Optimization of digital droplet polymerase chain reaction for quantification of genetically modified organisms
| Autor: | Ulrich Busch, Lars Gerdes, Sven Pecoraro, Azuka N. Iwobi |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
0301 basic medicine
ABI LifeTechnologies (formerly AppliedBiosystems) fluorescein FAM F lcsh:QR1-502 01 natural sciences Biochemistry MWG Eurofins-MWG lcsh:Microbiology law.invention cp/cp (gene) copy per (gene) copy Matrix (chemical analysis) DNA deoxyribonucleic acid PCR polymerase chain reaction Structural Biology law VIC V fluorescent dye (LifeTechnologies) HEX H hexachlorfluorescein Experience matrix Digital polymerase chain reaction MS Microsoft Bio DNA Technology/Biosearch Technologies dPCR digital PCR MeanSignal mean fluorescence signal value lcsh:QH301-705.5 Polymerase chain reaction Mathematics Thermal cycler Tech technician EURL-GMFF European Reference Laboratory for GM Food and Feed MIQE minimal information for publication of quantitative digital PCR experiments Genetically modified organism Food/feed analysis Molecular Medicine Biological system Droplet digital PCR (ddPCR) Research Paper ERM Certified European Reference Material Positive reaction Nanotechnology MRPL minimum required performance limit 03 medical and health sciences qPCR (quantitative) real-time PCR EU European Union SD standard deviation (of fluorescence signals) Quantification L liter Reference gene TIB TIB Molbiol Molecular Biology VBA visual basic for applications GM genetically modified ComputingMethodologies_COMPUTERGRAPHICS GMO genetically modified organism 010401 analytical chemistry Cat. No. catalogue number 0104 chemical sciences TAMRA T tetramethylrhodamin 030104 developmental biology Genetically modified organism (GMO) lcsh:Biology (General) ddPCR droplet digital PCR EC European Commission gDNA genomic DNA Lec lectin gene of soy DNA - Deoxyribonucleic acid |
| Zdroj: | Biomolecular Detection and Quantification Biomolecular Detection and Quantification, Vol 7, Iss C, Pp 9-20 (2016) |
| ISSN: | 2214-7535 |
| Popis: | Graphical abstract Highlights • Experience matrix condenses ddPCR performance parameters in graphical presentation. • Assay separation value based on absolute fluorescence signal distance and variation. • Separation value and experience matrix simplify choice of best assay parameters. • Influence of oligonucleotide concentration and annealing/extension temperature. Digital PCR in droplets (ddPCR) is an emerging method for more and more applications in DNA (and RNA) analysis. Special requirements when establishing ddPCR for analysis of genetically modified organisms (GMO) in a laboratory include the choice between validated official qPCR methods and the optimization of these assays for a ddPCR format. Differentiation between droplets with positive reaction and negative droplets, that is setting of an appropriate threshold, can be crucial for a correct measurement. This holds true in particular when independent transgene and plant-specific reference gene copy numbers have to be combined to determine the content of GM material in a sample. Droplets which show fluorescent units ranging between those of explicit positive and negative droplets are called ‘rain’. Signals of such droplets can hinder analysis and the correct setting of a threshold. In this manuscript, a computer-based algorithm has been carefully designed to evaluate assay performance and facilitate objective criteria for assay optimization. Optimized assays in return minimize the impact of rain on ddPCR analysis. We developed an Excel based ‘experience matrix’ that reflects the assay parameters of GMO ddPCR tests performed in our laboratory. Parameters considered include singleplex/duplex ddPCR, assay volume, thermal cycler, probe manufacturer, oligonucleotide concentration, annealing/elongation temperature, and a droplet separation evaluation. We additionally propose an objective droplet separation value which is based on both absolute fluorescence signal distance of positive and negative droplet populations and the variation within these droplet populations. The proposed performance classification in the experience matrix can be used for a rating of different assays for the same GMO target, thus enabling employment of the best suited assay parameters. Main optimization parameters include annealing/extension temperature and oligonucleotide concentrations. The droplet separation value allows for easy and reproducible assay performance evaluation. The combination of separation value with the experience matrix simplifies the choice of adequate assay parameters for a given GMO event. |
| Databáze: | OpenAIRE |
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