Monitoring signal transduction in cancer: tyrosine kinase gene expression profiling.

Abnormal expression of tyrosine kinase (TK) genes is common in tumors, in which it is believed to alter cell growth and response to external stimuli such as growth factors and hormones. Although the etiology and pathogenesis of carcinomas of the thyroid or breast remain unclear, there is evidence that the expression of TK genes, such as receptor tyrosine kinases, or mitogen-activated protein kinases, is dysregulated in these tumors, and that overexpression of particular TK genes due to gene amplification, changes in gene regulation, or structural alterations leads to oncogenic transformation of epithelial cells. We developed a rapid scheme to measure semiquantitatively the expression levels of 50-100 TK genes. Our assay is based on RT-PCR with mixed based primers that anneal to conserved regions in the catalytic domain of TK genes to generate gene-specific fragments. PCR products are then labeled by random priming and hybridized to DNA microarrays carrying known TK gene targets. Inclusion of differently labeled fragments from reference or normal cells allows identification of TK genes that show altered expression levels during malignant transformation or tumor progression. Examples demonstrate how this innovative assay might help to define new markers for tumor progression and potential targets for disease intervention. (J Histochem Cytochem 49:673-674, 2001)     

Use of signal quality measurements to gain efficiency in the analysis of cDNA microarray datacDNA microarray data

This research provides a new way to measure error in microarray data in order to improve gene expression analysis. Microarray data contains many sources of error. In order to glean information about mRNA expression levels, the true signal must first be segregated from noise. This research focuses on the variation that can be captured at the spot level in cDNA microarray images. Variation at other levels, due to differences at the array, dye, and block levels, can be corrected for by a variety of existing normalization procedures. Two signal quality estimates that capture the reliability of each spot printed on a microarray are described. A parametric estimate of within-spot variance, referred to here as σ²_spot, assumes that pixels follow a normal distribution and are spatially correlated. A non-parametric estimate of error, called the mean square prediction error (MSPE), assumes that spots of high quality possess pixels that are similar to their neighbors. This paper will provide a framework to use either spot quality measure in downstream analysis, specifically as weights in regression models. Using these spot quality estimates as weights can result in greater efficiency, in a statistical sense, when modeling microarray data.     

Correlation statistics for cDNA microarray image analysis

In this paper, correlation of the pixels comprising a microarray spot is investigated. Subsequently, correlation statistics, namely, Pearson correlation and Spearman rank correlation, are used to segment the foreground and background intensity of microarray spots. The performance of correlation-based segmentation is compared to clustering-based (PAM, k-means) and seeded-region growing techniques (SPOT). It is shown that correlation-based segmentation is useful in flagging poorly hybridized spots, thus minimizing false-positives. The present study also raises the intriguing question of whether a change in correlation can be an indicator of differential gene expression.     

肺癌相关基因芯片的制作

探讨肺癌相关基因芯片的制作过程及其在基因表达谱方面的应用,同时对一些传统的技术环节进行了改进。首先有目的搜集了60个肺癌相关基因,用修饰后的引物将其分别克隆,经纯化、变性后,用Cartesian PinSys5500 cDNA Microarray点样仪以微阵列的形式将其点布于醛基化的玻璃片上;然后将人支气管上皮恶性转化细胞模型BEP2D细胞的原代、20代、35代的总RNA逆转录后再经LD PCR标记成荧光探讨与基因芯片进行杂交。ScanArray 3000荧光扫描仪扫描后显示,图象背景均匀、信号清晰,证实该芯片制作成功,并能取得良好的杂交效果。     

细胞信号传导和肿瘤

信号传递系统的存在及其过程是近年细胞生物学,分子生物学和医学领域的研究热点之一。信号传递异常与肿瘤等多种疾病的发生,发展和预后直接相关。本所述是迄今认识比较清楚的与肿瘤密切相关的几条信号传导途径,阐明它们的作用机制对于洞悉肿瘤发病机制并最终攻克肿瘤均有重要意义。     

Use of signal quality measurements to gain efficiency in the analysis of cDNA microarray data

This research provides a new way to measure error in microarray data in order to improve gene expression analysis.Microarray data contains many sources of error.In order to glean information about mRNA expression levels,the true signal must first be segregated from noise.This research focuses on the variation that can be captured at the spot level in cDNA microarray images.Variation at other levels,due to differences at the array,dye,and block levels,can be corrected for by a variety of existing normalizati...     

Use of signal quality measurements to gain efficiency in the analysis of cDNA microarray data

This research provides a new way to measure error in microarray data in order to improve gene expression analysis.Microarray data contains many sources of error.In order to glean information about mRNA expression levels,the true signal must first be segregated from noise.This research focuses on the variation that can be captured at the spot level in cDNA microarray images.Variation at other levels,due to differences at the array,dye,and block levels,can be corrected for by a variety of existing normalization procedures.Two signal quality estimates that capture the reliability of each spot printed on a microarray are described.A parametric estimate of within-spot vari ance,referred to here as σ s2pot,assumes that pixels follow a normal distribution and are spatially correlated.A non-parametric estimate of error,called the mean square prediction error(MSPE),assumes that spots of high quality possess pixels that are similar to their neighbors.This paper will provide a framework to use either spot quality measure in downstream analysis,specifically as weights in regression models.Using these spot quality estimates as weights can result in greater efficiency,in a statistical sense,when modeling microarray data.     

