Information

Can qPCR primer with several mismatch works?

Can qPCR primer with several mismatch works?


We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

I am designing primers for qPCR from conserved regions of a few different fish species.This is because gene sequence for my fish species are yet unavailable in NCBI database.

Is it OK (can the primer anneal to the target sequence) if the primers have several mismatch at the interior and towards it 5' end? At the 3' end, I can only design primer with maximum 5 conserved bases.

Thank you.


QPCR is extremely sensitive, so I would not do this. Instead I would get those primers you mention in your question and instead sequence using them instead of doing qpcr with them. Then I would make new primers specific for the region you want to do qpcr on based on the sequence.The advantage of this is that you would now know the sequence and so you would have better primers for qpcr.


Analytical sensitivity and efficiency comparisons of SARS-CoV-2 RT-qPCR primer-probe sets

The recent spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exemplifies the critical need for accurate and rapid diagnostic assays to prompt clinical and public health interventions. Currently, several quantitative reverse transcription-PCR (RT-qPCR) assays are being used by clinical, research and public health laboratories. However, it is currently unclear whether results from different tests are comparable. Our goal was to make independent evaluations of primer-probe sets used in four common SARS-CoV-2 diagnostic assays. From our comparisons of RT-qPCR analytical efficiency and sensitivity, we show that all primer-probe sets can be used to detect SARS-CoV-2 at 500 viral RNA copies per reaction. The exception for this is the RdRp-SARSr (Charité) confirmatory primer-probe set which has low sensitivity, probably due to a mismatch to circulating SARS-CoV-2 in the reverse primer. We did not find evidence for background amplification with pre-COVID-19 samples or recent SARS-CoV-2 evolution decreasing sensitivity. Our recommendation for SARS-CoV-2 diagnostic testing is to select an assay with high sensitivity and that is regionally used, to ease comparability between outcomes.


Introduction

CRISPR/Cas9 has become the major genome-editing technology and been widely used in different kinds of organisms for genome modification purpose 1,2,3,4 . In the CRISPR/Cas9 system, Cas9 nuclease is directed to target DNA containing the protospacer adjacent motif (PAM) by single guide RNA (sgRNA), then cleaves both strands of target DNA at a site 3 bp upstream of the PAM sequence and generates double-strand breaks (DSBs). This kind of DNA breaks is harmful to cells and can lead to mutagenesis or cell death if left unrepaired. Once sensed, the DSBs will be repaired mostly by two different kinds of intrinsic mechanisms, homology-directed repair (HDR) or non-homologous end joining (NHEJ) 5,6 . The former relies on a homologous sequence as the repair template and repairs DNA breaks in a high-fidelity manner. It is usually employed to introduce specific DNA modifications, to meet the needs in functional study of genetic variation, especially in clinical usage. The latter involves direct ligation of the broken ends without the need for a homologous template and repairs DNA breaks in an error-prone manner. The NHEJ usually leads to unpredictable insertion or deletion of bases in the genome, named indel, which will most likely disrupt the open reading frame of target gene. This makes it a very effective method in destroying gene expression for gene functional study and in clinical to remove pathogenic genes 7,8 .

Usually, for any experimental purpose prescreening the sgRNAs for high editing efficiency and specificity is essential and screening the single-cell clones or offspring bearing desired modification events are often obligatory. The present techniques for evaluating genome editing efficiency have been well discussed and compared in a review 9 . The widely used methods are mainly based on DNA sequencing or mismatch-specific nuclease 9,10 . Sanger sequencing method involves PCR amplification and cloning steps of the target region before each DNA sequence being read separately. This multistep method can provide detailed information of each mutation event induced by nuclease, but is quite time-consuming, costly and laborious 10 . To overcome these disadvantages, computational algorism was introduced to realize editing efficiency quantification based on direct Sanger sequencing of amplicon mixture of target DNA region. Whereas its reliability tends to be impeded by repetitive sequence around the cutting site and highly depends on the purity of PCR product and the quality of Sanger sequencing 11 . The next-generation DNA sequencing (NGS) technology was also applied in profiling DNA mutation induced by sgRNA directed Cas9 nuclease owing to its massive parallel capacity 12 . Several web-based online platforms have been developed to analyze the NGS data, including CRISPR-GA 13 , BATCH-GE 14 , CRISPResso 15,16 , Cas-analyzer 17 and CRISPRMatch 18 et al. However, even though effective, these NGS-based methods still require multi-step operations and are costly in time and money. The mismatch specific nuclease-based methods employ T7 endonuclease 1 (T7E1) or Surveyor nuclease to cleave mismatches formed between DNA strands containing sequence difference originated from nuclease cutting 19 . They require only basic laboratory equipment but not applicable to polymorphic loci and tend to miss single-nucleotide mutation as well as large deletions 20 . In addition, many other alternatives have been developed with improvement in certain aspects, including qEva-CRISPR 21 , engineered nuclease-induced translocations(ENIT) 22 , Cas9 nuclease based restriction fragment length polymorphism (RFLP) analysis 23 , Indel Detection by Amplicon Analysis (IDAA) 24 and the gene-editing frequency digital PCR (GEF-dPCR) 25 . However, most methods are multistep and quantify the editing efficiency based on pre-amplified PCR product coming from genomic DNA but not directly on the genomic DNA itself 9,10,11,12,13,14,15,16,17,18,19,20,22,23,24 . Sequence and length-dependent bias introduced during PCR amplification will unavoidably affect the detection accuracy 26,27,28 . Moreover, many methods demand specific devices, such as capillary electrophoresis apparatus 21,24 , digital PCR system 25 and NGS platform 12,13,14,15,16,17,18 that are expensive and not readily available in most laboratories. As for offspring genotyping and single-cell clone screening, besides Sanger sequencing and NGS based methods 29,30 , several other strategies have also been developed specifically for genotyping purpose including high-resolution melting (HRM) 31 and oligoribonucleotide interference-PCR (ORNi-PCR) 32 et al. In zebrafish 33 and plant 34 , PCR based methods have been developed for mutant screening, but limited accuracy and sensitivity restricted its wide applications.

