基因芯片技术与微生物学

基因芯片技术作为生物芯片技术一个发展最完备的分支,近十年来,已经成为国内外研究的一个热点,基因芯片可以分为cDNA芯片和寡核苷酸芯片,cDNA芯片有多种制备方法,在基因表达相关研究方面具有重大价值;寡核苷酸芯片以美国Affymetrix公司的GeneChip为代表,主要应用于杂交测序,单核苷酸多态性分析和突变检测。本文分别对这两种芯片的制备,样品处理,杂交和信号检测分析技术作一综述。对近年来基因芯片技术在微生物学领域的应用进行了介绍。     

等Tm检测探针寡核苷酸芯片在p53突变热点检测中的应用

用等长探针检测基因的点突变,不同GC含量探针的碱基错分辨率很难均一。尝试利用探针近似等Tm的原则设计、制备了检测抑癌基因p53外显子7中密码子245、248、249单碱基突变及缺失的寡核苷酸芯片。实验得到较好的碱基错配分辨率,检测不同位点的碱基错配分辨率较为一致,芯片检测结果与测序结果一致。实验结果为制备检测p53常见热点突变的寡核酸芯片奠定了基础。     

应用多重标记引物增强寡核苷酸芯片的荧光信号强度

寡核苷酸芯片技术是一种高通量发掘和采集生物信息的强大技术平台,目前已广泛应用于生物科学领域 . 为改善寡核苷酸芯片的分析性能,对影响芯片杂交结果的因素,如片基表面的化学处理、探针的长度、间隔臂的长度、杂交条件等,进行了深入的研究和优化 . 对寡核苷酸芯片而言,仍有待解决的问题是如何产生更强的荧光信号来改善其检测灵敏度 . 利用两种类型的多个荧光分子标记的引物,来增强二维寡核苷酸芯片平面上的荧光信号强度 . 两种引物分别命名为：多标记线性引物和多标记分支引物 . 通过增加标记在目标 DNA 片段上的荧光分子数,可以显著增强寡核苷酸芯片上相应捕获探针的信号强度 . 实验表明,使用多标记引物能将所用的寡核苷酸微阵列的检测限 ( 以能够检测的最低模板量计算 ) 降低至单荧光标记引物的 1/100 以下,多重标记技术是一种有效增强微型化探针矩阵检测灵敏度的信号放大方法 .     

人类基因组DNA单核苷酸多态性的检测方法

单核苷酸多态性（SNP）作为新的遗传标记对基因定位及相关疾病的研究意义重大。本文对近年来9种SNP检测方法的原理、应用及优缺点,包括基于FRET原理的Taqman法和分子灯塔法;基于分子杂交技术的寡核苷酸连接分析、等位基因特异性寡核苷酸探针杂交法、动态等位基因特异性杂交法及DNA芯片法;及质谱法、变性－高压液相色谱法和单个碱基延伸标记法进行综述。     

应用寡核苷酸芯片筛选维甲酸诱导神经母细胞瘤细胞系SH-SY5Y分化成神经元过程中的差异表达基因

目的:应用寡核苷酸芯片筛选维甲酸(RA)诱导神经母细胞瘤细胞系SH-SY5Y分化成神经元过程中的差异表达基因。方法:从人胎脑及不同类型神经系统肿瘤组织中获取目的基因,查询相应基因mRNA序列,设计并合成探针,制备了含218种基因的神经功能相关的寡核苷酸芯片。应用RA诱导SH-SY5Y8d分化成成熟神经元,提取对照组和实验组每天的总RNA,通过逆转录荧光标记cDNA探针并与芯片杂交,洗片后扫描获取图像,数据分析获得差异表达基因,并通过RT-PCR进行验证。结果:发现13种基因表达上调,没有得到下调基因。RT-PCR验证结果基本与芯片结果一致。结论:SH-SY5Y经RA诱导分化成神经元存在一些差异表达的基因,寡核苷酸芯片技术可为研究SH-SY5Y诱导分化成神经元的分子作用机理提供技术平台。     

核酸适配体筛选技术研究进展

核酸适配体是一段特殊的寡核苷酸配基,对靶标有亲和力和特异性。核酸适配体发展十分迅速,但筛选方法的研究相对来说还不能满足适配体应用的需求。笔者从筛选过程出发,针对近年来较为新颖热门的非天然核苷酸替代传统核苷酸、镜像核酸库代替核酸库等核酸库优化方法,毛细管电泳筛选、微流控芯片技术、微阵列芯片、粒子展示等筛选策略改进技术,高通量测序等次级文库序列分析,碱基突变与修饰、序列截短等适配体结构优化等具体内容进行总结,探讨各种方法的研究进展及应用前景。     