Fundamentals of cDNA microarray data analysis

Microarray technology is a powerful approach for genomics research. The multi-step, data-intensive nature of this technology has created an unprecedented informatics and analytical challenge. It is important to understand the crucial steps that can affect the outcome of the analysis. In this review, we provide an overview of the contemporary trend on various main analysis steps in the microarray data analysis process, which includes experimental design, data standardization, image acquisition and analysis, normalization, statistical significance inference, exploratory data analysis, class prediction and pathway analysis, as well as various considerations relevant to their implementation.     

cDNA芯片阳性对照的制备及在芯片敏感性分析中的应用

cDNA芯片是一种高通量基因表达谱分析技术,在生理病理条件下细胞基因表达谱分析,新基因发现和功能研究等方面具有广阔应用前景。CDNA芯片阳性对照的选取以及CDNA芯片检测敏感性是芯片成功应用的关键问题之一。以在系统发育上与人类基因同源性小的荧火虫荧光素酶基因材料,制备了用于人类和其他动物基因表达谱CDNA芯片的通用型阳性对照探针和相应的mRNA参照物,经反转录对mRNA参照物进行Cy3荧光标记并与DNA芯片杂交后发现,mRNA参照物能特异性地与荧光酶基因cDNA片断杂交,而与人β-肌动蛋白基因,人G3PDH基因以及λDNA/HINDⅢ无杂交反应。把mRNA参照物以不同比例加入HepG2总RNA中,以反转录荧光标记后与CDNA芯片杂交,结果发现当总RNA中的MRNA含量为1/10^4稀释（即mRNA分子个数约为10^8个）时,CDNA芯片基本检测不出mRNA标记产物的杂交信号。而且,cDNA芯片检测的信号强度与芯片上固定的探针浓度密切相关,当探针浓度为2g/L时,杂交信号最强,随着探针浓度下降芯片的杂交信号趋于减弱。CDNA芯片通用型阳性参照物的制备以及应用于CDNA芯片检测敏感性研究为CDNA芯片应用于人和其他动物基因表达谱高通量分析和新基因功能研究提供了技术基础和理论依据。     

Microsystems for the analysis of cellular signal transduction

Drug development and cell biological research alike are confronted by the complexity of molecular signal transduction networks. These networks integrate information from different stimuli inside the cell. Progress in both disciplines will largely depend on experimental techniques that reflect the complexity of cellular signalling networks. The testing of combinations, either of drug candidates or of stimuli is considered one promising solution of this challenge. However, the systematic testing of combinations introduces a new dimension of experimental complexity. Miniaturization, parallelization and especially a reduction of the number of steps required for the preparation of samples are prerequisites for resolving this dilemma. As a solution for the testing of different combinations of drug candidates, we present the generation of substance mixtures by diffusion. For each combination of compounds only two pipetting steps are required for generating a virtually unlimited number of different test conditions. The potential of testing the response of cells to systematic combinations of different stimuli is demonstrated by using antibody microarrays where each spot carries a different combination of stimulatory antibodies. Both experimental strategies are combinatorial in nature. Through miniaturization and implementation of high-content read-outs a maximum of information is obtained with a minimum of manipulations. These authors contributed equally to this work.     

A concise guide to cDNA microarray analysis

Microarray expression analysis has become one of the most widely used functional genomics tools. Efficient application of this technique requires the development of robust and reproducible protocols. We have optimized all aspects of the process, including PCR amplification of target cDNA clones, microarray printing, probe labeling and hybridization, and have developed strategies for data normalization and analysis.     

cDNA microarray analysis of Helicobacter pylori-mediated alteration of gene expression in gastric cancer cells

Helicobacter pylori infection stimulates several intracellular signaling pathways and is accompanied by increased gene expression in gastric epithelial cells. High-density cDNA microarray was used to characterize the mRNA expression profile of genes in human gastric cancer cells (MKN45, AGS) cocultured with H. pylori. Coculture with cag pathogenicity island (PAI)-positive H. pylori (wild-type) significantly up-regulated mRNA expression in 8 of 2304 genes tested. In 6 (interleukin-8, I(kappaB)alpha, A20, ERF-1, keratin K7, glutathione peroxidase) of the 8 genes, up-regulation was confirmed by RT-PCR. In coculture with isogenic cagE-negative mutant ((Delta)cagE), which encodes a type IV secretion system with other genes in the cag PAI, no significant up-regulation was found. We further analyzed the role of A20. Transfection of expression vector encoding A20 resulted in an inhibition of H. pylori-mediated NF-kappaB activation, indicating that H. pylori-mediated A20 expression could be a negative regulator of NF-kappaB activation. Taken together, these results indicate the importance of microarray technology as a tool for analyzing the complex interplay between H. pylori and the host.     

Monitoring gene expression profile changes in ovarian carcinomas using cDNA microarray

The development of cancer is the result of a series of molecular changes occurring in the cell. These events lead to changes in the expression level of numerous genes that result in different phenotypic characteristics of tumors. In this report we describe the assembly and utilization of a 5766 member cDNA microarray to study the differences in gene expression between normal and neoplastic human ovarian tissues. Several genes that may have biological relevance in the process of ovarian carcinogenesis have been identified through this approach. Analyzing the results of microarray hybridizations may provides new leads for tumor diagnosis and intervention.     

CTAB-urea method purifies RNA from melanin for cDNA microarray analysis

Melanin represents a major problem for the study of melanoma by microarrays since it is retained during RNA extraction and inhibits the enzymatic reactions used for probe preparation. Here we report a new method for cleaning RNA from melanin, based on the use of the cationic detergent cetyl-trimethylammonium bromide (CTAB)-urea for RNA precipitation. This method is easy to perform and has a low cost. Purified RNA is recovered with high quality and good yield. CTAB-urea treated RNA from highly pigmented melanoma cells can be successfully reverse transcribed and labeled to obtain probes which can be subsequently used in cDNA microarray experiments, giving consistent and reproducible results.