Assessing the necessity of technical replicates in reverse transcription quantitative PCR

Abstract

Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is widely used for nucleic acid quantification. The use of technical triplicates in RT-qPCR aims to minimize variability and improve reliability but increases reagent consumption, labor, and time. This study systematically evaluates the necessity of technical replicates by analyzing 71,142 cycle threshold (Ct) values from 1,113 RT-qPCR runs across three instruments, two detection chemistries, and 30 operators. Variability within replicates was assessed using metrics such as the coefficient of variation (CV), while the impacts of operator expertise, detection chemistry, instrument calibration, and initial template concentration were explored. The findings challenge the assumption that variability increases at low template concentrations, revealing no correlation between Ct values and CV. While inexperienced operators exhibited slightly higher variability, their replicates were still consistent, with acceptable CVs and low outlier frequencies. Dye-based detection showed greater variability than probe-based. Time since calibration had negligible effects on replicate consistency. Notably, duplicate or single replicates sufficiently approximated triplicate means. These results challenge traditional assumptions about RT-qPCR variability and provide a data-driven framework for optimizing experimental design. This study offers potential for resource savings without compromising data quality, particularly in high-throughput applications or laboratories with limited funds. The data underlying this article are available at https://doi.org/10.5281/zenodo.15072870.

Graphical abstract

METHOD SUMMARY

We developed an automated method to assess the necessity of technical replicates in RT-qPCR by systematically analyzing cycle threshold (Ct) values obtained from multiple runs, instruments, detection chemistries, and operator groups. The approach calculates variability metrics such as the coefficient of variation and outlier frequency, and directly compares the performance of duplicate and single replicates against standard triplicates.

HIGHLIGHTS

• We analyzed 71,142 cycle threshold (Ct) values from 1,113 RT-qPCR runs spanning three instruments, two detection chemistries, 30 operators, and six years of laboratory work, making this the largest single-lab assessment of technical replicate necessity to date.

• Our data do not support the common assumption that low template concentration inflates the variability of technical replicate Ct values.

• Inexperienced operators exhibited slightly higher technical variability yet still produced replicates within widely accepted precision limits.

• Instrument calibration did not affect the variability of technical replicate Ct values.

• Moving from technical triplicates to duplicates or singles can cut reagent use, instrument time, and labor by 33–66%, offering substantial savings for high-throughput projects and resource-limited laboratories without affecting precision.

• We provide a data-driven framework for deciding when technical triplicates are warranted, and we encourage context-dependent experimental design rather than default repetition.

1. Introduction

Reverse transcription quantitative polymerase chain reaction (RT-qPCR) remains one of the most widely utilized techniques in molecular biology for quantifying nucleic acids. Despite its widespread use, the necessity of technical replicates (e.g., triplicates) in RT-qPCR assays continues to be debated. While triplicates are traditionally employed to account for technical variability and ensure precision, they also increase sample and reagent consumption, labor, and instrument time. These considerations are particularly critical in high-throughput or resource-limited settings, where reducing the number of replicates could substantially improve efficiency. Nevertheless, dispensing with technical triplicates must not be misconstrued as a recommendation to forego biological replication. Independent biological samples remain indispensable for capturing true biological variability and enabling valid statistical inference in most experimental techniques, including RT-qPCR [1–4].

Variability in RT-qPCR measurements can arise from multiple factors, including instrument performance, assay chemistry, operator technique, and random fluctuations inherent in the amplification process. The latter is thought to become particularly relevant at low template concentrations, defined here as reactions containing a low copy number of target RNA (e.g., fewer than 100 copies per reaction) or samples with reduced total RNA yield or integrity (e.g., following suboptimal extraction or partial degradation). Under these conditions, stochastic effects and template distribution can lead to significant variability in amplification efficiency and Ct (cycle threshold) values. As initial template concentration decreases, the RT-qPCR reaction becomes more susceptible to random factors, such as the uneven distribution of template molecules, enzyme efficiency variations, and even pipetting errors. These random sources of variability can compromise the reliability of the results, making the choice of replicates crucial to ensuring accuracy. Although such technical noise can be mitigated by careful assay optimization, this is generally minor compared with the variance introduced by sampling different biological individuals or preparations [1].