三色荧光技术在SNP检测中的应用

利用三色荧光标记的A、C、T双脱氧核苷酸单碱基延伸的方法结合编码寡核苷酸芯片技术检测单核苷酸多态性 (SNP)的基因型。以beta地中海贫血样本基因 (HBB基因 )突变作为模型的研究结果显示该方法能同时对多位点的SNP进行检测。     

肿瘤相关基因表达检测寡核苷酸芯片的制备及其初步应用

为研制肿瘤相关寡核苷酸芯片,并实现其在抗肿瘤反义核酸“癌泰得”作用机理研究方面的初步应用,制备了包含近450种肿瘤相关基因特异寡核苷酸探针的寡核苷酸芯片,建立了相应的质控标准.“癌泰得”用脂质体转染HepG2肿瘤细胞,提取细胞总RNA反转录并荧光标记cDNA,用制备的寡核苷酸芯片检测肝癌细胞HepG2的肿瘤相关基因表达水平,用软件分析获得其差异基因表达谱.0.4 μmol/L的反义核酸“癌泰得”作用于HepG2细胞15 h后,MDNCF、DHS等基因mRNA表达下调,MUC2、MPP11、LAT、HRIF-B、JNK3A1等mRNA基因表达上调,初步检测到了“癌泰得”的抗肿瘤作用可能的相关基因,为进一步的分子作用机理的探讨奠定基础.结果表明,制备的肿瘤相关芯片敏感度高、特异性高、重复性均较好,可用于检测肿瘤相关基因的表达谱,为临床诊断和基础研究提供了技术平台.     

HBV耐药相关DNA突变体的构建及在寡核苷酸基因芯片分析中的应用

拉米呋啶（Lamivudine)是近年来开发成功的一种治疗HBV慢性感染病人的核苷类药物。随着拉米呋啶在临床上广泛使用,HBV耐药现象已成为临床实践中的棘手问题。建立HBV耐药测定技术成为HBV基础研究和临床实践中企待解决的一个重要问题。寡核苷酸芯片（Oligochip)是近年来发展并逐步成熟的一种高通量基因检测技术,已成功应用于基因突变快速和高通量检测。本研究在构建HBV拉米呋啶抗性相关HBV多聚酶基因突变体基础上,设计并制备了HBV耐药寡核苷酸芯片（HBV－Lam Oligochip)。根据HBV拉米呋啶抗药性相关突变主要位于HBV多聚酶基因的L526、A546、M550和V553等氨基酸位点,设计了28条寡核苷酸探针。探针对应序列为HBV DNA聚合酶基因反义链,HBVHBVhb长度为15－18nt。探针合成时在其3′端接氨基和间隔臂（spacer)等特定修饰;探针纯化与定量后,用基因芯片点样仪点到醛基修饰载玻片上,制成HBV耐药寡核苷酸芯片（HBV－Lam Oligochip)。为了分析基因芯片的性能,克隆了833号HBV病人血清中HBV DNA聚合酶基因,测序证实为HBV DNA野生型（即未发生突变）。以该DNA为模板,用PCR法构建了HBV耐药相关突变体。用HBV－Lam Oligochip对HBV野生型DNA和人工构建DNA突变体进行了检测。结果发现,HBV－Lam Oligochip能有效检测出野生型DNA序列。检测突变体时,HBV－Lam Oligochip检测结果与相应突变体DNA序列一致,表明HBV－Lam Oligochip不仅可检测出野生型序列,而且可有效地分辨出单碱基突变,可应用于HBV耐药基因突变临床检测。     

寡核苷酸芯片在微生物检测中的应用

近几年来发展起来的基因组研究技术———基因芯片技术为微生物检测提供了一种强有力的手段。目前国内外已广泛地开展了利用寡核苷酸芯片对多种微生物 (主要是病毒和细菌 ,少量有真菌 )进行相关检测的研究 ,并在对微生物病原体检测、种类鉴定、功能基因检测、基因分型、突变检测、基因组监测等方面获得了成功。由于寡核苷酸探针具有可根据研究需要任意设计、特异性高等特点 ,寡核苷酸芯片在微生物检测中有着巨大的应用价值 ,具有广阔的应用前景。     

Microarray analysis: basic strategies for successful experiments

Microarrays offer a powerful approach to the analysis of gene expression that can be used for a wide variety of experimental purposes. However, there are several types of microarray platforms that are available. In addition, microarray experiments are expensive and generate complicated data sets that can be difficult to interpret. Success with microarray approaches requires a sound experimental design and a coordinated and appropriate use of statistical tools. Here, the advantages and pitfalls of utilizing microarrays are discussed, as are practical strategies to help novice users succeed with this method that can empower them with the ability to assay changes in gene expression at the whole genome level.     

