Abstract
BackgroundQuantitative PCR (qPCR) is a powerful tool that is particularly well-suited to measure mRNA levels in clinical samples, especially those with relatively low cell counts. However, a caveat of this approach is that reliable, stably expressed reference (housekeeping) genes are vital in order to ensure reproducibility and appropriate biological inference. In this study, we evaluated the expression stability of six reference genes in peripheral blood mononuclear cells (PBMCs) and isolated CD3+ T-cells from young and old adults (n = 10), following ex vivo stimulation with mock (unstimulated) or live influenza virus. Our genes included: β-actin (ACTB), glyercaldehyde-3-phostphate dehydrogenase (GAPDH), ribosomal protein L13a (RPL13a), ribosomal protein S18 (RPS18), succinate dehydrogenase complex flavoprotein subunit A (SDHA), and ubiquitin-conjugating enzyme E2D2 (UBE2D2).ResultsReference gene expression varied significantly depending on cell type and stimulation conditions, but not age. Using the comparative ΔCt method, and the previously published software BestKeeper, NormFinder, and geNorm, we show that in PBMCs and T-cells, UBE2D2 and RPS18 were the most stable reference genes, followed by ACTB; however, the expression of UBE2D2 and RPS18 was found to increase with viral stimulation in isolated T-cells, while ACTB expression did not change significantly. No age-related differences in stability were observed for any geneConclusionsThis study suggests the use of a combination of UBE2D2, RPS18, and ACTB for the study of influenza responses in PBMCs and T-cells, although ACTB alone may be the most optimal choice if choosing to compare target gene expression before and after viral stimulation. Both GAPDH and RPL13a were found to be poor reference genes and should be avoided for studies of this nature.
Highlights
Quantitative PCR is a powerful tool that is well-suited to measure mRNA levels in clinical samples, especially those with relatively low cell counts
In addition to comparing reference gene stability with and without live influenza challenge ex vivo, we compared stability between young and old adults, given that age is a major determinant of susceptibility to infection and can alter peripheral blood mononuclear cells (PBMCs) mRNA expression profiles significantly [10, 11]
In isolated T-cells, four of six candidate genes were significantly different between treatments: glyercaldehyde-3-phostphate dehydrogenase (GAPDH) (p < 0.01), ribosomal protein L13a (RPL13a) (p < 0.01), ribosomal protein S18 (RPS18) (p < 0.001), and ubiquitin-conjugating enzyme E2D2 (UBE2D2) (p < 0.001) (Fig. 1B)
Summary
Quantitative PCR (qPCR) is a powerful tool that is well-suited to measure mRNA levels in clinical samples, especially those with relatively low cell counts. A powerful tool one can use to measure gene expression in immune cells, including human peripheral blood mononuclear cells (PBMCs) and T-cells, is quantitative polymerase chain reaction (qPCR). This technology provides high sensitivity when measuring mRNA [1], making it an ideal tool for gene expression analysis in fresh PBMCs and isolated Tcells. RPS18 was included as it has been shown to be fairly stable in PBMCs from other species [17] and in tumour neovascularization studies [18] We compared these genes using four methods, each of which estimating stability and/or reliability in a slightly differ manner: geNorm [7] determines gene expression stability M) by calculating the average pairwise variation of each reference gene; NormFinder [19] uses an ANOVA based approached to calculate the candidate gene stability value by estimating the expression variation within the overall group (intragroup) and between groups (intergroup); Bestkeeper [20] estimates reliability according to the standard deviation of Cq values and the Pearson correlation between a given gene and an index of the most stable reference genes, as determined by the software; Lastly, the comparative ΔCt method, proposed by Silver and colleagues [21], compares the relative expression of pairs of reference genes within the sample and uses the average standard deviation of the ΔCt (or ΔCq) for each reference gene as a measure of stability
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