Understanding the magnitude and sources of this variability is essential for determining whether technical triplicates are universally required or if fewer replicates can yield comparable data quality. Although the MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiments) guidelines recommend performing technical replicates (duplicates) at the cDNA synthesis step to improve reliability, they do not mandate the use of technical replicates during the RT-qPCR stage itself [5]. In contrast, other sources, such as an article that introduced a mathematical model for relative quantification in RT-qPCR [6], and a study that validated internal control genes for normalization in RT-qPCR [7], employed technical replicates in their experiments. These studies highlight the practical use of technical replicates to minimize variability in RT-qPCR results, despite the lack of explicit recommendations for replicates in the RT-qPCR step. Yet even in those examples, power analyses reveal that adding additional biological samples yields a far greater gain in statistical power than repeating the same cDNA in extra wells [1,2].

The need for triplicates has often been justified by the observation that lower template concentrations lead to higher variability in Ct values, owing to both the stochastic nature of PCR amplification and technical factors. This variability can be exacerbated by instrument performance, differences in reagent efficiency, and even minor deviations in handling. Therefore, it is hypothesized that the necessity for triplicates may depend not only on initial template concentration but also on factors such as detection method, instrument type, and operator experience. By systematically analyzing 71,142 Ct values from 1,113 RT-qPCR runs across three instruments and two detection chemistries, the present study seeks to address these concerns and assess the impact of variability on the necessity of triplicates.

The primary aims of this study are to (i) quantify the variability of technical replicates across different RT-qPCR conditions, (ii) evaluate the sufficiency of duplicates and single replicates compared to triplicates, and (iii) provide evidence-based recommendations for optimizing RT-qPCR experimental designs.

2. Materials and methods

2.1. Dataset used

For this study, the necessity of technical triplicates in reverse transcription quantitative polymerase chain reaction (RT-qPCR) assays was analyzed. The dataset consisted of 71,142 Ct (cycle threshold) values obtained as technical triplicates (i.e., 23,714 × 3) from 1,113 different RT-qPCR runs, performed between September 2018 and November 2024. These runs were conducted across four different qPCR instruments: an Mx4000 with a 0.2 ml block, two separate QuantStudio^™ 3 machines (one with a 0.1 ml block and the other with a 0.2 ml block), and a QuantStudio^™ 7 Flex with a 0.1 ml block. All RT-qPCR reactions were prepared either by six experienced operators (PhD students and postdocs with >6 months of continuous laboratory experience, including pipetting experience) or by 24 inexperienced operators (undergraduate and Master’s students with <6 months of experience). Each operator used their own set of micropipettes across all runs, which were calibrated once a year.

Two types of RT-qPCR assays were used: dye-based assays, which used SYBR Green (a fluorescent dye that binds to DNA) as the reporter, and probe-based assays, which used FAM (a fluorescent dye attached to a probe) as the reporter. For dye-based RT-qPCR, custom-designed primers were used to amplify a variety of genes in mouse and human cDNA samples that were prepared from total RNA using the High-capacity RNA-to-cDNA kit (Thermo Fisher Scientific, 4388950). For probe-based RT-qPCR, TaqMan^™ Advanced miRNA Assays (Fisher, 15412184) were used for detecting microRNAs (miRNAs), PIWI interacting RNAs (piRNAs), or transfer RNA (tRNA) fragments in mouse and human cDNA samples that were prepared from total RNA using the TaqMan^™ Advanced miRNA cDNA Synthesis kit (Thermo Fisher Scientific, A28007). The specific RNA targets associated with the Ct values analyzed can be found in Supplementary Table 1. The source of the RNA samples included tissue lysates (such as mouse brain, spinal cord, or skeletal muscle), or cultured cell lysates (such as BV-2 cells, primary mouse microglia, mouse embryonic fibroblasts, or embryonic stem cell-derived motor neurons), for the detection of intracellular mRNA and non-coding RNA (ncRNA) targets. Other sources of RNA samples included human/mouse blood serum, human cerebrospinal fluid, or cell culture supernatant for the detection of extracellular ncRNA targets. All RT-qPCR reactions followed standard protocols for both assay types. Reactions were carried out in 96-well PCR plates or single PCR tubes in a final volume of 10 μL. All RT-qPCR reactions were run in technical triplicates within the same RT-qPCR run (i.e., the technical triplicates were never spread across different qPCR runs/plates).

