Abstract
DNA microarray technology enables investigators to measure the expression of several 1000 mRNA species simultaneously in a biological specimen. However, the reliability of the microarray technology to detect transcriptional differences representative of the original samples is affected by the quality of the extracted RNA. Thus, it is of critical importance to standardize sample-handling protocols and to perform a quality assessment of RNA preparations. In this report, 59 human tissue samples were used to evaluate the relationships between RNA quality and gene expression. From Affymetrix GeneChip array data analysis of these samples, we compared the performance of the 28S/18S ratio, two computer methods (RIN and degradometer) and our in-house RNA quality scale (RQS) in assessing RNA quality. The optimal RNA reliability threshold was determined for each method using statistical discrimination measures. We showed that RQS, RIN and degradometer have a similar capacity to detect reliable RNA samples whereas the 28S/18S ratio leads to a misleading categorization. Furthermore, we developed a new approach, based on clustering analyses of full chip expression, to control RNA quality after hybridization experiments. The combination of these methods, allowing monitoring of RNA quality prior to and after the hybridization experiments, ensured reliable and reproducible microarray data.