Possible sources of dye-related signal correlation bias in two-color DNA microarray assays

DNA microarray analyses commonly use two spectrally distinct fluorescent labels to simultaneously compare different mRNA pools. Signal correlation bias currently limits accepted resolution to twofold changes in gene expression. This bias was investigated by (i) examining fluorescence and absorption spectra and changes in relative fluorescence of DNAs labeled with the Cy3, Cy5, Alexa Fluor 555, and Alexa Fluor 647 dyes and by (ii) using homotypic hybridization assays to compare the Cy dye pair with the Alexa Fluor dye pair. Cy3 or Cy5 dye-labeled DNA exhibited reduced fluorescence and absorption anomalies that were eliminated by nuclease treatment, consistent with fluorescence quenching that arises from dye-dye or dye-DNA-dye interactions. Alexa Fluor 555 and Alexa Fluor 647 dye-labeled DNA exhibited little or no such anomalies. In microarray hybridization, the Alexa Fluor dye pair provided higher signal correlation coefficients (R2) than did the Cy dye pair; at the 95% prediction level, a 1.3-fold change in gene expression was significant using the Alexa Fluor dye pair. Lowered signal correlation of the Cy dye pair was associated with high variance in Cy5 dye signals. These results indicate that fluorescence quenching may be a source of signal bias associated with the Cy dye pair.     

Elimination of laboratory ozone leads to a dramatic improvement in the reproducibility of microarray gene expression measurements

Background  

Environmental ozone can rapidly degrade cyanine 5 (Cy5), a fluorescent dye commonly used in microarray gene expression studies. Cyanine 3 (Cy3) is much less affected by atmospheric ozone. Degradation of the Cy5 signal relative to the Cy3 signal in 2-color microarrays will adversely reduce the Cy5/Cy3 ratio resulting in unreliable microarray data.     

Impact of DNA chips on haematological oncology

As the Human Genome Project hurtles towards completion, DNA microarray technology offers the potential to open wide new windows into the study of genome complexity. DNA chips can be used for many different purposes, most prominently to measure levels of gene expression (messenger RNA abundance) for tens of thousands of genes simultaneously. But how much of this data is useful and is some superfluous? Can array data be used to identify a handful of critical genes that will lead to a more detailed taxonomy of haematological malignancies and can this or similar array data be used to predict clinical outcome? It is still too early to predict what the ultimate impact of DNA chips will be on our understanding of cancer biology. There are many critically important questions about this new field that are yet unaddressed. By the publication of this article, it is hoped that the technology of DNA chips will be opened up and demystified, and that additional opportunities for creative exploration will be catalysed.     

Development of a toxicological gene array and quantitative assessment of this technology

High-density arrays of DNA bound to solid substrates offer a powerful approach to identifying changes in gene expression in response to toxicants. While DNA arrays have been used to explore qualitative changes in gene regulation, less attention has focused on the quantitative aspects of this technology. Arrays containing expressed sequence tags for xenobiotic metabolizing enzymes, proteins associated with glutathione regulation, DNA repair enzymes, heat shock proteins, and housekeeping genes were used to examine gene expression in response to beta-naphthoflavone (beta-NF). Upregulation of cytochrome P4501a1 (Cyp1a1) and 1a2 in mouse liver was maximal 8 h after beta-NF administration. Significant upregulation of Cyp1a2 was noted at beta-NF doses as low as 0.62 and 1.2 mg/kg when gene expression was measured by microarray or Northern blotting, respectively. Maximal Cyp1a2 induction is 5-fold by Northern analysis and 10-fold by microarray. Induction of Cyp1a1 was 15- and 20-fold by Northern and microarray analysis, respectively. The coefficient of variation for spot to spot and slide to slide comparisons was <15%; this variability was smaller than interanimal variability (18-60%). Comparison of mRNA expression in control animals indicated that there are differences in labeling/detection associated with Cy3/Cy5 dyes; accordingly, experiments must include methods for establishing baseline signals for all genes. We conclude that the dynamic range and sensitivity of DNA microarrays on glass slides is comparable to Northern blotting analysis and that variability of the data introduced during spotting and hybridization is less than the interanimal variability.     