The majority of RT-qPCR runs in this study were conducted with 40 amplification cycles, in accordance with standard protocols for mid-to-high initial target concentration. However, approximately half of the runs utilizing probe-based detection used 50 amplification cycles to detect targets with extremely low abundance, where standard amplification settings might fail to reach detectable fluorescence thresholds.

2.2. Data analysis

Data analysis was conducted using MATLAB R2024b with custom-written code, which is publicly available on Zenodo, alongside the complete dataset used in this study (https://doi.org/10.5281/zenodo.15072870). The Ct values for all RT-qPCR reactions were extracted from the raw output files of the four instruments (one Mx4000, two separate QuantStudio^™ 3, and one QuantStudio^™ 7 Flex) and consolidated into a master dataset. Each data point was annotated with metadata, including the instrument used, the detection method (SYBR or Probe), the operator status (experienced or inexperienced), the date of the RT-qPCR run, and the last calibration date of the instrument. The mathematical equations underlying the calculation of specific variables analyzed are outlined in the Supplementary Information file.

2.3. Statistical analysis

Linear regression was performed to assess the relationship between variables, such as the coefficient of variation (CV) and the mean Ct values, as well as the CV and the time since last instrument calibration. The linear fit was performed using MATLAB’s “polyfit” function, which fits a polynomial of a specified degree to the data. In this case, a first-degree polynomial (linear fit) was used to model the relationships. The strength and significance of the linear relationship were assessed by calculating the correlation coefficient and the corresponding p-value, using MATLAB’s “corr” function. For this, the data were first tested for normality using the Kolmogorov–Smirnov test with MATLAB’s “kstest” function. If the data passed the normality test, the Pearson’s correlation coefficient was calculated to assess the strength and direction of the linear relationship. Otherwise, Spearman’s rank correlation was used. The final chosen correlation tests are indicated in the figure legends.

Where statistical comparisons were required between two groups, the normality of the data was assessed as described above. If the data were found to be normally distributed, an F-test for equal variances was performed using the “vartest2” function, followed by a t-test for mean difference using the “ttest2” function. If normality was violated, the Wilcoxon rank-sum test (equivalent to the Mann–Whitney U test) was applied using the “ranksum” function. The final chosen statistical tests are indicated in the figure legends. In cases where the Wilcoxon test indicated statistically significant differences, but the distribution of the data was skewed, the effect sizes were minuscule, and the sample sizes highly unbalanced, bootstrapping was also performed to provide a more accurate estimation of the true differences between groups. Bootstrapping is a resampling technique that involves repeatedly drawing random samples (with replacement) from the observed data to create many simulated datasets. For each resample, the statistic of interest (i.e., the median difference between groups) was calculated, and the p-value was estimated by determining the proportion of bootstrapped samples where the observed statistic exceeded the calculated value from the original data. The bootstrapping was performed using a custom implementation, where 10,000 resamples were generated using MATLAB’s “datasample” function.

3. Results and discussion

Ct values obtained in techinical triplicates from 1,113 different RT-qPCR runs conducted by 30 different operators across six years in the same laboratory were analyzed. These RT-qPCRs were performed as part of various different experimental projects unrelated to the current study. The individual contribution of each operator to the dataset is summarized in Supplementary Table 2. Overall, 80.46% of the dataset consists of data collected by experienced operators.

3.1. Impact of initial template concentration on technical replicate variability

The first aim was to examine the relationship between the mean Ct value of triplicates and the variability within those triplicates, as quantified by the coefficient of variation (CV). The CV provides a measure of precision by expressing the standard deviation of the Ct values relative to their mean, with higher CV values indicating greater variability. By correlating the mean Ct value with the CV, the aim was to determine whether more consistent (low CV) measurements were associated with a higher or lower average Ct value. If there is a strong negative or positive correlation, it would suggest that variability in the triplicates is related to the magnitude of the Ct value, potentially reflecting the efficiency or reliability of the assay across different template amounts. Conversely, a weak or no correlation would suggest that the consistency of replicate measurements does not depend on initial template concentration. The Spearman’s correlation coefficient (r = 0.20, p < 0.0001) indicated no relationship between the mean Ct value and the CV of the triplicates (Figure 1). Repeating this analysis separately for each detection method (dye or probe) also indicated no strong relationship between the mean Ct value and the CV of the triplicates (Supplementary Figure 1). This finding contrasts with the commonly held belief [8,9] that variability in RT-qPCR increases with decreasing initial template concentration, as discussed in the introduction. Instead, these results suggest that variability in triplicates is independent of template concentration.