Here we developed a real-time PCR based method, namely genome editing test PCR (getPCR) by combining the sensitivity of Taq polymerase to mismatch at primer 3′ end with real-time PCR technique for its power in DNA quantification. Applications in Lenti-X 293 T cells on 9 sgRNA targets indicate that this technique could determine the genome editing efficiency accurately in all cases of genome editing including NHEJ induced indels, HDR and base editing. Meanwhile, this method exhibited great power in single-cell clone genotyping by its ability in telling exactly how many alleles were modified. This technique described here provides the most robust strategy by far that can be used not only in genome editing efficiency quantification but also single-cell clone genotyping in a high throughput way.


DESIGN AND IMPLEMENTATION

To maximize the utilization of the server resources, PCRTiler has been implemented as a multi-threaded application that designs as many primer pairs concurrently as the server has processors. Independent tiling requests are queued until the currently executing tiling job is finished. Users providing an email address will be notified when their request has finished processing. Others will have to use the link provided on the submission confirmation page to view their result.

To promote fair use of the system, the total number of primer pairs that can be designed in a single request is limited to 200, and the maximum duration of a tiling job is set to three hours. Users exceeding those limits can still use PCRTiler, either by installing the standalone PCRTiler application on their personal computer, installing the server version and disabling the limit, or splitting their large request into smaller regions.

PCRTiler will gracefully recover from server restarts. As soon as new tiling requests are submitted to the server, they are compressed and then saved to disk. In the event that the server is restarted, PCRTiler will transparently recover the queued tiling requests, preserving their original order, and resume execution of the run that was aborted.

Software and hardware requirements

PCRTiler requires the Java Runtime Environment (JRE) v1.6.0 and Tomcat 6 running on a computer using the Linux operating system. It should theoretically also run on any combination of platforms and operating systems for which implementations exist for the JRE, Tomcat 6, Primer3 and BLAST binaries, but this has not been tested and therefore is unsupported. During testing, we have validated that it behaves properly when viewed with the latest versions of Firefox, Safari and Internet Explorer.

The performance of PCRTiler is primarily dependent on the available memory. In our experience, for acceptable performance, you need enough memory for the BLAST database (800 MB for Homo sapiens, 5 MB for most bacteria), plus a maximum of 1 GB for PCRTiler. Therefore, 2 GB of memory should be enough. This amount of memory is commonly included in recent workstation computers and laptops. PCRTiler is a multi-threaded application, so it will make use of all available CPU cores, accelerating primer design proportionately to the number of cores. The PCRTiler server currently runs Mandriva Linux 2010 on a dedicated Quad-core Intel machine clocked at 2.4 Ghz with 4 GB of RAM. Including the BLAST databases of all 1169 genomes, PCRTiler requires <15 GB of hard disk space.

Standalone version

In addition to the server version, we provide a standalone Java-based application, which includes a graphical user interface and the same one-click genome management feature as the server version. It also handles all aspects of downloading genomes from GenBank and the creation of BLAST databases. Since the standalone and server versions share much of the same code base, they both provide the same functionality. However, the standalone version uses the resources of the client computer. Using the standalone version is the easiest option for most users who want to run PCRTiler locally. Please note that the standalone version does not require Tomcat. To date, it has been shown to work correctly on Linux i386, Windows Vista and Windows XP.

Data retention

PCRTiler results are kept on the server for 14 days. However, users have the option of deleting their result file from the server immediately using the appropriate button on the result page. Users that would like to hold on to a PCRTiler result for a longer time period can download the raw result file from the website, which can be viewed using the standalone version of PCRTiler.


To design your own set of degenerate primers, follow some basic guidelines:

15-20 base pairs.
5) Try to include amino acids methionine and tryptophan, which are coded by a single codon (three-letter nucleotide code), and avoid amino acids leucine, serine and arginine, which can each be coded by six codon combinations (Table 2).
6) For subsequent cloning procedures and to increase primer length (and therefore annealing temperature) add a 5’ tail (6-9 base pairs) containing a restriction enzyme site.
7) If there is complete degeneracy (no matches among any given species), consider using the base inosine (structurally similar to guanine) as it can pair with any of the four bases, although it will bind to cytosine preferentially. Alternatively, insert N for aNy base to ensure equimolar concentrations of each base at that position in your primer mix.
8) Avoid degeneracy at the 3’ terminus (this would not be a good place to insert inosine).

Depending on the goal, the play-off between primer specificity and efficiency can be modified by altering the degeneracy of the primer. For example, the more degenerate the primers, the less specific annealing will be however, decreased degeneracy will allow more potential to identify unknown variants.


1 SequenceTracer removes the missing sequences in ROI. The exclusion criterion of missing sequences was clarified with editorial approval after Stage 1 acceptance and prior to observation of the data.