Plant–Pathogen Interactions: What Microarray Tells About It?

Plant defense responses are mediated by elementary regulatory proteins that affect expression of thousands of genes. Over the last decade, microarray technology has played a key role in deciphering the underlying networks of gene regulation in plants that lead to a wide variety of defence responses. Microarray is an important tool to quantify and profile the expression of thousands of genes simultaneously, with two main aims: (1) gene discovery and (2) global expression profiling. Several microarray technologies are currently in use; most include a glass slide platform with spotted cDNA or oligonucleotides. Till date, microarray technology has been used in the identification of regulatory genes, end-point defence genes, to understand the signal transduction processes underlying disease resistance and its intimate links to other physiological pathways. Microarray technology can be used for in-depth, simultaneous profiling of host/pathogen genes as the disease progresses from infection to resistance/susceptibility at different developmental stages of the host, which can be done in different environments, for clearer understanding of the processes involved. A thorough knowledge of plant disease resistance using successful combination of microarray and other high throughput techniques, as well as biochemical, genetic, and cell biological experiments is needed for practical application to secure and stabilize yield of many crop plants. This review starts with a brief introduction to microarray technology, followed by the basics of plant–pathogen interaction, the use of DNA microarrays over the last decade to unravel the mysteries of plant–pathogen interaction, and ends with the future prospects of this technology.     

Comparison of Alexa Fluor<Superscript>®</Superscript> and CyDye<Superscript>™</Superscript> for practical DNA microarray use

Microarrays are a powerful tool for comparison and understanding of gene expression levels in healthy and diseased states. The method relies upon the assumption that signals from microarray features are a reflection of relative gene expression levels of the cell types under investigation. It has previously been reported that the classical fluorescent dyes used for microarray technology, Cy3 and Cy5, are not ideal due to the decreased stability and fluorescence intensity of the Cy5 dye relative to the Cy3, such that dye bias is an accepted phenomena necessitating dye swap experimental protocols and analysis of differential dye affects. The incentive to find new fluorophores is based on alleviating the problem of dye bias through synonymous performance between counterpart dyes. Alexa Fluor 555 and Alexa Fluor 647 are increasingly promoted as replacements for CyDye in microarray experiments. Performance relates to the molecular and steric similarities, which will vary for each new pair of dyes as well as the spectral integrity for the specific application required. Comparative analysis of the performance of these two competitive dye pairs in practical microarray applications is warranted towards this end. The findings of our study showed that both dye pairs were comparable but that conventional CyDye resulted in significantly higher signal intensities (P < 0.05) and signal minus background levels (P < 0.05) with no significant difference in background values (P > 0.05). This translated to greater levels of differential gene expression with CyDye than with the Alexa Fluor counterparts. However, CyDye fluorophores and in particular Cy5, were found to be less photostable over time and following repeated scans in microarray experiments. These results suggest that precautions against potential dye affects will continue to be necessary and that no one dye pair negates this need.     

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.     

用基因表达谱芯片研究人正常肝和肝细胞癌中差异表达的基因

以基因表达谱芯片对人正常肝及肝癌组织基因表达的差异性进行了研究比较。奖４０９６条人ｃＤＮＡ用点样仪点在特制玻片上制备成表达谱芯片;利用肝和肝癌组织的ｍＲＮＡ通过逆转录方法,将Ｃｙ３和Ｃｙ５２种荧光分别标记到两种组织的ｃＤＮＡ上,制备成ｃＤＮＡ探针,并与表达谱芯片进行杂交及扫描,重复４次实验,通过计算机数据处理判定基因是否在上述２种组织中有表达差异,筛选出差异表达的基因共９０３条。基因芯片技术可同时     

