Improving DNA array data quality by minimising 'neighbourhood' effects

Gene expression studies using cDNA arrays require robust and sensitive detection methods. Being extremely sensitive, radioactive detection suffers from the influence of signals positioned in each other's vicinity, the 'neighbourhood' effect. This limits the gene density of arrays and the quality of the results obtained. We have investigated the quantitative influence of different parameters on the 'neighbourhood' effect. By using a model experimental system, we could show that the effect is linear and depends only on the intensity of the hybridisation signal. We identified a common factor that can describe the influence of the neighbour spots based on their intensities. This factor is <1%, but it has to be taken into account if a high dynamic range of gene expression is to be detected. We could also derive the factor, although with less precision, from comparison of duplicate spots on arrays of 4565 different clones and replication of the hybridisation experiments. The calculated coefficient applied to our actual experimental results not only revealed previously undetected tissue or cell-specific expression differences, but also increased the dynamic range of detection. It thus provides a relatively simple way of improving DNA array data quality with few experimental modifications.     

Improving DNA array data quality by minimising ‘neighbourhood’ effects

Gene expression studies using cDNA arrays require robust and sensitive detection methods. Being extremely sensitive, radioactive detection suffers from the influence of signals positioned in each other’s vicinity, the ‘neighbourhood’ effect. This limits the gene density of arrays and the quality of the results obtained. We have investigated the quantitative influence of different parameters on the ‘neighbourhood’ effect. By using a model experimental system, we could show that the effect is linear and depends only on the intensity of the hybridisation signal. We identified a common factor that can describe the influence of the neighbour spots based on their intensities. This factor is <1%, but it has to be taken into account if a high dynamic range of gene expression is to be detected. We could also derive the factor, although with less precision, from comparison of duplicate spots on arrays of 4565 different clones and replication of the hybridisation experiments. The calculated coefficient applied to our actual experimental results not only revealed previously undetected tissue or cell-specific expression differences, but also increased the dynamic range of detection. It thus provides a relatively simple way of improving DNA array data quality with few experimental modifications.     

Analysis of repeatability in spotted cDNA microarrays

We report a strategy for analysis of data quality in cDNA microarrays based on the repeatability of repeatedly spotted clones. We describe how repeatability can be used to control data quality by developing adaptive filtering criteria for microarray data containing clones spotted in multiple spots. We have applied the method on five publicly available cDNA microarray data sets and one previously unpublished data set from our own laboratory. The results demonstrate the feasibility of the approach as a foundation for data filtering, and indicate a high degree of variation in data quality, both across the data sets and between arrays within data sets.     

Variance stabilization applied to microarray data calibration and to the quantification of differential expression

We introduce a statistical model for microarray gene expression data that comprises data calibration, the quantification of differential expression, and the quantification of measurement error. In particular, we derive a transformation h for intensity measurements, and a difference statistic Deltah whose variance is approximately constant along the whole intensity range. This forms a basis for statistical inference from microarray data, and provides a rational data pre-processing strategy for multivariate analyses. For the transformation h, the parametric form h(x)=arsinh(a+bx) is derived from a model of the variance-versus-mean dependence for microarray intensity data, using the method of variance stabilizing transformations. For large intensities, h coincides with the logarithmic transformation, and Deltah with the log-ratio. The parameters of h together with those of the calibration between experiments are estimated with a robust variant of maximum-likelihood estimation. We demonstrate our approach on data sets from different experimental platforms, including two-colour cDNA arrays and a series of Affymetrix oligonucleotide arrays.     

Combining SSH and cDNA microarrays for rapid identification of differentially expressed genes

Comparing patterns of gene expression in cell lines and tissues has important applications in a variety of biological systems. In this study we have examined whether the emerging technology of cDNA microarrays will allow a high throughput analysis of expression of cDNA clones generated by suppression subtractive hybridization (SSH). A set of cDNA clones including 332 SSH inserts amplified by PCR was arrayed using robotic printing. The cDNA arrays were hybridized with fluorescent labeled probes prepared from RNA from ER-positive (MCF7 and T47D) and ER-negative (MDA-MB-231 and HBL-100) breast cancer cell lines. Ten clones were identified that were over-expressed by at least a factor of five in the ER-positive cell lines. Northern blot analysis confirmed over-expression of these 10 cDNAs. Sequence analysis identified four of these clones as cytokeratin 19, GATA-3, CD24 and glutathione-S-transferase mu-3. Of the remaining six cDNA clones, four clones matched EST sequences from two different genes and two clones were novel sequences. Flow cytometry and immunofluorescence confirmed that CD24 protein was over-expressed in the ER-positive cell lines. We conclude that SSH and microarray technology can be successfully applied to identify differentially expressed genes. This approach allowed the identification of differentially expressed genes without the need to obtain previously cloned cDNAs.     

Assessment of representational difference analysis (RDA) to construct informative cDNA microarrays for gene expression analysis of species with limited transcriptome information, using red and green tomatoes as a model

Microarray technology makes it feasible to analyse the expression of thousands of different gene elements in a single experiment. Most informative are 'whole genome' arrays, where all gene expression products of a single species or variety are represented. Such arrays are now available for a limited number of model species. However, for other, less well-documented species other routes are still necessary to obtain informative arrays. This includes the use of cDNA libraries. To enhance the amount of information that can be obtained from cDNA libraries, redundancy needs to be minimised, and the number of cDNAs relevant for the conditions of interest needs to be increased. Here, we used representational difference analysis (RDA), a mRNA subtraction procedure, as a tool to enhance the efficiency of cDNA libraries to be used to generate microarrays. Tomato was chosen as a model system for a less well-documented species. cDNA libraries for two distinct physiological conditions of tomato fruits, red and green, were made. The libraries were characterized by sequencing and hybridisation analysis. The RDA procedure was shown to be effective in selecting for genes of relevance for the physiological conditions under investigation, and against constitutively expressed genes. At the same time, redundancy was reduced, but complete normalisation was not obtained, and subsequent sequence analysis will be required to obtain non-redundant arrays. Further, known and putative ripening-related cDNAs were identified in hybridisation experiments on the basis of RNA populations as isolated from the green and red stage of ripening.     

