The research, published in Molecular Biology Reports by Ivan Zivotic, Ivana Kolic, Mariana Seke, Milan Stefanovic, Ljiljana Stojkovic, Aleksandra Stankovic, Maja Zivkovic and colleagues, set out to systematically evaluate whether slow, intermediate and fast ramp rates produce measurable shifts in cycle threshold values, the Ct values that serve as the primary readout of qPCR experiments. The team used a commercial TaqMan SNP genotyping assay alongside two well-characterized housekeeping genes, GAPDH and PPIA, providing both a genotyping application and a gene expression application within the same experimental framework. Eleven heterozygous DNA samples and three RNA samples were analyzed, with each ramp rate condition tested across two independent runs to distinguish genuine systematic effects from ordinary run-to-run noise.

The technical logic behind the question is straightforward. During each PCR cycle, DNA strands must separate at high temperature, primers and probes must bind at lower temperatures, and the polymerase must extend the new strand. If the instrument transitions too quickly between these temperature plateaus, reagents may not fully equilibrate, annealing may be incomplete, and enzyme activity may be briefly suboptimal. Conversely, slower ramps extend the total run time, which is a practical concern for high-throughput laboratories and for point-of-care settings where speed is critical. Earlier work has shown that ramp rate can matter in specific contexts. Studies of rapid microfluidic thermocyclers and ultrafast PCR platforms have demonstrated that carefully engineered fast cycling can work, but also that faster quantitative protocols may lose sensitivity or show increased variability under some conditions.

The clinical stakes of this question are not trivial. Research on tuberculosis diagnostics, for example, has documented that widespread use of incorrect ramp rate settings can negatively impact the performance of commercial line-probe assays used to detect drug-resistant Mycobacterium tuberculosis, leading to calls for better thermocycler configuration in diagnostic laboratories. Those findings created a plausible expectation that ramp rate could similarly distort Ct values in qPCR, potentially affecting quantification of viral loads, expression of disease-related genes, and genotyping calls across countless laboratories. The Serbian team’s results, however, point in a different direction for standard probe-based qPCR chemistry.

Across both the genotyping assay and the gene expression assays targeting GAPDH and PPIA, the researchers observed no consistent effect of ramp rate on Ct values. When they compared slow, intermediate and fast ramp settings within individual runs, they did identify isolated statistically significant or borderline significant differences, including for the FAM-labeled probe used in the genotyping assay and for both housekeeping gene expression measurements. Yet these differences failed a crucial test of reliability: they were not reproduced consistently across independent runs or across pairwise comparisons between ramp rate conditions. In other words, a difference that appeared in one run might vanish in the next, a pattern more consistent with ordinary experimental variability than with a genuine systematic influence of heating speed.

The authors conclude that the observed effects appeared to be assay- and run-dependent rather than attributable to a consistent influence of ramp rate itself. This distinction carries real methodological weight. Ct values are inherently sensitive to many sources of noise, including pipetting variation, reagent preparation, optical fluctuations, sample quality and the stochastic behavior of amplification at low template concentrations. When a sporadic difference between two ramp rate conditions falls within the range of ordinary run-to-run variation, attributing it to the ramp rate would be a statistical overreach. By requiring reproduction across runs before claiming an effect, the study models the kind of cautious interpretation that the updated MIQE 2.0 guidelines encourage for quantitative PCR reporting more broadly.

The findings align with a body of work suggesting that modern qPCR chemistry is more forgiving of thermal cycling parameters than early protocols assumed. Probe-based detection chemistries such as TaqMan rely on the 5′ nuclease activity of the polymerase releasing a fluorescent reporter, and the accumulation of fluorescence over cycles integrates the kinetics of many individual extension events. Small perturbations in the seconds spent transitioning between temperatures may be buffered by the exponential nature of amplification, where each cycle’s yield feeds into the next. Nearest-neighbor thermodynamic analyses of nucleic acid hybridization also indicate that primer and probe binding equilibria are governed primarily by sequence and temperature rather than by the speed at which those temperatures are reached, provided the hold times at each step are adequate.

Nevertheless, the researchers are careful to frame their work as exploratory rather than definitive. The sample size was modest, with eleven heterozygous DNA samples and three RNA samples, and the assay panel was limited to a single commercial SNP genotyping assay and two housekeeping genes. The authors explicitly note that larger studies involving more assays and samples are needed to determine whether PCR ramp rate has a meaningful impact on qPCR performance and reproducibility under different experimental conditions. Different amplicon lengths, different polymerase formulations, different detection chemistries such as SYBR Green intercalating dyes, and different instrument architectures could all behave differently. Studies of amplicon length effects on real-time PCR results, for instance, show that the physical dimensions of the amplified product can influence outcomes, and it remains possible that longer amplicons interact with ramp rate in ways the short targets tested here do not.

