ddPCR: a more accurate tool for SARS-CoV-2 detection in low viral load specimens

Tao Suo, Xinjin Liu, Jiangpeng Feng, Ming Guo, Wenjia Hu, Dong Guo, Hafiz Ullah, Yang Yang, Qiuhan Zhang, Xin Wang, Muhanmmad Sajid, Zhixiang Huang, Liping Deng, Tielong Chen, Fang Liu, Ke Xu, Yuan Liu, Qi Zhang, Yingle Liu, Yong Xiong, Guozhong Chen, Ke Lan, Yu Chen, Tao Suo, Xinjin Liu, Jiangpeng Feng, Ming Guo, Wenjia Hu, Dong Guo, Hafiz Ullah, Yang Yang, Qiuhan Zhang, Xin Wang, Muhanmmad Sajid, Zhixiang Huang, Liping Deng, Tielong Chen, Fang Liu, Ke Xu, Yuan Liu, Qi Zhang, Yingle Liu, Yong Xiong, Guozhong Chen, Ke Lan, Yu Chen

Abstract

Quantitative real time PCR (RT-PCR) is widely used as the gold standard for clinical detection of SARS-CoV-2. However, due to the low viral load specimens and the limitations of RT-PCR, significant numbers of false negative reports are inevitable, which results in failure to timely diagnose, cut off transmission, and assess discharge criteria. To improve this situation, an optimized droplet digital PCR (ddPCR) was used for detection of SARS-CoV-2, which showed that the limit of detection of ddPCR is significantly lower than that of RT-PCR. We further explored the feasibility of ddPCR to detect SARS-CoV-2 RNA from 77 patients, and compared with RT-PCR in terms of the diagnostic accuracy based on the results of follow-up survey. 26 patients of COVID-19 with negative RT-PCR reports were reported as positive by ddPCR. The sensitivity, specificity, PPV, NPV, negative likelihood ratio (NLR) and accuracy were improved from 40% (95% CI: 27-55%), 100% (95% CI: 54-100%), 100%, 16% (95% CI: 13-19%), 0.6 (95% CI: 0.48-0.75) and 47% (95% CI: 33-60%) for RT-PCR to 94% (95% CI: 83-99%), 100% (95% CI: 48-100%), 100%, 63% (95% CI: 36-83%), 0.06 (95% CI: 0.02-0.18), and 95% (95% CI: 84-99%) for ddPCR, respectively. Moreover, 6/14 (42.9%) convalescents were detected as positive by ddPCR at 5-12 days post discharge. Overall, ddPCR shows superiority for clinical diagnosis of SARS-CoV-2 to reduce the false negative reports, which could be a powerful complement to the RT-PCR.

Keywords: RT-PCR; SARS-CoV-2; clinical detection; droplet digital PCR; false negative.

Conflict of interest statement

No potential conflict of interest was reported by the author(s).

Figures

Figure 1.
Figure 1.
Plot of results from a linearity experiment to determine the reportable range of ddPCR and RT-PCR targeting ORF1ab and N of SARS-CoV-2. (A and B) Expected values (converted to log10) were plotted on the X-axis versus measured values of ddPCR (converted to log10) on the Y-axis using Graph Pad Prism targeting (A) ORF1ab and (B) N. (C and D) Expected values (converted to log10) were plotted on the X-axis versus measured Ct values of RT-PCR on the Y-axis using Graph Pad Prism targeting (C) ORF1ab and (D) N. Data are representative of three independent experiments with 3 replicates for each concentration (means ± SD).
Figure 1.
Figure 1.
Plot of results from a linearity experiment to determine the reportable range of ddPCR and RT-PCR targeting ORF1ab and N of SARS-CoV-2. (A and B) Expected values (converted to log10) were plotted on the X-axis versus measured values of ddPCR (converted to log10) on the Y-axis using Graph Pad Prism targeting (A) ORF1ab and (B) N. (C and D) Expected values (converted to log10) were plotted on the X-axis versus measured Ct values of RT-PCR on the Y-axis using Graph Pad Prism targeting (C) ORF1ab and (D) N. Data are representative of three independent experiments with 3 replicates for each concentration (means ± SD).
Figure 2.
Figure 2.
Probit analysis sigmoid curve reporting the LoD of ddPCR and RT-PCR. Replicate reactions of (A) ORF1ab and (B) N of ddPCR or (C) ORF1ab and (D) N of RT-PCR were done at concentrations around the detection end point determined in preliminary dilution experiments. The X-axis shows expected concentration (copies/reaction). The Y-axis shows fraction of positive results in all parallel reactions performed. The inner line is a probit curve (dose-response rule). The outer lines are 95% confidence interval (95% CI). Data are representative of three independent experiments with 8 replicates for each concentration.
Figure 2.
Figure 2.
Probit analysis sigmoid curve reporting the LoD of ddPCR and RT-PCR. Replicate reactions of (A) ORF1ab and (B) N of ddPCR or (C) ORF1ab and (D) N of RT-PCR were done at concentrations around the detection end point determined in preliminary dilution experiments. The X-axis shows expected concentration (copies/reaction). The Y-axis shows fraction of positive results in all parallel reactions performed. The inner line is a probit curve (dose-response rule). The outer lines are 95% confidence interval (95% CI). Data are representative of three independent experiments with 8 replicates for each concentration.
Figure 3.
Figure 3.
Flowchart of this research design. (A) Research design for suspected outpatients and (B) supposed convalescents. These results were acquired in blind from hospitals and laboratory independently. The official approved RT-PCR were conducted by hospitals. The follow-up survey and clinical information of enrolled patients were used to evaluate the performance of ddPCR and RT-PCR.
Figure 3.
Figure 3.
Flowchart of this research design. (A) Research design for suspected outpatients and (B) supposed convalescents. These results were acquired in blind from hospitals and laboratory independently. The official approved RT-PCR were conducted by hospitals. The follow-up survey and clinical information of enrolled patients were used to evaluate the performance of ddPCR and RT-PCR.

