Impact of RNA degradation on gene expression profiles:: Assessment of different methods to reliably determine RNA quality

被引:138
作者
Copois, Virginie
Bibeau, Frederic
Bascoul-Mollevi, Caroline
Salvetat, Nicolas
Chalbos, Patrick
Bareil, Corinne
Candeil, Laurent
Fraslon, Caroline
Conseiller, Emmanuel
Granci, Virginie
Maziere, Pierre
Kramar, Andrew
Ychou, Marc
Pau, Bernard
Martineau, Pierre
Molina, Franck
Del Rio, Maguy
机构
[1] CRLC Val Aurelle Paul Lamarque, Ctr Rech Cancerol, CNRS, UMR 5160, F-34298 Montpellier 5, France
[2] CRLC Val Aurelle Paul Lamarque, Serv Anatomopathol, F-34298 Montpellier, France
[3] CRLC Val Aurelle Paul Lamarque, Serv Oncol Digest, F-34298 Montpellier 5, France
[4] CRLC Val Aurelle Paul Lamarque, Biostat Unit, F-34298 Montpellier 5, France
[5] Sanofiaventis, Oncol Therapeut Dept, Vitry Sur Seine, France
关键词
expression profiles; gene; quality; RNA; scale;
D O I
10.1016/j.jbiotec.2006.07.032
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
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 (R) GeneChip (R) 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. (c) 2006 Elsevier B.V. All rights reserved.
引用
收藏
页码:549 / 559
页数:11
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