For working laboratories, the practical message is one of measured reassurance rather than license for indiscriminate protocol changes. The results suggest that laboratories running standard probe-based qPCR assays need not worry excessively about modest differences in ramp rate settings between instruments, provided that validation is performed on the actual platform in use. This matters for multi-site studies, for laboratories that must transfer validated assays between different thermal cycler models, and for clinical settings where instrument availability varies. At the same time, the tuberculosis diagnostic literature demonstrates that some assays are genuinely sensitive to thermal cycling parameters, so blanket assumptions in either direction would be unwise. The sensible approach, consistent with the study’s conclusions, is empirical verification: run the assay under the intended conditions, confirm that Ct values and amplification efficiency remain within established acceptance criteria, and document the settings used.

The study also arrives at a moment when the qPCR field is actively revisiting its reporting standards. The MIQE 2.0 revision of the minimum information guidelines reflects growing recognition that technical parameters, instrument characteristics and data analysis choices all shape the reproducibility of quantitative PCR results. By systematically probing one such parameter and reporting both the significant and non-significant findings, the Belgrade team contributes to a more honest empirical picture of what actually drives Ct variability. Their work was supported by the Ministry of Science, Technological Development and Innovation of the Republic of Serbia, and the underlying data are available within the paper and its supplementary materials. While the question of ramp rate effects is not fully closed, this exploratory study provides a useful data point: for the genotyping and gene expression assays tested, the speed at which the thermal cycler moved between temperatures left the fundamental quantitative output of qPCR essentially unchanged, an encouraging result for anyone who has ever worried that a slightly slower machine might quietly be skewing their numbers.

Subject of Research: Effect of thermal cycler ramp rate on cycle threshold values in quantitative real-time PCR genotyping and gene expression assays

Article Title: Limited effect of PCR ramp rate on qPCR Ct values: an exploratory study

Article References: Zivotic, I., Kolic, I., Seke, M., Stefanovic, M., Stojkovic, L., Stankovic, A., & Zivkovic, M. (2026). Limited effect of PCR ramp rate on qPCR Ct values: an exploratory study. Molecular Biology Reports, 53(1), Article 1690. https://doi.org/10.1007/s11033-026-12881-w

Image Credits: AI Generated

DOI: 10.1007/s11033-026-12881-w

Keywords: qPCR, ramp rate, Ct values, thermal cycler, TaqMan assay, GAPDH, PPIA, genotyping, gene expression, amplification efficiency, MIQE guidelines, reproducibility

Real-time quantitative polymerase chain reaction, or RT-qPCR, remains one of the most widely used techniques in breast cancer research. It allows scientists to quantify precisely how active particular genes are in a sample, which is critical for identifying biomarkers, validating molecular targets, and understanding how tumors differ from healthy tissue at the transcript level. But the raw output of an RT-qPCR experiment—the cycle threshold, or Ct value at which fluorescence crosses a detection limit—depends not only on the target gene but also on how much starting material was loaded, how efficiently the RNA was extracted, and how well the reverse transcription step worked. Normalization is the mathematical correction that strips away these technical variables, and it is typically done by measuring a reference gene assumed to be expressed at a constant level in every sample.

The trouble is that this assumption of constancy is often wrong. Genes chosen decades ago as ‘housekeeping’ controls because of their essential roles in basic cellular metabolism can vary substantially between tumor and normal tissue, between molecular subtypes, and even between individual patients. If the reference gene itself fluctuates with disease state, the apparent expression changes in the genes under study may be artifacts of the normalization rather than real biology. The MIQE guidelines, published in 2009, formalized the requirement that researchers validate their reference genes for every specific tissue and experimental context rather than borrowing them uncritically from the literature, and a growing body of work has shown that conventionally used controls frequently fail this test in human cancer studies.

To address this gap for breast tissue, the Moroccan team analyzed twenty breast tissue samples—thirteen tumors and seven normal adjacent tissues—collected at the Military Hospital of Instruction Mohammed V in Rabat, with ethical approval from the Ethics Committee for Biomedical Research in Rabat and written informed consent from all participants. They measured the expression of ten candidate reference genes spanning a range of cellular functions: ACTB, which encodes beta-actin, a core component of the cytoskeleton; GAPDH, a central enzyme of glycolysis; GUSB, a lysosomal enzyme; HNRNPL, an RNA-binding protein involved in splicing; PCBP1, another RNA-binding protein; PPIA, a peptidyl-prolyl isomerase; PUM1, a translational regulator; RER1, an endoplasmic reticulum sorting receptor; TBP, the TATA-box binding protein central to transcription initiation; and 18S ribosomal RNA.