Statistical design of reverse dye microarrays

MOTIVATION: In cDNA microarray experiments all samples are labelled with either Cy3 dye or Cy5 dye. Certain genes exhibit dye bias-a tendency to bind more efficiently to one of the dyes. The common reference design avoids the problem of dye bias by running all arrays 'forward', so that the samples being compared are always labelled with the same dye. But comparison of samples labelled with different dyes is sometimes of interest. In these situations, it is necessary to run some arrays 'reverse'-with the dye labelling reversed-in order to correct for the dye bias. The design of these experiments will impact one's ability to identify genes that are differentially expressed in different tissues or conditions. We address the design issue of how many specimens are needed, how many forward and reverse labelled arrays to perform, and how to optimally assign Cy3 and Cy5 labels to the specimens. RESULTS: We consider three types of experiments for which some reverse labelling is needed: paired samples, samples from two predefined groups, and reference design data when comparison with the reference is of interest. We present simple probability models for the data, derive optimal estimators for relative gene expression, and compare the efficiency of the estimators for a range of designs. In each case, we present the optimal design and sample size formulas. We show that reverse labelling of individual arrays is generally not required.     

Evaluation of experimental designs for two-color cDNA microarrays.

The major goal of two-color cDNA microarray experiments is to measure the relative gene expression level (i.e., relative amount of mRNA) of each gene between samples in studies of gene expression. More specifically, given an N-sample experiment, we need all N(N - 1)/2 relative expression levels of all sample pairs of each gene for identification of the differentially expressed genes and for clustering of gene expression patterns. However, the intensities observed from two-color cDNA microarray experiments do not simply represent the relative gene expression level. They are composed of signal (gene expression level), noise, and other factors. In discussions on the experimental design of two-color cDNA microarray experiments, little attention has been given to the fact that different combinations of test and control samples will produce microarray intensities data with varying intrinsic composition of factors. As a consequence, not all experimental designs for two-color cDNA microarray experiments are able to provide all possible relative gene expression levels. This phenomenon has never been addressed. To obtain all possible relative gene expression levels, a novel method for two-color cDNA microarray experimental design evaluation is necessary that will allow the making of an accurate choice. In this study, we propose a model-based approach to illustrate how the factor composition of microarray intensities changed with different experimental designs in two-color cDNA microarray experiments. By analyzing 12 experimental designs (including 5 general forms), we demonstrate that not all experimental designs are able to provide all possible relative gene expression levels due to the differences in factor composition. Our results indicate that whether an experimental design can provide all possible relative expression levels of all sample pairs for each gene should be the first criterion to be considered in an evaluation of experimental designs for two-color cDNA microarray experiments.     

Genomic Portraits of the Nervous System in Health and Disease

As the human genome project moves toward its goal of sequencing the entire human genome, gene expression profiling by DNA microarray technology is being employed to rapidly screen genes for biological information. In this review, we will introduce DNA microarray technology, outline the basic experimental paradigms and data analysis methods, and then show with some examples how gene expression profiling can be applied to the study of the central nervous system in health and disease.     

cDNA microarray technology and its applications

The cDNA microarray is the most powerful tool for studying gene expression in many different organisms. It has been successfully applied to the simultaneous expression of many thousands of genes and to large-scale gene discovery, as well as polymorphism screening and mapping of genomic DNA clones. It is a high throughput, highly parallel RNA expression assay technique that permits quantitative analysis of RNAs transcribed from both known and unknown genes. This technique provides diagnostic fingerprints by comparing gene expression patterns in normal and pathological cells, and because it can simultaneously track expression levels of many genes, it provides a source of operational context for inference and predication about complex cell control systems. This review describes this recently developed cDNA microarray technology and its application to gene discovery and expression, and to diagnostics for certain diseases.     

