- Research article
- Open Access
Selection of reliable reference genes for quantitative real-time PCR in human T cells and neutrophils
© Kreth et al; licensee BioMed Central Ltd. 2011
- Received: 2 August 2011
- Accepted: 20 October 2011
- Published: 20 October 2011
The choice of reliable reference genes is a prerequisite for valid results when analyzing gene expression with real-time quantitative PCR (qPCR). This method is frequently applied to study gene expression patterns in immune cells, yet a thorough validation of potential reference genes is still lacking for most leukocyte subtypes and most models of their in vitro stimulation. In the current study, we evaluated the expression stability of common reference genes in two widely used cell culture models-anti-CD3/CD28 activated T cells and lipopolysaccharide stimulated neutrophils-as well as in unselected untreated leukocytes.
The mRNA expression of 17 (T cells), 7 (neutrophils) or 8 (unselected leukocytes) potential reference genes was quantified by reverse transcription qPCR, and a ranking of the preselected candidate genes according to their expression stability was calculated using the programs NormFinder, geNorm and BestKeeper. IPO8, RPL13A, TBP and SDHA were identified as suitable reference genes in T cells. TBP, ACTB and SDHA were stably expressed in neutrophils. TBP and SDHA were also the most stable genes in untreated total blood leukocytes. The critical impact of reference gene selection on the estimated target gene expression is demonstrated for IL-2 and FIH expression in T cells.
The study provides a shortlist of suitable reference genes for normalization of gene expression data in unstimulated and stimulated T cells, unstimulated and stimulated neutrophils and in unselected leukocytes.
- Reference Gene
- Normalization Strategy
- Candidate Reference Gene
- Stable Reference Gene
- Potential Reference Gene
Due to its high sensitivity, specificity and resolution, quantitative real-time PCR (qPCR) has become the method of choice for gene expression analyses of selected genes [1–3]. However, reverse transcription (RT) qPCR measurements are influenced by a variety of unspecific factors, including the amount and quality of the isolated RNA and efficiencies of reverse transcription and PCR amplification, which makes accurate normalization a prerequisite for reliable results [1, 4–6]. The most commonly applied normalization strategy involves the use of reference genes as internal controls, whose expression should be constant in all samples under investigation . Since it has become clear, though, that conventional reference genes, such as glyceraldehyde-3-phosphate dehydrogenase (GAPDH) or β-actin (ACTB), are regulated under certain circumstances leading to invalid results [7, 8], it is essential to validate the suitability of potential reference genes for the specific experimental conditions.
The study of gene expression patterns in immune cells is a promising approach to gain insight into complex regulatory mechanisms associated with immune-mediated disease . Although RT-qPCR is frequently employed for gene expression analysis in leukocytes, a thorough validation of reference gene stability has not been described yet. Data are not only missing for the appropriate normalization of mRNA levels in unselected leukocytes, but are also scarce with respect to leukocyte subtypes or activation procedures [10–12]. Stimulating T cells with anti-CD3/CD28 beads to mimic the activation by antigen-presenting cells , for example, or treating neutrophils with lipopolysaccharide (LPS) [14–16] are two well-established in vitro models in the investigation of inflammatory, infectious or autoimmune disease; a systematic validation of reference gene stability has thus far been lacking for either model, though.
In the present study we investigated the expression stability of potential reference genes in unstimulated and anti-CD3/CD28 activated T cells and in unstimulated and LPS-stimulated neutrophils, using the three software applications geNorm , NormFinder  and BestKeeper . Based on these results, we further identified reference genes that can be used as universal normalizers in gene expression studies in unselected leukocyte populations. Furthermore, we show that the use of unstable reference genes is prone to cause highly misleading results, which underlines the importance of a thorough selection and evaluation of reference genes for RT-qPCR experiments in immune cells.
Isolation and stimulation of T lymphocytes and neutrophils
Blood withdrawal from healthy volunteers was approved by the institutional ethics committee of the Ludwig Maximilians University, Munich, Germany, and written informed consent was obtained. T cells were isolated from peripheral blood mononuclear cells by negative selection using the Pan T cell isolation kit II (Miltenyi Biotec) according to the manufacturer's instructions. Neutrophils were separated from whole blood by continous percoll gradient density centrifugation as previously described . Cells were cultured in RPMI-1640 medium (Sigma-Aldrich) supplemented with 10% heat-inactivated fetal calf serum (Biochrom) and L-glutamine (Gibco) at 37°C in 5% CO2. T cells (1 × 106/ml) were stimulated with anti-CD3/CD28 beads (Invitrogen) at a bead-to-cell ratio of 1:1 and harvested after 24 hours. Neutrophils (1.5 × 106/ml) were stimulated for 6 hours with 100 ng/ml LPS (E.O55.B5, Sigma-Aldrich).