Intra-Platform Repeatability and Inter-Platform Comparability of MicroRNA Microarray Technology

Over the last decade, DNA microarray technology has provided a great contribution to the life sciences. The MicroArray Quality Control (MAQC) project demonstrated the way to analyze the expression microarray. Recently, microarray technology has been utilized to analyze a comprehensive microRNA expression profiling. Currently, several platforms of microRNA microarray chips are commercially available. Thus, we compared repeatability and comparability of five different microRNA microarray platforms (Agilent, Ambion, Exiqon, Invitrogen and Toray) using 309 microRNAs probes, and the Taqman microRNA system using 142 microRNA probes. This study demonstrated that microRNA microarray has high intra-platform repeatability and comparability to quantitative RT-PCR of microRNA. Among the five platforms, Agilent and Toray array showed relatively better performances than the others. However, the current lineup of commercially available microRNA microarray systems fails to show good inter-platform concordance, probably because of lack of an adequate normalization method and severe divergence in stringency of detection call criteria between different platforms. This study provided the basic information about the performance and the problems specific to the current microRNA microarray systems.     

A parallel study of different array-CGH platforms in a set of Spanish patients with developmental delay and intellectual disability

Developmental delay and intellectual disability, which occur in 1–3% of the population, account for a large number of the cases regularly seen in genetic units. Chromosomal microarray analysis has been shown to be a valuable clinical diagnostic assay and it should be the first-tier clinical diagnostic test for individuals with these conditions. However and due to several difficulties such as the platform resolution, the cost, and the inexperience with genomic data bases, the implementation of this test in many cytogenetic laboratories has been delayed. In an attempt to provide more insights of the benefits derived by using the chromosomal microarray analysis, this study presents the experience of two clinical centers using three different microarray platforms. The results obtained using a custom microarray (KaryoArray®) and two different commercial medium- and high-resolution whole-genome oligonucleotide microarrays have been compared. An overall diagnostic yield of around 15% has been obtained. However, the custom microarray platform has been shown to be more convenient for a clinical setting, since it allows the detection of more pathogenic copy number variants and less common variants.     

The end of the microarray Tower of Babel: will universal standards lead the way?

Microarrays are the most common method of studying global gene expression, and may soon enter the realm of FDA-approved clinical/diagnostic testing of cancer and other diseases. However, the acceptance of array data has been made difficult by the proliferation of widely different array platforms with gene probes ranging in size from 25 bases (oligonucleotides) to several kilobases (complementary DNAs or cDNAs). The algorithms applied for image and data analysis are also as varied as the microarray platforms, perhaps more so. In addition, there is a total lack of universally accepted standards for use among the different platforms and even within the same array types. Due to this lack of coherency in array technologies, confusion in interpretation of data within and across platforms has often been the norm, and studies of the same biological phenomena have, in many cases, led to contradictory results. In this commentary/review, some of the causes of this confusion will be summarized, and progress in overcoming these obstacles will be described, with the goal of providing an optimistic view of the future for the use of array technologies in global expression profiling and other applications.     

Are data from different gene expression microarray platforms comparable?

Many commercial and custom-made microarray formats are routinely used for large-scale gene expression surveys. Here, we sought to determine the level of concordance between microarray platforms by analyzing breast cancer cell lines with in situ synthesized oligonucleotide arrays (Affymetrix HG-U95v2), commercial cDNA microarrays (Agilent Human 1 cDNA), and custom-made cDNA microarrays from a sequence-validated 13K cDNA library. Gene expression data from the commercial platforms showed good correlations across the experiments (r = 0.78-0.86), whereas the correlations between the custom-made and either of the two commercial platforms were lower (r = 0.62-0.76). Discrepant findings were due to clone errors on the custom-made microarrays, old annotations, or unknown causes. Even within platform, there can be several ways to analyze data that may influence the correlation between platforms. Our results indicate that combining data from different microarray platforms is not straightforward. Variability of the data represents a challenge for developing future diagnostic applications of microarrays.     

RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis

While meta-analysis provides a powerful tool for analyzing microarray experiments by combining data from multiple studies, it presents unique computational challenges. The Bioconductor package RankProd provides a new and intuitive tool for this purpose in detecting differentially expressed genes under two experimental conditions. The package modifies and extends the rank product method proposed by Breitling et al., [(2004) FEBS Lett., 573, 83-92] to integrate multiple microarray studies from different laboratories and/or platforms. It offers several advantages over t-test based methods and accepts pre-processed expression datasets produced from a wide variety of platforms. The significance of the detection is assessed by a non-parametric permutation test, and the associated P-value and false discovery rate (FDR) are included in the output alongside the genes that are detected by user-defined criteria. A visualization plot is provided to view actual expression levels for each gene with estimated significance measurements. AVAILABILITY: RankProd is available at Bioconductor http://www.bioconductor.org. A web-based interface will soon be available at http://cactus.salk.edu/RankProd     

