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RT-qPCR Testing of SARS-CoV-2: A Primer

1. Mullis K., Faloona F., Saiki R., Horn G., Erlich H., Scharf S. Specific Enzymatic Amplification of DNA In Vitro: The Polymerase Chain Reaction. Cold Spring Harb. Symp. Quant. Boil. 1986;51:263–273. doi: 10.1101/SQB.1986.051.01.032. [PubMed] [CrossRef] [Google Scholar]

2. Bustin S. Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. J. Mol. Endocrinol. 2000;25:169–193. doi: 10.1677/jme.0.0250169. [PubMed] [CrossRef] [Google Scholar]

3. Myers T.W., Gelfand D.H. Reverse transcription and DNA amplification by a Thermus thermophilus DNA polymerase. Biochemistry. 1991;30:7661–7666. doi: 10.1021/bi00245a001. [PubMed] [CrossRef] [Google Scholar]

4. Bustin S.A. A-Z of Quantitative PCR. IUL Press; La Jolla, CA, USA: 2004. [Google Scholar]

5. Bustin S., Benes V., Garson J.A., Hellemans J., Huggett J., Kubista M., Mueller R., Nolan T., Pfaffl M., Shipley G.L., et al. The MIQE Guidelines: Minimum Information for Publication of Quantitative Real-Time PCR Experiments. Clin. Chem. 2009;55:611–622. doi: 10.1373/clinchem.2008.112797. [PubMed] [CrossRef] [Google Scholar]

6. Hellemans J., Mortier G., De Paepe A., Speleman F., Vandesompele J. qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Boil. 2007;8:R19. doi: 10.1186/gb-2007-8-2-r19.[PMC free article] [PubMed] [CrossRef] [Google Scholar]

7. Sanders R., Bustin S., Huggett J., Mason D. Improving the standardization of mRNA measurement by RT-qPCR. Biomol. Detect. Quantif. 2018;15:13–17. doi: 10.1016/j.bdq.2018.03.001.[PMC free article] [PubMed] [CrossRef] [Google Scholar]

8. Sanders R., Huggett J.F., Bushell C.A., Cowen S., Scott D.J., Foy C.A. Evaluation of digital PCR for absolute DNA quantification. Anal Chem. 2011;83:6474–6484. doi: 10.1021/ac103230c. [PubMed] [CrossRef] [Google Scholar]

9. Dong L., Zhou J., Niu C., Wang Q., Pan Y., Sheng S., Wang X., Zhang Y., Yang J., Liu M., et al. Highly accurate and sensitive diagnostic detection of SARS-CoV-2 by digital PCR. medRxiv. 2020 doi: 10.1101/2020.03.14. [CrossRef] [Google Scholar]

10. Yu F., Yan L., Wang N., Yang S., Wang L., Tang Y., Gao G., Wang S., Ma C., Xie R., et al. Quantitative Detection and Viral Load Analysis of SARS-CoV-2 in Infected Patients. Clin Infect Dis. 2020 doi: 10.1093/cid/ciaa345.[PMC free article] [PubMed] [CrossRef] [Google Scholar]

11. Bustin S. How to speed up the polymerase chain reaction. Biomol. Detect. Quantif. 2017;12:10–14. doi: 10.1016/j.bdq.2017.05.002.[PMC free article] [PubMed] [CrossRef] [Google Scholar]

12. Farrar J.S., Wittwer C.T. Extreme PCR: Efficient and Specific DNA Amplification in 15–60 Seconds. Clin. Chem. 2015;61:145–153. doi: 10.1373/clinchem.2014.228304. [PubMed] [CrossRef] [Google Scholar]

13. Notomi T. Loop-mediated isothermal amplification of DNA. Nucleic Acids Res. 2000;28:63. doi: 10.1093/nar/28.12.e63.[PMC free article] [PubMed] [CrossRef] [Google Scholar]

14. Lu R., Wu X., Wan Z., Li Y., Zuo L., Qin J., Jin X., Zhang C. Development of a Novel Reverse Transcription Loop-Mediated Isothermal Amplification Method for Rapid Detection of SARS-CoV-2. Virol. Sin. 2020:1–4. doi: 10.1007/s12250-020-00218-1.[PMC free article] [PubMed] [CrossRef] [Google Scholar]

15. Belk W.P., Sunderman F.W. A Survey of the Accuracy of Chemical Analyses in Clinical Laboratories. Am. J. Clin. Pathol. 1947;17:853–861. doi: 10.1093/ajcp/17.11.853. [PubMed] [CrossRef] [Google Scholar]

16. Lippi G., Simundic A.M. European Federation for Clinical Chemistry; Laboratory Medicine (EFLM) Working Group for Preanalytical Phase WG-PRE. The EFLM strategy for harmonization of the preanalytical phase. Clin. Chem. Lab. Med. 2018;56:1660–1666. doi: 10.1515/cclm-2017-0277. [PubMed] [CrossRef] [Google Scholar]

17. Bustin S.A., Nolan T. Template Handling, preparation and quantification. In: Bustin S.A., editor. A-Z of Quantitative PCR. International University Line; La Jolla, CA, USA: 2004. pp. 143–213. [Google Scholar]

18. Dheda K., Huggett J., Bustin S., Johnson M.A., Rook G., Zumla A. Validation of housekeeping genes for normalizing RNA expression in real-time PCR. Biotechniques. 2004;37:112–119. doi: 10.2144/04371RR03. [PubMed] [CrossRef] [Google Scholar]

19. Bustin S., Dhillon H.S., Kirvell S., Greenwood C., Parker M., Shipley G.L., Nolan T. Variability of the Reverse Transcription Step: Practical Implications. Clin. Chem. 2015;61:202–212. doi: 10.1373/clinchem.2014.230615. [PubMed] [CrossRef] [Google Scholar]

20. Bustin S., Huggett J. qPCR primer design revisited. Biomol. Detect. Quantif. 2017;14:19–28. doi: 10.1016/j.bdq.2017.11.001.[PMC free article] [PubMed] [CrossRef] [Google Scholar]

21. Corman V.M., Landt O., Kaiser M., Molenkamp R., Meijer A., Chu D.K., Bleicker T., Brünink S., Schneider J., Schmidt M.L., et al. Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Eurosurveillance. 2020;25:2000045. doi: 10.2807/1560-7917.ES.2020.25.3.2000045.[PMC free article] [PubMed] [CrossRef] [Google Scholar]

22. Chan J.F.-W., Yip C.C.-Y., To K.K.-W., Tang T.H.-C., Wong S.C.-Y., Leung K.-H., Fung A.Y.-F., Ng A.C.-K., Zou Z., Tsoi H.-W., et al. Improved molecular diagnosis of COVID-19 by the novel, highly sensitive and specific COVID-19-RdRp/Hel real-time reverse transcription-polymerase chain reaction assay validated in vitro and with clinical specimens. J. Clin. Microbiol. :2020. doi: 10.1128/JCM.00310-20.[PMC free article] [PubMed] [CrossRef] [Google Scholar]

23. Wang Y., Kang H., Liu X., Tong Z.-H. Combination of RT-qPCR testing and clinical features for diagnosis of COVID-19 facilitates management of SARS-CoV-2 outbreak. J. Med. Virol. 2020;92:538–539. doi: 10.1002/jmv.25721.[PMC free article] [PubMed] [CrossRef] [Google Scholar]

Sours: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7215906/

What are the differences between PCR, RT-PCR, qPCR, and RT-qPCR?


Polymerase chain reaction (PCR) is a relatively simple and widely used molecular biology technique to amplify and detect DNA and RNA sequences. Compared to traditional methods of DNA cloning and amplification, which can often take days, PCR requires only a few hours. PCR is highly sensitive and requires minimal template for detection and amplification of specific sequences. Basic PCR methods have further advanced from simple DNA and RNA detection. Below, we have provided an overview of the different PCR methods and the reagents we provide at Enzo Life Sciences for your research needs. We aim to help scientists quickly access PCR reagents to use in their next research project!

PCR

For standard PCR, all you need is a DNA polymerase, magnesium, nucleotides, primers, the DNA template to be amplified and a thermocycler. The PCR mechanism is as simple as its purpose: 1) double-stranded DNA (dsDNA) is heat denatured, 2) primers align to the single DNA strands and 3) the primers are extended by DNA polymerase, resulting in two copies of the original DNA strand. The denaturation, annealing, and elongation process over a series of temperatures and times is known as one cycle of amplification. Each step of the cycle should be optimized for the template and primer set used. This cycle is repeated approximately 20-40 times and the amplified product can then be analyzed. PCR is widely used to amplify DNA for subsequent experimental use. PCR also has applications in genetic testing or for the detection of pathogenic DNA.

As PCR is a highly sensitive method and very small volumes are required for single reactions, preparation of a master mix for several reactions is recommended. The master mix must be well mixed and then split by the number of reactions, ensuring that each reaction will contain the same amount of enzyme, dNTPs and primers. Many suppliers, such as Enzo Life Sciences, also offer PCR mixes that already contain everything except primers and the DNA template.

Guanine/Cytosine-rich (GC-rich) regions represent a challenge in standard PCR techniques. GC-rich sequences are more stable than sequences with lower GC content. Furthermore, GC-rich sequences tend to form secondary structures, such as hairpin loops. As a result, GC-rich double strands are difficult to completely separate during the denaturation phase. Consequently, DNA polymerase cannot synthesize the new strand without hindrance. A higher denaturation temperature can improve this and adjustments towards a higher annealing temperature and shorter annealing time can prevent unspecific binding of GC-rich primers. Additional reagents can improve the amplification of GC-rich sequences. DMSO, glycerol and betaine help to disrupt the secondary structures that are caused by GC interactions and thereby facilitate separation of the double strands.


Hot Start PCR

Unspecific amplification is a problem that can occur during PCR. Most DNA polymerases that are used for PCR, work best at 68 - 72°C. Therefore, the chosen extension temperature should be in this range. The enzyme can, however, also be active to a lesser degree, at lower temperatures. At temperatures that are far below the annealing temperature, primers tend to bind non-specifically, which can lead to non-specific amplification, even if the reaction is set up on ice. This can be prevented by using polymerase inhibitors that dissociate from the DNA polymerase only once a certain temperature is reached. The inhibitor can be an antibody that binds the polymerase and denatures at the initial denaturation temperature.


