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Quantitative assessment of a novel flow-through porous microarray for the rapid analysis of gene expression profiles.

ABSTRACT: A novel microarray system that utilizes a porous aluminum-oxide substrate and flow-through incubation has been developed for rapid molecular biological testing. To assess its utility in gene expression analysis, we determined hybridization kinetics, variability, sensitivity and dynamic range of the system using amplified RNA. To show the feasibility with complex biological RNA, we subjected Jurkat cells to heat-shock treatment and analyzed the transcriptional regulation of 23 genes. We found that trends (regulation or no change) acquired on this platform are in good agreement with data obtained from real-time quantitative PCR and Affymetrix GeneChips. Additionally, the system demonstrates a linear dynamic range of 3 orders of magnitude and at least 10-fold decreased hybridization time compared to conventional microarrays. The minimum amount of transcript that could be detected in 20 microl volume is 2-5 amol, which enables the detection of 1 in 300,000 copies of a transcript in 1 microg of amplified RNA. Hybridization and subsequent analysis are completed within 2 h. Replicate hybridizations on 24 identical arrays with two complex biological samples revealed a mean coefficient of variation of 11.6%. This study shows the potential of flow-through porous microarrays for the rapid analysis of gene expression profiles in clinical applications.

SUBMITTER: Wu Y

PROVIDER: S-EPMC516077 | biostudies-literature | 2004

REPOSITORIES: biostudies-literature

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Quantitative assessment of a novel flow-through porous microarray for the rapid analysis of gene expression profiles.

Wu Ying Y de Kievit Peggy P Vahlkamp Lars L Pijnenburg Dirk D Smit Maarten M Dankers Martijn M Melchers Diana D Stax Martijn M Boender Piet J PJ Ingham Colin C Bastiaensen Niek N de Wijn Rik R van Alewijk Dirk D van Damme Henk H Raap Anton K AK Chan Alan B AB van Beuningen Rinie R

Nucleic acids research 20040827 15

A novel microarray system that utilizes a porous aluminum-oxide substrate and flow-through incubation has been developed for rapid molecular biological testing. To assess its utility in gene expression analysis, we determined hybridization kinetics, variability, sensitivity and dynamic range of the system using amplified RNA. To show the feasibility with complex biological RNA, we subjected Jurkat cells to heat-shock treatment and analyzed the transcriptional regulation of 23 genes. We found tha ...[more]

PMID: 15333674

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Project description:BACKGROUND: Meta-analysis of gene expression microarray datasets presents significant challenges for statistical analysis. We developed and validated a new bioinformatic method for the identification of genes upregulated in subsets of samples of a given tumour type ('outlier genes'), a hallmark of potential oncogenes. METHODOLOGY: A new statistical method (the gene tissue index, GTI) was developed by modifying and adapting algorithms originally developed for statistical problems in economics. We compared the potential of the GTI to detect outlier genes in meta-datasets with four previously defined statistical methods, COPA, the OS statistic, the t-test and ORT, using simulated data. We demonstrated that the GTI performed equally well to existing methods in a single study simulation. Next, we evaluated the performance of the GTI in the analysis of combined Affymetrix gene expression data from several published studies covering 392 normal samples of tissue from the central nervous system, 74 astrocytomas, and 353 glioblastomas. According to the results, the GTI was better able than most of the previous methods to identify known oncogenic outlier genes. In addition, the GTI identified 29 novel outlier genes in glioblastomas, including TYMS and CDKN2A. The over-expression of these genes was validated in vivo by immunohistochemical staining data from clinical glioblastoma samples. Immunohistochemical data were available for 65% (19 of 29) of these genes, and 17 of these 19 genes (90%) showed a typical outlier staining pattern. Furthermore, raltitrexed, a specific inhibitor of TYMS used in the therapy of tumour types other than glioblastoma, also effectively blocked cell proliferation in glioblastoma cell lines, thus highlighting this outlier gene candidate as a potential therapeutic target. CONCLUSIONS/SIGNIFICANCE: Taken together, these results support the GTI as a novel approach to identify potential oncogene outliers and drug targets. The algorithm is implemented in an R package (Text S1).

Dataset Information

Quantitative assessment of a novel flow-through porous microarray for the rapid analysis of gene expression profiles.

Publications

Quantitative assessment of a novel flow-through porous microarray for the rapid analysis of gene expression profiles.

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