2 The threshold was decided before Stage 1 acceptance. However, it was not clearly mentioned in the Stage 1 protocol and a previous study was referenced only.

Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare.c.5012597.

Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

References

2020 A new coronavirus associated with human respiratory disease in China . Nature 579, 265-269. (doi:10.1038/s41586-020-2008-3) Crossref, PubMed, ISI, Google Scholar

2020 A pneumonia outbreak associated with a new coronavirus of probable bat origin . Nature 579, 270-273. (doi:10.1038/s41586-020-2012-7) Crossref, PubMed, ISI, Google Scholar

2020 A novel coronavirus from patients with pneumonia in China, 2019 . N. Engl. J. Med. 382, 727-733. (doi:10.1056/NEJMoa2001017) Crossref, PubMed, ISI, Google Scholar

2020 A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster . Lancet 395, 514-523. (doi:10.1016/S0140-6736(20)30154-9) Crossref, PubMed, ISI, Google Scholar

. 2020 A familial cluster of infection associated with the 2019 novel coronavirus indicating possible person-to-person transmission during the incubation period . J. Infect. Dis. 221, 1757-1761. (doi:10.1093/infdis/jiaa077) Crossref, PubMed, Google Scholar

2020 Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding . Lancet 395, 565-574. (doi:10.1016/S0140-6736(20)30251-8) Crossref, PubMed, ISI, Google Scholar

2020 Improved molecular diagnosis of COVID-19 by the novel, highly sensitive and specific COVID-19-RdRp/Hel real-time reverse transcription-PCR assay validated in vitro and with clinical specimens . J. Clin. Microbiol. 58, e00310-20. (doi:10.1128/JCM.00310-20) Crossref, PubMed, ISI, Google Scholar

2020 Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China . Lancet 395, 497-506. (doi:10.1016/S0140-6736(20)30183-5) Crossref, PubMed, ISI, Google Scholar

2020 Comparative performance of SARS-CoV-2 detection assays using seven different primer/probe sets and one assay kit . J. Clin. Microbiol. 58, e00557-20. (doi:10.1128/JCM.00557-20) Crossref, PubMed, Google Scholar

Niu P, Lu R, Zhao L, Wang H, Huang B, Ye F, Wang W, Tan W

. 2020 Three novel real-time RT-PCR assays for detection of COVID-19 virus. China CDC Weekly. Google Scholar

2020 Development of a laboratory-safe and low-cost detection protocol for SARS-CoV-2 of the coronavirus disease 2019 (COVID-19) . Exp. Neurobiol. 29, 107-119. (doi:10.5607/en20009) Crossref, PubMed, Google Scholar

2020 Development of a novel, genome subtraction-derived, SARS-CoV-2-specific COVID-19-nsp2 real-time RT-PCR assay and its evaluation using clinical specimens . Int. J. Mol. Sci. 21, 2574. (doi:10.3390/ijms21072574) Crossref, Google Scholar

2020 Epidemiologic features and clinical course of patients infected with SARS-CoV-2 in Singapore . JAMA 323, 1488-1494. (doi:10.1001/jama.2020.3204) Crossref, PubMed, ISI, Google Scholar

Lippi G, Simundic AM, Plebani M

. 2020 Potential preanalytical and analytical vulnerabilities in the laboratory diagnosis of coronavirus disease 2019 (COVID-19) . Clin. Chem. Lab. Med. (doi:10.1515/cclm-2020-0285) Crossref, Google Scholar

. 2005 Sequence variation in primer targets affects the accuracy of viral quantitative PCR . J. Clin. Virol. 34, 104-107. (doi:10.1016/j.jcv.2005.02.010) Crossref, PubMed, ISI, Google Scholar

2019 Missed detections of influenza A(H1)pdm09 by real-time RT-PCR assay due to haemagglutinin sequence mutation, December 2017 to March 2018, northern Viet Nam . Western Pac. Surveill. Response J . 10, 32-38. (doi:10.5365/wpsar.2018.9.3.003) Crossref, PubMed, ISI, Google Scholar

Klungthong C, Chinnawirotpisan P, Hussem K, Phonpakobsin T, Manasatienkij W, Ajariyakhajorn C, Rungrojcharoenkit K, Gibbons RV, Jarman RG

. 2010 The impact of primer and probe-template mismatches on the sensitivity of pandemic influenza A/H1N1/2009 virus detection by real-time RT-PCR . J. Clin. Virol. 48, 91-95. (doi:10.1016/j.jcv.2010.03.012) Crossref, PubMed, ISI, Google Scholar

Lee HK, Lee CK, Loh TP, Chiang D, Koay ES, Tang JW

. 2011 Missed diagnosis of influenza B virus due to nucleoprotein sequence mutations, Singapore, April 2011 . Euro Surveill. 16, 19943. PubMed, Google Scholar

2014 Newly emerging mutations in the matrix genes of the human influenza A(H1N1)pdm09 and A(H3N2) viruses reduce the detection sensitivity of real-time reverse transcription-PCR . J. Clin. Microbiol. 52, 76-82. (doi:10.1128/JCM.02467-13) Crossref, PubMed, ISI, Google Scholar

Kamau E, Agoti CN, Lewa CS, Oketch J, Owor BE, Otieno GP, Bett A, Cane PA, Nokes DJ

. 2017 Recent sequence variation in probe binding site affected detection of respiratory syncytial virus group B by real-time RT-PCR . J. Clin. Virol. 88, 21-25. (doi:10.1016/j.jcv.2016.12.011) Crossref, PubMed, ISI, Google Scholar

