Technical validation of cDNA based microarray as screening technique to identify candidate genes in synovial tissue biopsy specimens from patients with spondyloarthropathy. Issue 5 (13th April 2004) - Rihl, M; Baeten, D; Seta, N; Gu, J; De Keyser, F; Veys, E M; Kuipers, J G; Zeidler, H; Yu, D T Y
Abstract : Objectives: To validate the use of cDNA based microarray on synovial biopsies by analysing the experimental variability due to amplification of RNA, reproducibility of the assay, heterogeneity of the tissue, and statistical analysis. Methods: Total RNA was extracted from three spondyloarthropathy (SpA) and three osteoarthritis (OA) synovial tissue biopsy specimens and from the peripheral blood mononuclear cells (PBMC) of four healthy donors. Exponential RNA amplification by SMART-PCR was compared with linear amplification. Reproducibility was tested by comparing different microarray systems and by performing duplicate experiments. Sample heterogeneity was assessed by comparing overall gene expression profiles, histopathology, and analysis of genes expressed in the synovium and normal PBMC. Statistical analysis using t test and Bonferroni adjustment was verified by permutation of class labels. Results: Gene expression was concordant in 12/14 (86%) cytokine/chemokine genes between both microarrays and different RNA amplification systems. When one microarray system was used, expressed genes were 78–95% concordant in duplicate experiments. Gene expression profiles had a higher degree of similarity between SpA synovium than between PBMC or OA synovium despite clear histopathological differences between synovial samples. Comparison of SpA synovium with OA synovium and with PBMC yielded 11 and 18 expressed transcripts, respectively; six were shared in both comparisons. Permutations of SpA and OA samples yielded only one expressed gene in 19 comparisons. Conclusions: These data provide evidence that microarrays can be used for analysis of synovial tissue biopsies with high reproducibility and low variability of the generated gene expression profiles.