- Methodology article
- Open Access
An optimized protocol for microarray validation by quantitative PCR using amplified amino allyl labeled RNA
© Jeanty et al; licensee BioMed Central Ltd. 2010
- Received: 1 February 2010
- Accepted: 7 October 2010
- 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.
- qPCR Primer
- Amino Allyl
- Gaussian Data
- qPCR Efficiency
- Renal Graft Rejection
Gene expression profiling using microarrays has rapidly become an analytical tool of choice for translational research laboratories. Genome-wide or more dedicated microarrays are generally used as a fishing expedition to identify candidate genes or pathways that can be used either for their prognostic performance and/or for their therapeutic potential in many diseases. The technique relies on the relative quantification of mRNA expression in cells or tissues. Circulating blood cells can be used as an alternative to tissue biopsies when these are not available. This alternative nevertheless assumes that a systemic biosignature of the pathological state exists and can be assessed through gene expression profiling of blood cells. Consistently, while biosignatures of blood cells were originally reported to be a useful prognostic tool for acute myeloid leukemia [1, 2], several studies later showed that these biosignatures can also aid in the development of biomarkers of several diseases affecting vital organs such as the brain  and the coronary arteries . Both peripheral blood mononuclear cells [3, 4] and whole blood cells  have been used in such profiling experiments. One has nonetheless to keep in mind that the method of RNA collection, either from blood cells using the PAXgene™ technology for instance , or from buffy coats [3, 4], is a critical variable when designing research protocols using microarray studies . The PAXgene™ system is attractive because it stabilizes RNA immediately after collection without the need of rapidly isolating the leukocyte compartment. This is particularly relevant when designing clinical protocols in which patients are included any time of the day (patients with acute myocardial infarction for instance). In addition, this system requires only a very limited volume of blood. However, the reliability of this system to consistently detect all gene transcripts may be questioned .
In addition to the type of blood collection, every steps of the microarray technique can influence the quality of the results. When minute starting amounts of RNA are available, additional steps of amplification have to be performed [8, 9]. This scenario is frequent when using the PAXgene™ system since RNA is extracted from only 2.5 mL of blood. Such RNA is generally processed through a multiple steps procedure to generate amplified amino allyl RNA (AA-aRNA) coupled with fluorescent dyes. First, RNA is reverse transcribed, then amplified with incorporation of amino allyl UTP (AA-UTP) to serve as an arm to facilitate dye binding, and finally coupled with fluorescent dyes before hybridization onto microarrays. This fastidious protocol introduces supplementary bias in the microarray technique, sometimes leading to false positive discovery and erroneous results [10–13]. Some alternatives have been developed, such as the Universal Linkage System technology (Kreatech Diagnostics, Amsterdam, The Netherlands) or the Ovation® technology (NuGEN, San Carlos, CA, USA). Optimization of the amplification procedure has been tackled by previous investigators, such as Waddell et al. who reported two different methods for amplification of bacterial RNA to be assessed in microarray experiments . A popular approach to balance the problem of false discovery is to validate microarray data using an independent technique, such as Northern blot hybridization, RNase protection assay or real-time quantitative PCR (qPCR), the latter being the more widely used . Most commonly performed on cDNA obtained from reverse transcription (RT) of total RNA, qPCR may also be performed on AA-aRNA leftover from microarray experiments [Ambion Tips from the Bench, Using Excess Labeled aRNA for Microarray Validation, TechNotes Volume 14(1)]. This is particularly valuable when limited amount of RNA is available for validation - in case of research protocols using the PAXgene™ system for instance. However, care should be taken when designing such validation experiments. Here, we report our experience with microarray validation by qPCR on AA-aRNA and we present an optimized protocol that improves the reliability of this validation.
