Comparison of fluorescent tag DNA labeling methods used for expression analysis by DNA microarrays

Gene expression profiling by DNA microarrays has found wide application in many fields of biomedical research. The protocols for this technique are not yet standardized, and for each given step in microarray analysis a number of different protocols are in use. As a consequence, results obtained in different laboratories can be difficult to compare. Of particular importance in this respect are the methods for the preparation of fluorescent cDNA probes that should quantitatively reflect the abundance of different mRNAs in the two samples to be compared. Here we systematically evaluate and compare five different published and/or commercial principles for the synthesis offluorescently labeled probes for microarray analysis (direct labeling, 77 RNA polymerase amplification, aminoallyl labeling, hapten-antibody enzymatic labeling, and 3-D multi-labeled structures). We show that individual labeling methods can significantly influence the expression pattern obtained in a microarray experiment and discuss the respective benefits and limitations of each method.     

Comparison of commercial probe labeling kits for microarray: towards quality assurance and consistency of reactions

Microarray technology is readily available to scientists interested in gene expression. Commensurate with this availability is the growing market in accessory products offering convenience but potentially variable performance. Here we evaluate seven commercial kits for probe labeling against a human apoptosis oligonucleotide array. All kits were found to label probes successfully using the manufacturers' instructions. The Stratagene Fairplay Microarray Labeling Kit was the most sensitive, with an overall call rate of 74% and the lowest rate of indeterminant calls for the HEK and HepG2 cell lines. The Invitrogen SuperScript Indirect cDNA Labeling System showed the most reproducible gene expression pattern and the least technical variation, both in terms of signal strength and between replicates on each array. The Promega Pronto! Plus System showed the least dye bias however, a higher level of variation between replicates was observed. Pairwise comparisons revealed that the Promega Pronto! Plus System and Invitrogen SuperScript Indirect cDNA Labeling System had the most similarity in their patterns of gene expression. Results obtained suggest variability in the performance of commercial kits between different manufacturers. This study supports the need to conduct comparative evaluations of commercial microarray probe labeling kits and the need for validation prior to use.     

An evaluation of tyramide signal amplification and archived fixed and frozen tissue in microarray gene expression analysis

Archival formalin-fixed, paraffin-embedded and ethanol-fixed tissues represent a potentially invaluable resource for gene expression analysis, as they are the most widely available material for studies of human disease. Little data are available evaluating whether RNA obtained from fixed (archival) tissues could produce reliable and reproducible microarray expression data. Here we compare the use of RNA isolated from human archival tissues fixed in ethanol and formalin to frozen tissue in cDNA microarray experiments. Since an additional factor that can limit the utility of archival tissue is the often small quantities available, we also evaluate the use of the tyramide signal amplification method (TSA), which allows the use of small amounts of RNA. Detailed analysis indicates that TSA provides a consistent and reproducible signal amplification method for cDNA microarray analysis, across both arrays and the genes tested. Analysis of this method also highlights the importance of performing non-linear channel normalization and dye switching. Furthermore, archived, fixed specimens can perform well, but not surprisingly, produce more variable results than frozen tissues. Consistent results are more easily obtainable using ethanol-fixed tissues, whereas formalin-fixed tissue does not typically provide a useful substrate for cDNA synthesis and labeling.     

A rapid and inexpensive labeling method for microarray gene expression analysis

Background  

Global gene expression profiling by DNA microarrays is an invaluable tool in biological research. However, existing labeling methods are time consuming and costly and therefore often limit the scale of microarray experiments and sample throughput. Here we introduce a new, fast, inexpensive method for direct random-primed fluorescent labeling of eukaryotic cDNA for gene expression analysis and compare the results obtained on the NimbleGen microarray platform with two other widely-used labeling methods, namely the NimbleGen-recommended double-stranded cDNA protocol and the indirect (aminoallyl) method.     

Comparative analysis of eukaryotic marine microbial assemblages from 18S rRNA gene and gene transcript clone libraries by using different methods of extraction

Eukaryotic marine microbes play pivotal roles in biogeochemical nutrient cycling and ecosystem function, but studies that focus on the protistan biogeography and genetic diversity lag-behind studies of other microbes. 18S rRNA PCR amplification and clone library sequencing are commonly used to assess diversity that is culture independent. However, molecular methods are not without potential biases and artifacts. In this study, we compare the community composition of clone libraries generated from the same water sample collected at the San Pedro Ocean Time Series (SPOTs) station in the northwest Pacific Ocean. Community composition was assessed using different cell lysis methods (chemical and mechanical) and the extraction of different nucleic acids (DNA and RNA reverse transcribed to cDNA) to build Sanger ABI clone libraries. We describe specific biases for ecologically important phylogenetic groups resulting from differences in nucleic acid extraction methods that will inform future designs of eukaryotic diversity studies, regardless of the target sequencing platform planned.     

LSimpute: accurate estimation of missing values in microarray data with least squares methods

Microarray experiments generate data sets with information on the expression levels of thousands of genes in a set of biological samples. Unfortunately, such experiments often produce multiple missing expression values, normally due to various experimental problems. As many algorithms for gene expression analysis require a complete data matrix as input, the missing values have to be estimated in order to analyze the available data. Alternatively, genes and arrays can be removed until no missing values remain. However, for genes or arrays with only a small number of missing values, it is desirable to impute those values. For the subsequent analysis to be as informative as possible, it is essential that the estimates for the missing gene expression values are accurate. A small amount of badly estimated missing values in the data might be enough for clustering methods, such as hierachical clustering or K-means clustering, to produce misleading results. Thus, accurate methods for missing value estimation are needed. We present novel methods for estimation of missing values in microarray data sets that are based on the least squares principle, and that utilize correlations between both genes and arrays. For this set of methods, we use the common reference name LSimpute. We compare the estimation accuracy of our methods with the widely used KNNimpute on three complete data matrices from public data sets by randomly knocking out data (labeling as missing). From these tests, we conclude that our LSimpute methods produce estimates that consistently are more accurate than those obtained using KNNimpute. Additionally, we examine a more classic approach to missing value estimation based on expectation maximization (EM). We refer to our EM implementations as EMimpute, and the estimate errors using the EMimpute methods are compared with those our novel methods produce. The results indicate that on average, the estimates from our best performing LSimpute method are at least as accurate as those from the best EMimpute algorithm.     

芯片杂交中荧光标记样品定量与非定量的比较研究

比较在芯片杂交中,荧光标记样品定量与非定量对杂交结果的影响。其方法是,提取经As2O3作用K562细胞前后的总RNA,逆转录成cDNA第一链,并分别用Cy3／Cy5标记。标记后的样品再次定量或不定量,但均取相同体积上样与K562芯片杂交,用扫描仪扫描并分析。其结果,标记后样品定量与不定量杂交的结果都与理论推测一致,但以样品定量进行杂交的效果更好,标记样品杂交前再次定量的,分析发现2个基因表达下调;杂交前不定量仅取相同体积进行杂交的,发现6个基因片段表达下调,其中只有2个基因与细胞凋亡通路密切相关。认为在芯片的杂交检测中,对荧光标记样品杂交前再次定量可大大提高杂交结果的可靠性。