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Optimization of extraction method for circulating tumor DNA measurements by digital droplet PCR

ABSTRACT: A study of pre-analytical variables and optimization of extraction method for circulating tumor DNA measurements by digital droplet PCR

PROVIDER: PRJEB30048 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	ERR2938972.fastq.gz	Fastqsanger.gz
	ERR2938972_1.fastq.gz	Fastqsanger.gz
	ERR2938972_2.fastq.gz	Fastqsanger.gz
	ERR2938973.fastq.gz	Fastqsanger.gz
	ERR2938973_1.fastq.gz	Fastqsanger.gz

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Project description:Droplet digital PCR (ddPCR) is being advocated as a reference method to measure rare genomic targets. It has consistently been proven to be more sensitive and direct at discerning copy numbers of DNA than other quantitative methods. However, one of the largest obstacles to measuring microRNA (miRNA) using ddPCR is that reverse transcription efficiency depends upon the target, meaning small RNA nucleotide composition directly effects primer specificity in a manner that prevents traditional quantitation optimization strategies. Additionally, the use of reagents that are optimized for miRNA measurements using quantitative real-time PCR (qRT-PCR) appear to either cause false positive or false negative detection of certain targets when used with traditional ddPCR quantification methods. False readings are often related to using inadequate enzymes, primers and probes. Given that two-step miRNA quantification using ddPCR relies solely on reverse transcription and uses proprietary reagents previously optimized only for qRT-PCR, these barriers are substantial. Therefore, here we outline essential controls, optimization techniques, and an efficacy model to improve the quality of ddPCR miRNA measurements. We have applied two-step principles used for miRNA qRT-PCR measurements and leveraged the use of synthetic miRNA targets to evaluate ddPCR following cDNA synthesis with four different commercial kits. We have identified inefficiencies and limitations as well as proposed ways to circumvent identified obstacles. Lastly, we show that we can apply these criteria to a model system to confidently quantify miRNA copy number. Our measurement technique is a novel way to quantify specific miRNA copy number in a single sample, without using standard curves for individual experiments. Our methodology can be used for validation and control measurements, as well as a diagnostic technique that allows scientists, technicians, clinicians, and regulators to base miRNA measures on a single unit of measurement rather than a ratio of values.

Project description: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.

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Optimization of extraction method for circulating tumor DNA measurements by digital droplet PCR

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