Graphical abstract

Keywords: SARS-CoV-2, COVID-19, qPCR, Ct
Dear Editor,
We read with interest the review by Walsh et al.1 summarizing data on detection patterns and viral loads of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the course of infection. We agree with them that determination of SARS-CoV-2 viral load in clinical samples will aid the interpretation of laboratory assays and in the management of isolation and contact tracing protocols, but it should be noted that currently there is no standard measure of viral load in clinical samples. It is becoming common to assume that the Ct values from real-time (quantitative) reverse transcription polymerase chain reaction (qPCR) diagnostic tests are direct measures of viral load, and the use of Ct values has been proposed as a tool to identify those patients who might not be infectious in spite of being positive2 or to correlate the PCR results with infectivity in cell cultures in order to predict which samples are actually infectious.3 , 4 While it is true that Cts are related to the starting amount of template in the reaction this is not a linear relation and the use of raw Ct values understates the dispersion of the measurements.5 Another problem is that most diagnostic SARS-CoV-2 qPCR tests are done on suspensions from nasopharyngeal swabs, and these are samples from a surface and have an intrinsic variability that depends on the operator and on the tolerance of the patients.6 Moreover, the concept of viral load itself is dubious in the absence of a reference mass or volume unit. Finally, the different nucleic acid extraction and amplification systems used by are additional variability sources. For these reasons the assumption that there is a direct relation between the qPCR signal, the amount of virus collected and the amount of virus in the patient's nasopharynx may be misleading and should be taken with care.7
To illustrate these points, we take advantage of the design a commercial SARS-CoV-2 RT-qPCR that targets two SARS-CoV-2 genes (E and N) in two different reactions (SARS-CoV-2 Real Time PCR kit, Vircell, Granada, Spain). The tube targeting gene N includes an unrelated (and undisclosed) internal amplification control (primers, probe and a target RNA) in the reaction mix, while the tube targeting gene E includes primers and probes for human RNAse P. The housekeeping RNAse P is a ribozyme expressed in many tissues. Specific primers and probes detect both the gene (DNA) and its RNA product and are included for sample quality control in many SARS-CoV-2 and influenza virus commercial assays.8 To explore the use of human RNAseP to normalize the data we collected the results of a series of 145 randomly selected positive assays from our registers (March and April 2020). In this set the internal control in the N tube had an average Ct of 30 (range 25.3 to 35.5, IQR=2.1 cycles) (Fig. 1 A). The human RNAse P control in the E tube had an average Ct of 28.8 and a broader distribution: range 20.9 to 36.3 and IQR=3.5 cycles (Fig. 1B). The Ct values of the target genes were independent of those of the controls in both cases (Figs. 1A,B, r2 values not significantly different from zero), meaning that there were no interferences between the target and the control reactions. The variability of the internal controls in the N tubes must be due to experimental errors during the setting up of the PCR reactions, while the higher variability of the human RNAse P controls in the E tubes reflects, in addition, the variations in the amount of material collected with the swabs and in the nucleic acid extraction process.
Fig. 1.
Analysis of the SARS-CoV-2 Ct values obtained using a commercial RT-qPCR assay (Vircell) in a set of clinical samples. A) Cts of the Internal Control RNA plotted against the SARS-CoV-2 N gene Cts (r2 = 0.004). B). Cts of the human RNAse P plotted against the SARS-CoV-2 E gene Cts (r2 = 0.007). C) Normalized SARS-CoV-2 gene E Ct values (log(2−ΔCt)=log(2−Cttarget-Ctreference)) plotted against the SARS-CoV-2 E gene. The normalized Ct values are relative loads (ratios of viral target to human target) and are transformed to logarithmic scale for graphical representation. The black arrow illustrates the broad range spanned by any particular Ct value.
To correct for sampling variability we used the human RNAse P as a reference to normalize the viral load by the comparative Ct method (ΔCt)9 that transforms the Cts into relative loads (ratios of viral target to human target). Fig. 1C shows a plot of SARS-CoV-2 gene E Cts normalized with the human RNAse P Ct values against the gene E Cts. The plot shows an inverse linear correlation, which is expected because Ct values reflect, indeed, viral loads, but the dispersion of the data may reach up to four log units (ten thousand-fold) for any given Ct (black arrow). This is not a problem of this particular brand or PCR design, it could be observed in other commercial (TaqMan 2019-nCoV Assay Kit v1, Thermo Fisher Scientific, Waltham, MA, USA) and in-house10 assays.