An alternative to total RNA to perform microarrays validation by qPCR
We took profit of an ongoing protocol of our lab which aimed at identifying new prognostic biomarkers of renal graft rejection. Hypothesizing that inflammation in the organ donnor conditions the success of transplantation , we analyzed the transcriptome of whole blood cells of brain-dead organ donors by microarrays. The hypothesis beyond this protocol was that graft rejection by the receiver may be predicted by the transcriptomic analysis of blood cells from the donor. Total RNA of whole blood cells collected in PAXgene™ tubes from 22 brain-dead organ donors was extracted. Since a limited volume of blood (2.5 mL) was withdrawn in these tubes, only a low amount of RNA could be extracted. After taking out 1 μg of total RNA for microarrays, only 15 donors had enough remaining total RNA to perform validation experiments by qPCR. In an attempt to find an alternative material than total RNA to perform validation experiments, we tested whether qPCR could be performed on AA-aRNA leftover from microarray experiments.
Amplification and amino allyl labeling of RNA inhibits qPCR
Amplification and amino allyl labeling decrease qPCR sensitivity and efficiency
Cq values obtained by qPCR using RNA and AA-aRNA
Mean ± SD
20.7 ± 4.9
25.6 ± 7.0 *
Optimized protocol for reverse transcription (RT)
Protocol optimization improves RT yield
RNase treatment improves qPCR efficiency
Effect of protocol optimization and RNase H on qPCR efficiency and linearity
Mean ± SD
P vs -RNase H
P vs original protocol
P vs original protocol/- RNase H
- RNase H
-1.96 ± 1.10
+ RNase H
-3.43 ± 0.30
- RNase H
-1.36 ± 0.70
+ RNase H
-2.88 ± 0.29
Linearity (R 2 )
- RNase H
0.80 ± 0.18
+ RNase H
0.98 ± 0.01
- RNase H
0.90 ± 0.11
+ RNase H
0.98 ± 0.01
These data show that RNase H treatment improves qPCR efficiency. This result is consistent with previous data . Considering that RNase H degrades RNA paired to cDNA after RT, improvement of qPCR parameters by RNase H suggests that RNA/cDNA duplexes may have prime amplification by Taq polymerase, inducing the synthesis of other PCR products than those targeted by the specific qPCR primers. However, the observation that RNase H does not modify Cq values (Figure 4) is consistent with a minor effect of RNA/cDNA duplexes on qPCR outcome. Also, fusion curves obtained for each qPCR consistently showed a single peak, attesting for the specificity of the amplification (not shown). Together with the previous observation that RT protocol optimization improved RT yield and qPCR sensitivity, these results demonstrate that our optimized RT protocol with RNase H treatment provides an optimal cDNA from AA-aRNA to be used in qPCR experiments.
Importance of the distance from 3' end in the design of qPCR primers
List of primers used in this study
cDNA length (bp)
Genbank accession number
Distance from 3' end (bp)
Validation of microarray results by qPCR using AA-aRNA
Correlation between microarrays and qPCR
Microarrays vs original RT protocol
Microarrays vs optimized RT protocol
We have implemented an optimized protocol for the validation of microarrays data by qPCR. This protocol allows using AA-aRNA leftover from microarray experiments when limited amount of RNA is available. It can aid in the quantification of low-abundant genes and provides a significant level of correlation between microarrays and qPCR. In addition, this protocol provides high-quality material that can be used to validate expression of relevant genes that may be highlighted by other approaches than microarrays. Such approaches, some of them being increasingly used in the field of biomarker or therapeutic targets discovery, include bioinformatic analysis of functional networks or signaling pathways [24, 25].
Two types of RNA were used in this study. First, RNA was extracted from whole blood cells of 22 brain-dead organ donors. Median age was 50 (36-64), 14 donors were males, and the initial events that led to brain death were cerebrovascular accident (n = 10), brain trauma (n = 6), acute onset of brain hypoxia (n = 2), suicide (n = 2) and trauma (n = 2). According to the French legislation, studies on brain-dead patients do not require informed consent. The French "Agence de la Biomédecine" approved the protocol and blood was withdrawn after signature of next of kin for all scientific studies of the brain-dead patient. Diagnosis of brain death was established according to the criteria of the French "Agence de la Biomédecine" . Arterial blood (2.5 ml) was withdrawn from the arterial catheter in PAXgene™ blood RNA tubes (PreAnalytix®, BD Europe, Erembodegem, Belgium), in the operation room, just before organ harvesting. PAXgene™ tubes were stored at -20°C before RNA extraction. Total blood RNA was isolated using the PAXgene™ Blood RNA kit (Qiagen, Courtaboeuf, France) according to the manufacturer's instructions. RNA quantity was assessed with a Nanodrop (Thermo Scientific, Wilmington, USA) and quality was evaluated using the Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA). All RNAs used in the present study were of high quality and un-degraded (OD260/OD280 > 1.9 and OD260/OD230 > 1.7, RNA integrity number (RIN) > 8). Second, we used the universal human reference RNA comprising total RNA from 10 human cell lines (Stratagene Europe, Amsterdam, The Netherlands). All nucleic acid samples were stored at -80°C until use.