Figure 1.

Correlation between the mean cycle threshold (Ct) value of triplicates (proxy for initial template concentration) and the coefficient of variation between the triplicates. Spearman’s correlation coefficient, r = 0.20, p < 0.0001. The red line represents the linear fit of the data. N = 23,714 mean (from triplicates) Ct values.

3.2. Impact of instrument and detection method on technical replicate variability

The next aim was to determine whether the instrument or detection method used for the RT-qPCR run had an effect on the frequency of outliers in Ct values within each set of technical replicates. Outliers were first defined as Ct values that deviated from the mean of the triplicates by more than ±2 Ct values, which is a commonly used threshold for identifying unusually discrepant data points [10–17]. Data from experienced operators were available from three instruments: Stratagene Mx4000, QuantStudio^™ 3, and QuantStudio^™ 7 Flex, and two detection methods: Dye and Probe. In addition, data from inexperienced operators were only available for the two QuantStudio^™ 3 instruments. The results showed varying outlier rates for each combination (Figure 2(A)). Importantly, outlier frequency was observed to be higher with inexperienced operators for the same instrument + detection method combination than with experienced operators. However, this was not assessed statistically due to the highly unbalanced sample sizes.

Figure 2.

Outlier frequency within technical triplicate cycle threshold (Ct) values. (A) Outlier frequency based on deviation of each Ct value from the triplicate mean Ct. Outliers were defined as Ct values that deviated from the mean of the triplicates by more than ±2 Ct values. (B) Outlier frequency based on replicate concordance. Outliers were defined as cases where two replicates were closely aligned (difference ≤ 2 Ct), while the third replicate deviated significantly (difference > 2 Ct). (C) Average absolute deviation of each replicate from the mean of the triplicates, per instrument + detection method combination. N-numbers over the bars represent the number of sets of triplicate Ct values analyzed. O-numbers represent the number of operators. QS3, QuantStudio^™ 3; QS7, QuantStudio^™ 7 Flex. “QS3(A)” and “QS3(B)” refer to two separate QS3 machines.

To expand the analysis, outliers within triplicate Ct values were redefined as cases where two replicates were closely aligned (difference ≤ 2 Ct), while the third replicate deviated significantly (difference > 2 Ct). The rationale for this redefinition was to capture instances of replicate concordance, where two values are consistent, but one deviates markedly. While this approach is less commonly cited than the “deviation from the mean” method, it is a recognized way of identifying outliers in RT-qPCR experiments [8,18,19]. Additionally, this replicate concordance rule is formally identical to the one-outlier case of the Dixon Q test and to the single-point Grubbs test when the sample size is three [20,21]. The results showed a similar pattern of outlier rates for each combination as that seen with the first definition of outliers (Figure 2(B)).

These results highlight that both the instrument and the detection method can influence the frequency of outliers, as previously suggested [22–24], with the combination of QS7 + Dye showing a particularly high outlier rate. Importantly, the comparison of results from experienced and inexperienced operators underscores the role that operator experience plays in influencing data variability. For instance, with the QS3 + Dye combination, outlier frequencies were markedly higher for inexperienced operators, with a 3.04-fold increase (first outlier definition) or a 2.42-fold increase (second outlier definition), compared to experienced operators, but such marked differences were not observed for the QS3 + Probe combination (only 1.19- and 1.21-fold increase for the respective outlier definition). This suggests that variability introduced by less experienced operators may magnify the impact of other factors, such as the detection method. Therefore, the use of triplicates in assays involving certain setups may be desirable to ensure data accuracy and reliability. Conversely, the relatively low outlier frequency (<5%) observed for all other combinations (except for Mx4000 + Probe when analyzing replicate concordance) suggests that technical replicates may not be necessary under these conditions if the operators are experienced.