Koo C, Kaur S, Teh ZY, Xu H, Nasir A, Lai YL, Khan E, Ng LC, Hapuarachchi HC

. 2016 Genetic variability in probe binding regions explains false negative results of a molecular assay for the detection of dengue virus . Vector Borne Zoonotic Dis. 16, 489-495. (doi:10.1089/vbz.2015.1899) Crossref, PubMed, ISI, Google Scholar

Hughes GJ, Smith JS, Hanlon CA, Rupprecht CE

. 2004 Evaluation of a TaqMan PCR assay to detect rabies virus RNA: influence of sequence variation and application to quantification of viral loads . J. Clin. Microbiol. 42, 299-306. (doi:10.1128/jcm.42.1.299-306.2004) Crossref, PubMed, ISI, Google Scholar

Christopherson C, Sninsky J, Kwok S

. 1997 The effects of internal primer-template mismatches on RT-PCR: HIV-1 model studies . Nucleic Acids Res. 25, 654-658. (doi:10.1093/nar/25.3.654) Crossref, PubMed, ISI, Google Scholar

Acharya A, Vaniawala S, Shah P, Parekh H, Misra RN, Wani M, Mukhopadhyaya PN

. 2014 A robust HIV-1 viral load detection assay optimized for Indian sub type C specific strains and resource limiting setting . Biol. Res. 47, 22. (doi:10.1186/0717-6287-47-22) Crossref, PubMed, ISI, Google Scholar

Liu C, Chang L, Jia T, Guo F, Zhang L, Ji H, Zhao J, Wang L

. 2017 Real-time PCR assays for hepatitis B virus DNA quantification may require two different targets . Virol. J. 14, 94. (doi:10.1186/s12985-017-0759-8) Crossref, PubMed, ISI, Google Scholar

Chow CK, Qin K, Lau LT, Cheung-Hoi Yu A

. 2011 Significance of a single-nucleotide primer mismatch in hepatitis B virus real-time PCR diagnostic assays . J. Clin. Microbiol. 49, 4418-4419 author reply 4420. (doi:10.1128/JCM.05224-11) Crossref, PubMed, ISI, Google Scholar

Chan JF, Kok KH, Zhu Z, Chu H, To KK, Yuan S, Yuen KY

. 2020 Genomic characterization of the 2019 novel human-pathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan . Emerg. Microbes Infect. 9, 221-236. (doi:10.1080/22221751.2020.1719902) Crossref, PubMed, ISI, Google Scholar

Ogando NS, Ferron F, Decroly E, Canard B, Posthuma CC, Snijder EJ

. 2019 The curious case of the nidovirus exoribonuclease: its role in RNA synthesis and replication fidelity . Front. Microbiol. 10, 1813. (doi:10.3389/fmicb.2019.01813) Crossref, PubMed, ISI, Google Scholar

Smith EC, Sexton NR, Denison MR

. 2014 Thinking outside the triangle: replication fidelity of the largest RNA viruses . Annu. Rev. Virol. 1, 111-132. (doi:10.1146/annurev-virology-031413-085507) Crossref, PubMed, ISI, Google Scholar

2020 Phylogenetic analysis of the first four SARS-CoV-2 cases in Chile . J. Med. Virol. (doi:10.1002/jmv.25797) Crossref, PubMed, ISI, Google Scholar

Forster P, Forster L, Renfrew C, Forster M

. 2020 Phylogenetic network analysis of SARS-CoV-2 genomes . Proc. Natl Acad. Sci. USA 117, 9241-9243. (doi:10.1073/pnas.2004999117) Crossref, PubMed, ISI, Google Scholar

. 2020 Genetic diversity and evolution of SARS-CoV-2 . Infect. Genet. Evol. 81, 104260. (doi:10.1016/j.meegid.2020.104260) Crossref, PubMed, ISI, Google Scholar

2020 On the origin and continuing evolution of SARS-CoV-2 . Natl Sci. Rev. nwaa036. (doi:10.1093/nsr/nwaa036) Crossref, Google Scholar

Wang C, Liu Z, Chen Z, Huang X, Xu M, He T, Zhang Z

. 2020 The establishment of reference sequence for SARS-CoV-2 and variation analysis . J. Med. Virol. 92, 667-674. (doi:10.1002/jmv.25762) Crossref, ISI, Google Scholar

2020 Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) real-time RT-PCR N gene 2020 (Wuhan-N 2019-nCoV-related test). protocols.io. (doi:10.17504/protocols.io.bchwit7e) Google Scholar

Sharfstein JM, Becker SJ, Mello MM

. 2020 Diagnostic testing for the novel coronavirus . JAMA 323, 1437. (doi:10.1001/jama.2020.3864) Crossref, ISI, Google Scholar

Wang X, Yao H, Xu X, Zhang P, Zhang M, Shao J, Xiao Y, Wang H

. 2020 Limits of detection of six approved RT-PCR kits for the novel SARS-coronavirus-2 (SARS-CoV-2) . Clin. Chem. (doi:10.1093/clinchem/hvaa099) Crossref, Google Scholar

Li D, Wang D, Dong J, Wang N, Huang H, Xu H, Xia C

. 2020 False-negative results of real-time reverse-transcriptase polymerase chain reaction for severe acute respiratory syndrome coronavirus 2: role of deep-learning-based CT diagnosis and insights from two cases . Korean J. Radiol. 21, 505-508. (doi:10.3348/kjr.2020.0146) Crossref, PubMed, ISI, Google Scholar