RT-PCR

Reverse transcription PCR, or RT-PCR, allows the use of RNA as a template. An additional step allows the detection and amplification of RNA. The RNA is reverse transcribed into complementary DNA (cDNA), using reverse transcriptase. The quality and purity of the RNA template is essential for the success of RT-PCR. The first step of RT-PCR is the synthesis of a DNA/RNA hybrid. Reverse transcriptase also has an RNase H function, which degrades the RNA portion of the hybrid. The single stranded DNA molecule is then completed by the DNA-dependent DNA polymerase activity of the reverse transcriptase into cDNA. The efficiency of the first-strand reaction can affect the amplification process. From here on, the standard PCR procedure is used to amplify the cDNA. The possibility to revert RNA into cDNA by RT-PCR has many advantages. RNA is single-stranded and very unstable, which makes it difficult to work with. Most commonly, it serves as a first step in qPCR, which quantifies RNA transcripts in a biological sample.


qPCR and RT-qPCR

Quantitative PCR (qPCR) is used to detect, characterize and quantify nucleic acids for numerous applications. Commonly, in RT-qPCR, RNA transcripts are quantified by reverse transcribing them into cDNA first, as described above and then qPCR is subsequently carried out. As in standard PCR, DNA is amplified by 3 repeating steps: denaturation, annealing and elongation. However, in qPCR, fluorescent labeling enables the collection of data as PCR progresses. This technique has many benefits due to a range of methods and chemistries available.

In dye-based qPCR (typically green), fluorescent labeling allows the quantification of the amplified DNA molecules by employing the use of a dsDNA binding dye. During each cycle, the fluorescence is measured. The fluorescence signal increases proportionally to the amount of replicated DNA and hence the DNA is quantified in “real time”. The disadvantages to dye-based qPCR are that only one target can be examined at a time and that the dye will bind to any ds-DNA present in the sample.

In probe-based qPCR, many targets can be detected simultaneously in each sample but this requires optimization and design of a target specific probe(s), used in addition to primers. There are several types of probe designs available, but the most common type is a hydrolysis probe, which incorporates the use of a fluorophore and quencher. Fluorescence resonance energy transfer (FRET) prevents the emission of the fluorophore via the quencher while the probe is intact. However, during the PCR reaction, the probe is hydrolyzed during primer extension and amplification of the specific sequence it is bound to. The cleavage of the probe separates the fluorophore from the quencher and results in an amplification-dependent increase in fluorescence. Thus, the fluorescence signal from a probe-based qPCR reaction is proportional to the amount of the probe target sequence present in the sample. Because probe-based qPCR is more specific than dye-based qPCR, it is often the technology used in qPCR diagnostic assays.

Further details can be found on our website: AMPIGENE® PCR & qPCR Solutions. Enzo’s mission is to provide low-cost, easily adaptable, and effective solutions to the diagnostic market. Our AMPIPROBE®platform uses novel PCR design to enable the quantification of nucleic acids for clinically relevant targets. Our assays are easily adaptable for laboratory use and cost-effective, without compromising on quality and performance. Compatible on open PCR platforms, AMPIPROBE® assayscan be validated on existing instrumentation, eliminating the need for capital expenditures.


The summary table below highlights the PCR products available for PCR, RT-PCR and qPCR. Please click on the product of interest for more information or contact our Technical Support Teamfor further assistance.
Check out our 10 tips for successful Real Time qPCR!
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Real-Time PCR: Revolutionizing Detection and Expression Analysis of Genes

1. Schaad NW, Fredrick RD. Real-time PCR and its application for rapid plant disease diagnostics. Can J Plant Pathol. 2002;24:250–258.[Google Scholar]

2. Monis PT, Giglio S. Nucleic acid amplification-based techniques for pathogen detection and identification. Infect Genet Evo. 2006;6:2–12.[PMC free article] [PubMed] [Google Scholar]

3. Kalow W. Pharmacogenetics and personalised medicine. Fundament Clin Pharmacol. 2002;16:337–342. [PubMed] [Google Scholar]

4. Severino G, Del Zompo M. Adverse drug reactions: role of pharmacogenomics. Pharmacol Res. 2004;49:363–373. [PubMed] [Google Scholar]

5. Kalow W. Pharmacogenetics and pharmacogenomics: origin, status, and the hope for personalized medicine. Pharmacogenomics J. 2006;6:162–165. [PubMed] [Google Scholar]

6. Kirk BW, Feinsod M, Favis R, Kliman RM, Barany F. Single nucleotide polymorphism seeking long term association with complex disease. Nucleic Acids Res. 2002;30:3295–3311.[PMC free article] [PubMed] [Google Scholar]

7. Alaoui-Jamali MA, Xu YJ. Proteomic technology for biomarker profiling in cancer: an update. J Zhejiang Univ Sci B. 2006;7:411–420.[PMC free article] [PubMed] [Google Scholar]

8. Liefers GJ, Tollenaar RAEM. Cancer genetics and their application to individualized medicine. Eur J Cancer. 2002;38:872–879. [PubMed] [Google Scholar]

9. Stahlberg A, Zoric N, Aman P, Kubista M. Quantitative real-time PCR for cancer detection: the lymphoma case. Expert Rev Mol Diagn. 2005;5:221–230. [PubMed] [Google Scholar]

10. Khanna C, Helman LJ. Molecular approaches in pediatric oncology. Annu Rev Med. 2006;57:83–97. [PubMed] [Google Scholar]

11. Stahlberg A, Zoric N, Aman P, Kubista M. Quantitative real-time PCR for cancer detection: the lymphoma case. Expert Rev Mol Diagn. 2005;5:221–230. [PubMed] [Google Scholar]

12. Mackay IM. Real-time PCR in the microbiology laboratory. Clin Microbiol Infect. 2004;10:190–212. [PubMed] [Google Scholar]

13. Hoebeeck JVDL, Poppe R, De Smet B, Yigit E, Claes N, Zewald N, de Jong R, De Paepe GJ, Speleman A, Vandesompele F. Rapid detection of VHL exon deletions using real-time quantitative PCR. Lab Investig. 2005;85:24–33. [PubMed] [Google Scholar]

14. Epsy MJ, Uhl JR, Sloan LM, Buckwalter SP, Jones MF, Vetter EA, Yao JDC, Wengenack NL, Rosenblatt JE, Cockerill FR, Smith TF. Real-time PCR in clinical microbiology: Applications for routine laboratory testing. Clin Microbiol Rev. 2006;19:165–256.[PMC free article] [PubMed] [Google Scholar]

15. Kaltenboeck B, Wang C. Advances in real-time PCR: application to clinical laboratory diagnostics. Adv Clin Chem. 2005;40:219–259.[PMC free article] [PubMed] [Google Scholar]

16. Weidmann M, Meyer-König U, Hufert FT. Rapid detection of herpes simplex virus and varicella-zoster virus infections by real-time PCR. J Clin Microbiol. 2003;41:1565–1568.[PMC free article] [PubMed] [Google Scholar]

17. Legoff J, Bouhlal H, Gresenguet G, Weiss H, Khonde N, Hocini H, Desire N, Si Mohamed A, Longo JD, Chemin C, Frost E, Pepin J, Malkin JE, Mayaud P, Belec L. Real-time PCR quantification of genital shedding of herpes simplex virus (HSV) and human immunodeficiency virus (HIV) in women coinfected with HSV and HIV. J Clin Microbiol. 2006;44:423–432.[PMC free article] [PubMed] [Google Scholar]

18. Fleming DT, McQuillan GM, Johnson RE, Nahmias AJ, Aral SO, Lee FK, St Louis ME. Herpes simplex virus type 2 in the United States, 1976 to 1994. N Engl J Med. 1997;337:1105–1111. [PubMed] [Google Scholar]

19. Ryncarz AJ, Goddard J, Wald A, Huang ML, Roizman B, Corey L. Development of a high-throughput puantative assay for detecting herpes simplex virus DNA in clinical samples. J Clin Microbiol. 1999;37:1941–1947.[PMC free article] [PubMed] [Google Scholar]

20. Espy MJ, Ross TK, Teo R, Svien KA, Wold AD, Uhl JR, Smith TF. Evaluation of LightCycler PCR for implementation of laboratory diagnosis of herpes simplex virus infections. J Clin Microbiol. 38:3116–3118. 2000a. [PMC free article] [PubMed] [Google Scholar]

21. Espy MJ, Teo R, Ross TK, Svien KA, Wold AD, Uhl JR, Smith TF. Diagnosis of varicella-zoster virus infections in the clinical laboratory by LightCycler PCR. J Clin Microbiol. 38:3187–3189. 2000b. [PMC free article] [PubMed] [Google Scholar]

22. Koenig M, Reynolds KS, Aldous W, Hickman M. Comparison of Light-Cycler PCR, enzyme immunoassay, and tissue culture for detection of herpes simplex virus. Diagn Microbiol Infect Dis. 2001;40:107–110. [PubMed] [Google Scholar]

23. Burrows J, Nitsche A, Bayly B, Walker E, Higgins G, Kok T. Detection and subtyping of Herpes simplex virus in clinical samples by LightCycler PCR, enzyme immunoassay and cell culture. BMC Microbiol. 2002;2:12.[PMC free article] [PubMed] [Google Scholar]

24. Aldea C, Alvarez CP, Folgueira L, Delgado R, Otero JR. Rapid detection of herpes simplex virus DNA in genital ulcers by real-time PCR using SYBR green I dye as the detection signal. J Clin Microbiol. 2002;40:1060–1062.[PMC free article] [PubMed] [Google Scholar]

25. Wald A, Huang ML, Carrell D, Selke S, Corey L. Polymerase chain reaction for detection of herpes simplex virus (HSV) DNA on mucosal surfaces: comparison with HSV isolation in cell culture. J Infect Dis. 2003;188:1345–1351. [PubMed] [Google Scholar]

26. van Doornum GJ, Guldemeester J, Osterhaus AD, Niesters HG. Diagnosing herpesvirus infections by real-time amplification and rapid culture. J Clin Microbiol. 2003;41:576–580.[PMC free article] [PubMed] [Google Scholar]

27. Schmutzhard J, Riedel HM, Wirgart BZ, Grillner L. Detection of herpes simplex virus type 1, herpes simplex virus type 2 and varicella-zoster virus in skin lesions. Comparison of real-time PCR, nested PCR and virus isolation. J Clin Virol. 2004;29:120–126. [PubMed] [Google Scholar]

28. Kimura H, Morita M, Yabuta Y, Kuzushima K, Kato K, Kojima S, Matsuyama T, Morishima T. Quantative analysis of Epstein-Barr virus load by using a real-time PCR assay. J Clin Microbiol. 1999;37:132–136.[PMC free article] [PubMed] [Google Scholar]

29. MacKenzie J, Gallagher A, Clyton RA, Perry J, Eden OB, Ford AM, Greaves MG, Jarrett RF. Screening for herpesvirus genomes in common acute lymphoblastic leukemia. Leukemia. 2001;15:415–421. [PubMed] [Google Scholar]

30. Templeton KE, Scheltinga SA, Beersma MF, Kroes AC, Claas EC. Rapid and sensitive method using multiplex real-time PCR for diagnosis of infections by influenza A and influenza B viruses, respiratory syncytial virus, and parainfluenza viruses 1, 2, 3, and 4. J Clin Microbiol. 2004;42:1564–1569.[PMC free article] [PubMed] [Google Scholar]