RNA amplification and amino allyl labeling
Messenger RNAs were amplified using the Amino Allyl MessageAmp® kit (Ambion, Cambridgeshire, United Kingdom) according to the manufacturer's protocol, starting with one μg of total RNA. This protocol is based on the Eberwine RNA amplification procedure . Briefly, the generation of multiple antisense RNA copies of each mRNA is obtained by first strand cDNA synthesis with an oligo(dT) primer tagged with a T7 promoter. After second strand synthesis, an in vitro transcription reaction is performed with T7 RNA polymerase in presence of 5-(3-aminoallyl)-UTP (AA-UTP). This produces amplified amino allyl RNA (AA-aRNA) that can be coupled with fluorescent dyes for microarray experiments.
Reverse transcription of RNA and AA-aRNA
The same RT protocol was applied to RNA (un-amplified and un-labeled RNA) and AA-aRNA (amplified and amino allyl labeled RNA but not coupled with fluorescent dye). 1 μg of RNA and 100 ng of AA-aRNA were reverse transcribed into cDNA using the SuperScript II reverse transcriptase (Invitrogen, Merelbeke, Belgium) with the following protocol: RNA or AA-aRNA was mixed with the 5× RT buffer, random hexamers, dNTPs and DTT in a total volume of 19 μl. Samples were then heated to 42°C for 2 min, and 1 μL of SuperScript II was added to a total volume of 20 μl. Final concentrations were: 50 mM Tris-HCl, 75 mM KCl, 3 mM MgCl2, 0.5 mM dNTPs, 10 mM DTT, 200 U of SuperScript II, 180 ng of random hexamers (Invitrogen). RT was allowed for 50 min at 42°C and was followed by enzyme inactivation at 70°C for 15 min. The absence of contaminating DNA was checked using no RT assays.
Optimized RT protocol. RNA or AA-aRNA were mixed with 180 ng random hexamers and dNTPs to a total volume of 13 μL, heated to 65°C for 5 min and rapidly chilled on ice for 5 min. 4 μl of 5× RT buffer and 2 μl DTT were then added and samples were incubated for 2 min at 25°C. Then 1 μL of Superscript II was added and samples were pre-incubated for 10 min at 25°C before RT for 50 min at 42°C. RT was stopped by heating to 70°C for 15 min. Finally, 1 μL (2 U) of RNase H (Ambion) was added, and incubation was continued for 20 min at 37°C. Reagent concentrations were the same as above.
Quantitative real-time PCR (qPCR)
cDNAs obtained from RT of RNA or AA-aRNA were diluted 10-fold and 4 μL were mixed with 16 μL of SYBR®Green Master Mix (Biorad, Nazareth, Belgium) containing 300 nM of each primer (final volume 20 μL). Amplification was carried out in the IQ5 thermal cycler (BioRad) under the following conditions: heating for 3 minutes at 95°C, 40 cycles of denaturation for 30 seconds at 95°C, followed by an annealing/extension for 1 min. A negative control without cDNA template was run in every assay and measures were performed in duplicates. Primers were designed with the Beacon Designer Pro 7.0 software (Premier Biosoft) and their characteristics are indicated in Table 3. Primers specificity was assessed using the NCBI BLAST tool http://0-www.ncbi.nlm.nih.gov.brum.beds.ac.uk/BLAST/Blast.cgi. HPLC-purified primers were obtained from TIB MOLBIOL (Berlin, Germany). Expression levels were calculated using the freely available GENEX Expression Macro (Biorad) which takes into account primer efficiency. Melting curves were analyzed and amplicons were sequenced to confirm the specificity of the reaction. See 'additional file 1' for MIQE checklist.
