Ribosomal RNA Passes Its Test as a Steady Reference in High-Throughput Plasma qPCR

Every quantitative PCR experiment lives or dies by its reference gene. The technique can measure nucleic acids with exquisite sensitivity, but only if the measurements can be anchored to something that does not itself waver between samples. For decades, molecular biologists have leaned on housekeeping genes and ribosomal RNAs to serve as these internal anchors, yet the field has repeatedly learned that no control gene is universally stable. A new study published in Molecular Biology Reports now offers one of the most systematic assessments to date of 18 S ribosomal RNA, the workhorse control of countless qPCR experiments, in a setting that is notoriously unforgiving: high-throughput, pre-amplified RNA measured from human blood plasma on the TaqMan OpenArray platform.

The research, led by Habib Francis of the Sydney Clinical Trials Centre at The University of Sydney together with colleagues across Australia, Hong Kong and India, drew on plasma samples from the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) trial, a landmark randomised controlled trial of nearly 10,000 adults with type 2 diabetes. Because the samples came from a large, well-characterised clinical cohort, the team could interrogate the behaviour of 18 S rRNA across hundreds of real biobanked specimens rather than a handful of laboratory controls. In total, 789 plasma RNA samples analysed across 17 separate OpenArray runs formed the backbone of the stability assessment, giving the study a statistical weight that most reference-gene validations never approach.

The technical challenge the authors set out to resolve is a genuine worry in multiplexed qPCR. Microfluidic OpenArray plates pack thousands of nanolitre-scale reactions onto a single plate, and the tiny input of plasma RNA, which is naturally dilute and dominated by fragmented extracellular species, usually forces a pre-amplification step. During those 14 cycles of targeted pre-amplification, all the primer pairs in the pool compete for the same enzymes, nucleotides and reaction space. Highly abundant transcripts such as 18 S rRNA, which can be present at levels orders of magnitude above the messenger RNA targets of interest, could in theory monopolise shared reagents and quietly suppress the amplification of scarcer targets. If that happened, Ct values would shift in ways that no amount of normalisation could correct, corrupting every downstream comparison.

To test this directly, the researchers ran a paired experiment on 23 plasma samples. Each sample was pre-amplified twice, once with a primer pool containing the 18 S rRNA assay and once with an otherwise equivalent pool lacking it. The team then measured four representative target genes, ACTG1, ACTB, CLU and PFN1, under both conditions and compared the raw cycle threshold values sample by sample. Because not every gene was detectable in every sample, the effective sample sizes ranged from eight to seventeen per assay, a limitation the authors acknowledge candidly. Even so, the paired design is the cleanest possible way to isolate the effect of a single primer from all other sources of variation.

The verdict was reassuring. Median paired Ct differences between the two pre-amplification conditions ranged from just 0.07 to 0.52 cycles, and none of the four targets showed a statistically significant shift, with all p-values exceeding 0.05. Bland–Altman analysis, which plots the difference between paired measurements against their average, confirmed the picture: the mean bias across all four genes combined was a negligible −0.18 Ct, and the data points for each gene were distributed similarly, mostly falling within the 95 percent limits of agreement. In plain terms, adding 18 S rRNA to the pre-amplification pool did not measurably distort the amplification of the co-amplified messenger RNA targets under the conditions tested. The feared competition effect, at least in this workflow, did not materialise.

The stability data were equally encouraging. Across the 789 samples and 17 runs, 18 S rRNA was consistently detectable, with a median Ct of 14.5 and an interquartile range of 13.4 to 16.3. The distribution of Ct values was unimodal, clustering tightly around the median without the bimodality or systematic shifts that would hint at batch effects or hidden technical artefacts. Inter-run variability was low, with a coefficient of variation of just 3.9 percent and no significant differences between runs. Notably, these results were obtained using archived plasma samples that had endured three freeze–thaw cycles, conditions typical of longitudinal clinical biobanking, suggesting that 18 S rRNA detection remains technically robust even when samples have been handled the way real-world studies demand.

The study also tackled a practical problem that haunts long-running biomarker projects: what happens when a primer pool runs out? The FIELD analyses had relied on an archived pre-amplification pool containing the 18 S rRNA assay, stored unopened at −80 °C for more than five years. The researchers compared 18 S rRNA Ct values in 774 paired plasma samples amplified with either that archived pool or a newly manufactured one. Amplification succeeded under both conditions, but the new pool produced slightly lower Ct values, a statistically significant shift with a mean bias of 0.53 Ct. More concerning was the spread: the standard deviation of paired differences was 3.82 Ct, with 95 percent limits of agreement stretching from −6.95 to 8.01 Ct, and variability appeared to widen at higher average Ct values, where template abundance is lowest.

That combination of a small average bias and wide individual-level variability carries an important lesson for anyone managing longitudinal studies. The authors conclude that data generated with archived and new primer pools remain broadly comparable at the group level, but the two pools should not be treated as directly interchangeable for individual paired measurements. They recommend practical safeguards: keeping detailed records of primer sequences, concentrations and manufacturing lots; running bridging experiments on a representative subset of samples whenever a pool must be replaced; and using paired comparisons with Bland–Altman analysis to quantify any systematic bias before transitioning. Where the bias is small and consistent, calibration may suffice; where variability is large, reanalysis with a common pool or an alternative validated assay may be the only defensible option.

The authors are careful to frame what this study does and does not prove. Their findings establish the technical performance of 18 S rRNA within plasma-based OpenArray workflows, not its biological stability across disease states or experimental conditions, and they cannot be assumed to extend to other sample types, platforms or pre-amplification chemistries. Plasma is a peculiar analytical matrix, rich in fragmented extracellular RNA and poor in total messenger RNA, and pre-analytical variables such as blood collection tube type can reshape extracellular RNA profiles entirely. Still, by combining large-scale stability assessment, paired technical comparisons and a real-world test of reagent longevity, the study offers a template for validating endogenous controls in any high-throughput qPCR setting, and it gives biomarker researchers working with scarce clinical plasma a solid reason to trust one of molecular biology’s oldest internal controls.

Subject of Research: Validation of 18 S rRNA as an endogenous control gene in high-throughput plasma RNA qPCR workflows

Article Title: Evaluation of 18 S rRNA as an endogenous control in OpenArray® plasma RNA qPCR workflows

Article References: Francis, H., Januszewski, A. S., Mangani, A., Ema, F. K., Hardikar, A. A., Joglekar, M. V., Huang, M. L., Sullivan, D. R., Ma, R. C., Galande, S., Gebski, V., Simes, R. J., Jenkins, A. J., Molloy, M. P., & Keech, A. C. (2026). Evaluation of 18 S rRNA as an endogenous control in OpenArray® plasma RNA qPCR workflows. Molecular Biology Reports, 53(1), Article 1641. https://doi.org/10.1007/s11033-026-12820-9

Image Credits: AI Generated

DOI: 10.1007/s11033-026-12820-9

Keywords: qPCR, 18S rRNA, OpenArray, plasma RNA, endogenous control, pre-amplification, reference gene, Bland-Altman analysis, FIELD trial, biomarkers, gene expression normalisation, technical validation