问号钩端螺旋体基因组DNA芯片的研制

目的 研制并初步评估问号钩端螺旋体(简称钩体)赖型赖株的基因组DNA芯片。方法 利用Primegens引物设计软件筛选出问号钩体赖型赖株全基因组中的特异性基因进行引物设计。对成功设计出相应引物的3 290个基因用聚合酶链反应方法进行扩增,以纯化后的产物点样制备芯片。并用双色荧光杂交策略对芯片质量进行了初步平估。结果 共获得3 290个基因产物用于点样。参考株自身杂交实验结果表明:该芯片有较高的点一致性、信噪比和较低的假阳性率。结论 成功制备了包含问号钩体赖型赖株3 290个目的基因的基因组DNA芯片,并可用于基于该芯片的问号钩体比较基因组学的研究。     

Coupled analysis of gene expression and chromosomal location

Microarray technology can be used to assess simultaneously global changes in expression of mRNA or genomic DNA copy number among thousands of genes in different biological states. In many cases, it is desirable to determine if altered patterns of gene expression correlate with chromosomal abnormalities or assess expression of genes that are contiguous in the genome. We describe a method, differential gene locus mapping (DIGMAP), which aligns the known chromosomal location of a gene to its expression value deduced by microarray analysis. The method partitions microarray data into subsets by chromosomal location for each gene interrogated by an array. Microarray data in an individual subset can then be clustered by physical location of genes at a subchromosomal level based upon ordered alignment in genome sequence. A graphical display is generated by representing each genomic locus with a colored cell that quantitatively reflects its differential expression value. The clustered patterns can be viewed and compared based on their expression signatures as defined by differential values between control and experimental samples. In this study, DIGMAP was tested using previously published studies of breast cancer analyzed by comparative genomic hybridization (CGH) and prostate cancer gene expression profiles assessed by cDNA microarray experiments. Analysis of the breast cancer CGH data demonstrated the ability of DIGMAP to deduce gene amplifications and deletions. Application of the DIGMAP method to the prostate data revealed several carcinoma-related loci, including one at 16q13 with marked differential expression encompassing 19 known genes including 9 encoding metallothionein proteins. We conclude that DIGMAP is a powerful computational tool enabling the coupled analysis of microarray data with genome location.     

nm23-H1基因转染L9981肺癌细胞前后基因表达谱的变化

应用基因芯片检测L9981细胞转染nm23-H1基因前后细胞基因表达谱的改变.提取L9981细胞转染nm23-H1基因前后细胞的总RNA,纯化为mRNA后再转录为cDNA.cDNA经限制性内切酶Sau3AI消化后,cDNA片段分别用cy3和cy5标记,与定制的包含14000个基因芯片杂交.杂交结果经扫描和软件分析,nm23-H1基因转染L9981细胞后发现1156(8.26%,1156/14000)个基因表达上调,而642(4.59%,642/14000)个基因表达下调.涉及基因包括信号传导、癌基因与抑癌基因、转移相关基因、细胞周期与凋亡、细胞外基质与细胞骨架相关基因,以及细胞因子和转录因子等.nm23-H1基因是通过对转移相关基因的调节来发挥其抑制肺癌细胞株L9981侵袭和转移作用的.     

Linear amplification of catalyzed reporter deposition technology on nylon membrane microarray

The application of microarray analysis to gene expression from limited tissue samples has not been very successful because of the poor signal qualityfrom the genes expressed at low levels. Here we discussed the use of catalyzed reporter deposition (CARD) technology to amplify signals from limited RNA samples on nylon membrane cDNA microarray. When the input RNA level was greater than 10 microg, the genes expressed at high levels did not amplify in proportion to those expressed at low levels. Compared to conventional colorimetric detection, the CARD method requires less than 10% of the total RNA used for amplification of signal displayed onto a nylon membrane cDNA microarray. Total RNA (5-10 microg), as one can extract from a limited amount of specimen, was determined to produce a linear correlation between the colorimetric detection and CARD methods. Beyond this range, it can cause a nonlinear amplification of highly expressed and low-abundance genes. These results suggest that when amplification is needed for any applications using the CARD method, including DNA microarray experiments, precaution has to be taken in the amount of RNA used to avoid skew amplification and thus misleading conclusions.     