Chen Z, Li Y, Wu B, Hou Y, Bao J, Deng X

. 2020 A patient with COVID-19 presenting a false-negative reverse transcriptase polymerase chain reaction result . Korean J. Radiol. 21, 623. (doi:10.3348/kjr.2020.0195) Crossref, PubMed, ISI, Google Scholar

Li Y, Yao L, Li J, Chen L, Song Y, Cai Z, Yang C

. 2020 Stability issues of RT-PCR testing of SARS-CoV-2 for hospitalized patients clinically diagnosed with COVID-19 . J. Med. Virol. (doi:10.1002/jmv.25786) ISI, Google Scholar

Dong X, Cao YY, Lu XX, Zhang JJ, Du H, Yan YQ, Akdis CA, Gao YD

. 2020 Eleven faces of coronavirus disease 2019 . Allergy. (doi:10.1111/all.14289) Crossref, Google Scholar

Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, Tao Q, Sun Z, Xia L

. 2020 Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases . Radiology . (doi:10.1148/radiol.2020200642) Crossref, PubMed, Google Scholar

Patel R, Babady E, Theel ES, Storch GA, Pinsky BA, St George K, Smith TC, Bertuzzi S

. 2020 Report from the American Society for Microbiology COVID-19 International Summit, 23 March 2020: value of diagnostic testing for SARS-CoV-2/COVID-19 . mBio 11, e00722-20. (doi:10.1128/mBio.00722-20) Crossref, ISI, Google Scholar

2020 Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR . Euro Surveill. 25, 2000045. (doi:10.2807/1560-7917.ES.2020.25.3.2000045) Crossref, Google Scholar

2020 Development of genetic diagnostic methods for novel coronavirus 2019 (nCoV-2019) in Japan . Jpn. J. Infect. Dis. (doi:10.7883/yoken.JJID.2020.061) Crossref, Google Scholar

2020 Molecular diagnosis of a novel coronavirus (2019-nCoV) causing an outbreak of pneumonia . Clin. Chem. 66, 549-555. (doi:10.1093/clinchem/hvaa029) Crossref, PubMed, ISI, Google Scholar

. 2017 GISAID: global initiative on sharing all influenza data—from vision to reality . Euro Surveill. 22, 30494. (doi:10.2807/1560-7917.ES.2017.22.13.30494) Crossref, PubMed, Google Scholar

Das J, Do Q-T, Shaines K, Srikant S

. 2013 U.S. and them: the geography of academic research . J. Dev. Econ. 105, 112-130. (doi:10.1016/j.jdeveco.2013.07.010) Crossref, ISI, Google Scholar

2017 Changing trends and persisting biases in three decades of conservation science . Glob. Ecol. Conserv. 10, 32-42. (doi:10.1016/j.gecco.2017.01.008) Crossref, ISI, Google Scholar

. 2020 Keep up with the latest coronavirus research . Nature 579, 193. (doi:10.1038/d41586-020-00694-1) Crossref, PubMed, Google Scholar

Arab-Zozani M, Hassanipour S

. 2020 Features and limitations of LitCovid hub for quick access to literature about COVID-19 . Balkan Med. J. (doi:10.4274/balkanmedj.galenos.2020.2020.4.67) Crossref, PubMed, Google Scholar

Katoh K, Misawa K, Kuma K, Miyata T

. 2002 MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform . Nucleic Acids Res. 30, 3059-3066. (doi:10.1093/nar/gkf436) Crossref, PubMed, ISI, Google Scholar

Katoh K, Rozewicki J, Yamada KD

. 2019 MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization . Brief Bioinform. 20, 1160-1166. (doi:10.1093/bib/bbx108) Crossref, PubMed, ISI, Google Scholar

. 2014 AliView: a fast and lightweight alignment viewer and editor for large datasets . Bioinformatics 30, 3276-3278. (doi:10.1093/bioinformatics/btu531) Crossref, PubMed, ISI, Google Scholar

Nagy A, Jirinec T, Jirincova H, Cernikova L, Havlickova M

. 2019 In silico re-assessment of a diagnostic RT-qPCR assay for universal detection of Influenza A viruses . Sci. Rep. 9, 1630. (doi:10.1038/s41598-018-37869-w) Crossref, PubMed, ISI, Google Scholar

Zhao Z, Li H, Wu X, Zhong Y, Zhang K, Zhang YP, Boerwinkle E, Fu YX

. 2004 Moderate mutation rate in the SARS coronavirus genome and its implications . BMC Evol. Biol. 4, 21. (doi:10.1186/1471-2148-4-21) Crossref, PubMed, ISI, Google Scholar

2020 Genomic diversity of SARS-CoV-2 in coronavirus disease 2019 patients . Clin. Infect. Dis. (doi:10.1093/cid/ciaa203) Crossref, Google Scholar

. 2016 Mechanisms of viral mutation . Cell. Mol. Life Sci. 73, 4433-4448. (doi:10.1007/s00018-016-2299-6) Crossref, PubMed, ISI, Google Scholar

Carrasco-Hernandez R, Jacome R, Lopez Vidal Y, Ponce de Leon S

. 2017 Are RNA viruses candidate agents for the next global pandemic? A review . ILAR J. 58, 343-358. (doi:10.1093/ilar/ilx026) Crossref, PubMed, ISI, Google Scholar

