Potential pitfalls in analysing a SARS-CoV-2 RT-PCR assay and how to standardise data interpretation.

The emergence of SARS-CoV-2 in December 2019 lead to the rapid implementation of assays for virus detection, with real-time RT-PCR arguably considered the gold-standard. In our laboratory Altona RealStar SARS-Cov-2 RT-PCR kits are used with Applied Biosystems QuantStudio 7 Flex thermocyclers. Real-time PCR data interpretation is potentially complex and time-consuming, particularly for SARS-CoV-2, where the laboratory handles up to 2000 samples each day. To simplify this, an automated system that rapidly interprets the curves, developed by diagnostics.ai was introduced. QuantStudio software provides two methods for interpretation, relative threshold and baseline threshold. Many of our assays are analysed using relative threshold and directly exported into pcr.ai software, however, in some rare cases the QuantStudio software assigns positive results to 'ambiguous' curves, flagged by pcr.ai, requiring manual intervention. Due to the sample numbers processed and the proportionate increase in curves flagged by pcr.ai, the two methods were investigated. An audit was carried out to determine the frequency of these curves, involving 138 samples tested during November 2020, including 97 serial samples from 38 patients and it was determined that the relative threshold method produced unreliable results in many of these cases. In addition, we present a solution to simplify the interpretation and automate the process.

Investigating the sequence variation in the influenza A matrix genes during the 2017-2018 and 2018-2019 seasons in samples from a local population in London.

Recent publications have highlighted the emergence of mutations in the M1 gene of both influenza A H1N1pdm09 and H3N2 subtypes affecting the performance of commercial RT-PCR assays. Respiratory samples from the 2018/2019 season positive by our in-house RT-PCR for influenza A were analysed for the prevalence and impact of any M1 gene mutations. Sequence information was used to re-design primers for our routine assay and their performance assessed. Forty-five samples, consisting of 11 H1N1pdm09 and 34 H3N2 subtypes, together with the NIBSC H1N1 control were sequenced. All samples displayed the core mutations for H1N1 M1(C154T; G174A and G238A) and for H3N2 M1(C153T; C163T and G189T); three of the H1N1pdm09 viruses also showed a small number of point mutations. None of the mutations appeared to affect either the sensitivity or efficiency of the RT-PCR when compared to the re-designed primers. Although the mutations we found agreed with those in the publications cited we did not encounter any problems with our routine diagnostic assay and no improvements were found when the primers were modified to suit those mutations. However, it is likely that the influenza A virus M1 gene will accumulate further mutations that could impact RT-PCR assays and, therefore, it would be prudent to implement routine sequencing of samples during the influenza seasons to ensure no loss in assay performance.