Transcriptomic profiles of whole blood cells from 22 brain-dead organ donors were obtained using oligonucleotide microarrays representing 25,000 genes . Total RNA extracted from whole blood cells was used in combination with reference RNA (Universal Human Reference RNA) to provide an internal standard for comparisons of relative gene expression levels across arrays. Messenger RNAs were amplified using the Amino Allyl MessageAmp™ kit (Ambion®, Cambridgeshire, United Kingdom) according to the manufacturer's protocol, starting with one μg of total RNA. Five μg of each amino allyl aRNA were labeled with Cy3 or Cy5 (Amersham, Buckinghamshire, United Kingdom). Dye coupling to amino allyl aRNA was measured using the ND-1000 spectrophotometer. Dye coupling yield >5% was a prerequisite for further analysis. 750 ng of each amino allyl aRNA labeled Cy3 or Cy5 (reference RNA or donor RNA) were combined and hybridized on oligonucleotide microarrays representing 25,000 genes. Four microarrays per patient were hybridized and a dye-swap was performed (2 microarrays patient-Cy3/reference-Cy5 and 2 microarrays patient-Cy5/reference-Cy3). Hybridization steps were performed using the Agilent Technologies system. Briefly, RNA was fragmented with a fragmentation buffer before mixing with a hybridization buffer. Microarrays were blocked with 50 mM ethanolamine in 50 mM borate buffer pH = 9.0. Agilent's hybridization chambers and rotating oven were used for hybridization at 60°C for 17 h at 4 rpm. Microarrays were washed for 10 min in 6X SSC, 0.005% Triton X-102, for 5 min in 0.1X SSC, 0.005% Triton X-102, and were then dried by centrifugation before scanning using an Axon 4000B microarray scanner and the GenePix Pro 6® software (Molecular Devices, Berks, UK). Self photomultiplicator gain adjustment and 0.1% saturated spots were allowed during scanning. Spot finding and raw data quantification of all four microarrays for each patient were performed in a batch analysis using the MAIA® freeware. A Lowess non linear normalization step was performed with the Acuity® software (Molecular Devices) to compensate for uneven Cy3-Cy5 distribution. The normalized log ratio Cy3/Cy5 was used in subsequent steps. A filtering step was then performed to remove genes that were not present in at least three microarrays out of four. The quality and reproducibility of each of the four microarrays per patient were evaluated using ANOVA, correlation coefficients and Self Organizing Maps drawn with the Acuity® software. Data are stored in the Web based Microarray Data manager MEDIANTE and are available at the Gene Expression Omnibus database (http://0-www.ncbi.nlm.nih.gov.brum.beds.ac.uk/geo/) under the accession number GSE8723. Before statistical analysis, genes not present in at least 50% of the patients were filtered out. Supervised analysis was performed using the Significance Analysis of Microarrays (SAM) software which correlates gene expression with an external variable such as EF value. Two class unpaired t-test and 100 permutations were used. Gene missing values imputation was performed via a K Nearest Neighbour algorithm normalization using 10 neighbours.
Results are presented as mean ± SD or as median (interquartile range) for description of demographic characteristics. Comparisons between two groups were performed with two-tailed t-test for Gaussian data and Mann-Whitney test for non Gaussian data. Comparisons between multiple groups were performed with one way ANOVA for Gaussian data and Kruskal-Wallis one way ANOVA on ranks for non Gaussian data. Paired data among multiple groups were compared with one way repeated measures ANOVA and all pairwise multiple comparison procedures (Holm-Sidak method). Correlation tests were performed using the Pearson product moment correlation method. Statistical significance tests were generated with the SigmaPlot v11.0 software and the SigmaStat software (for Windows version 3; SPPS Inc. Chicago, Illinois, USA). A P value < 0.05 was considered statistically significant.
This study was supported by grants from the "Agence de Biomédecine" (France), the Centre de Recherche Public-Santé (Luxembourg), and the Ministry of Culture, Higher Education and Research (Luxembourg).
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