Current issues for DNA microarrays: platform comparison, double linear amplification, and universal RNA reference

DNA microarray technology has been widely used to simultaneously determine the expression levels of thousands of genes. A variety of approaches have been used, both in the implementation of this technology and in the analysis of the large amount of expression data. However, several practical issues still have not been resolved in a satisfactory manner, and among the most critical is the lack of agreement in the results obtained in different array platforms. In this study, we present a comparison of several microarray platforms [Affymetrix oligonucleotide arrays, custom complementary DNA (cDNA) arrays, and custom oligo arrays printed with oligonucleotides from three different sources] as well as analysis of various methods used for microarray target preparation and the reference design. The results indicate that the pairwise correlations of expression levels between platforms are relative low overall but that the log ratios of the highly expressed genes are strongly correlated, especially between Affymetrix and cDNA arrays. The microarray measurements were compared with quantitative real-time-polymerase chain reaction (QRT-PCR) results for 23 genes, and the varying degrees of agreement for each platform were characterized. We have also developed and tested a double amplification method which allows the use of smaller amounts of starting material. The added round of amplification produced reproducible results as compared to the arrays hybridized with single round amplified targets. Finally, the reliability of using a universal RNA reference for two-channel microarrays was tested and the results suggest that comparisons of multiple experimental conditions using the same control can be accurate.     

Analysis of matched mRNA measurements from two different microarray technologies

MOTVIATION: The existence of several technologies for measuring gene expression makes the question of cross-technology agreement of measurements an important issue. Cross-platform utilization of data from different technologies has the potential to reduce the need to duplicate experiments but requires corresponding measurements to be comparable. METHODS: A comparison of mRNA measurements of 2895 sequence-matched genes in 56 cell lines from the standard panel of 60 cancer cell lines from the National Cancer Institute (NCI 60) was carried out by calculating correlation between matched measurements and calculating concordance between cluster from two high-throughput DNA microarray technologies, Stanford type cDNA microarrays and Affymetrix oligonucleotide microarrays. RESULTS: In general, corresponding measurements from the two platforms showed poor correlation. Clusters of genes and cell lines were discordant between the two technologies, suggesting that relative intra-technology relationships were not preserved. GC-content, sequence length, average signal intensity, and an estimator of cross-hybridization were found to be associated with the degree of correlation. This suggests gene-specific, or more correctly probe-specific, factors influencing measurements differently in the two platforms, implying a poor prognosis for a broad utilization of gene expression measurements across platforms.     

Correlation analysis of external RNA controls reveals its utility for assessment of microarray assay

Quality control of a microarray experiment has become an important issue for both research and regulation. External RNA controls (ERCs), which can be either added to the total RNA level (tERCs) or introduced right before hybridization (cERCs), are designed and recommended by commercial microarray platforms for assessment of performance of a microarray experiment. However, the utility of ERCs has not been fully realized mainly due to the lack of sufficient data resources. The US Food and Drug Administration (FDA)-led community-wide Microarray Quality Control (MAQC) study generates a large amount of microarray data with implementation of ERCs across several commercial microarray platforms. The utility of ERCs in quality control by assessing the ERCs’ concentration-response behavior was investigated in the MAQC study. In this work, an ERC-based correlation analysis was conducted to assess the quality of a microarray experiment. We found that the pairwise correlations of tERCs are sample independent, indicating that the array data obtained from different biological samples can be treated as technical replicates in analysis of tERCs. Consequently, the commonly used quality control method of applying correlation analysis on technical replicates can be adopted for assessing array performance based on different biological samples using tERCs. The proposed approach is sensitive to identifying outlying assays and is not dependent on the choice of normalization method.     

Evaluation of DNA microarray results with quantitative gene expression platforms

We have evaluated the performance characteristics of three quantitative gene expression technologies and correlated their expression measurements to those of five commercial microarray platforms, based on the MicroArray Quality Control (MAQC) data set. The limit of detection, assay range, precision, accuracy and fold-change correlations were assessed for 997 TaqMan Gene Expression Assays, 205 Standardized RT (Sta)RT-PCR assays and 244 QuantiGene assays. TaqMan is a registered trademark of Roche Molecular Systems, Inc. We observed high correlation between quantitative gene expression values and microarray platform results and found few discordant measurements among all platforms. The main cause of variability was differences in probe sequence and thus target location. A second source of variability was the limited and variable sensitivity of the different microarray platforms for detecting weakly expressed genes, which affected interplatform and intersite reproducibility of differentially expressed genes. From this analysis, we conclude that the MAQC microarray data set has been validated by alternative quantitative gene expression platforms thus supporting the use of microarray platforms for the quantitative characterization of gene expression.