Expression cloning screening of a unique and full-length set of cDNA clones is an efficient method for identifying genes involved in Xenopus neurogenesis

Functional screens, where a large numbers of cDNA clones are assayed for certain biological activity, are a useful tool in elucidating gene function. In Xenopus, gain of function screens are performed by pool screening, whereby RNA transcribed in vitro from groups of cDNA clones, ranging from thousands to a hundred, are injected into early embryos. Once an activity is detected in a pool, the active clone is identified by sib-selection. Such screens are intrinsically biased towards potent genes, whose RNA is active at low quantities. To improve the sensitivity and efficiency of a gain of function screen we have bioinformatically processed an arrayed and EST sequenced set of 100,000 gastrula and neurula cDNA clones, to create a unique and full-length set of approximately 2500 clones. Reducing the redundancy and excluding truncated clones from the starting clone set reduced the total number of clones to be screened, in turn allowing us to reduce the pool size to just eight clones per pool. We report that the efficiency of screening this clone set is five-fold higher compared to a redundant set derived from the same libraries. We have screened 960 cDNA clones from this set, for genes that are involved in neurogenesis. We describe the overexpression phenotypes of 18 single clones, the majority of which show a previously uncharacterised phenotype and some of which are completely novel. In situ hybridisation analysis shows that a large number of these genes are specifically expressed in neural tissue. These results demonstrate the effectiveness of a unique full-length set of cDNA clones for uncovering players in a developmental pathway.     

cDNA文库法在HCV-1b检测芯片研制中的应用

制备丙型肝炎病毒(HCV) 1b亚型诊断芯片并进行初步验证评价.采用cDNA文库法制备探针,用限制性内切酶Sau3AⅠ消化HCV 1b全长cDNA ,所得的酶切片段72℃补平加A ,AT克隆,PCR初步鉴定,并测序.将筛选出的片段打印在氨基修饰的玻片上制备成检测芯片并进行杂交验证分析.运用cDNA文库法,得到2 2个大小相对一致(2 5 0～75 0bp)的基因片段.序列分析表明,均属于HCV 1b基因,可以作为诊断芯片探针;样品标记采用限制性显示(restrictiondisplay ,RD)技术,标记后进行杂交.杂交结果显示,样品和诊断基因芯片杂交的敏感性和特异性均佳.批内和批间精密度CV值分别为5 4 %和6 8% ,表明用cDNA文库法收集片段是一种快速、简便制备芯片探针的实用方法.     

Expressed sequence tags and their application for plant research

Expressed Sequence Tags (ESTs) are short, usually unedited sequences obtained by single-pass sequencing of cDNA clones from any cDNA library. Analyzing and comparing ESTs can provide information on gene expression, function and evolution. Large-scale EST sequencing has become an attractive alternative to plant genome sequencing. Currently, plant EST collections comprise over 3.8 million sequences from about 200 species. They have proved to be a valuable tool for gene discovery and plant metabolism analysis. Several plant-specific EST databases have been created which provide access to sequence data and bioinformatics-based tools for data mining. Searching EST collections allows pre-selection of genes for preparing cDNA arrays, targeted to bring maximum information on specialized processes, like stress response, symbiotic nitrogen fixation etc. Also, ESt-based molecular markers such as SNP, SSR, and indels are fast developing tools for breeders and researchers.     

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.     

Construction of a Normalized cDNA Library for the Trypanosoma cruzi Genome Project

Sequencing of the Trypanosoma cruzi genome is underway. Expressed sequence tags, obtained from cDNA libraries, facilitate mapping and gene discovery. The efficiency of large-scale generation of such tags is increased when using normalized cDNA libraries, where the frequency of individual clones is brought within a narrow range. Repetitive sequencing of abundant clones is therefore minimized. We constructed a normalized cDNA library from epimastigotes of clone CL Brener, and the efficiency of normalization of representative clones was assessed and shown to be adequate. The normalized cDNA library has been distributed to several groups and large-scale sequencing is currently in progress.     

Quantitative oligonucleotide microarray data analysis with an artificial standard probe strategy

Quantitative data analysis is an important element in several applications of DNA microarray, including mRNA expression profiling and estimation of infectious doses for pathogens. Here, we introduce an artificial standard probe strategy for quantitative pathogen detection using an oligonucleotide chip as a model system. The standard capture probe sequence was artificially designed to prevent non-specific hybridization with bacterial targets. Based on the fluorescence intensities of artificial standard spots, the raw fluorescence intensity data for specific spots could be corrected to generate linear correlations with target concentrations. Therefore, our novel artificial standard probe may be effectively applied for the correction of chip-to-chip variations and quantitative data analysis of a one-color labeled DNA microarray system.     

cDNA文库法制备HCV-1诊断芯片探针的应用性研究

探讨利用eDNA文库法制备HCV-1诊断基因芯片探针的可行性．用限制性内切酶Sau3AI消化HCVla及lb全长eDNA,所得的酶切片段72℃补平加A,AT克隆,PCR初步鉴定,并测序．结果显示：HCV两个亚型1a、1b的全长eDNA得到57个大小相对一致(200-1000bp)的片段,平均每个亚型约28个,PCR及序列分析表明,所扩增的片段均属于HCV-1的特异基因,可做为HCV-1诊断基因芯片探针．利用eDNA文库法收集片段是一种快速、简便制备芯片探针的实用方法．