References

    1. Wu F, Zhao S, Yu B, et al. . A new coronavirus associated with human respiratory disease in China. Nature. 2020;579:265–269.
    1. Chen L, Liu W, Zhang Q, et al. . RNA based mNGS approach identifies a novel human coronavirus from two individual pneumonia cases in 2019 Wuhan outbreak. Emerg Microbes Infect. 2020;9:313–319. doi: 10.1080/22221751.2020.1725399
    1. World Health Organization . Laboratory testing for 2019 novel coronavirus (2019-nCoV) in suspected human cases [Internet]. 2020 [cited 2020 Feb 4]. p. 1–7. Available from: .
    1. General Office of the National Health and Health Commission O of the SA of TCM . Diagnosis and treatment of pneumonitis with a new type of coronavirus infection (trial version 5) [Internet]. 2020 [cited 2020 Feb 6]. Available from: .
    1. Qiu J. Covert coronavirus infections could be seeding new outbreaks [Internet]. Nat. news. 2020 [cited 2020 Mar 20]. Available from: 10.1038/d41586-020-00822-x.
    1. Wu Z, McGoogan JM.. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the chinese center for disease control and prevention. Jama [Internet]. 2020;2019:25–28. Available from: .
    1. Klein D. Quantification using real-time PCR technology: applications and limitations. Trends Mol Med. 2002;8:257–260. doi: 10.1016/S1471-4914(02)02355-9
    1. Guo Y, Wang K, Zhang Y, et al. . Comparison and analysis of the detection performance of six new coronavirus nucleic acid detection reagents. Chongqing Med. 2020;14:1671–8348.
    1. Kuypers J, Jerome KR.. Applications of digital PCR for clinical microbiology. J Clin Microbiol. 2017;55:1621–1628. doi: 10.1128/JCM.00211-17
    1. Vogelstein B, Kinzler KW.. Digital PCR. Proc Natl Acad Sci USA. 1999;96:9236–9241. doi: 10.1073/pnas.96.16.9236
    1. Pohl G, Shih I-M.. Principle and applications of digital PCR. Expert Rev Mol Diagn. 2004;4:41–47. doi: 10.1586/14737159.4.1.41
    1. Sanders R, Mason DJ, Foy CA, et al. . Evaluation of digital PCR for absolute RNA quantification. PLoS One. 2013;8:e75296. doi: 10.1371/journal.pone.0075296
    1. White RA, Blainey PC, Fan HC, et al. . Digital PCR provides sensitive and absolute calibration for high throughput sequencing. BMC Genomics. 2009;10:110–116. doi: 10.1186/1471-2164-10-110
    1. Hindson BJ, Ness KD, Masquelier DA, et al. . High-throughput droplet digital PCR system for absolute quantitation of DNA copy number. Anal Chem. 2011;83:8604–8610. doi: 10.1021/ac202028g
    1. Hindson CM, Chevillet JR, Briggs HA, et al. . Absolute quantification by droplet digital PCR versus analog real-time PCR. Nat Methods. 2013;10:1003–1005. doi: 10.1038/nmeth.2633
    1. Postel M, Roosen A, Laurent-Puig P, et al. . Droplet-based digital PCR and next generation sequencing for monitoring circulating tumor DNA: a cancer diagnostic perspective. Expert Rev Mol. Diagn. 2018;18:7–17. doi: 10.1080/14737159.2018.1400384
    1. Miyaoka Y, Mayerl SJ, Chan AH, et al. . Detection and quantification of HDR and NHEJ induced by genome editing at endogenous gene loci using droplet digital PCR. In: Karlin-Neumann G, Bizouarn F, editors. Digit. PCR methods Protoc. New York, NY: Springer New York; 2018. p. 349–362. [Internet]. Available from: 10.1007/978-1-4939-7778-9_20.
    1. National Institute For viral Disease Control and prevention of PRC . Specific primers and probes for detection 2019 novel coronavirus [Internet]. 2020 [cited 2020 Apr 10]. Available from: .
    1. Burd EM. Validation of laboratory-developed molecular assays for infectious diseases. Clin Microbiol Rev. 2010;23:550–576. doi: 10.1128/CMR.00074-09
    1. Li M, Lei P, Zeng B, et al. . Coronavirus disease (COVID-19): spectrum of CT findings and temporal progression of the disease. Acad Radiol. 2020: 1–6. 10.1016/j.acra.2020.03.003.

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