In addition to outlier frequency, the average absolute deviation of each replicate from the mean of the triplicates was calculated for each combination of instrument and detection method (Figure 2(C)). The values in Figure 2(C) represent the mean deviation of each replicate from the average Ct value of the triplicates, with higher values indicating greater variability within replicates. The results again showed a similar pattern as that seen with the two different definition of outliers. These findings suggest that the Mx4000 + Probe and QS7 + Dye combinations exhibit the highest variability, showing an average deviation of 0.38 and 0.47 Ct, respectively. In general, a variability of <0.5 Ct, as in these cases, may not be considered excessively high for most RT-qPCR applications, as it corresponds to a modest level of variation in gene expression measurements. Notably, operator experience also contributed to variability, as seen earlier. For the QuantStudio^™ 3 instrument, the average deviation for inexperienced operators was 2.03-fold higher with Dye and 1.32-fold higher with Probe, compared to the experienced operators. This further underscores the importance of operator expertise in reducing replicate variability.

To examine whether technical replicate variability decreased over time as operators gained experience, the mean CV for each individual operator was plotted over time, with a trendline fitted and the slope calculated. Among the six experienced operators, the slopes were minimal, ranging from -0.0258 to 0.0004, indicating essentially no significant improvement in CV over time (Figure 3(A)). This suggests that experienced operators maintained a consistent level of precision in their measurements throughout the analyzed period, likely due to their established proficiency with pipetting. In contrast, the slopes for inexperienced operators varied widely, ranging from -0.5888 to 0.1627. The majority of slopes were negative, suggesting improvement in precision over time, while a few were positive, indicating increased variability for some operators (Figure 3(B)). Notably, operators with initially wide CV ranges (e.g., an operator with a CV range of 7.63%, and another one with a CV range of 6.82%) exhibited stronger negative slopes (−0.1949 and −0.1719, respectively), suggesting substantial improvements as they gained experience. Conversely, operators with narrow CV ranges (e.g., an operator with a range of 0.68%, and another one with a range of 0.43%) generally showed minimal slope changes (−0.0017 and 0.0003, respectively), reflecting consistent performance across time. These observations highlight the importance of both the initial variability and the rate of improvement when assessing operator performance. Operators with initially high variability demonstrated the greatest potential for improvement. On the other hand, operators with low initial variability may already be performing at a high level, leaving limited room for further improvement. Overall, these findings emphasize the dynamic nature of operator performance among less experienced individuals and the critical role of training and experience in achieving replicate consistency.

Figure 3.

Mean coefficient of variation (CV) of triplicate cycle threshold (Ct) values for individual experienced (A) and inexperienced (B) operators over time. The red dashed line represents a linear trendline, and the slope of this line is indicated in red. The subplots are sorted from lowest to highest trendline slope. Two inexperienced operators are not shown, since they collected all their data on the same day.

3.3. Comparison of technical replicate variability between different machines of the same model

Since data from two different QuantStudio^™ 3 machines were available in the analyzed dataset, the triplicate CV values between the two machines were compared to evaluate the variability within technical replicates. The analysis focused on data from inexperienced operators only, as experienced operator data were only available for one of the two QuantStudio^™ 3 machines. To ensure a fair comparison, the analysis was restricted to data using the Dye detection method, as this was the only detection method represented for both machines. While this approach aimed to minimize confounding factors, the ideal comparison would involve the exact same RT-qPCR reactions being set up by the same operator and run on both machines, ensuring that variability due to experimental setup or operator skill is entirely controlled. Nevertheless, this analysis provides an approximation of variability differences between the machines under less controlled conditions. The mean CV for QS3(A) was 2.18%, while for QS3(B) it was 1.76%. A Wilcoxon rank-sum test revealed no statistically significant difference (p = 0.9722) between the machines (Supplementary Figure S1). These findings indicate that the two machines perform similarly in terms of technical replicate variability under the conditions analyzed.