31. Klaschik S, Lehmann LE, Raadts A, Book M, Hoeft A, Stuber F. Real-time PCR for detection and differentiation of gram-positive and gram-negative bacteria. J Clin Microbiol. 2002;40:4304–4307.[PMC free article] [PubMed] [Google Scholar]

32. Kraus G, Cleary T, Miller N, Seivright R, Young AK, Spruill G, Hnatyszyn HJ. Rapid and specific detection of the mycobacterium tuberculosis complex using fluorogenic probes and real time PCR. Mol Cell Probes. 2001;15:375–383. [PubMed] [Google Scholar]

33. Lachnik J, Ackermann B, Bohrssen A, Maass S, Diephaus C, Puncken A, Stermann M, Bange FC. Rapid-cycle PCR and fluorimetry for detection of mycobacteria. J Clin Microbiol. 2002;40:3364–3373.[PMC free article] [PubMed] [Google Scholar]

34. Coppenraet ESBV, Lindeboom JA, Prins JM, Peeters MF, Claas ECJ, Kuijper EJ. Real-time PCR assay using fine-needle aspirates and tissue biopsy specimens for rapid diagnosis of Mycobacterial lymphadenitis in children. J Clin Microbiol. 2004;42:2644–2650.[PMC free article] [PubMed] [Google Scholar]

35. Sedlacek L, Rifai M, Feldmann K, Bange FC. LightCyclerbased differentiation of Mycobacterium abscessus and Myco-bacterium chelonae. J Clin Microbiol. 2004;42:3284–3287.[PMC free article] [PubMed] [Google Scholar]

36. Stermann M, Bohrssen A, Diephaus C, Maass S, Bange FC. Polymorphic nucleotide within the promoter of nitrate reductase (NarGHJI) is specific for Mycobacterium tuberculosis. J Clin Microbiol. 2003;41:3252–3259.[PMC free article] [PubMed] [Google Scholar]

37. Broccolo F, Scarpellini P, Locatelli G, Zingale A, Brambilla AM, Cichero P, Sechi A, Lazzarin A, Lusso P, Malnati MS. Rapid diagnosis of mycobacterial infections and quantitation of Mycobacterium tuberculosis load by two real-time calibrated PCR assays. J Clin Microbiol. 2003;41:4565–4572.[PMC free article] [PubMed] [Google Scholar]

38. Desjardin LE, Chen Y, Perkins MD, Teixeira L, Cave MD, Eisenach KD. Comparison of the ABI 7700 system (TaqMan) and competitive PCR for quantification of IS6110 DNA in sputum during treatment of tuberculosis. J Clin Microbiol. 1998;36:1964–1968.[PMC free article] [PubMed] [Google Scholar]

39. Rhee JT, Piatek AS, Small PM, Harris LM, Chaparro SV, Kramer FR, Alland D. Molecular epidemiologic evaluation of transmissibility and virulence of Mycobacterium tuberculosis. (1999) J Clin Microbiol. 1999;37:1764–1770.[PMC free article] [PubMed] [Google Scholar]

40. Rondini S, Mensah-Quainoo E, Troll H, Bodmer T, Pluschke G. Development and application of real-time PCR assay for quantification of Mycobacterium ulcerans DNA. J Clin Microbiol. 2003;41:4231–4237.[PMC free article] [PubMed] [Google Scholar]

41. Viedma DGD, del Sol Diaz Infantes M, Lasala F, Chaves F, Alcala L, Bouza E. New real-time PCR able to detect in a single tube multiple rifampin resistance mutations and high-level isoniazid resistance mutations in Mycobacterium tuberculosis. J Clin Microbiol. 2002;40:988–995.[PMC free article] [PubMed] [Google Scholar]

42. Marı’n M, de Viedma DG, Ruı’z-Serrano MJ, Bouza E. Rapid direct detection of multiple rifampin and isoniazid resistance mutations in Mycobacterium tuberculosis in respiratory samples by real-time PCR. Antimicrob Agents Chemother. 2004;48:4293–4300.[PMC free article] [PubMed] [Google Scholar]

43. Torres MJ, Criado A, Palomares JC, Aznar J. Use of real-time PCR and fluorimetry for rapid detection of rifampin and isoniazid resistance-associated mutations in Mycobacterium tuberculosis. J Clin Microbiol. 2000;38:3194–3199.[PMC free article] [PubMed] [Google Scholar]

44. Piatek AS, Telenti A, Murray MR, El-Hajj H, Jacobs WR, Kramer FR, Alland D. Genotypic analysis of Mycobacterium tuberculosis in two distinct populations using molecular beacons: implications for rapid susceptibility testing. Antimicrob Agents Chemother. 2000;44:103–110.[PMC free article] [PubMed] [Google Scholar]

45. Wada T, Maeda S, Tamaru A, Imai S, Hase A, Kobayashi K. Dual-probe assay for rapid detection of drug-resistant Mycobacterium tuberculosis by real-time PCR. J Clin Microbiol. 2004;42:5277–5285.[PMC free article] [PubMed] [Google Scholar]

46. Bell CA, Uhl JR, Hadfield TL, David JC, Meyer RF, Smith TF, Cockerill FR. Detection of Bacillus anthracis DNA by LightCycler PCR. J Clin Microbiol. 2002;40:2897–2902.[PMC free article] [PubMed] [Google Scholar]

47. Moser MJ, Christensen DR, Norwood D, Prudent JR. Multiplexed detection of anthrax-related toxin genes. J Mol Diagn. 2006;8:89–96.[PMC free article] [PubMed] [Google Scholar]

48. Pham AS, Tarrand JJ, May GS, Lee MS, Kontoyiannis DP, Han XY. Diagnosis of invasive mold infection by real-time quantitative PCR. Am J Clin Pathol. 2003;119:38–44. [PubMed] [Google Scholar]

49. Sanguinetti M, Posteraro B, Pagano L, Pagliari G, Fianchi L, Mele L, La Sorda M, Franco A, Fadda G. Comparison of real-time PCR, conventional PCR, and galactomannan antigen detection by enzymelinked immunosorbent assay using bronchoal-veolar lavage fluid samples from hematology patients for diagnosis of invasive pulmonary aspergillosis. J Clin Microbiol. 2003;41:3922–3925.[PMC free article] [PubMed] [Google Scholar]

50. Bowman JC, Abruzzo GK, Anderson JW, Flattery AM, Gill CJ, Pikounis VB, Schmatz DM, Liberator PA, Douglas CM. Quantitative PCR assay to measure Aspergillus fumigatus burden in a murine model of disseminated aspergillosis: demonstration of efficacy of caspofungin acetate. Antimicrob Agents Chemother. 2001;45:3474–3481.[PMC free article] [PubMed] [Google Scholar]

51. Costa C, Vidaud D, Olivi M, Bart-Delabesse E, Vidaud M, Bretagne S. Development of two real-time quantitative TaqMan PCR assays to detect circulating Aspergillus fumigatus DNA in serum. J Microbiol Methods. 44:263–269. 2001a. [PubMed] [Google Scholar]

52. Maaroufi Y, De Bruyne JM, Duchateau V, Georgala A, Crokaert F. Early detection and identification of commonly encountered Candida species from simulated blood cultures by using a real-time PCR based assay. J Mol Diagn. 2004;6:108–114.[PMC free article] [PubMed] [Google Scholar]

53. White PL, Williams DW, Kuriyama T, Samad SA, Lewis MA, Barnes RA. Detection of Candida in concentrated oral rinse cultures by real-time PCR. J Clin Microbiol. 2004;42:2101–2107.[PMC free article] [PubMed] [Google Scholar]

54. Costa JM, Ernault P, Gautier E, Bretagne S. Prenatal diagnosis of congenital toxoplasmosis by duplex real-time PCR using fluorescence resonance energy transfer hybridization probes. Prenatal Diagnosis. 21:85–88. 2000b. [PubMed] [Google Scholar]

55. Palladino S, Kay I, Fonte R, Flexman J. Use of real-time PCR and the LightCycler system for the rapid detection of Pneumocystis carinii in respiratory specimens. Diagn Microbiol Infect Dis. 2001;39:233–236. [PubMed] [Google Scholar]

56. Bialek R, Kern J, Herrmann T, Tijerina R, Cecenas L, Reischl U, Gonzalez GM. PCR assays for identification of Coc-cidioides posadasii based on the nucleotide sequence of the antigen 2/proline-rich antigen. J Clin Microbiol. 2004;42:778–783.[PMC free article] [PubMed] [Google Scholar]

57. Imhof A, Schaer C, Schoedon G, Schaer DJ, Walter RB, Schaffner A, Schneemann M. Rapid detection of pathogenic fungi from clinical specimens using LightCycler real-time fluorescence PCR. Eur J Clin Microbiol Infect Dis. 2003;22:558–560. [PubMed] [Google Scholar]

58. Hsu MC, Chen KW, Lo HJ, Chen YC, Liao MH, Lin YH, Li SY. Species identification of medically important fungi by use of real-time LightCycler PCR. J Med Microbiol. 2003;52:1071–1076. [PubMed] [Google Scholar]

59. Martagon-Villamil J, Shrestha N, Sholtis M, Isada CM, Hall GS, Bryne T, Lodge BA, Reller LB, Procop GW. Identification of Histoplasma capsulatum from culture extracts by real-time PCR. J Clin Microbiol. 2003;41:1295–1298.[PMC free article] [PubMed] [Google Scholar]

60. Marques ER, Ferreira ME, Drummond RD, Felix JM, Menossi M, Savoldi M, Travassos LR, Puccia R, Batista WL, Carvalho KC, Goldman MH, Goldman GH. Identification of genes preferentially expressed in the pathogenic yeast phase of Paracoccidioides brasiliensis, using suppression subtraction hybridization and differential macroarray analysis. Mol Genet Genomics. 2004;271:667–677. [PubMed] [Google Scholar]

61. Meliani L, Develoux M, Marteau-Miltgen M, Magne D, Barbu V, Poirot VL, Roux P. Real time quantitative PCR assay for Pneumocystis jirovecii detection. J Eukaryot Microbiol. 2003;50:651. [PubMed] [Google Scholar]

62. Year H, Tzen MZ, Dupouy-Camet J. Molecular biology for detection and characterization of protozoan infections in humans. Eur J Prostistol. 2003;39:435–443.[Google Scholar]

63. Blessmann J, Buss H, Nu PA, Dinh BT, Ngo QT, Van AL, Alla MD, Jackson TF, Ravdin JI, Tannich E. Real-time PCR for detection and differentiation of Entamoeba histolytica and Entamoeba dispar in fecal samples. J Clin Microbiol. 2002;40:4413–4417.[PMC free article] [PubMed] [Google Scholar]

64. Freitas JM, Lages-Silva E, Crema E, Pena SD, Macedo AM. Real time PCR strategy for the identification of major lineages of Trypanosoma cruzi directly in chronically infected human tissues. Int J Parasitol. 2005;35:411–417. [PubMed] [Google Scholar]

