Hybridization kinetics analysis of an oligonucleotide microarray for microRNA detection

MicroRNA (miRNA) microarrays have been successfully used for profiling miRNA expression in many physiological processes such as development, differentiation, oncogenesis, and other disease processes. Detecting miRNA by miRNA microarray is actually based on nucleic acid hybridization between target molecules and their corresponding complementary probes. Due to the small size and high degree of similarity among miRNA sequences, the hybridization condition must be carefully optimized to get specific and reliable signals. Previously, we reported a microarray platform to detect miRNA expression. In this study, we evaluated the sensitivity and specificity of our microarray platform. After systematic analysis, we determined an optimized hybridization condition with high sensitivity and specificity for miRNA detection. Our results would be helpful for other hybridization-based miRNA detection methods, such as northern blot and nuclease protection assay.     

Monitoring of cDNA microarray with common primer target and hybridization specificity with selected targets

Academic researchers are increasingly producing and using cDNA microarrays. Their quality and hybridization specificity are crucial in determining whether the generated data are accurate and interpretable. Here, we describe two methods of monitoring microarray production, the sustainability of DNA attachment, and the specificity of hybridization. The first method consists of labeling an oligonucleotide, which is one of the primers used to amplify all cDNA probes on the array (except for beta-actin and GAPDH) with fluorescent dye and hybridize it to the cDNA microarray. Attachment of the cDNAs on the array after the hybridization procedure was monitored by visualizing fluorescent signals from the spots on the array. In the second method, two selected DNA targets, beta-actin and GAPDH, were labeled with fluorescent dye to hybridize to the cDNA array. Hence, hybridization specificity was demonstrated by obtaining fluorescent signals solely from the genes corresponding to the target.     

Standardization of protocols in cDNA microarray analysis

Systematic variations can occur at various steps of a cDNA microarray experiment and affect the measurement of gene expression levels. Accepted standards integrated into every cDNA microarray analysis can assess these variabilities and aid the interpretation of cDNA microarray experiments from different sources. A universally applicable approach to evaluate parameters such as input and output ratios, signal linearity, hybridization specificity and consistency across an array, as well as normalization strategies, is the utilization of exogenous control genes as spike-in and negative controls. We suggest that the use of such control sets, together with a sufficient number of experimental repeats, in-depth statistical analysis and thorough data validation should be made mandatory for the publication of cDNA microarray data.     

Performance comparison of one-color and two-color platforms within the MicroArray Quality Control (MAQC) project

Microarray-based expression profiling experiments typically use either a one-color or a two-color design to measure mRNA abundance. The validity of each approach has been amply demonstrated. Here we provide a simultaneous comparison of results from one- and two-color labeling designs, using two independent RNA samples from the Microarray Quality Control (MAQC) project, tested on each of three different microarray platforms. The data were evaluated in terms of reproducibility, specificity, sensitivity and accuracy to determine if the two approaches provide comparable results. For each of the three microarray platforms tested, the results show good agreement with high correlation coefficients and high concordance of differentially expressed gene lists within each platform. Cumulatively, these comparisons indicate that data quality is essentially equivalent between the one- and two-color approaches and strongly suggest that this variable need not be a primary factor in decisions regarding experimental microarray design.     

基于通用荧光标签的microRNA芯片检测平台

microRNA（miRNA）是一类生物内源性非编码小RNA分子,在细胞的生长、发育、增殖和细胞凋亡中具有重要的调控作用,并且与癌症的发生息息相关. miRNA表达谱是miRNA研究中的一个关键环节,使用芯片技术分析miRNA表达谱通常要求对miRNA样品进行耗时耗力的荧光标记,并且常规的芯片技术难以精确识别高度同源的miRNAs分子. 为了解决以上问题,本实验室建立了一个被命名为SHUT（Stacking-hybridized Universal Tag）的新型非标记miRNA芯片分析方法. 本文通过分别使用两步杂交法、一步杂交法对SHUT平台的miRNAs特异性检测进行优化. 研究结果表明,SHUT平台在48℃下杂交18 h,除了let-7b和P-let-7c,let-7c和P-let-7a分别存在超过32.5%和83.3%的假阳性信号之外,其它交叉杂交信号都低于10%,具有较好的特异性. 在该杂交条件下,靶标分子let-7d的最低检测浓度为20 fmol/L,并且在20 fmol/L至200 pmol/L之间具有较好的线性关系.     

