Dataset Information

Validation of a novel, fully integrated and flexible microarray benchtop facility for gene expression profiling.

ABSTRACT: Here we describe a novel microarray platform that integrates all functions needed to perform any array-based experiment in a compact instrument on the researcher's laboratory benchtop. Oligonucle otide probes are synthesized in situ via a light- activated process within the channels of a three-dimensional microfluidic reaction carrier. Arrays can be designed and produced within hours according to the user's requirements. They are processed in a fully automatic workflow. We have characterized this new platform with regard to dynamic range, discrimination power, reproducibility and accuracy of biological results. The instrument detects sample RNAs present at a frequency of 1:100 000. Detection is quantitative over more than two orders of magnitude. Experiments on four identical arrays with 6398 features each revealed a mean coefficient of variation (CV) value of 0.09 for the 6398 unprocessed raw intensities indicating high reproducibility. In a more elaborate experiment targeting 1125 yeast genes from an unbiased selection, a mean CV of 0.11 on the fold change level was found. Analyzing the transcriptional response of yeast to osmotic shock, we found that biological data acquired on our platform are in good agreement with data from Affymetrix GeneChips, quantitative real-time PCR and--albeit somewhat less clearly--to data from spotted cDNA arrays obtained from the literature.

SUBMITTER: Baum M

PROVIDER: S-EPMC290286 | biostudies-literature | 2003 Dec

REPOSITORIES: biostudies-literature

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Validation of a novel, fully integrated and flexible microarray benchtop facility for gene expression profiling.

Baum Michael M Bielau Simone S Rittner Nicole N Schmid Kathrin K Eggelbusch Kathrin K Dahms Michael M Schlauersbach Andrea A Tahedl Harald H Beier Markus M Güimil Ramon R Scheffler Matthias M Hermann Carsten C Funk Jörg-Michael JM Wixmerten Anke A Rebscher Hans H Hönig Matthias M Andreae Claas C Büchner Daniel D Moschel Erich E Glathe Andreas A Jäger Evelyn E Thom Marc M Greil Andreas A Bestvater Felix F Obermeier Frank F Burgmaier Josef J Thome Klaus K Weichert Sigrid S Hein Silke S Binnewies Tim T Foitzik Volker V Müller Manfred M Stähler Cord Friedrich CF Stähler Peer Friedrich PF

Nucleic acids research 20031201 23

Here we describe a novel microarray platform that integrates all functions needed to perform any array-based experiment in a compact instrument on the researcher's laboratory benchtop. Oligonucle otide probes are synthesized in situ via a light- activated process within the channels of a three-dimensional microfluidic reaction carrier. Arrays can be designed and produced within hours according to the user's requirements. They are processed in a fully automatic workflow. We have characterized thi ...[more]

PMID: 14627841

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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).

Project description:BackgroundThis report describes a method for the generation of global gene expression profiles from low frequent B-cell subsets by using fluorescence-activated cell sorting and RNA amplification. However, some of the differentiating compartments involve a low number of cells and therefore it is important to optimize and validate each step in the procedure.MethodsNormal lymphoid tissues from blood, tonsils, thymus and bone marrow were immunophenotyped by the 8-colour Euroflow panel using multiparametric flow cytometry. Subsets of B-cells containing cell numbers ranging from 800 to 33,000 and with frequencies varying between 0.1 and 10 percent were sorted, subjected to mRNA purification, amplified by the NuGEN protocol and finally analysed by the Affymetrix platform.ResultsFollowing a step by step strategy, each step in the workflow was validated and the sorting/storage conditions optimized as described in this report. First, an analysis of four cancer cell lines on Affymetrix arrays, using either 100 ng RNA labelled with the Ambion standard protocol or 1 ng RNA amplified and labelled by the NuGEN protocol, revealed a significant correlation of gene expressions (r ? 0.9 for all). Comparison of qPCR data in samples with or without amplification for 8 genes showed that a relative difference between six cell lines was preserved (r ? 0.9). Second, a comparison of cells sorted into PrepProtect, RNAlater or directly into lysis/binding buffer showed a higher yield of purified mRNA following storage in lysis/binding buffer (p < 0.001). Third, the identity of the B-cell subsets validated by the cluster of differentiation (CD) membrane profile was highly concordant with the transcriptional gene expression (p-values <0.001). Finally, in normal bone marrow and tonsil samples, eight evaluated genes were expressed in accordance with the biology of lymphopoiesis (p-values < 0.001), which enabled the generation of a gene-specific B-cell atlas.ConclusionA description of the implementation and validation of commercially available kits in the laboratory has been examined. This included steps for cell sorting, cell lysis/stabilization, RNA isolation, RNA concentration and amplification for microarray analysis. The workflow described in this report will enable the generation of microarray data from minor sorted B-cell subsets.