65. Rolao N, Cortes S, Rodrigues OR, Campino L. Quantification of Leishmania infantum parasites in tissue biopsies by real-time polymerase chain reaction and polymerase chain reaction-enzyme-linked immunosorbent assay. J Parasitol. 2004;90:1150–1154. [PubMed] [Google Scholar]

66. Guy RA, Xiao C, Horgen PA. Real-time PCR assay for detection and genotype differentiation of Giardia lamblia in stool specimens. J Clin Microbiol. 2004;42:3317–3320.[PMC free article] [PubMed] [Google Scholar]

67. Varma M, Hester JD, Schaefer FW, Ware MW, Lindquist HD. Detection of Cyclospora cayetanensis using a quantitative real-time PCR assay. J Microbiol Methods. 2003;53:27–36. [PubMed] [Google Scholar]

68. Chabbert E, Lachaud L, Crobu L, Bastien P. Comparison of two widely used PCR primer systems for detection of Toxoplasma in amniotic fluid, blood, and tissues. J Clin Microbiol. 2004;42:1719–1722.[PMC free article] [PubMed] [Google Scholar]

69. Menotti J, Cassinat B, Porcher R, Sarfati C, Derouin F, Molina JM. Development of a real-time polymerase-chainreaction assay for quantitative detection of Enterocytozoon bieneusi DNA in stool specimens from immunocompromised patients with intestinal microsporidiosis. J Infect Dis. 2003;187:1469–1474. [PubMed] [Google Scholar]

70. Boothroyd JC, Blader I, Cleary M, Singh U. DNA micro-arrays in parasitology: strengths and limitations. Trends Parasitol. 2003;19:470–476. [PubMed] [Google Scholar]

71. Leutenegger CM, Klein D, Hofmann-Lehmann R, Mislin C, Hummel U, Böni J, Boretti F, Guenzburg WH, Lutz H. Rapid feline immunodeficiency virus provirus quantitation by polymerase chain reaction using the TaqMan fluorogenic realtime detection system. J Virol Methods. 1999;78:105–116. [PubMed]

Sours: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2430684/
Quantitative real time PCR (qPCR)

A Basic Guide to Real Time PCR in Microbial Diagnostics: Definitions, Parameters, and Everything

Introduction

The phrase “Polymerase chain reaction” (PCR) was first used more than 30 years ago in a paper describing a novel enzymatic amplification of DNA (Saiki et al., 1985). The first applications of PCR were rather unpractical due to the usage of thermolabile Klenow fragment for amplification, which needed to be added to the reaction after each denaturation step. The crucial innovation which enabled routine usage of PCR was utilization of thermostable polymerase from Thermus aquaticus (Saiki et al., 1988). This improvement, together with the availability of PCR cyclers and chemical components, led to the worldwide recognition of PCR as the tool of choice for the specific enzymatic amplification of DNA in vitro. It must be noted that the general concept of PCR, which includes primers, DNA polymerase, nucleotides, specific ions, and DNA template, and consisting of cycles that comprise steps of DNA denaturation, primer annealing, and extension, have not been changed since 1985. The invention of PCR has greatly boosted research in various areas of biology and this technology has significantly contributed to the current level of human knowledge in many spheres of research.

The most substantial milestone in PCR utilization was the introduction of the concept of monitoring DNA amplification in real time through monitoring of fluorescence (Holland et al., 1991; Higuchi et al., 1992). In real time PCR (also denoted as quantitative PCR—qPCR; usage of RT-PCR is inappropriate as this abbreviation is dedicated to reverse transcription PCR), fluorescence is measured after each cycle and the intensity of the fluorescent signal reflects the momentary amount of DNA amplicons in the sample at that specific time. In initial cycles the fluorescence is too low to be distinguishable from the background. However, the point at which the fluorescence intensity increases above the detectable level corresponds proportionally to the initial number of template DNA molecules in the sample. This point is called the quantification cycle (Cq; different manufactures of qPCR instruments use their own terminology, but since 2009, the term Cq is used exclusively) and allows determination of the absolute quantity of target DNA in the sample according to a calibration curve constructed of serially diluted standard samples (usually decimal dilutions) with known concentrations or copy numbers (Yang and Rothman, 2004; Kubista et al., 2006; Bustin et al., 2009).

Moreover, qPCR can also provide semi-quantitative results without standards but with controls used as a reference material. It this case, the observed results can be expressed as higher or lower multiples with reference to control. This application of qPCR has been extensively used for gene expressions studies (Bustin et al., 2009), but did not obtain the same success in microbiology quantification since it is unable to produce absolute quantitative values.

There are two strategies for the real time visualization of amplified DNA fragments—non-specific fluorescent DNA dyes and fluorescently labeled oligonucleotide probes. These two approaches were developed in parallel (Holland et al., 1991; Higuchi et al., 1992) and are used in pathogen detection; however, probe-based chemistry prevails. This is due to its higher specificity mediated by the additional oligonucleotide—the probe—and the lower susceptibility to visualize non-specific PCR products, e.g., primer dimers (Bustin, 2000; Kubista et al., 2006).

To fully understand the possibilities of qPCR in detecting and quantifying target DNA in samples it is essential to describe the mathematical principle of this method. The PCR is an exponential process where the number of DNA molecules theoretically doubles after each cycle (if the efficiency of the reaction is 100%). More generally, the amplification reaction follows this equation:

where Nn is the number of PCR amplicons after n cycles, N0 is the initial number of template copies in the sample, E is the PCR efficiency that can assume values in the range from 0 to 1 (0–100%) and n is number of cycles. In a scenario where there is initially one copy of the template in the reaction and PCR efficiency is 100%, it is possible to simplify the equation as follows:

If a calibration curve is run, usually 10-fold serial dilutions are used. The difference in Cq values between two 10-fold serial dilutions could be expressed as

Then n = 3.322. When E should be determined the (1) is starting point and the equation is

If n is taken to be 3.322, then E = 1, i.e., 100%.

The PCR efficiency is therefore a significant factor for the quantification of the target DNA in unknown samples. The reliability of the calibration curve in enabling quantification is then determined by the spacing of the serial dilutions. If the Log10 of the concentration or copy number of each standard is plotted against its Cq value (Figure 1), the E can be derived from the regression equation describing the linear function:

Where x and y, the concentration/amount of target and Cq values respectively, characterize the coordinates in the plot, k is the regression coefficient or slope and c is the intercept. Taking the model regression equation from Figure 1, the slope is −3.322, which mean that E = 100% according to (4). The intercept shows the Cq value when one copy would be theoretically detected (Kubista et al., 2006; Johnson et al., 2013). The concentration or amount of target nucleic acid in unknown samples is then calculated according to the Cq value through Equation (5).

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Figure 1. Model calibration curve with the regression equation (characterized by the slope and intercept) and regression coefficient.

From the definitions above it is evident that Cq values are instrumental readings, and must be recalculated to values with specific units, e.g., copies of organism, ng of DNA, various concentrations, etc., (Bustin et al., 2009; Johnson et al., 2013). However, referral to Cq values in scientific papers is widespread and interpretations based on Cq values can lead to misleading conclusions. Concentrations in qPCR are expressed in the logarithmic scale (Figure 1) and Cq differences between 10-fold serial dilutions are theoretically always 3.322 cycles. Therefore, although the numerical difference between Cq 20 and 35 is rather negligible, the difference in real numbers (copies, ng) is almost five orders of magnitude (Log10).

This feature must be reflected in the subsequent calculations. For example, the coefficient of variation (CV, ratio between standard deviation and mean) calculated from the Cq values and real numbers results in profoundly different results. The same applies for any statistical tests where Cq values are used, even for cases where the logarithm of Cq values is used for the normalization of data before the statistical evaluation. The correct procedure should include initial recalculation to real numbers followed by logarithmic transformation.

Pros and Cons of Using qPCR in Detection and Quantification of Pathogens

Since PCR is capable of amplifying a specific fragment of DNA, it has been used in pathogen diagnostics. With the increasing amount of sequencing data available, it is literally possible to design qPCR assays for every microorganism (groups and subgroups of microorganisms, etc.) of interest. The main advantages of qPCR are that it provides fast and high-throughput detection and quantification of target DNA sequences in different matrices. The lower time of amplification is facilitated by the simultaneous amplification and visualization of newly formed DNA amplicons. Moreover, qPCR is safer in terms of avoiding cross contaminations because no further manipulation with samples is required after the amplification. Other advantages of qPCR include a wide dynamic range for quantification (7–8 Log10) and the multiplexing of amplification of several targets into a single reaction (Klein, 2002). The multiplexing option is essential for detection and quantification in diagnostic qPCR assays that rely on the inclusion of internal amplification controls (Yang and Rothman, 2004; Kubista et al., 2006; Bustin et al., 2009).

qPCR assays are used not only for the detection, but also to determine the presence of specific genes and alleles, e.g., typing of strains and isolates, antimicrobial resistance profiling, toxin production, etc., However, the mere presence of genes responsible for resistance to antimicrobial compounds or fungal toxin production does not automatically mean their expression or production. Therefore, although qPCR-based typing tests are faster, their results should be correlated with phenotypic and biochemical tests (Levin, 2012; Osei Sekyere et al., 2015).

As for the microbial diagnostics, there are different considerations in detecting and quantifying viral, bacterial, and parasitic agents. These considerations are based on the target (DNA or RNA), cultivability, interpretation of results, and clinical significance of qPCR results.

qPCR plays an important role in the detection, quantification, and typing of viral pathogens. This is because detection of important clinical and veterinary viruses using culture methods is time-consuming or impossible, while ELISA tests are not universally available and suffer from comparatively low sensitivity and specificity. qPCR (with the inclusion of reverse transcription for the diagnostics of RNA viruses) provides the appropriate sensitivity and specificity (Hoffmann et al., 2009). Moreover, determination of the viral load by (RT)-qPCR is used as an indicator of the response to antiviral therapies (Watzinger et al., 2006). For these reasons (RT)-qPCR has become an indispensable tool in virus diagnostics (Yang and Rothman, 2004).

The situation is similar in the case of intestinal protozoan diagnostics (Rijsman et al., 2016). The gold standard technique for the detection of protozoan agents, the microscopic examination of feces, is laborious, time-consuming, and requires specifically trained personnel. Similarly, ELISA testing suffers from low sensitivity and specificity (Rijsman et al., 2016). Therefore, qPCR is now emerging as a powerful tool in the routine detection, quantification, and typing of intestinal parasitic protozoa.

In contrast to viral and protozoan detection and quantification, many bacteria of clinical, veterinary, and food safety significance, can be cultured. For this reason, culture is considered as the gold standard in bacterial detection and quantification. However, in cases when critical and timely intervention for infectious disease is required, the traditional, slow, and multistep culture techniques cannot provide results in a reasonable time. This limitation is compounded by the necessity of culturing fastidious pathogens and additional testing (species determination, identification of virulence factors, and antimicrobial resistance). qPCR is capable of providing the required information in a short time; however, the phenotypic and biochemical features must be confirmed from bacterial isolates (Yang and Rothman, 2004).

