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Open Access Methodology article

An optimized protocol for microarray validation by quantitative PCR using amplified amino allyl labeled RNA

Céline Jeanty1, Dan Longrois2, Paul-Michel Mertes3, Daniel R Wagner14 and Yvan Devaux1*

Author Affiliations

1 Laboratory of Cardiovascular Research, Centre de Recherche Public-Santé, Luxembourg

2 Department of Anesthesia and Intensive Care, Hopital Bichat-Claude-Bernard, Université Paris-VII, France

3 Department of Anesthesia and Intensive Care, Centre Hospitalier et Universitaire de Nancy, Hôpital Central, Nancy, France

4 Division of Cardiology, Centre Hospitalier, Luxembourg

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BMC Genomics 2010, 11:542  doi:10.1186/1471-2164-11-542

Published: 7 October 2010



Validation of microarrays data by quantitative real-time PCR (qPCR) is often limited by the low amount of available RNA. This raised the possibility to perform validation experiments on the amplified amino allyl labeled RNA (AA-aRNA) leftover from microarrays. To test this possibility, we used an ongoing study of our laboratory aiming at identifying new biomarkers of graft rejection by the transcriptomic analysis of blood cells from brain-dead organ donors.


qPCR for ACTB performed on AA-aRNA from 15 donors provided Cq values 8 cycles higher than when original RNA was used (P < 0.001), suggesting a strong inhibition of qPCR performed on AA-aRNA. When expression levels of 5 other genes were measured in AA-aRNA generated from a universal reference RNA, qPCR sensitivity and efficiency were decreased. This prevented the quantification of one low-abundant gene, which was readily quantified in un-amplified and un-labeled RNA. To overcome this limitation, we modified the reverse transcription (RT) protocol that generates cDNA from AA-aRNA as follows: addition of a denaturation step and 2-min incubation at room temperature to improve random primers annealing, a transcription initiation step to improve RT, and a final treatment with RNase H to degrade remaining RNA. Tested on universal reference AA-aRNA, these modifications provided a gain of 3.4 Cq (average from 5 genes, P < 0.001) and an increase of qPCR efficiency (from -1.96 to -2.88; P = 0.02). They also allowed for the detection of a low-abundant gene that was previously undetectable. Tested on AA-aRNA from 15 brain-dead organ donors, RT optimization provided a gain of 2.7 cycles (average from 7 genes, P = 0.004). Finally, qPCR results significantly correlated with microarrays.


We present here an optimized RT protocol for validation of microarrays by qPCR from AA-aRNA. This is particularly valuable in experiments where limited amount of RNA is available.