3.4. Impact of instrument calibration on technical replicate variability

To further explore the sources of variability, the impact of instrument calibration on the CV of triplicate Ct values was assessed. Calibration data were available for the two QuantStudio^™ 3 instruments and the QuantStudio^™ 7 Flex instrument, but not for the Mx4000 instrument. Calibration status was categorized into “valid” and “expired” based on the manufacturer’s recommendation of recalibration every two years (for QuantStudio^™ 3) or six months (for QuantStudio^™ 7 Flex). Statistical analysis using Wilcoxon rank-sum tests revealed significant differences in variability based on calibration status for three of the four instrument + detection method combinations (Figure 4(A)). However, the large imbalance in sample sizes could have influenced the results, particularly the statistical power and sensitivity to detect differences. Additionally, while the Wilcoxon test is statistically significant, the medians suggest that the effect size may not be practially meaningful, raising questions about whether the significance is driven by sample size or genuine differences. To address this, bootstrapping was performed to estimate the sampling distribution of the test statistic (i.e., the difference in medians) by resampling with replacement from the observed data. Figure 4(B) shows the bootstrap distributions of the differences in medians between the Valid and Expired calibration groups for each instrument and detection method combination. The vertical dashed line in each plot represents the observed difference in medians calculated directly from the data prior to bootstrappoing. In all cases, the observed difference falls near the center of the bootstrap distributions, indicating that it is consistent with the variability expected under random resampling. Furthermore, the bootstrap-derived probabilities, calculated as the proportion of bootstrap differences as extreme as or more extreme than the observed difference, and shown as p-values in Figure 4(B), are all greater than the significance threshold of 0.05. This suggests that the observed differences are typical of what could arise by chance and are not statistically significant. Together, these results indicate that there is weak evidence for meaningful differences in variability between calibration states. The observed effect sizes are negligible and unlikely to have practical significance. While the Wilcoxon rank-sum tests identified statistically significant differences for some comparisons, these results appear to be driven by random variation or the large imbalance in sample sizes, rather than genuine differences. Overall, these findings suggest that instrument calibration status does not play a significant role in influencing triplicate Ct variability.

Figure 4.

(A) Effect of instrument calibration on triplicate cycle threshold (Ct) variability. Data are shown as median (blue bars) ± interquartile range/2 (error bars), and these values are also indicated next to the bars. Data were analyzed by Wilcoxon rank-sum tests, with the p-values indicated on each subplot. N-numbers next to the bars represent the number of sets of triplicate Ct values analyzed. (B) Bootstrap probability distributions of the differences in medians between the Valid and Expired calibration groups for each instrument and detection method combination. The vertical dashed lines represent the observed difference in medians between the Valid and Expired calibration groups calculated directly from the data in the absence of bootstrapping (these observed medians are displayed in panel A Figure 4). The p-values on each subplot represent the bootstrap-derived probabilities, calculated as the proportion of bootstrap differences as extreme as or more extreme than the observed difference. QS3, QuantStudio^™ 3; QS7, QuantStudio^™ 7 Flex. QS3, QuantStudio^™ 3; QS7, QuantStudio^™ 7 Flex; cal., calibration.

To further investigate the effect of expired calibration on data variability, the relationship between the time elapsed since the last calibration and the CV of triplicate Ct values was examined. It was hypothesized that the variability observed with expired calibration might be related to the length of time since the last calibration, with longer intervals potentially leading to increased variability. For the QuantStudio^™ 3 instrument, the manufacturer recommends that calibration be performed every 2 years to ensure optimal instrument performance. For the QuantStudio^™ 7 Flex instrument, calibration is recommended every 6 months. To test this, the CV of triplicate Ct values against the time since the last calibration was plotted. A linear regression analysis using Spearman’s correlation coefficients revealed no relationship between the time since calibration and the variability in Ct values for either of the instruments or detection methods (Figure 5). This indicates that the time since the last calibration does not affect the variability of triplicate Ct values. However, expired calibration may still introduce systematic errors in the absolute Ct values. For instance, an expired calibration could lead to discrepancies in the measured Ct values, even if the variability within technical replicates is unaffected. Although the data does not support a direct impact on variability, it remains plausible that expired instruments could compromise the accuracy of measurements, suggesting caution in their use.

Figure 5.

Effect of post-calibration instrument stability on cycle threshold (Ct) variability. Spearman’s correlation coefficients (r) and p-values are indicated on each subplot. The red lines represent the linear fit of the data. The black vertical dashed lines indicate the calibration expiry threshold of 2 years (for QS3) or 6 months (for QS7). N = 5666, 8579, 896, and 6826 mean (from triplicates) Ct values for QS3 + Dye, QS3 + Probe, QS7 + Dye, and QS7 + Probe, respectively. QS3, QuantStudio^™ 3; QS7, QuantStudio^™ 7 Flex.