In food safety, all international standards for food quality rely on the determination of pathogenic microorganisms using traditional culture methods. qPCR techniques represent an excellent alternative to existing standard culture methods as they enable reliable detection and quantification (for several pathogens) and harbor many other advantages as discussed above. However, there are limitations with respect to the sensitivity of assays based on qPCR. As culture methods rely on the multiplication of bacteria during the pre-culture steps (pre-enrichment), samples for DNA isolation usually initially contain very low numbers of target bacteria (Rodriguez-Lazaro et al., 2013). This limitation leads to the most important disadvantage of qPCR, which is its inherent incapability of distinguishing between live and dead cells. The usage of qPCR itself is therefore limited to the typing of bacterial strains, identification of antimicrobial resistance, detection, and possibly quantification in non-processed and raw food. It is important to note that processed food can still contain amplifiable DNA even if all the potentially pathogenic bacteria in food are devitalized and the foodstuff is microbiologically safe for consumption (Rodriguez-Lazaro et al., 2013). To overcome this problem, a pre-enrichment of sample in culture media could be placed prior to the qPCR. This step may include non-selective enrichment in buffered peptone water or specific selective media for the respective bacterium. This procedure is primarily intended to allow resuscitation/recovery and subsequent multiplication of the bacteria for the downstream qPCR detection; the second advantage is dilution and elimination of possible PCR inhibitors present into the sample (presence of salts, conservation substances, etc.). The extraction of the DNA from the culture media is easier than that from the food samples, which are much more heterogeneous in terms of composition (Margot et al., 2015).

Although qPCR itself cannot distinguish among viable and dead cells attempts have been made to adapt qPCR for viability detection. It was shown that RNA has low stability and should be degraded in dead cells within minutes. However, the correlation of cell viability with the persistence of nucleic acid species must be well characterized for a particular situation before an appropriate amplification-based analytical method can be adopted as a surrogate for more traditional culture techniques (Birch et al., 2001). Moreover, difficulties connected with RNA isolation from samples like food, feces or environmental samples can provide false-negative results especially when low numbers of target cells are expected.

Another option for determination of viability using qPCR is the deployment of intercalating fluorescent dyes like propidium monoazide (PMA) and ethidium monoazide (EMA; Nocker and Camper, 2009). In these methods, the criterion for viability determination is membrane integrity. Metabolically active cells (regardless of their cultivability) with full membrane integrity keep the dyes outside the cells and are therefore considered as viable. However, if plasma membrane integrity is compromised, the dyes penetrate the cells, or react with the DNA outside of dead cells. The labeled DNA is then not available for the amplification by qPCR and the difference between treated and untreated cells provides information about the proportion of viable cells in the sample. The limitation of this method is the necessity to have the cells in a light-transparent matrix, e.g., water samples, cell cultures, etc., as the intercalation of the dye to DNA requires exposure to light. Therefore, samples of insufficient light transparency do not permit the application of these dyes. There is a preference for PMA over EMA, as it was shown that EMA penetrates the membranes of live bacterial cells (Nocker et al., 2006).

Moreover, another topic we want to just to mention here is the generation and use of standards required for the calibration curves. In general, two are the most diffused approaches for the generation of calibration curves. One employs dilutions of target genomic nucleic acid and the other plasmid standards. Both strategies can lead to a final quantification of the target, but plasmids containing specific target sequences offer the advantages of easy production, stability, and cheapness. On the other hand, in principle, PCR efficiency obtained by plasmid standards sometimes could differ compared to the efficiency obtained using genomic standard, which instead, for organisms fastidious to growth, could be isolated only starting from a given matrix, and thus susceptible to degradation and losses (Chaouachi et al., 2013). Finally, the production and validation of international quantification standards for qPCR assays is technically demanding and these standards are currently available only for a few targets (Pavšič et al., 2015).

qPCR Parameters in Microbial Detection and Quantification

Analytical Specificity (Selectivity)

This parameter in qPCR refers to the specificity of primers for target of interest. Analytical specificity consists of two concepts: inclusivity describes the ability of the method to detect a wide range of targets with defined relatedness e.g., taxonomic, immunological, genetic composition (Anonymous, 2009, 2015a). Another definition describes inclusivity as the strains or isolates of the target analyte(s) that the method can detect (Anonymous, 2012). ISO 16140 and other standards recommend that inclusivity should be determined on 20–50 well-defined (certified) strains of the target organism (Anonymous, 2009, 2011, 2012, 2015a; Broeders et al., 2014), or for Salmonella, it is recommended that 100 serovars should be included for inclusivity testing (Anonymous, 2012).

On the other hand, exclusivity describes the ability of the method to distinguish the target from similar but genetically distinct non-targets. In other words, exclusivity can also be defined as the lack of interference from a relevant range of non-target strains, which are potentially cross-reactive (Anonymous, 2009, 2011, 2012, 2015a). The desirable number of positive samples in exclusivity testing is zero (Johnson et al., 2013).

Analytical Sensitivity (Limit of Detection, LOD)

Many official documents have discussed theories and procedures for the correct definition of the LOD for different methods. A general consensus was reached around the definition of the LOD as the lowest amount of analyte, which can be detected with more than a stated percentage of confidence, but, not necessarily quantified as an exact value (Anonymous, 2011, 2013, 2014). In this regard, the confidence level obtained or requested for the definition of LOD can reflect the number of replicates (both technical and experimental) needed by the assay in order to reach the requested level of confidence (e.g., 95%). It is clear that the more replicates are tested, the narrower will be the interval of confidence. Another definition describes the LOD as the lowest concentration level that can be determined as statistically different from a blank at a specified level of confidence. This value should be determined from the analysis of sample blanks and samples at levels near the expected LOD (Anonymous, 2015a). However, it should be noted that LOD definitions described above were reported for chemical methods, and are not perfectly suited for PCR methods (Burns and Valdivia, 2008). This is because, for limited concentrations of analyte (nucleic acids), the output of the reaction can be a success (amplification), or a failure (no amplification at all), without any blank, or critical level at which it is possible to set a cut-off value over which the sample can be considered as positive one. Moreover, it should be remembered here that, by definition, a blank sample should never be positive in PCR.

Since the definitions reported above are not practicable for PCRs, other approaches have been proposed. A conservative approach is to consider the LOD value as the minimum concentration of nucleic acid or number of cells, which always gives a positive PCR result in all replicates tested, or in the major part (over 95%) of them (Nutz et al., 2011). In practice, multiple aliquots of a specific matrix are spiked with serial dilutions of the target organism and undergo the whole process of nucleic acid isolation and qPCR. The LOD is then defined as the spike amount of target organism in dilution that could be detected in 95% of replicates. For example, 10 replicates of milk samples were spiked with serial dilutions of Campylobacter jejuni in amounts of 105–100 cells per 1 ml of milk. The experimentally determined LOD of the method for the detection of C. jejuni in milk is approximately 1.56 × 103 cells/ml of milk (Figure 2). In order to better define the most precise value, more dilutions can be tested before reaching a final LOD value as close as possible to the real one. The number of replicates tested should be at least six (Slana et al., 2008; Kralik et al., 2011); however, the more replicates (10 or 15 and more; Ricchi et al., 2016) performed, the higher level of confidence of the LOD that can be achieved (Anonymous, 2015b).

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Figure 2. Graphical representation of the determination of limit of detection (LOD) in qPCR. The data in the table show the number of positive samples/all analyzed samples (signal ratio).

According to the Poisson distribution, it was concluded that the LOD for PCR cannot be lower than at least three copies of the nucleic acid targets (Bustin et al., 2009; Johnson et al., 2013). However, this value refers to the theoretical LOD of the qPCR methodology, which is capable of detecting a single target DNA molecule in the sample. Assuming this, such LOD for all optimized qPCR assays will be similar. Therefore, as stated above, the LOD must be related to the whole method that includes nucleic acid preparation and qPCR. Only under these conditions can it represent a valid parameter that describes the features of the respective qPCR method (Anonymous, 2015a).

However, sometimes it is not possible to obtain large numbers of replicates, for both financial and technical reasons. To overcome these problems, an increasing number of reports utilize Probit or Logit approaches for determining the LOD for PCR methods (Burns and Valdivia, 2008; Anonymous, 2014; Pavšic et al., 2016; Ricchi et al., 2016). Briefly, both mathematical functions are regressions used to analyse binomial response variables (positive or negative) and are able to transform the sigmoid dose-response curve, typical of a binomial variable, to a straight line that can then be analyzed by regression either through least squares or maximum likelihood methods. The final end-point of the analysis is a concentration (coupled with relative intervals of confidence), associated to a probability (e.g., 95%) to detect the nucleic acid. Moreover, Probit regression is exploitable only for normally distributed data, while Logit function can also be used for data not normally distributed; however, in this context, both functions have the same meaning.

Finally, it must be noted that LOD is not a limiting value and therefore, that Cq values below the LOD cannot automatically be considered as negative. From the definition of LOD, it is evident that values below the LOD are absolutely valid in terms of microorganism presence; however, the probability of their repeated detection is lower than 95%. This feature is connected with the Poisson distribution when working with small numbers.

Limit of Quantification (LOQ)

The documents already cited for the LOD definitions also contain analog definitions for the LOQ. The LOQ was defined as the smallest amount of analyte, which can be measured and quantified with defined precision and accuracy under the experimental conditions by the method under validation (Armbruster and Pry, 2008; Anonymous, 2011, 2013). An alternative definition is that the LOQ is the lowest amount or concentration of analyte that can be quantitatively determined with an acceptable level of uncertainty (Anonymous, 2015a). It is clear that, according to the previous definitions, the LOQ can never be lower than the LOD.

In practice, the LOQ is determined as is the LOD, on replicates of spiked samples, but the assessment of results is quantitative. Numerically, the LOQ is defined as the lowest concentration of analyte, which gives a predefined variability, generally reported as the coefficient of variation (CV). For qPCR, this value has been proposed to be fixed under 25% (Broeders et al., 2014; Dreo et al., 2014; Anonymous, 2015b; Pavšic et al., 2016), bearing in mind that the Cq values must be recalculated to copies or g of nucleic acids before performing the evaluation of the CV (Bustin et al., 2009; Johnson et al., 2013). Hoverer, this value was proposed based on the experience accrued in GMO detection laboratories (Broeders et al., 2014; Anonymous, 2015b), and there is no general agreement regarding any technical standards for molecular methods in microbiology. Therefore, we propose here to define the LOQ in the molecular diagnosis of microorganisms as the lowest concentration, amount, or number of analytes with a CV < 25%.

