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Updated: Oct 28, 2025

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Two-Step Reverse Transcription Droplet Digital PCR Protocols for SARS-CoV-2 Detection and Quantification
Published on: March 31, 2021
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RT-PCR based SARS-CoV-2 variant screening assays require careful quality control
Jeremy V Camp1, Christoph Buchta2, Jovana Jovanovic2
1Center for Virology, Medical University of Vienna, Kinderspitalgasse 15, 1090 Vienna, Austria.
Summary
Diagnostic labs assessed SARS-CoV-2 variants of concern (VOC) using molecular assays. Accurate VOC identification requires detecting multiple mutations and sufficient viral load, indicating a need for further training in some labs.
Area of Science:
- Virology
- Molecular Diagnostics
- Public Health
Background:
- Emergence of SARS-CoV-2 variants of concern (VOC) with increased transmissibility and immune evasion poses a global health challenge.
- Clinical laboratories are mandated to identify and report these VOC.
Purpose of the Study:
- To evaluate the accuracy and reliability of molecular diagnostic assays used by laboratories for identifying SARS-CoV-2 VOC.
- To assess the scope of different molecular diagnostic approaches in VOC detection.
Main Methods:
- An external quality assessment scheme was implemented.
- Twenty-five laboratories participated, using various RT-qPCR-based assays.
- Laboratories analyzed five patient-derived samples and reported methods, results, and interpretations.
Main Results:
- Laboratories utilized diverse RT-qPCR assays targeting spike protein mutations characteristic of three VOC lineages.
- Detection of VOC-associated mutations at four specific sites correlated with higher correct classification rates.
- Low template copy number and additional genetic variations in samples reduced assay accuracy and confidence.
Conclusions:
- Melting-curve assays offer faster, less bioinformatically intensive VOC identification compared to sequencing.
- Accurate VOC classification depends on detecting multiple variant sites and adequate sample template (viral load).
- Some laboratories may need enhanced training for interpreting complex genetic data from evolving SARS-CoV-2 strains.