3.5. Impact of reducing technical replicates

Next, to evaluate replicate consistency, the frequency distribution of the mean absolute difference between replicates for each set of triplicate Ct values was examined. This measure quantifies the variability of Ct values within a set of triplicates. The results revealed that 80% of the data exhibited mean differences between replicates of ≤ 0.29 Ct, while 90% of the data had differences of ≤ 0.50 Ct, and 95% fell within ≤ 0.82 Ct (Figure 6). These findings indicate that the majority of replicates demonstrated a high degree of consistency, with relatively small differences in Ct values. The 80^th and 90^th percentiles, representing fold-changes of approximately 1.22 and 1.41 in target concentration, highlight low variability across most measurements. However, the 95th percentile, with a mean difference of 0.82 Ct (equivalent to a 1.76-fold change in target concentration), reflects the inherent variability of RT-qPCR experiments and could be significant in studies requiring high precision. While this level of variability is unlikely to affect conclusions in experiments focusing on large-scale differences in gene expression, it may warrant attention in studies where even small changes are biologically meaningful.

Figure 6.

Frequency of mean absolute differences between replicates in each set of triplicate cycle threshold (Ct) values. N = 23,714 sets of triplicate Ct values.

To assess the sufficiency of duplicate replicates compared to triplicates in RT-qPCR experiments, the variation in mean Ct values when one replicate was excluded from triplicate datasets was examined. Without accounting for instrument, detection method, or operator experience, the residuals (differences between the triplicate mean Ct and the pairwise mean Ct values) were analyzed. This analysis revealed that the majority of data points had small residuals, indicating minimal deviation from the triplicate mean, with only 0.59% of pairwise means exhibiting more than 2 Ct values away from the zero residual line (Figure 7(A)). Therefore, the duplicate replicates still perform adequately and align closely with the triplicate mean.

Figure 7.

Residuals of pairwise mean cycle threshold (Ct) values (A) and single Ct values (B) versus the corresponding triplicate mean Ct. The grey points represent the residuals [difference between triplicate mean Ct and either each possible pairwise mean Ct (A) or each single Ct value (B)], while the red dots indicate the residuals where the absolute deviation exceeds a defined threshold (±2 Ct, indicated by the blue horizontal dashed lines). These significant residuals indicate Ct values that deviate substantially from the triplicate mean. The horizontal black dashed line at zero represents perfect agreement, where the residual would be zero. Red points represent 0.59% and 1.73% of the data in panels A and B, respectively. N = 23,714 sets of triplicate Ct values.

Building upon this, the residuals of single Ct values (differences between each individual Ct value and the corresponding triplicate mean Ct) were also examined. It was observed that only 1.73% of the single Ct values deviated by more than ±2 Ct from the triplicate mean (Figure 7(B)). This low percentage of significant residuals highlights the consistency and reliability of individual Ct values in reflecting the triplicate mean. Importantly, these significant residuals occurred across the full range of Ct values and were not concentrated at higher Ct values (i.e., lower initial template concentration), which contradicts the commonly held notion that variability increases with decreasing template concentration. Instead, the residuals were relatively evenly distributed, indicating that significant deviations from the triplicate mean are not more likely at higher Ct values.

4. Conclusions

This study evaluated the necessity of technical triplicates in RT-qPCR assays by analyzing data from various instruments, detection methods, and operator experience levels. The results demonstrate that while technical triplicates improve precision, their necessity is often overstated. Even inexperienced operators produced relatively consistent replicates, with acceptable CVs and low outlier frequencies, a parameter that, to our knowledge, has not been systematically quantified in previous studies. Importantly, the analysis revealed that duplicates or even single replicates closely approximate the triplicate means in most cases, highlighting that technical replicates are not strictly required under well-controlled conditions.

Furthermore, contrary to the commonly held belief that variability in technical replicates increases at lower template concentrations due to stochastic effects in the RT-qPCR amplification process [8,9], this study did not observe a clear correlation between template concentration (as indicated by the Ct value) and variability in replicate measurements. Stochastic variations, such as random fluctuations in template distribution or enzyme efficiency, are often thought to be more pronounced at lower template concentrations, where fewer target molecules are available for amplification. However, in this analysis, significant deviations from the triplicate mean (residuals greater than 2 Ct values) occurred across the full range of Ct values and were not concentrated in reactions with higher Ct values (i.e., lower template amounts). This suggests that variability between replicates is not strongly driven by template concentration alone, supporting previous evidence that well-optimized assays can maintain low variance even when copy number is limiting [25].