Lefever S, Pattyn F, Hellemans J, Vandesompele J

. 2013 Single-nucleotide polymorphisms and other mismatches reduce performance of quantitative PCR assays . Clin. Chem. 59, 1470-1480. (doi:10.1373/clinchem.2013.203653) Crossref, PubMed, ISI, Google Scholar

Stadhouders R, Pas SD, Anber J, Voermans J, Mes TH, Schutten M

. 2010 The effect of primer-template mismatches on the detection and quantification of nucleic acids using the 5′ nuclease assay . J. Mol. Diagn. 12, 109-117. (doi:10.2353/jmoldx.2010.090035) Crossref, PubMed, ISI, Google Scholar

Armstrong PM, Prince N, Andreadis TG

. 2012 Development of a multi-target TaqMan assay to detect eastern equine encephalitis virus variants in mosquitoes . Vector Borne Zoonotic Dis. 12, 872-876. (doi:10.1089/vbz.2012.1008) Crossref, PubMed, ISI, Google Scholar

Garson JA, Ferns RB, Grant PR, Ijaz S, Nastouli E, Szypulska R, Tedder RS

. 2012 Minor groove binder modification of widely used TaqMan probe for hepatitis E virus reduces risk of false negative real-time PCR results . J. Virol. Methods. 186, 157-160. (doi:10.1016/j.jviromet.2012.07.027) Crossref, PubMed, ISI, Google Scholar

Brault AC, Fang Y, Dannen M, Anishchenko M, Reisen WK

. 2012 A naturally occurring mutation within the probe-binding region compromises a molecular-based West Nile virus surveillance assay for mosquito pools (Diptera: Culicidae) . J. Med. Entomol. 49, 939-941. (doi:10.1603/me11287) Crossref, PubMed, ISI, Google Scholar

2020 Analytical sensitivity and efficiency comparisons of SARS-COV-2 qRT-PCR assays . medRxiv . (doi:10.1101/2020.03.30.20048108) Google Scholar

West CP, Montori VM, Sampathkumar P

. 2020 COVID-19 testing: the threat of false-negative results . Mayo Clin. Proc. 20, 30365-30367. (doi:10.1016/j.mayocp.2020.04.004) Google Scholar

Drame M, Teguo MT, Proye E, Hequet F, Hentzien M, Kanagaratnam L, Godaert L

. 2020 Should RT-PCR be considered a gold standard in the diagnosis of Covid-19? J. Med. Virol. (doi:10.1002/jmv.25996) Crossref, PubMed, Google Scholar

2020 BioLaboro: a bioinformatics system for detecting molecular assay signature erosion and designing new assays in response to emerging and reemerging pathogens. bioRxiv. (doi:10.1101/2020.04.08.031963) Google Scholar

2020 Comparison of SARS-CoV-2 detection from nasopharyngeal swab samples by the Roche cobas(R) 6800 SARS-CoV-2 test and a laboratory-developed real-time RT-PCR test . J. Med. Virol. (doi:10.1002/jmv.25988) Crossref, PubMed, ISI, Google Scholar

2020 Interpret with caution: an evaluation of the commercial AusDiagnostics versus in-house developed assays for the detection of SARS-CoV-2 virus . J. Clin. Virol. 127, 104374. (doi:10.1016/j.jcv.2020.104374) Crossref, PubMed, Google Scholar

Nagy A, Jirinec T, Cernikova L, Jirincova H, Havlickova M

. 2015 Large-scale nucleotide sequence alignment and sequence variability assessment to identify the evolutionarily highly conserved regions for universal screening PCR assay design: an example of influenza A virus . Methods Mol. Biol. 1275, 57-72. (doi:10.1007/978-1-4939-2365-6_4) Crossref, PubMed, Google Scholar

Singer JB, Thomson EC, McLauchlan J, Hughes J, Gifford RJ

. 2018 GLUE: a flexible software system for virus sequence data . BMC Bioinf. 19, 532. (doi:10.1186/s12859-018-2459-9) Crossref, PubMed, ISI, Google Scholar


Instrument faults can have an insidious onset and can, therefore, be difficult to diagnose. To prevent expensive repair costs, ensure that all operators of instruments are fully trained and initially supervised. Some instrument faults cause catastrophic failures, resulting in no amplification or fluorescent data while others distort data or treat samples in a non-uniform manner creating artificial differences between identical biological samples. The use of control samples with control assays is invaluable for troubleshooting. When an instrument fault is suspected, a reliable, optimized assay should be run in all wells. This uniformity check will reveal problems that are specific to regions of the instrument as well as separate assay and instrument issues.

After running a well-planned PCR there are several diagnostic tools available for troubleshooting:

  • Control samples and assays
  • End-point gel/SYBR Green I dye reagent
  • Amplification Plots (check replicates and amplification plot profile)
  • Standard Curves (gradient and R2)/dilution series
  • Melting/Dissociation Plots (SYBR Green I dye, Molecular Beacons, Scorpions ® Probes)
  • Raw data/multicomponent views

Control Samples/Reactions

The use of controls is strongly recommended. It is almost impossible to troubleshoot a failed assay without information from an appropriate suite of controls.

Figure 11.4. A) Undiluted template fails to amplify whereas dilutions show improved amplification efficiency. B) Addition of 0.3% BSA to the qPCR mix supports amplification from the undiluted template.