RNA extraction and cDNA synthesis
Total RNA was isolated using the RNAqueous Kit (Ambion) followed by DNase treatment (TurboDNase, Ambion) according to the manufacturer's instructions. Total blood leukocyte RNA was extracted from 10 ml whole blood by use of the LeukoLOCK system (Ambion) following the suggested protocol. RNA quantity and purity were measured with a NanoDrop 2000 spectrophotometer (Thermo Scientific), and only samples with A260/A280 ratios between 1.80 and 2.00 were analyzed further. The integrity of RNA samples was confirmed by electrophoresis on a 1% agarose gel. First-strand cDNA was synthesized from equal amounts of RNA (1000 ng) using Superscript III reverse transcriptase (Invitrogen) and random hexamers and oligo(dT) primers as described .
Quantitative real-time PCR
17 commonly used reference genes were selected as candidate genes (Table 1). Real-time PCR was performed in duplicate on a LightCycler®480 instrument (Roche Diagnostics) using equal amounts (10 ng) of reverse transcribed total RNA and pre-validated probe-based RealTime ready® assays (Roche Diagnostics; see Additional file 1 Table S1 for Assay ID and amplicon location). Interleukin-2 (IL-2) and factor inhibiting hypoxia inducible factor (FIH) were chosen as exemplary target genes, using the following primers and Universal ProbeLibrary (UPL) probes (Roche Diagnostics): IL-2: 5' AAGTTTTACATGCCCAAGAAGG 3' (forward primer), 5' AAGTGAAAGTTTTTGCTTTGAGCTA 3' (reverse primer), UPL probe #65; FIH: 5' ACCCTGTTCATCACCCATGT 3' (forward primer), 5' TCTCGTAGTCGGGATTGTCA 3' (reverse primer), UPL probe #21. With the exception of 18S, all assays were designed to span at least one intron. Negative controls without the addition of cDNA were included to verify the absence of contamination. To avoid inter-run variation, the same gene was tested in the same run on different samples . The cycling conditions comprised an inital denaturation phase at 95°C for 5 min, followed by 45 amplification cycles at 95°C for 10 s, 60°C for 30 s and 72°C for 15 s. Quantification cycle (Cq) values were calculated employing the "second derivative maximum" method as computed by the LightCycler software. Amplification efficiencies were determined for all qPCR assays by calculating calibration curves from 5- to 10-fold serial dilutions from pooled cDNA using the equation E = 10[-1/slope]. Efficiencies ranged from 89.2% (ALAS) to 107.5% (ACTB) with r2 > 1.98 (see Table S1 for E and r2 values for each assay).
Candidate reference genes evaluated in this study.
cytoskeletal structural protein
5-aminolevulinate synthase 1
heme biosynthetic pathway
β-chain of MHC I molecules
heme biosynthetic pathway
hypoxanthine phosphoribosyl-transferase 1
purine salvage pathway
nuclear import of proteins
phosphoglycerate kinase 1
peptidylprolyl isomerase A
ribosomal protein, large, P0
ribosomal protein, translation
ribosomal protein L13A
ribosomal protein, translation
succinate dehydrogenase complex, subunit A
mitochondrial respiratory chain
TATA box binding protein
general RNA polymerase II transcription factor
transferrin receptor (p90, CD71)
cellular iron homeostasis
tyrosine-3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta polypeptide
binding to phosphorylated serine residues, signal transduction
RNA, 18S ribosomal 1
ribosomal RNA, translation
Statistical data analysis
The Kolmogorov-Smirnov test was applied to determine whether the distribution of the differences between Cq values of paired samples deviated from a normal distribution. Intergroup comparisons were performed by paired t-test or Wilcoxon signed rank test, if data were normally or not normally distributed, respectively, and candidate genes showing differential expression (p < 0.05) were ruled out from further analyses. Expression stability of potential reference genes was evaluated by applying three generally accepted  Excel-based software tools-BestKeeper , geNorm  and NormFinder -according to the instructions provided by the developers. The BestKeeper software suggests a preliminary ranking of candidate reference genes based on Cq variation in expression. Furthermore, it estimates the expression stability by performing a pair-wise correlation analysis for each pair of candidate genes. The program geNorm provides a measure of gene expression stability (M) by calculating the average pairwise variation of each control gene from all the other control gene candidates. In addition, it performs a ranking of the candidate genes by stepwise exclusion of the worst scoring gene and repeated recalculation of the average M value. Unlike geNorm and BestKeeper, NormFinder employs a model-based approach, which does not only estimate the overall variation of the candidate genes but also the variation between sample subgroups. All analyses were done correcting for different amplification efficiencies. Cq values were transformed into relative quantities for data processing by geNorm and NormFinder using the comparative Cq method and E as base . To assess the expression stability of candidate reference genes in paired samples of unstimulated and stimulated cells, and to evaluate the impact of different normalization strategies on target gene expression, relative expression ratios (R) were calculated for reference genes, combinations of reference genes and target genes using the equation R = EΔCq where E is the efficiency of the respective real-time PCR assay and ΔCq = Cq(stimulated sample)-Cq(unstimulated control). These ratios or the geometric means, respectively, were used for calculation of normalized relative expression ratios as described by Pfaffl et al. . Differences in target gene expression were tested for statistical significance (p < 0.05) using paired t-test and Bonferroni correction to account for multiple comparisons.
