Evidence map›Paper›PMID 40616447›Full record

ArticleBioTechniques2025

Assessing the necessity of technical replicates in reverse transcription quantitative PCR.

Eleni Christoforidou, Majid Hafezparast

Abstract read
In one paragraph

Article in BioTechniques, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Eleni ChristoforidouDepartment of Neuroscience, University of Sussex, Brighton, United Kingdom.
Majid HafezparastDepartment of Neuroscience, University of Sussex, Brighton, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Real-Time Polymerase Chain ReactionReverse Transcriptase Polymerase Chain ReactionCalibrationReproducibility of ResultsAssay variabilityhigh-throughputqPCRreverse transcription quantitative PCRRT-qPCRtechnical replicates

Identifiers

PMID40616447
PMCPMC12315840

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.