Investigations into a completely failed assay can be difficult because there is little information to work with for troubleshooting. Since many assay failures are the result of some catastrophic error, the first check should be to verify the experiment set up and then repeat the PCR. If this fails, the troubleshooting process is dependent on information regarding each component of the experiment (Figure 11-5).

Figure 11.5. The basic troubleshooting process for PCR.

When a qPCR experiment completely fails, the first step is to check assay design, the oligo sequences and the QC data from the oligo manufacturer. Although the assay may have failed, qPCR multicomponent/raw data can be used to provide further information. Figure 11.6A shows the raw data plot for two assays containing either a 6-FAM™ or a HEX™ (VIC ® ) labeled probe. Although both assays show amplification, the HEX signal is approximately half of that of the FAM signal. Since this is an inherently weaker dye, this is a normal observation. The agarose gel analysis (Figure 11.6B) shows both reactions yield a similar concentration of products, supporting the observation that the qPCR Cq values are similar.

Figure 11.6. A) The raw data plots of a duplex assay containing a FAM and a HEXlabeled probe. The FAM probe naturally yields higher fluorescence. B) The agarose gel showing that equal quantities of product were produced in each reaction and confirms the qPCR Cq observation.

Examination of the raw data is a useful check to verify that the probe is labeled correctly and was added to the reaction. Figure 11.7 shows the raw data for the amplification of three targets in a triplex experiment. The probes specific to each target are labeled with FAM, HEX and TAMRA. The HEX and TAMRA probes show a low background and efficient amplification, however the FAM signal is consistently high throughout the experiment and there is no evidence of amplification. This is consistent with too high probe concentration in the reaction or a fault with the probe such that there is no initial quenching of the signal. In such cases the probe concentration and assay design should be verified, ensuring that the probe has a compatible label and quencher and, if required, a new probe tested.

Figure 11.7. A triplex reaction was used to detect three targets using probes labeled with FAM, HEX and TAMRA. The HEX and TAMRA probes yielded amplification from the targets but the FAM probe showed no amplification. Examination of the raw data revealed that the background fluorescence was exceptionally high and no difference was observed throughout the reaction. This is consistent with too high a probe concentration in the reaction or a faulty probe with inadequate quenching.

If the original experiment relied upon probe detection the assay should be repeated using SYBR Green I reagents, including a positive and negative control (but not precious samples). Alternatively the products of a failed reaction can be checked on an ethidium bromide stained agarose gel. Adopting the SYBR Green I approach for repeating the experiment is preferable because it avoids the risk of contamination and provides a repeat experiment to verify the initial failure. If the SYBR Green I experiment provides data, it is possible that the original probe failure was due to either a technical error or probe fault. To differentiate between experimental error or a fault with the probe, repeat the probe experiment if the reaction fails again, replace the probe. This approach can be adopted to investigate reactions that are producing poor data. In the example shown in Figure 11.8, the probe reaction was sub-optimal and when compared to the reaction run using SYBR Green I, it can be seen that the probe signal does not reflect the experiment. In such cases the assay design should be verified and a new probe tested.

Figure 11.8. Identical reactions were run containing either a qPCR probe or SYBR Green I dye (as indicated). The SYBR Green I reaction was approximately eleven cycles more sensitive and yielded much higher end-point fluorescence. This is indicative of a fault in the probe or problem with the probe design (data kindly provided by Prof. Stephen Bustin, UK).

Validating probe labeling

The raw data or multicomponent plot is a useful diagnostic tool to investigate whether the appropriate concentration of probe has been included into the reaction and that the probe is adequately labeled and quenched. Figure 11.9 shows the multicomponent plot for a reaction containing three probes. The first two generate amplification plots and background fluorescence is evident. There is no data from the third probe and an examination of the raw data reveals that the background fluorescence is equivalent to the water blank control which does not contain any probe. Therefore, this data is the result of absence of fluorescence in the reaction. This would be due to an error during set up in which the probe was not included or that the probe was not labeled.

Figure 11.9. Three genes were detected in the same template sample. Two reactions resulted in amplification (1 and 2), however the 3rd was negative. An examination of the multicomponent view reveals that the background fluorescence for the 3rd reaction is equivalent to the water control indicating an absence of signal.

A further check of the probe labeling can be performed using a DNase I digestion. This must be performed with extreme care to ensure that probe and primer stocks are not contaminated with enzyme, which would lead to catastrophic results. An aliquot of a failing probe (Figure 11.10A) equivalent to that included in a reaction, e.g., 300 nM is incubated with and without DNase I. This can be carried out in real time (Figure 11.10B) such that the fluorescent yield is measured with respect to time or alternatively, the initial and end point (after 10 min) reading provide sufficient information. When performing this test it is important to compare the data to a probe that is functioning well and has the same fluorescent label and quencher (Figure 11.10B).

Figure 11.10. A) Two templates were detected using different probes, both FAM labeled. While detection using one probe resulted in a high fluorescent signal, the second was much weaker. B) A control and a test probe (300 nM) were incubated at 37 °C in a real time instrument in DNase I buffer in the presence or absence of DNase I enzyme. The fluorescent release from probe 1 was approximately twice that from probe 2, demonstrating that probe 2 labeling was inadequate.