Another approach for the determination of the LOQ of qPCR is based on the use of the Youden index (J) and receiver operating characteristic curves (Nutz et al., 2011). This last index was defined as J = sensitivity + specificity −1 (Fluss et al., 2005). A series of spiked samples with different concentrations of target DNA were analyzed and the J-values were calculated for each PCR cycle. The LOQ was then fixed as the concentrations of DNA where the J-values were highest (Nutz et al., 2011).

Finally, an issue that should be addressed for the determination of the LOQ as well as LOD is the efficiency of recovery of target molecules during the nucleic acid extraction phases. Generally, nucleic acids are extracted from different complex matrices, like food, feces, or other samples using different procedures. The efficiency of DNA recovery is usually around 30% and lower (Slana et al., 2008; Kralik et al., 2011; Ricchi et al., 2016) and neglecting this parameter leads to underestimation of the true number of target microorganisms in the original sample, which is then reflected by the lower LOD and LOQ values. Therefore, determination of DNA isolation efficiency should be part of the LOD and LOQ. DNA isolation efficiency is a quotient between the number of microorganisms recovered after the entire procedure (nucleic acid extraction + qPCR) and the number of microorganisms used for spiking the negative matrices (Slana et al., 2008; Kralik et al., 2011; Ricchi et al., 2016). Due to the fact that these data are provided during the determination of the LOD and LOQ, it is not necessary to perform additional experiments. It is recommended that the median of mean DNA isolation values from different dilutions is used as the practical overall DNA isolation efficiency (Kralik et al., 2011).

Similarly to the LOD, quantity can also be assessed in samples with numbers of organisms or concentrations of DNA lower than the LOQ, but the confidence of such quantification will be lower than that declared by the definition of LOQ. Moreover, there are possibilities of how to refer to such quantities in terms of semi-quantitative interpretation, e.g., range of values (102–101 cells/g).

Amplification Efficiency of qPCR (E)

This parameter was mentioned above in the section dedicated to the mathematical description of qPCR (Equation 4). PCR efficiency should be in the range of 0–1 (0–100%); when E = 1 this means that the number of newly formed DNA amplicons is doubled in each cycle. This is difficult to reach repeatedly over time. In practice, this parameter is likely to be in the range 90–105% (Johnson et al., 2013). This parameter can be estimated from the slope of the calibration curve.

In connection to this issue, the lowest and highest concentrations of the standard included in the calibration curve, which can be truly quantified, should be determined according to the linear dynamic range of over at least 6 Log10. The dynamic range is defined by the MIQE guidelines as the range over which a reaction is linear (Bustin et al., 2009).

The determination of PCR efficiency by the standard curve actually provides two pieces of information. If an inhibitor would be present in the most concentrated sample, there would be a visible increase in Cq values in these and therefore a diminishment of the 3.322 Cq span at higher concentrations. However, this is not a frequent phenomenon, as standards are usually well-characterized and therefore, any inhibition is rather unlikely. If there would be a similar situation in lower concentration samples, this suggests a possible pipetting error rather than the presence of inhibitors. An important function to assess this is the coefficient of determination (R2 value), that should be higher than 0.98 (Johnson et al., 2013). In reality, it is much more important to determine the PCR inhibition and subsequent diminishment of the PCR efficiency in analyzed samples. There are approaches based on the analysis of the fluorescent curve of each sample by specific software (LinRegPCR), which can calculate the PCR efficiency of each sample without the series of dilutions. However, this approach is not flawless as it does not take into account all possible variables that can affect the analysis (Ruijter et al., 2009).

Accuracy of PCR

The following parameters of qPCR deal with ways of how to compare novel qPCR methods with reference methods or materials. Accuracy is defined as a measure of the degree of conformity of a value generated by a specific procedure to the assumed or accepted true value (Anonymous, 2015a). In other words, accuracy describes the level of agreement between reference and measured values. There are several aspects that need to be considered in terms of defining accuracy. In binary classification tests (qualitative detection), the samples analyzed by a novel (alternative) test that needs to be verified (typically a novel qPCR assay) are categorized according to their concordance with the reference method in four basic categories (Table 1). This division originates from the statistical classification known as error matrix and allows determination of several parameters that describe the diagnostic potential of the qPCR method.

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Table 1. Parameters for comparison of qPCR results with a reference method in a 2 × 2 error matrix contingency table.

Diagnostic sensitivity, which is described as TP/(TP + FN), refers to the ability of the new test to correctly identify samples identified by the reference method as positive. The lower the diagnostic sensitivity, the poorer will be the inclusivity of the tested qPCR. Another explanation could be that the analytical sensitivity (LOD) of the reference method is higher than the tested qPCR.

Diagnostic specificity is defined as the TN/(TN + FP) and refers to the ability of the test to correctly identify samples that were found to be negative by the reference method. The lower the diagnostic specificity, the poorer will be the exclusivity of the tested qPCR. Another explanation could be that the sensitivity of the reference method is quite bad, and the new qPCR method is capable of identifying more positive samples than the reference method.

Relative accuracy is defined as the (TP + TN)/(TP + TN + FP + FN) and describes the proportion of all correctly identified samples among all samples (Anonymous, 2009). If no FN and FP are detected, then it is 100%. In all other cases, this value is lower than 100%.

In quantitative determination, the accuracy numerically describes the distance of the value from the novel tested qPCR and some reference (true) value. For this reason, accuracy is referred to as trueness in quantitative classification (Anonymous, 1994). Trueness is defined as the degree of agreement of the expected value with the true value or accepted reference value. This is related to systematic error (Anonymous, 2015a,b). In GMO testing the trueness must be within 25% of the accepted reference value (Anonymous, 2015b). There are no fixed values of trueness that the novel tested qPCR method must meet in microbiological diagnostics. This might be caused by the fact that the trueness in qPCR can be determined by the comparison with some certified reference material, with the reference method or by proficiency testing. Certified reference material with a quantified number of target organisms is available only for a limited number of organisms (especially viruses like HIV, HBV, HCV, HAV, HPV, CMV, EBV), while for the remainder of clinically significant organisms, these materials are often available only for the qualitative analysis, and are therefore not suitable for trueness determination. Reference methods usually have varying diagnostic sensitivities and specificities and often they do not fit for the purposes of the quantitative assessment of novel qPCR methods. Moreover, the organization of proficiency testing via ring trials is expensive and requires a supplier of the reference material (like QCMD). These are the main reasons why determination of trueness in qPCR methods for the microbial detection in clinical, and especially in veterinary food safety areas, is rather limited.

Precision of qPCR

Precision is defined as the degree of agreement of measurements under specified conditions. The precision is described by statistical methods such as SD or confidence limit (Anonymous, 2015a). From the definition of precision, it is evident that this qPCR parameter is quantitative. For practical determination of precision, two conditions termed repeatability, and reproducibility were introduced (Anonymous, 1994). These two parameters are used to describe the variability of measurements introduced by the operator, equipment, and its calibration, environmental factors that can influence the measurement like temperature, humidity etc., and time between measurements (Anonymous, 1994). Repeatability is described as the closeness of agreement between successive and independent results obtained by the same method on identical test material under the same conditions (apparatus, operator, laboratory, and short intervals of time) and expresses within-laboratory variations (Anonymous, 1994, 2009, 2015a). Repeatability consists of two different variables: intra- and inter-assay variation. The intra-assay variation describes the variability of the replicates conducted in the same experiment; the inter-assay variation describes the variability between different experiments conducted on different days. Numerically, the repeatability is characterized as the SD of replicates at each concentration of each matrix for each method (Anonymous, 2012). The interval characterized by the SD of the replicates is called the repeatability limit (r) and is defined as the value less than or equal to the expected absolute difference, with a probability of 95%, between two tests results obtained under repeatability conditions (Anonymous, 1994, 2009, 2015b; Broeders et al., 2014). If the measured value lies outside the SD, it should be considered as suspect (Anonymous, 2009). It is necessary to perform the estimation of repeatability on 15 repeats at least (Anonymous, 1994, 2015b). Testing of repeatability requires analysis of the spiked relevant matrix at least at four levels—high, medium, low (near to the LOD) and negative in at least duplicates (Anonymous, 2009). For more rigorous testing the use of five replicates and the addition of one more sample spiked with a competitor strain that gives similar results in the given detection system is recommended. Natural background microflora can fulfill this requirement as long as they are present in the matrix at a level 1 Log10 greater than the target analyte (Anonymous, 2015a). In clinical, veterinary and food microbial detection, there are no specific recommendations for the repeatability SD value in terms of its proportion with respect to the mean. In GMO detection the repeatability SD must be ≤25% established on samples containing 0.1% GM related to the mass fraction of GM material (Broeders et al., 2014; Anonymous, 2015b).

On the other hand, reproducibility is the closeness of agreement between single test results on identical test material using the same method, obtained in different laboratories using different equipment and expresses the variation between laboratories (Anonymous, 1994, 2009, 2015a). Numerically, the reproducibility is characterized as the SD replicates at each concentration for each matrix across all laboratories (Anonymous, 2012). Similarly to repeatability, the reproducibility limit (R), as the interval characterized by the SD of the replicates, is defined as a value less than or equal to which the absolute difference between two test results obtained under reproducibility conditions is expected to have a probability of 95% (Anonymous, 1994, 2009, 2015b). If the difference between two results from different laboratories exceeds R, the results must be considered suspect (Anonymous, 2009). The reproducibility is usually defined by collaborative studies, which determine the variability of the results obtained by the given method in different laboratories using identical samples (Anonymous, 2009, 2012; Molenaar-de Backer et al., 2016). The number of laboratories with valid results which should be included in the collaborative study is at least eight. Therefore, it is advisable to select 10–12 labs (Anonymous, 2009, 2015a). The requirements for the minimal number of testing samples are identical to the repeatability determination (Anonymous, 2009, 2015a). Similarly, there are no specific recommendations for SD values of reproducibility with regard to the mean in clinical, veterinary, and food microbial detection. Again, in GMO testing the SD of reproducibility should be <35% over the whole dynamic range. However, at relative concentrations <0.2% or at an amount <100 copies SD values <50% are deemed acceptable (Broeders et al., 2014; Anonymous, 2015b).

Although determination of qPCR precision requires quantitative data, there is also the possibility of determining the precision of the method qualitatively. The mechanism of precision determination remains identical as for the quantitative estimation, including the validation within collaborative studies. However, the results are evaluated only qualitatively (positive/negative). This approach can be used for the validation of the specific new qPCR method in different laboratories, but it is preferably used for the validation and routine control of various qPCR methods in different laboratories on a set of reference samples. Such samples are provided by certain authorities (reference laboratories) or private companies (QCMD), which collect data from different laboratories and in the case of success, provide certificates regarding participation in such testing.