Normalization is not as straightforward as suggested by this example. A full characterization of the linear ranges and a calibration using standards11 should be done for every different target and primer/probe design. Other reference genes might be explored as well, although human RNAseP has been widely used and might enable to exploit the huge amount of data already collected in many laboratories around the world.
Using Ct values obtained in diagnostic PCR reactions as direct measures of SARS-CoV-2 viral loads is simple, but at the cost of introducing errors that cannot be neglected. Normalization using some marker of the cell mass or the mucosal surface sampled should be integrated into commercial diagnostic kits to make the different assays comparable and to evaluate the potential of quantitative PCR for the clinical management of COVID-19 patients.
Acknowledgments
E. D. has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Individual Fellowship grant agreement No. 796084.
References
- 1.Walsh Kieran A., Karen Jordan, Barbara Clyne, Daniela Rohde, Linda Drummond, Paula Byrne. SARS-CoV-2 detection, viral load and infectivity over the course of an infection. J Infect. 2020;81(3):357–371. doi: 10.1016/j.jinf.2020.06.067. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Tom Michael R., Mina Michael J. To interpret the SARS-CoV-2 test, consider the cycle threshold value. Clin Infect Dis. 2020:1–19. doi: 10.1093/cid/ciaa619. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Bernard La Scola, Marion Le Bideau, Julien Andreani, Thuan Hoang Van, Clio Grimaldier, Philippe Colson. Viral RNA load as determined by cell culture as a management tool for discharge of SARS-CoV-2 patients from infectious disease wards. Eur J Clin Microbiol Infect Dis. 2020;39(6):1059–1061. doi: 10.1007/s10096-020-03913-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Jared Bullard, Kerry Dust, Duane Funk, Strong James E., David Alexander, Lauren Garnett. Predicting infectious SARS-CoV-2 from diagnostic samples. Clin Infect Dis. 2020;954162(478):1–4. doi: 10.1093/cid/ciaa638. [DOI] [Google Scholar]
- 5.Livak K.J., Schmittgen T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001;25(4):402–408. doi: 10.1006/meth.2001.1262. [DOI] [PubMed] [Google Scholar]
- 6.Daniela Basso, Ada Aita, Filippo Navaglia, Elisa Franchin, Paola Fioretto, Stefania Moz. SARS-CoV-2 RNA identification in nasopharyngeal swabs: issues in pre-analytics. Clin Chem Lab Med. 2020:1–8. doi: 10.1515/cclm-2020-0749. [DOI] [PubMed] [Google Scholar]
- 7.Binnicker Matthew J. Challenges and controversies related to testing for COVID-19. J Clin Microbiol. 2020;(August):1–16. doi: 10.1128/JCM.01695-20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Ying Yan, Le Chang, Lunan Wang. Laboratory testing of SARS-CoV, MERS-CoV, and SARS-CoV-2 (2019-nCoV): current status, challenges, and countermeasures. Rev Med Virol. 2020;30(3):1–14. doi: 10.1002/rmv.2106. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.André Peinnequin, Catherine Mouret, Olivier Birot, Antonia Alonso, Jacques Mathieu, Didier Clarençon. Rat pro-inflammatory cytokine and cytokine related mRNA quantification by real-time polymerase chain reaction using SYBR green. BMC Immunol. 2004;5(1):3. doi: 10.1186/1471-2172-5-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Corman Victor M., Olfert Landt, Marco Kaiser, Richard Molenkamp, Adam Meijer, Chu Daniel K.W. Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Eurosurveillance. 2020;25(3):1–8. doi: 10.2807/1560-7917.ES.2020.25.3.2000045. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Giannella M., Alonso M., de Viedma D Garcia, Roa P Lopez, Catalán P., Padilla B. Prolonged viral shedding in pandemic influenza A(H1N1): clinical significance and viral load analysis in hospitalized patients. Clin Microbiol Infect. 2011;17(8):1160–1165. doi: 10.1111/j.1469-0691.2010.03399.x. [DOI] [PubMed] [Google Scholar]