Raw Cq values are summarized in Additional File 2 Table S2. Candidate reference genes were evaluated in a stepwise procedure: First, 17 commonly used reference genes were evaluated in unstimulated and stimulated T cells. Second, candidate genes stably expressed in T cells were further evaluated in unstimulated and stimulated neutrophils. Finally, candidate reference genes stably expressed in both T cells and neutrophils were analyzed in total blood leukocytes in order to identify universal leukocyte normalizers.
Reference gene evaluation in unstimulated and anti-CD3/CD28 stimulated T cells
Results of BestKeeper, geNorm and NormFinder analyses in unstimulated and anti-CD3/CD28 stimulated T cells.
SD [± Cq]#
CV [% Cq]#
Stability ranking of candidate reference genes in T cells, neutrophils and unselected blood leukocytes by NormFinder, geNorm and BestKeeper.
Total Blood Leukocytes
Reference gene evaluation in unstimulated and LPS-stimulated neutrophils
Results of BestKeeper, geNorm and NormFinder analyses in unstimulated and LPS-stimulated neutrophils.
SD [± Cq]#
CV [% Cq]#
Reference gene evaluation in total blood leukocytes
Results of BestKeeper, geNorm and NormFinder analyses in total blood leukocytes (n = 12).
SD [± Cq]#
CV [% Cq]#
Optimal number of reference genes
Regulation of reference gene expression in T cells and neutrophils upon stimulation
Influence of the normalization strategy on the estimated target gene expression
Quantitative real-time PCR has become a standard method for gene expression analysis, allowing accurate quantification of mRNA levels over a wide dynamic range . If handled improperly, however, the results can be misleading. One of the most critical points is the selection of appropriate reference genes to control for experimental error between samples [3, 7]. In the current study, we evaluated, to our knowledge for the first time, the expression stability of common reference genes separately in two widely-used cell culture models of stimulated leukocyte subtypes: T cells activated by anti-CD3/CD28 beads, and LPS-stimulated neutrophils. A major finding of our study was that several conventional "housekeeping genes" proved to be unreliable controls, which is in line with previous reports about an unstable expression of commonly used reference genes, such as GAPDH, ACTB or HPRT1, in various experimental setups [11, 20–22]. Of note, IPO8 and ACTB behaved considerably differently regarding their stability in neutrophils or T cells, and candidate genes we found inappropriate for normalization in activated T cells have been reported to be stably expressed in LPS-treated monocytes (B2M, PPIA, ACTB) or B cells from chronic lymphocytic leukemia patients (B2M, HPRT1). These findings underscore the necessity of careful individual validation of reference genes for every leukocyte subtype and every experimental condition.
