ArticleBMC genomics2010
An optimized protocol for microarray validation by quantitative PCR using amplified amino allyl labeled RNA.
Article in BMC genomics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- Contributions and Challenges of High Throughput qPCR for Determining Antimicrobial Resistance in the Environment: A Critical Review.Molecules (Basel, Switzerland) · 2019Pooled it
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- Blood Transcriptome Profiling Highlights the Role of Intestinal Bacterial Translocation in Severe COVID-19.Pathogens (Basel, Switzerland) · 2025Article
- Use of Metatranscriptomics in Microbiome Research.Bioinformatics and biology insights · 2016Review
- FCRL5 Delineates Functionally Impaired Memory B Cells Associated with Plasmodium falciparum Exposure.PLoS pathogens · 2015Article
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundValidation 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.
resultsqPCR 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.
conclusionWe 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.
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