Microarray detection of food-borne pathogens using specific probes prepared by comparative genomics

In order to design a method for the accurate detection and identification of food-borne pathogens, we used comparative genomics to select 70-mer oligonucleotide probes specific for 11 major food-borne pathogens (10 overlapping probes per pathogen) for use in microarray analysis. We analyzed the hybridization pattern of this constructed microarray with the Cy3-labeled genomic DNA of various food-borne pathogens and other bacteria. Our microarray showed a highly specific hybridization pattern with the genomic DNA of each food-borne pathogen; little unexpected cross-hybridization was observed. Microarray data were analyzed and clustered using the GenePix Pro 6.0 and GeneSpring GX 7.3.1 programs. The analyzed dendrogram revealed the discriminating power of constructed microarray. Each food-borne pathogen clustered according to its hybridization specificity and non-pathogenic species were discriminated from pathogenic species. Our method can be applied to the rapid and accurate detection and identification of food-borne pathogens in the food industry. In addition, this study demonstrates that genome sequence comparison and DNA microarray analysis have a powerful application in epidemiologic and taxonomic studies, as well as in the food safety and biodefense fields.     

Assessment of repeated microarray experiments using mixed tissue RNA reference samples

Genome-scale gene expression technologies are increasingly being applied for biological research as a whole and toxicological screening in particular. In order to monitor data quality and process drift, we adopted the use of two rat-tissue mixtures (brain, liver, kidney, and testis) previously introduced as RNA reference samples. These samples were processed every time a microarray experiment was hybridized, thereby verifying the comparability of the resulting expression data for cross-study comparison. This study presents the analysis of 21 technical replicates of these two mixed-tissue samples using Affymetrix RAE230_2 GeneChip over a period of 12 months. The results show that detection sensitivity, measured by the number of present and absent sequences, is robust, and data correlation, indicated by scatter plots, varies little over time. Receiver operating characteristic (ROC) curves show the sensitivity and specificity of the current measurements are consistent with arrays previously classified as well performing. Overall, this paper shows that the inclusion of standard samples during microarray labeling and hybridization experiments is useful to benchmark the performance of microarray experiments over time and allows discovery of any process drift that, if it occurs, may confound the comparison of these datasets.     

Ensemble dependence model for classification and prediction of cancer and normal gene expression data

MOTIVATION: DNA microarray technologies make it possible to simultaneously monitor thousands of genes' expression levels. A topic of great interest is to study the different expression profiles between microarray samples from cancer patients and normal subjects, by classifying them at gene expression levels. Currently, various clustering methods have been proposed in the literature to classify cancer and normal samples based on microarray data, and they are predominantly data-driven approaches. In this paper, we propose an alternative approach, a model-driven approach, which can reveal the relationship between the global gene expression profile and the subject's health status, and thus is promising in predicting the early development of cancer. RESULTS: In this work, we propose an ensemble dependence model, aimed at exploring the group dependence relationship of gene clusters. Under the framework of hypothesis-testing, we employ genes' dependence relationship as a feature to model and classify cancer and normal samples. The proposed classification scheme is applied to several real cancer datasets, including cDNA, Affymetrix microarray and proteomic data. It is noted that the proposed method yields very promising performance. We further investigate the eigenvalue pattern of the proposed method, and we discover different patterns between cancer and normal samples. Moreover, the transition between cancer and normal patterns suggests that the eigenvalue pattern of the proposed models may have potential to predict the early stage of cancer development. In addition, we examine the effects of possible model mismatch on the proposed scheme.     

Relationship between gene expression and observed intensities in DNA microarrays--a modeling study

A theoretical study of the physical properties which determine the variation in signal strength from probe to probe on a microarray is presented. A model which incorporates probe-target hybridization, as well as the subsequent dissociation which occurs during stringent washing of the microarray, is introduced and shown to reasonably describe publicly available spike-in experiments carried out at Affymetrix. In particular, this model suggests that probe-target dissociation during the stringent wash plays a critical role in determining the observed hybridization intensities. In addition, it is demonstrated that non-specific hybridization introduces uncertainties which significantly limit the ability of any model to accurately quantify absolute gene expression levels while, in contrast, target folding appears to have little effect on these results. Finally, for data from target spike-in experiments, our model is shown to compare favorably with an existing statistical model in determining target concentration levels.     