Amplification Plots

The structure of the amplification plots and the reproducibility of technical replicates provide a wealth of information regarding the quality of the qPCR assay and may also provide the first warning indications that all is not as it should be. The amplification plots in Figure 11.11A are non-typical, very noisy and would be difficult to interpret accurately. A further examination of the dR fluorescence values reveals that the end-point fluorescent yield is only 400 units, indicating that the reaction is inadequate but the amplification plots have been generated by the instrument software and autoscaled. Similarly, the data in Figure 11.11B have a pronounced foxtail (decreasing curve) at the beginning of the profile, before increasing again after a baseline section. The appearance of the foxtail is consistent across two reactions, but one reaction has a much lower end-point (Figure 11.11C) resulting in an amplified, relative foxtail.

Figure 11.11. A) Amplification plots that are noisy due to autoscaling by the instrument software of poor data with low fluorescence. B) Reactions yielding low end point dR have a pronounced initial foxtail. C) The foxtail is seen as a normal effect when in proportion to the high quality assay.

Similarly, the amplification plots in Figure 11.12A are clearly abnormal and could not be used as they are presented. An amplification plot which dips below zero dR (Figure 11.12A) is a classic indication of inappropriate baseline settings having been applied. Examination of the raw data for this reaction (Figure 11.12B) shows that the actual amplification plots have a normal profile, confirming that the analyzed data are the result of an instrument software issue. The appropriate baseline can be deduced from the raw data and applied in the software. In this case cycles 6 to 16 represent the initial linear, baseline phase of the reaction and when applied, result in normal amplification plots (Figure 11.12C).

Figure 11.12. A) Amplification plots were clearly abnormal with a section of the profile dipping below the baseline. B) Examination of the raw data plot reveals that the reaction data are as expected. C) Setting the instrument baseline according to the appropriate cycles restores the normal profile to the data of analyzed amplification plots.

The amplification plot profile can also be interpreted to give information about the quality of the assay and optimization. Figure 11.13 shows the attempted amplification of a 10‑fold serial dilution of template with each concentration run in duplicate qPCR. The reproducibility between replicates is poor, the cycle difference (ΔCq) between the data is not constant and it is not 3.323 cycles, as expected for a 10-fold serial dilution. Examination of the amplification plots, with consideration to this being a standard curve, reveals that the assay is below standard and could not be used for analysis. The reasons would need further investigation but could be the result of poor assay design (see PCR/qPCR/dPCR Assay Design), sub-optimal assay conditions (see Assay Optimization and Validation), or poor pipetting (repeat assay).

Figure 11.13. A cDNA sample was diluted through a 10-fold serial dilution and the specific template detected using duplicate qPCR for each dilution. The replicates are poor, indicating a problem with pipetting or with assay optimization.

Figure 11.14. During a standard qPCR the data suddenly spike upward with a non-typical profile.

Figure 11.15. For declining or hooked fluorescence plots, the possible cause may be due to the complementary strand competing with the primer and/or probe for annealing to template. Ignore as long as Ct is not affected


The equation for using multiple reference genes to calculate the relative gene expression is displayed below.

The first thing I will say is: don’t panic! It is actually not as confusing as it looks. It is actually very similar to the Pfaffl equation, the only difference here being the geometric averaging of all the relative quantities (RQ), i.e. the (EREF) ∆Ct REF part, of the multiple reference genes used on the denominator (bottom) part of the equation.

The E in the equation refers to the base of exponential amplification. A value of 2, like in the delta-delta Ct method, indicates that after each PCR cycle, the amount of product will double. In other words, a value represents a 100% efficient reaction.


RESULTS

Efficiencies were calculated above 97% for all TaqMan and SYBR Green analysis [ Table 2 ]. Standard curves for SYBR Green and TaqMan analysis showed in [Figures ​ [Figures1 1 and ​ and2] 2 ] respectively.

Table 2

Calculated efficiencies of two methods

Standard curves of SYBR Green method, (a) A1 adenosine receptor, (b) A2A Adenosine receptor, (c) A2B adenosine receptor (d) A3 adenosine receptor, (e) B. actin

Standard curves of TaqMan method, (a) A1 adenosine receptor, (b) A2A Adenosine receptor, (c) A2B adenosine receptor (d) A3 adenosine receptor, (e) B. actin

The average values of normalized adenosine receptors gene expression levels were 1.44, 2.38, 3.79 and 3.55 for A1, A2A, A2B and A3 adenosine receptors for SYBR Green method and value for TaqMan method were 1.38, 2.43, 3.84 and 3.58 [ Table 3 ] respectively.

Table 3

Adenosine receptors expression levels in breast cancer

In the case of association between data of gene expression resulting from TaqMan and SYBR Green, Pearson analysis showed a significant and positive correlation between all method-pair gene expression analysis Table 4 .

Table 4

Correlation between results of SYBR Green and TaqMan methods


Advantages of Multiplex PCR

1. Internal Controls
Potential problems in a simple PCR include false negatives due to reaction failure or false positives due to contamination. False negatives are often revealed in multiplex assays because each amplicon provides an internal control for the other amplified fragments.

2. Efficiency
The expense of reagents and preparation time is less in multiplex PCR than in systems where several tubes of uniplex PCRs are used. A multiplex reaction is ideal for conserving costly polymerase and templates in short supply.

3. Indication of Template Quality
The quality of the template may be determined more effectively in multiplex than in a simple PCR reaction.

4. Indication of Template Quantity
The exponential amplification and internal standards of multiplex PCR can be used to assess the amount of a particular template in a sample. To quantitate templates accurately by multiplex PCR, the amount of reference template, the number of reaction cycles, and the minimum inhibition of the theoretical doubling of product for each cycle must be accounted.