The limited guidelines in the literature regarding the use of technical replicates in RT-qPCR are noteworthy. The MIQE guidelines suggest that technical replicates be used for cDNA synthesis but do not make any recommendations about whether they are necessary for RT-qPCR. Yet, researchers often employ technical replicates during RT-qPCR to account for technical variability. For example, a previously published methods paper provides practical guidance for RT-qPCR experiments, recommending the use of three technical replicates [2]. However, this recommendation appears precautionary rather than based on empirical evidence. This divergence in recommendations underscores the need for context-driven decisions. While technical replicates at the cDNA synthesis step can help mitigate variability in cDNA preparation, the decision to use replicates at the RT-qPCR step should depend on the experimental goals, the precision required, the potential sources of variability in each setup, and follow-up experimental plans.

Although this study demonstrates that technical replicates are often unnecessary under standard conditions, some experimental contexts may still benefit from their inclusion. For example, a previous study demonstrated that increased technical replicates can enhance quantitative resolution and improve the ability to distinguish small differences in copy number variation (CNV) analyses [25]. Their study focuses on a specialized application where high precision is essential for detecting subtle changes in gene copy number, making replicates more critical due to the inherent variability and complexity of CNV assays. In contrast, the present study evaluates standard RT-qPCR workflows, which typically involve more straightforward experimental setups aimed at quantifying larger differences in nucleic acid abundance. This distinction underscores the importance of tailoring the number of technical replicates to the specific precision requirements of the assay.

While this study provides valuable insights into the necessity of technical replicates in RT-qPCR, several limitations must be acknowledged. First, the dataset was derived exclusively from a single laboratory, potentially limiting the generalizability of the findings to other settings. Variability in other laboratories could arise from differences in training protocols, instrument maintenance, environmental conditions, or assay execution. Additionally, the analysis focused on a subset of qPCR platforms and detection chemistries, leaving unanswered questions about other widely used chemistries (e.g., EvaGreen) or platforms (e.g., Bio-Rad or Roche systems). Furthermore, the focus on Ct variability alone, while informative, does not capture other important quality metrics like amplification efficiency or melt curve consistency, which may also impact the reliability of reduced replicates. Finally, while the study investigated variability across mid-to-low template abundances, the findings may not fully extrapolate to high-abundance targets, multiplexed assays, or other complex experimental setups. Addressing these limitations in future studies would help validate and extend the conclusions of this work.

4.1. Future perspective

Our study challenges the longstanding convention of performing technical triplicates in RT-qPCR assays, suggesting that duplicates or even single replicates can yield reliable results under many experimental conditions. The use of triplicates requires three times the amount of samples and reagents compared to single replicates, which can substantially impact availability of samples and budgets for large-scale studies. Performing triplicates also triples the processing time per sample, limiting throughput in high-demand experiments. The decision to employ triplicates should thus balance the precision required against the additional costs, with triplicates reserved for experiments requiring high confidence in variability-prone setups or critical endpoints. Moreover, this optimized replicate strategy not only benefits high-throughput laboratories but also significantly reduces operational expenses for resource-limited settings, including laboratories in developing or lower-income countries and research groups funded by charitable organizations, thereby enhancing the accessibility and sustainability of RT-qPCR assays across diverse research environments. By continuing to push the boundaries of assay efficiency without sacrificing data quality, our work lays the foundation for more sustainable and accessible molecular diagnostics practices worldwide. These future directions not only underscore the potential for significant cost and time savings but also highlight the broader impact on research reproducibility and accessibility across the global scientific community.

Funding Statement

This work was supported by the Motor Neurone Disease Association of England Wales and Northern Ireland (Hafezparast/Apr21/880-791). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author contributions

Eleni Christoforidou: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing.

Majid Hafezparast: Funding acquisition, Resources, Supervision, Writing – review & editing.

Disclosure statement

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

No writing assistance was utilized in the production of this manuscript.

Data availability statement

The data underlying this article are available at https://doi.org/10.5281/zenodo.15072870.