procedure corrects the q(com)-dependent dye biases in terms of both the location and the spread of the ratio distribution. In addition, we propose a statistical model for false positive rate determination based on the design and the quality of a microarray experiment. The model predicts that a lower limit of 0.5 for the replicate concordance rate is needed in order to be certain of true positives. Our work demonstrates the importance and advantages of having a quantitative quality control scheme for microarrays.     

Ground based studies of gene expression in Arabidopsis exposed to gravity stresses

As a link in the preparation of the MULTIGEN experiment, which will take place on the International Space Station, ground based studies of the gene expression in Arabidopsis thaliana were performed. Microarray technology was used to screen Arabidopsis seedlings exposed to simulated hypogravity on a Random Positioning Machine and a 1 x g control sample. This screening showed differential expression in 177 out of approximately 8000 genes. Some of these genes can be grouped into functional categories, e.g. general metabolism, biogenesis of cellular components, cellular transport and transport facilitation, and cell rescue and defense response. However, about 50% of the genes encode proteins with unknown function. Based on the above results a new "in-house" cDNA microarray was constructed. Some of the selected genes on this microarray (e.g. Xyloglucan endotransglycosylase, At2g18800) showed differential expression both in Arabidopsis exposed to hypergravity and simulated hypogravity by use of a centrifuge and a Random Positioning Machine.     

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.     

Statistical designs for two-color spotted microarray experiments

Two-color cDNA or oligonucleotide-based spotted microarrays have been commonly used in measuring the expression levels of thousands of genes simultaneously. To realize the immense potential of this powerful new technology, budgeted within limited resources or other constraints, practical designs with high efficiencies are in demand. In this study, we address the design issue concerning the arrangement of the mRNA samples labeled with fluorescent dyes and hybridized on the slides. A normalization model is proposed to characterize major sources of systematic variation in a two-color microarray experiment. This normalization model establishes a connection between designs for two-color microarray experiments with a particular class of classical row-column designs. A heuristic algorithm for constructing A-optimal or highly efficient designs is provided. Statistical optimality results are found for some of the designs generated from the algorithm. It is believed that the constructed designs are the best or very close to the best possible for estimating the relative gene expression levels among the mRNA samples of interest.     

Impact of RNA extraction from limited samples on microarray results

To move microarray technology into the diagnostic realm, the impact of technical parameters, such as sample preparation and RNA extraction, needs to be understood and minimized. We evaluated the impact of two RNA extraction methods, DNase treatment and the amount of hybridized cDNA probe, on the outcome of microarray results. The results for both RNA extraction methods were comparable, although one method resulted in residual DNA that slightly affected the microarray results. As little as one microgram of total RNA could be used to synthesize a cDNA probe and resulted in a gene expression profile that was similar to one produced using 5 micrograms total RNA, even though the overall signal intensity was lower. These experiments illustrate that microarray technology holds great promise for the use of limited clinical samples in the diagnostic setting.     

The implications of microarray technology for animal use in scientific research

Microarray technology has the potential to affect the number of laboratory animals used, the severity of animal experiments, and the development of non-animal alternatives in several areas scientific research. Microarrays can contain hundreds or thousands of microscopic spots of DNA, immobilised on a solid support, and their use enables global patterns of gene expression to be determined in a single experiment. This technology is being used to improve our understanding of the operation of biological systems during health and disease, and their responses to chemical insults. Although it is impossible to predict with certainty any future trends regarding animal use, microarray technology might not initially reduce animal use, as is often claimed to be the case. The accelerated pace of research as a result of the use of microarrays could increase overall animal use in basic and applied biological research, by increasing the numbers of interesting genes identified for further analysis, and the number of potential targets for drug development. Each new lead will require further evaluation i n studies that could involve animals. In toxicity testing, microarray studies could lead to increases in animal studies, if further confirmatory and other studies are performed. However, before such technology can be used more extensively, several technical problems need to be overcome, and the relevance of the data to biological processes needs to be assessed. Were microarray technology to be used in the manner envisaged by its protagonists, there need to be efforts to increase the likelihood that its application will create new opportunities for reducing, refining and replacing animal use. This comment is a critical assessment of the possible implications of the application of microarray technology on animal experimentation in various research areas, and makes some recommendations for maximising the application of the Three Rs.