Reliable reference genes for the quantification of mRNA in human T-cells and PBMCs stimulated with live influenza virus
Bibliographic record
Abstract
Abstract Background Quantitative 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). Results Reference 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 gene Conclusions This 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".