BestKeeper, geNorm and NormFinder outputs provided very similar stability rankings of the candidate genes, especially in T cells. As the programs are based on different algorithms [4–6], the consensus between them increases the reliability of the results. In neutrophils, there was some discrepancy in the ranking order: geNorm identified RPL13A as one of the two most stable genes, whereas RPL13A was assigned the last rank by NormFinder and BestKeeper analyses. In contrast to NormFinder, the pairwise comparison approach applied by geNorm is sensitive to co-regulation and shows a tendency to top rank candidates with correlated expression rather than minimal variation , which could be an explanation for differing results. In the present study, the combinations of the two most suitable genes proposed by geNorm (SDHA/RPL13A) and NormFinder (ACTB/TBP) showed a similarly low expression variation in paired samples of untreated and stimulated neutrophils, suggesting the suitability of both normalization approaches. Consistent with the recently published MIQE (minimum information for publication of quantitative real-time PCR experiments) guidelines , these results support the use of a normalization strategy that is based on several stably expressed genes, not just a single gene, to reduce variation. The number of reference genes used in a particular experiment will be a compromise between minimizing variability and considerations of practicability [4, 6]. NormFinder and geNorm consistently suggested the use of two reference genes (RPL13A and IPO8) for normalizing gene expression data in unstimulated and activated T cells. In neutrophils, results differed between geNorm and NormFinder with geNorm indicating the optimal number of reference genes with six, whereas according to NormFinder the combination of ACTB and TBP was sufficient. It is important to note that neither geNorm nor NormFinder claim absolutness of their results but recommend them as a guideline which has to be interpreted individually when selecting the number of reference genes to be used [4, 6]. Based on the results in paired samples, and considering that NormFinder, unlike geNorm, takes intergroup differences into account and is less susceptibel to co-regulation of genes, we recommend the use of at least two genes out of ACTB, TBP, SDHA and RPL13A for normalization in LPS-stimulated neutrophils. 18S, which is commonly used for normalization of qPCR data in various cell types , including leukocytes [25, 26], belonged to the stably expressed candidates in T cells. Due to its high expression, though, it will likely be inappropriate for the expression normalization of most genes of interest, as similar abundances of target and reference gene are important to ensure that they are both subject to the same PCR kinetics .
We intended to identify potential "universal leukocyte normalizers" (suitable for as many leukocyte subtypes as possible). Therefore, we limited the reference genes evaluated in neutrophils to those candidates that had performed well in T cells. As a consequence of this sequential procedure, it cannot be excluded that a subset of reference genes not tested in our study would be suitable for normalizing gene expression in neutrophils. Studying gene expression in total blood leukocytes, thereby circumventing the time-consuming purification of single leukocyte subtypes, appears as an attractive approach in the search for diagnostic or therapeutic targets in immune-mediated disease , although one has to be aware of its inherent limitations: changes in expression levels may not only be due to regulation of transcriptional activity but also reflect relative changes in the abundance of single cell populations with constant expression levels. The bias introduced will be especially pronounced if the control genes used for normalization show variable expression stabilities in different leukocyte subtypes. The expression stability of potential reference genes should therefore ideally be assessed in the single cell types prior to using them in mixed-cell approaches. Our results identified the combination of SDHA and TBP as a suitable normalizer in T cells as well as in neutrophils. In good agreement, a recent study recommends the use of SDHA as a reference gene in LPS stimulated porcine T cells . Furthermore, TBP has recently been reported to be stably expressed in LPS stimulated monocytes . We therefore hypothesized that TBP and SDHA could be suitable "universal" reference genes in unselected leukocytes. In support of our results, SDHA and TBP were listed among the three most stable genes in total blood leukocytes by all three analyzing softwares. Although NormFinder analyses found the use of a single reference gene (SDHA) to be sufficient in total blood leukocytes, we recommend as a general rule the use of at least two reference genes, and thus normalization to SDHA and TBP, as suggested by geNorm.
Whether a chosen normalization strategy is considered suitable or not in a given experimental setting also depends on the extent and required resolution of expression differences. When analyzing the expression of IL-2, a target gene that undergoes a strong upregulation in activated T cells, even the use of considerably instable reference genes correctly indicated an increase in IL-2 transcripts, which may be sufficient if only an on-/off response is to be detected. Usually, however, the investigated regulatory effect is much smaller, and estimating the exact expression change is important. In this case, the use of inappropriate reference genes leads to unreliable results and may even produce artificial changes, as is demonstrated by the comparison of different normalization approaches for the expression of FIH, a key component of the cellular oxygen-sensing machinery that controls the activity of the transcriptional regulator HIF-1α , but is not known to be regulated in T cells activated by anti-CD3/CD28 beads under normoxic conditions. Of note, adding a stable reference gene for normalization did considerably compensate for the distorting effect of using a single unstable reference gene, thus supporting the use of more than one reference gene . However, even when combined with the most stable gene, using an unstably expressed gene led to erronous FIH expression results; a careful selection of all the reference genes used for normalization is therefore required.
Our study clearly demonstrates the need to carefully select appropriate reference genes for normalization of gene expression data obtained by RT-qPCR. We recommend the use of two genes out of RPL13A, IPO8, TBP and SDHA and at least two genes out of ACTB, TBP, SDHA and RPL13A as RT-qPCR control genes in T cells and neutrophils, respectively. Furthermore, SDHA and TBP were shown to be suitable gene expression normalizers in unselected leukocytes.
The data sets supporting the results of this article are included within the article and its additional files.
We thank J. Rink for her excellent technical assistance.
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