Conclusion

qPCR technology represents a powerful tool in microbial diagnostics. In viral and parasitical detection, quantification and typing, the suitability of this technique is beyond doubt; in the area of bacterial diagnostics it can replace culture techniques, especially when rapid and sensitive diagnostic assays are required. The spread of qPCR to different areas of routine microbial diagnostics together with the lack of standard procedures for the determination of basic functional parameters of qPCR has led to a scenario in which standardization of methods is performed according to different rules by different laboratories. This issue was partially solved by the publication of MIQE guidelines (Bustin et al., 2009); however, there are differences in attitude to validation and standardization of qPCR assays across clinical, veterinary and food safety areas. Any contribution to the unification of standardization and validation procedures will improve the quality of qPCR assays in microbial detection, quantification and typing.

Author Contributions

Both authors listed, have made substantial, direct and intellectual contribution to the work, and approved it for publication.

Funding

The work was supported by the MA CR RO0516 and MEYS CR NPU I program (LO1218). The funder had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors would like to thank Neysan Donnelly (Max-Planck-Institute of Biochemistry, Germany) for grammatical corrections of the manuscript.

References

Anonymous (1994). ISO 5725-1 - Accuracy (Trueness and Precision) of Measurement Methods and Results — Part 1: General Principles and Definitions. Geneva: ISO - International Organization for Standardization.

Anonymous (2009). Protocol for the Validation of Alternative Microbiological Methods. Søborg: NordVal International /Nordic Committee on Food Analysis, NMKL.

Anonymous (2011). ISO 16140 - Microbiology of Food and Animal Feeding Stuffs - Protocol for the Validation of Alternative Methods. Geneva: ISO - International Organization for Standardization.

Anonymous (2012). Methods Committee Guidelines for Validation of Microbiological Methods for Food and Environmental Surfaces. Rockville, MD: AOAC INTERNATIONAL.

Anonymous, R. (2013). Terrestrial Manual, 7th Edn, Chapter 1.1.5. Principles and Methods of Validation of Diagnostic Assays for Infectious Diseases (Version adopted in May, 2013). Paris: World Organisation for Animal Health.

Google Scholar

Anonymous, R. (2014). OIE Validation Guidelines, 3.6.5. Statistical Approaches To Validation. Paris: World Organisation for Animal Health.

Google Scholar

Anonymous, R. (2015a). Guidelines for the Validation of Analytical Methods for the Detection of Microbial Pathogens in Foods and Feeds, 2nd Edn. Silver Spring, MD: US Food & Drug Administration; Office of Foods and Veterinary Medicine.

Google Scholar

Anonymous, R. (2015b). JRC Technical Report - Definition of Minimum Performance Requirements for Analytical Methods of GMO Testing. Ispra: European Commission, Joint Research Centre; Institute for Health and Consumer Protection.

Google Scholar

Armbruster, D. A., and Pry, T. (2008). Limit of blank, limit of detection and limit of quantitation. Clin. Biochem. Rev. 29(Suppl. 1), S49–S52.

PubMed Abstract | Google Scholar

Birch, L., Dawson, C. E., Cornett, J. H., and Keer, J. T. (2001). A comparison of nucleic acid amplification techniques for the assessment of bacterial viability. Lett. Appl. Microbiol. 33, 296–301. doi: 10.1046/j.1472-765X.2001.00999.xc

PubMed Abstract | CrossRef Full Text | Google Scholar

Broeders, S., Huber, I., Grohmann, L., Berben, G., Taverniers, I., Mazzara, M., et al. (2014). Guidelines for validation of qualitative real-time PCR methods. Trends Food Sci. Technol. 37, 115–126. doi: 10.1016/j.tifs.2014.03.008

CrossRef Full Text | Google Scholar

Burns, M., and Valdivia, H. (2008). Modelling the limit of detection in real-time quantitative PCR. Eur. Food Res. Technol. 226, 1513–1524. doi: 10.1007/s00217-007-0683-z

CrossRef Full Text | Google Scholar

Bustin, S. A. (2000). Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. J. Mol. Endocrinol. 25, 169–193. doi: 10.1677/jme.0.0250169

PubMed Abstract | CrossRef Full Text | Google Scholar

Bustin, S. A., Benes, V., Garson, J. A., Hellemans, J., Huggett, J., Kubista, M., et al. (2009). The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 55, 611–622. doi: 10.1373/clinchem.2008.112797

PubMed Abstract | CrossRef Full Text | Google Scholar

Chaouachi, M., Bérard, A., and Saïd, K. (2013). Relative quantification in seed GMO analysis: state of art and bottlenecks. Transgenic Res. 22, 461–476. doi: 10.1007/s11248-012-9684-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Dreo, T., Pirc, M., Ramšak, Ž., Pavšic, J., Milavec, M., Zel, J., et al. (2014). Optimising droplet digital PCR analysis approaches for detection and quantification of bacteria: a case study of fire blight and potato brown rot. Anal. Bioanal. Chem. 406, 6513–6528. doi: 10.1007/s00216-014-8084-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Higuchi, R., Dollinger, G., Walsh, P. S., and Griffith, R. (1992). Simultaneous amplification and detection of specific DNA sequences. Biotechnology 10, 413–417.

PubMed Abstract | Google Scholar

Hoffmann, B., Beer, M., Reid, S. M., Mertens, P., Oura, C. A., van Rijn, P. A., et al. (2009). A review of RT-PCR technologies used in veterinary virology and disease control: sensitive and specific diagnosis of five livestock diseases notifiable to the World Organisation for Animal Health. Vet. Microbiol. 139, 1–23. doi: 10.1016/j.vetmic.2009.04.034

PubMed Abstract | CrossRef Full Text | Google Scholar

Holland, P. M., Abramson, R. D., Watson, R., and Gelfand, D. H. (1991). Detection of specific polymerase chain reaction product by utilizing the 5′—3′ exonuclease activity of Thermus aquaticus DNA polymerase. Proc. Natl. Acad. Sci. U.S.A. 88, 7276–7280.

PubMed Abstract | Google Scholar

Kralik, P., Slana, I., Kralova, A., Babak, V., Whitlock, R. H., and Pavlik, I. (2011). Development of a predictive model for detection of Mycobacterium avium subsp. paratuberculosis in faeces by quantitative real time PCR. Vet. Microbiol. 149, 133–138. doi: 10.1016/j.vetmic.2010.10.009

PubMed Abstract | CrossRef Full Text | Google Scholar

Kubista, M., Andrade, J. M., Bengtsson, M., Forootan, A., Jonak, J., Lind, K., et al. (2006). The real-time polymerase chain reaction. Mol. Aspects Med. 27, 95–125. doi: 10.1016/j.mam.2005.12.007

PubMed Abstract | CrossRef Full Text | Google Scholar

Margot, H., Zwietering, M. H., Joosten, H., O'Mahony, E., and Stephan, R. (2015). Evaluation of different buffered peptone water (BPW) based enrichment broths for detection of Gram-negative foodborne pathogens from various food matrices. Int. J. Food Microbiol. 214, 109–115. doi: 10.1016/j.ijfoodmicro.2015.07.033

PubMed Abstract | CrossRef Full Text | Google Scholar

Molenaar-de Backer, M. W., de Waal, M., Sjerps, M. C., and Koppelman, M. H. (2016). Validation of new real-time polymerase chain reaction assays for detection of hepatitis A virus RNA and parvovirus B19 DNA. Transfusion 56, 440–448. doi: 10.1111/trf.13334

PubMed Abstract | CrossRef Full Text | Google Scholar

Nocker, A., and Camper, A. K. (2009). Novel approaches toward preferential detection of viable cells using nucleic acid amplification techniques. FEMS Microbiol. Lett. 291, 137–142. doi: 10.1111/j.1574-6968.2008.01429.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Nocker, A., Cheung, C. Y., and Camper, A. K. (2006). Comparison of propidium monoazide with ethidium monoazide for differentiation of live vs. dead bacteria by selective removal of DNA from dead cells. J. Microbiol. Methods 67, 310–320. doi: 10.1016/j.mimet.2006.04.015

PubMed Abstract | CrossRef Full Text | Google Scholar

Nutz, S., Döll, K., and Karlovsky, P. (2011). Determination of the LOQ in real-time PCR by receiver operating characteristic curve analysis: application to qPCR assays for Fusarium verticillioides and F. proliferatum. Anal. Bioanal. Chem. 401, 717–726. doi: 10.1007/s00216-011-5089-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Osei Sekyere, J., Govinden, U., and Essack, S. Y. (2015). Review of established and innovative detection methods for carbapenemase-producing Gram-negative bacteria. J. Appl. Microbiol. 119, 1219–1233. doi: 10.1111/jam.12918

PubMed Abstract | CrossRef Full Text | Google Scholar

Pavšič, J., Devonshire, A. S., Parkes, H., Schimmel, H., Foy, C. A., Karczmarczyk, M., et al. (2015). Standardization of nucleic acid tests for clinical measurements of bacteria and viruses. J. Clin. Microbiol. 53, 2008–2014. doi: 10.1128/JCM.02136-14

PubMed Abstract | CrossRef Full Text | Google Scholar

Pavšic, J., Žel, J., and Milavec, M. (2016). Assessment of the real-time PCR and different digital PCR platforms for DNA quantification. Anal. Bioanal. Chem. 408, 107–121. doi: 10.1007/s00216-015-9107-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Ricchi, M., Savi, R., Bolzoni, L., Pongolini, S., Grant, I. R., De Cicco, C., et al. (2016). Estimation of Mycobacterium avium subsp. paratuberculosis load in raw bulk tank milk in Emilia-Romagna Region (Italy) by qPCR. Microbiologyopen 5, 551–559. doi: 10.1002/mbo3.350

PubMed Abstract | CrossRef Full Text | Google Scholar

Rijsman, L. H., Monkelbaan, J. F., and Kusters, J. G. (2016). Clinical consequences of PCR based diagnosis of intestinal parasitic infections. J. Gastroenterol. Hepatol. 31, 1808–1815. doi: 10.1111/jgh.13412

PubMed Abstract | CrossRef Full Text | Google Scholar

Rodriguez-Lazaro, D., Cook, N., and Hernandez, M. (2013). Real-time PCR in food science: PCR diagnostics. Curr. Issues Mol. Biol. 15, 39–44.

PubMed Abstract | Google Scholar

Ruijter, J. M., Ramakers, C., Hoogaars, W. M., Karlen, Y., Bakker, O., van den Hoff, M. J., et al. (2009). Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data. Nucleic Acids Res. 37:e45. doi: 10.1093/nar/gkp045

PubMed Abstract | CrossRef Full Text | Google Scholar

Saiki, R. K., Gelfand, D. H., Stoffel, S., Scharf, S. J., Higuchi, R., Horn, G. T., et al. (1988). Primer-directed enzymatic amplification of DNA with a thermostable DNA polymerase. Science 239, 487–491.

PubMed Abstract | Google Scholar

Saiki, R. K., Scharf, S., Faloona, F., Mullis, K. B., Horn, G. T., Erlich, H. A., et al. (1985). Enzymatic amplification of beta-globin genomic sequences and restriction site analysis for diagnosis of sickle cell anemia. Science 230, 1350–1354.