The methodological heart of the study lay in how stability was assessed. Rather than relying on a single algorithm, the researchers used the RefFinder tool, which integrates four independent approaches: geNorm, which ranks candidates based on the pairwise variation between them and identifies the optimal number of genes to combine; NormFinder, a model-based method that estimates both intra- and inter-group variance and is particularly good at accounting for differences between tumor and normal samples; BestKeeper, which uses pairwise correlations of raw Ct values to identify candidates that co-vary least with the rest of the panel; and the comparative delta-Ct method, which compares each candidate directly against all others within each sample. Each algorithm produces its own ranking, and RefFinder condenses these into a geometric mean ranking that reflects the consensus across methods.

The results were strikingly consistent. TBP emerged as the most stable reference gene overall, with a RefFinder geometric mean of 2.21, followed closely by PUM1 at 2.34 and RER1 at 2.78. The three genes occupied closely aligned positions across the individual algorithms, which strengthens confidence in the ranking, since agreement between statistically independent methods is a hallmark of genuine stability. At the other end of the spectrum, ACTB and GAPDH—the two genes most deeply entrenched in the qPCR literature—consistently ranked as the least stable candidates across all four algorithms. In other words, the genes most likely to be chosen by default were the ones most likely to introduce systematic error into a breast tissue expression study.

The finding that GAPDH is unstable in breast tumors is biologically plausible rather than merely statistical. Tumor cells undergo a metabolic shift toward aerobic glycolysis, the Warburg effect, and GAPDH sits at the center of that reprogramming, so its expression can rise with tumor aggressiveness and hypoxia. ACTB, meanwhile, is tied to cytoskeletal remodeling, a process intimately involved in invasion and metastasis. TBP, by contrast, is a basal transcription factor whose expression tends to track overall cellular transcriptional activity more evenly, and PUM1 and RER1 have shown stable expression in other cancer contexts, including rectal tumors and cancer cell line panels, lending external support to the new results.

The study also fits into a broader pattern in the normalization literature. Previous work in breast cancer cell lines, including the widely used MCF-7 line, has identified different optimal reference sets than those suited to fresh tissue, and studies in endometrial, ovarian, colorectal, and renal cancers have each concluded that no single reference gene is universally appropriate. Some groups have advocated normalizing to the geometric average of multiple reference genes rather than a single control, an approach pioneered by Vandesompele and colleagues in 2002, and the close agreement among TBP, PUM1, and RER1 in this study suggests that a combination of two or three of them could provide a robust normalization strategy for breast tissue work.

The authors are appropriately measured about the limitations of their work. The sample size of twenty tissues, while sufficient for a candidate-gene validation study of this design, is modest, and the tumor cohort of thirteen samples cannot capture the full heterogeneity of breast cancer, which spans estrogen receptor-positive, HER2-positive, and triple-negative subtypes with distinct molecular landscapes. The team explicitly calls for further validation on a larger scale before the findings are adopted as standard practice. They also note that the datasets generated in the study are available from the corresponding author on reasonable request, and that all summarized data supporting the conclusions are included in the published article, in keeping with transparency norms in the field.

Even with those caveats, the practical implications are immediate for laboratories working on breast cancer gene expression. Any study planning to use RT-qPCR on breast tumors or matched normal adjacent tissue now has a data-driven, multi-algorithm-validated shortlist of candidates—TBP, PUM1, and RER1—and a clear warning against defaulting to ACTB or GAPDH. As RT-qPCR continues to complement immunohistochemistry in molecular subtyping and to support the validation of therapeutic targets such as PIK3CA and BRCA1/2 in the era of precision oncology, the accuracy of the underlying normalization quietly underpins every downstream conclusion. A study like this one, though modest in scale, addresses one of the most consequential and least glamorous sources of error in cancer molecular biology, and it does so with the kind of methodological rigor that the MIQE guidelines were written to encourage.

Subject of Research: Validation of reference genes for RT-qPCR normalization in breast tumors and normal adjacent tissues

Article Title: Promising reference genes for RT-qPCR normalization in breast tumors and normal adjacent tissues

Article References: Oubaqui, F.-E., Oukabli, M., Kouach, J., Bakri, Y., Ameziane El Hassani, R., & Qmichou, Z. (2026). Promising reference genes for RT-qPCR normalization in breast tumors and normal adjacent tissues. Molecular Biology Reports, 53(1), Article 1646. https://doi.org/10.1007/s11033-026-12851-2

Image Credits: AI Generated

DOI: 10.1007/s11033-026-12851-2

Keywords: reference genes, RT-qPCR, breast cancer, gene expression normalization, TBP, PUM1, RER1, GAPDH, ACTB, housekeeping genes, molecular biomarkers, MIQE guidelines