Relationship between gene expression and observed intensities in DNA microarrays—a modeling study

A theoretical study of the physical properties which determine the variation in signal strength from probe to probe on a microarray is presented. A model which incorporates probe-target hybridization, as well as the subsequent dissociation which occurs during stringent washing of the microarray, is introduced and shown to reasonably describe publicly available spike-in experiments carried out at Affymetrix. In particular, this model suggests that probe-target dissociation during the stringent wash plays a critical role in determining the observed hybridization intensities. In addition, it is demonstrated that non-specific hybridization introduces uncertainties which significantly limit the ability of any model to accurately quantify absolute gene expression levels while, in contrast, target folding appears to have little effect on these results. Finally, for data from target spike-in experiments, our model is shown to compare favorably with an existing statistical model in determining target concentration levels.     

Application and validation of DNA microarrays for the 16S rRNA-based analysis of marine bacterioplankton

An oligonucleotide probe-based DNA microarray was evaluated for its ability to detect 16S rRNA targets in marine bacterioplankton samples without prior amplification by polymerase chain reaction (PCR). The results obtained were compared with those of quantitative fluorescence in situ hybridization (FISH). For extraction and direct labelling of total RNA, a fast and efficient protocol based on commercially available kits was established. A set of redundant and hierarchically structured probes was applied, and specificity of hybridization was assessed by additional control oligonucleotides comprising single central mismatches. The protocol was initially tested by microarray analysis of bacterial pure cultures. Complete discrimination of all control oligonucleotides was achieved, indicating a high degree of hybridization specificity. In a co-culture, abundant members were detected by microarray analysis, but signal ratios of positive probes did not correlate well with quantitative data from FISH experiments. A marine picoplankton sample from the German Bight was analysed. Bacterial populations with relative abundances of at least 5% were detected by hybridizing 0.1 microg of total RNA extracted from a sample of 375 ml equivalent to 4.1 x 10(8) cells. Our results demonstrate that major populations of marine bacterioplankton can be identified by microarray analysis in a fast and reliable way, even in relatively low volumes of sea water.     

Correcting for gene-specific dye bias in DNA microarrays using the method of maximum likelihood

MOTIVATION: In two-color microarray experiments, well-known differences exist in the labeling and hybridization efficiency of Cy3 and Cy5 dyes. Previous reports have revealed that these differences can vary on a gene-by-gene basis, an effect termed gene-specific dye bias. If uncorrected, this bias can influence the determination of differentially expressed genes. RESULTS: We show that the magnitude of the bias scales multiplicatively with signal intensity and is dependent on which nucleotide has been conjugated to the fluorescent dye. A method is proposed to account for gene-specific dye bias within a maximum-likelihood error modeling framework. Using two different labeling schemes, we show that correcting for gene-specific dye bias results in the superior identification of differentially expressed genes within this framework. Improvement is also possible in related ANOVA approaches. AVAILABILITY: A software implementation of this procedure is freely available at http://cellcircuits.org/VERA     

Detecting unknown sequences with DNA microarrays: explorative probe design strategies

Designing environmental DNA microarrays that can be used to survey the extreme diversity of microorganisms existing in nature, represents a stimulating challenge in the field of molecular ecology. Indeed, recent efforts in metagenomics have produced a substantial amount of sequence information from various ecosystems, and will continue to accumulate large amounts of sequence data given the qualitative and quantitative improvements in the next-generation sequencing methods. It is now possible to take advantage of these data to develop comprehensive microarrays by using explorative probe design strategies. Such strategies anticipate genetic variations and thus are able to detect known and unknown sequences in environmental samples. In this review, we provide a detailed overview of the probe design strategies currently available to construct both phylogenetic and functional DNA microarrays, with emphasis on those permitting the selection of such explorative probes. Furthermore, exploration of complex environments requires particular attention on probe sensitivity and specificity criteria. Finally, these innovative probe design approaches require exploiting newly available high-density microarray formats.