Genomics and microarray for detection and diagnostics

Genomics provided biomedical scientists an inventory of all genes and sequences present in a living being. This provides an unique opportunity to the scientists to predict and study biological functions of these genes. The changes in the gene expression regulated by genomic sequences therefore reflect changes in the molecular processes working in a cell or tissue in response to external factors including exposure to toxic compounds and pathogens. Microarray offers a biotechnological revolution with the help of DNA chemistry, silicon chip technology and optics to be used to monitor gene expression for thousands of genes in one single experiment. Briefly, 20,000 to 100,000 unique DNA molecules get applied by a robot to the surface of silicon wafers (approximately the size of a microscope slide). Using a single microarray experiment, the expression level of 20,000 to 100,000 genes will be examined in one single experiment. Genomics and microarray have a significant role and impact on the design and development of modern detection and diagnostic tools in several different ways. Microarray tools are now used on regular basis for monitoring gene expression of large number of genes and also frequently applied to DNA sequence analysis, immunology, genotyping, and molecular diagnosing. For diagnostics, these tools can be used to distinguish and differentiate between different DNA fragments that differ by as little as a single nucleotide polymorphism (SNP). These microarrays can be divided based on the gene density spots that will be high density (>10,000 spots) per slide, medium (< 1000 > 100) and low density (< 100). High-density arrays have proven to be very useful in disease diagnosis especially in diagnosis and classification of different types of cancers. These microarray tools hold tremendous potential for pathogen detection, which will be comprised, of unique sets of genes (also referred to as "signatures") able to unambiguously identify the species and strain of pathogens of interest.     

Genomic DNA standards for gene expression profiling in Mycobacterium tuberculosis

A fundamental problem in DNA microarray analysis is the lack of a common standard to compare the expression levels of different samples. Several normalization protocols have been proposed to overcome variables inherent in this technology. As yet, there are no satisfactory methods to exchange gene expression data among different research groups or to compare gene expression values under different stimulus–response profiles. We have tested a normalization procedure based on comparing gene expression levels to the signals generated from hybridizing genomic DNA (genomic normalization). This procedure was applied to DNA microarrays of Mycobacterium tuberculosis using RNA extracted from cultures growing to the logarithmic and stationary phases. The applied normalization procedure generated reproducible measurements of expression level for 98% of the putative mycobacterial ORFs, among which 5.2% were significantly changed comparing the logarithmic to stationary growth phase. Additionally, analysis of expression levels of a subset of genes by real time PCR technology revealed an agreement in expression of 90% of the examined genes when genomic DNA normalization was applied instead of 29–68% agreement when RNA normalization was used to measure the expression levels in the same set of RNA samples. Further examination of microarray expression levels displayed clusters of genes differentially expressed between the logarithmic, early stationary and late stationary growth phases. We conclude that genomic DNA standards offer advantages over conventional RNA normalization procedures and can be adapted for the investigation of microbial genomes.     

DNA芯片技术中利用内标对数据归一化后检测基因的表达变化

在DNA芯片技术中 ,通过反转录反应 ,由mRNA合成带有荧光标记物的cDNA的过程中 ,往往要参入已知质量的poly(A) + RNA ,以对DNA芯片的检测灵敏度进行归一化处理 .通过体外转录的方法 ,以真核生物的cDNA克隆中的DNA片段为模板合成poly(A) +RNA ,对之定量后 ,以不同的质量比参入到样品的反转录体系中 ,代表不同的RNA拷贝丰度 ,从而对DNA芯片检测的灵敏度进行了定量 ,并得到DNA芯片上杂交点的荧光信号强度与基因表达的RNA拷贝数成正相关的关系 .利用含有内标的DNA芯片检测了热击反应后酵母细胞的基因表达变化 ,结果与Northern印迹方法检测结果是相符的