PubMed Abstract | Google Scholar

Slana, I., Kralik, P., Kralova, A., and Pavlik, I. (2008). On-farm spread of Mycobacterium avium subsp. paratuberculosis in raw milk studied by IS900 and F57 competitive real time quantitative PCR and culture examination. Int. J. Food Microbiol. 128, 250–257. doi: 10.1016/j.ijfoodmicro.2008.08.013

PubMed Abstract | CrossRef Full Text | Google Scholar

Yang, S., and Rothman, R. E. (2004). PCR-based diagnostics for infectious diseases: uses, limitations, and future applications in acute-care settings. Lancet Infect. Dis. 4, 337–348. doi: 10.1016/S1473-3099(04)01044-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: quantitative PCR, limit of detection, limit of quantification, accuracy, precision, trueness

Citation: Kralik P and Ricchi M (2017) A Basic Guide to Real Time PCR in Microbial Diagnostics: Definitions, Parameters, and Everything. Front. Microbiol. 8:108. doi: 10.3389/fmicb.2017.00108

Received: 31 October 2016; Accepted: 16 January 2017;
Published: 02 February 2017.

Copyright © 2017 Kralik and Ricchi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Petr Kralik, [email protected]

Sours: https://www.frontiersin.org/articles/10.3389/fmicb.2017.00108/full

Qpcr rt pcr y

Introduction

Real-time PCR vs. traditional PCR vs. digital PCR at a glance

Digital PCRReal-time PCRTraditional PCR
OverviewMeasures the fraction of negative replicates to determine absolute copies.Measures PCR amplification as it occurs.Measures the amount of accumulated PCR product at the end of the PCR cycles.
Quantitative?Yes, the fraction of negative PCR reactions is fit to a Poisson statistical algorithm.Yes, because data is collected during the exponential growth (log) phase of PCR when the quantity of the PCR product is directly proportional to the amount of template nucleic acid.No, though comparing the intensity of the amplified band on a gel to standards of a known concentration can give you 'semi-quantitative' results.
Applications
  • Absolute quantification of viral load
  • Absolute quantification of nucleic acid standards
  • Absolute quantification of next-gen sequencing Libraries
  • Rare allele detection
  • Absolute quantification of gene expression
  • Enrichment and separation of mixtures
  • Quantitation of gene expression
  • Microarray verification
  • Quality control and assay validation
  • Pathogen detection
  • SNP genotyping
  • Copy number variation
  • MicroRNA Analysis
  • Viral quantitation
  • siRNA/RNAi experiments

Amplification of DNA for:

  • Sequencing
  • Genotyping
  • Cloning
Summary

Advantages of digital PCR:

  • No need to rely on references or standards
  • Desired precision can be achieved by increasing total number of PCR replicates
  • Highly tolerant to inhibitors
  • Capable of analyzing complex mixtures
  • Unlike traditional qPCR, digital PCR provides a linear response to the number of copies present to allow for small fold change differences to be detected

Adavantages of real-time PCR:

  • Increased dynamic range of detection
  • No post-PCR processing
  • Detection is capable down to a 2-fold change
  • Collects data in the exponential growth phase of PCR
  • An increase in reporter fluorescent signal is directly proportional to the number of amplicons generated
  • The cleaved probe provides a permanent record amplification of an amplicon

Disadvantages of traditional PCR:

  • Poor Precision
  • Low sensitivity
  • Short dynamic range < 2 logs
  • Low resolution
  • Non-automated
  • Size-based discrimination only
  • Results are not expressed as numbers
  • Ethidium bromide for staining is not very quantitative
  • Post-PCR processing

 To understand why traditional PCR is limiting, it is important to understand what happens during a PCR reaction. A basic PCR run can be broken up into three phases:

Exponential

Exact doubling of product is accumulating at every cycle (assuming 100% reaction efficiency). The reaction is very specific and precise. Exponential amplification occurs because all of the reagents are fresh and available, the kinetics of the reaction push the reaction to favor doubling of amplicon.

Linear (high variability)

As the reaction progresses, some of the reagents are being consumed as a result of amplification. The reactions start to slow down and the PCR product is no longer being doubled at each cycle.

Plateau (End-point: gel detection for traditional methods)

The reaction has stopped, no more products are being made and if left long enough, the PCR products will begin to degrade. Each tube or reaction will plateau at a different point, due to the different reaction kinetics for each sample. These differences can be seen in the plateau phase. The plateau phase is where traditional PCR takes its measurement, also known as end-point detection.

 Figure 1: PCR phases.

TOP

Traditional PCR measures at the plateau, giving you variable results

In Figure 2, three replicate samples, which had same amount of DNA in the beginning of the reaction, have different quantities of PCR product by the plateau phase of the reaction (due to variations in reaction kinetics). Therefore, it will be more precise to take measurements during the exponential phase, where the replicate samples are amplifying exponentially.

Figure 2: Identical samples produce different quantities of reaction product by the plateau phase of PCR.

TOP

Real-time PCR measures at the exponential phase for more accurate quantitation

Real-time PCR focuses on the exponential phase because it provides the most precise and accurate data for quantitation. Within the exponential phase, the real-time PCR instrument calculates two values. The Threshold line is the level of detection at which a reaction reaches a fluorescent intensity above background. The PCR cycle at which the sample reaches this level is called the Cycle Threshold, Ct. The Ct value is used in downstream quantitation or presence/absence detection. By comparing the Ct values of samples of unknown concentration with a series of standards, the amount of template DNA in an unknown reaction can be accurately determined.

Figure 3: The PCR cycle at which the sample reaches a fluorescent intensity above background is the Cycle Threshold or Ct.

TOP

Digital PCR counts individual molecules for absolute quantification

Digital PCR works by partitioning a sample into many individual real-time PCR reactions; some portion of these reactions contain the target molecule (positive) while others do not (negative). Following PCR analysis, the fraction of negative answers is used to generate an absolute answer for the exact number of target molecules in the sample, without reference to standards or endogenous controls.

Figure 4:  Digital PCR uses the ratio of positive (black) to negative (white) PCR reactions to count the number of target molecules.

TOP

Applied Biosystems TaqMan probe- and SYBR Green–based detection

Every real-time PCR reaction contains a fluorescent reporter molecule—an Applied Biosystems™ TaqMan® probe or SYBR™ Green dye, for example—to monitor the accumulation of PCR product. As the quantity of target amplicon increases, so does the amount of fluorescence emitted from the fluorophore.

Figure 5:  Advantages of real-time PCR vs. traditional PCR.

TOP

Applied Biosystems has a full range of real-time PCR products for routine and challenging applications

Applied Biosystems offers a comprehensive set of products for real-time PCR-based gene expression, miRNA, copy number variation, and SNP genotyping analysis, from off-the-shelf gene-specific probe and primer sets, to everyday reagents and plastics, instrument systems, software, and everything in between.

For Research Use Only. Not for use in diagnostic procedures.

Sours: https://www.thermofisher.com/us/en/home/life-science/pcr/real-time-pcr/real-time-pcr-learning-center/real-time-pcr-basics/real-time-vs-digital-vs-traditional-pcr.html
Reverse Transcriptase PCR (RT PCR)

qPCR and RT-qPCR

Real-time quantitative PCR, or qPCR, is a standard method for detecting and quantifying a specific target sequence, or quantifying gene expression levels in a sample. In qPCR, fluorescently labeled probes or nucleic acids, or fluorescent double-stranded DNA-binding dyes are incorporated into the PCR reaction and product formation is monitored in real time following each PCR cycle. 

Probe-based qPCR uses a fluorescent primer or probe to detect the amplification product. An advantage of this method is that it allows sequence-specific detection of the target and reduces the likelihood of detecting nonspecific artifacts. Probe-based detection also can accommodate multiplex amplification by using differentially labeled probes. There are several different approaches to probe-based qPCR detection including hydrolysis probes such as TaqMan®, hybridization probes, hairpin probes and labeled primers.

The use of fluorescent DNA-binding dyes, like BRYT® Green or SYBR® Green, is one of the most straightforward qPCR approaches. A dye is added to the reaction, and fluorescence is measured at each PCR cycle. Because fluorescence of these dyes increases dramatically in the presence of double-stranded DNA, DNA synthesis can be monitored as an increase in fluorescent signal.

The primary output from a real-time PCR experiment is an amplification curve showing the accumulation of amplified product as fluorescent signal versus the cycle number.

Quantifying the amount of starting material is done using the quantification cycle (Cq, also known as Ct). Cq or Ct (threshold cycle) is the amplification cycle at which the fluorescence signal detected reaches an amplification threshold in a specific well. The amplification threshold is calculated based on the background fluorescence over the baseline region of all the curves in the group and represents the point where the reporter signal is significantly higher than the background levels. Cq is inversely related to the amount of starting template in the reaction and is a relative measure of the concentration of target in the PCR reaction.

Sours: https://www.promega.es/products/pcr/qpcr-and-rt-qpcr/

You will also be interested:

What is Real-Time PCR (qPCR)?

To understand how real-time PCR works, we illustrate a qPCR analysis using a typical amplification plot (Figure 1). In this plot, the number of PCR cycles is shown on the x-axis, and the fluorescence from the amplification reaction, which is proportional to the amount of amplified product in the tube, is shown on the y-axis.

The amplification plot shows two phases, an exponential phase followed by a non-exponential plateau phase. During the exponential phase, the amount of PCR product approximately doubles in each cycle. As the reaction proceeds, however, reaction components are consumed, and ultimately one or more of the components becomes limiting. At this point, the reaction slows and enters the plateau phase (cycles 28–40 in Figure 1).

Real-time PCR (qPCR) amplification plot

Figure 1. Amplification plot. Baseline-subtracted fluorescence versus number of PCR cycles.

Initially, fluorescence remains at background levels, and increases in fluorescence are not detectable (cycles 1–18, Figure 1) even though product accumulates exponentially. Eventually, enough amplified product accumulates to yield a detectable fluorescence signal. The cycle number at which this occurs is called the quantification cycle, or Cq. Because the Cq value is measured in the exponential phase when reagents are not limited, real-time qPCR can be used to reliably and accurately calculate the initial amount of template present in the reaction based on the known exponential function describing the reaction progress.

The Cq of a reaction is determined mainly by the amount of template present at the start of the amplification reaction. If a large amount of template is present at the start of the reaction, relatively few amplification cycles will be required to accumulate enough product to give a fluorescence signal above background. Thus, the reaction will have a low, or early, Cq. In contrast, if a small amount of template is present at the start of the reaction, more amplification cycles will be required for the fluorescence signal to rise above background. Thus, the reaction will have a high, or late, Cq. This relationship forms the basis for the quantitative aspect of real-time PCR.

Sours: https://www.bio-rad.com/en-us/applications-technologies/what-real-time-pcr-qpcr?ID=LUSO4W8UU


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