Project description:The present study is aimed at identifying potential candidate genes as prognostic markers in human oral tongue squamous cell carcinoma (SCC) by large scale gene expression profiling.The gene expression profile of patients (n=37) with oral tongue SCC were analyzed using Affymetrix HG_U95Av2 high-density oligonucleotide arrays. Patients (n=20) from which there were available tumor and matched normal mucosa were grouped into stage (early vs. late) and nodal disease (node positive vs. node negative) subgroups and genes differentially expressed in tumor vs. normal and between the subgroups were identified. Three genes, GLUT3, HSAL2, and PACE4, were selected for their potential biological significance in a larger cohort of 49 patients via quantitative real-time RT-PCR.Hierarchical clustering analyses failed to show significant segregation of patients. In patients (n=20) with available tumor and matched normal mucosa, 77 genes were found to be differentially expressed (P< 0.05) in the tongue tumor samples compared to their matched normal controls. Among the 45 over-expressed genes, MMP-1 encoding interstitial collagenase showed the highest level of increase (average: 34.18 folds). Using the criterion of two-fold or greater as overexpression, 30.6%, 24.5% and 26.5% of patients showed high levels of GLUT3, HSAL2 and PACE4, respectively. Univariate analyses demonstrated that GLUT3 over-expression correlated with depth of invasion (P<0.0001), tumor size (P=0.024), pathological stage (P=0.009) and recurrence (P=0.038). HSAL2 was positively associated with depth of invasion (P=0.015) and advanced T stage (P=0.047). In survival studies, only GLUT3 showed a prognostic value with disease-free (P=0.049), relapse-free (P=0.002) and overall survival (P=0.003). PACE4 mRNA expression failed to show correlation with any of the relevant parameters.The characterization of genes identified to be significant predictors of prognosis by oligonucleotide microarray and further validation by real-time RT-PCR offers a powerful strategy for identification of novel targets for prognostication and treatment of oral tongue carcinoma.

Project description:BackgroundThe main limitation in performing genome-wide gene-expression profiling is the assay of low-expression genes. Approaches with high throughput and high sensitivity for assaying low-expression transcripts are urgently needed for functional genomic studies. Combination of the suppressive subtractive hybridization (SSH) and cDNA microarray techniques using the subtracted cDNA clones as probes printed on chips has greatly improved the efficiency for fishing out the differentially expressed clones and has been used before. However, it remains tedious and inefficient sequencing works for identifying genes including the great number of redundancy in the subtracted amplicons, and sacrifices the original advantages of high sensitivity of SSH in profiling low-expression transcriptomes.ResultsWe modified the previous combination of SSH and microarray methods by directly using the subtracted amplicons as targets to hybridize the pre-made cDNA microarrays (named as "SSH/microarray"). mRNA prepared from three pairs of hepatoma and non-hepatoma liver tissues was subjected to the SSH/microarray assays, as well as directly to regular cDNA microarray assays for comparison. As compared to the original SSH and microarray combination assays, the modified SSH/microarray assays allowed for much easier inspection of the subtraction efficiency and identification of genes in the subtracted amplicons without tedious and inefficient sequencing work. On the other hand, 5015 of the 9376 genes originally filtered out by the regular cDNA microarray assays because of low expression became analyzable by the SSH/microarray assays. Moreover, the SSH/microarray assays detected about ten times more (701 vs. 69) HCC differentially expressed genes (at least a two-fold difference and P < 0.01), particularly for those with rare transcripts, than did the regular cDNA microarray assays. The differential expression was validated in 9 randomly selected genes in 18 pairs of hepatoma/non-hepatoma liver tissues using quantitative RT-PCR. The SSH/microarray approaches resulted in identifying many differentially expressed genes implicated in the regulation of cell cycle, cell death, signal transduction and cell morphogenesis, suggesting the involvement of multi-biological processes in hepato-carcinogenesis.ConclusionThe modified SSH/microarray approach is a simple but high-sensitive and high-efficient tool for differentially profiling the low-expression transcriptomes. It is most adequate for applying to functional genomic studies.

Dataset Information

Validation of a novel, fully integrated and flexible microarray benchtop facility for gene expression profiling.

Publications

Validation of a novel, fully integrated and flexible microarray benchtop facility for gene expression profiling.

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