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Droplet digital polymerase chain reaction for rapid broad-spectrum detection of bloodstream infections.

ABSTRACT: The droplet digital polymerase chain reaction (ddPCR) is a novel molecular technique that allows rapid quantification of rare target DNA sequences. Aim of this study was to explore the feasibility of the ddPCR technique to detect pathogen DNA in whole blood and to assess the diagnostic accuracy of ddPCR to detect bloodstream infections (BSIs), benchmarked against blood cultures. Broad-range primers and probes were designed to detect bacterial 16S rRNA (and Gram stain for differentiation) and fungal 28S rRNA. To determine the detection limit of ddPCR, 10-fold serial dilutions of E. coli and C. albicans were spiked in both PBS and whole blood. The diagnostic accuracy of ddPCR was tested in historically collected frozen blood samples from adult patients suspected of a BSI and compared with blood cultures. Analyses were independently performed by two research analysts. Outcomes included sensitivity and specificity of ddPCR. Within 4 h, blood samples were drawn, and DNA was isolated and analysed. The ddPCR detection limit was approximately 1-2 bacteria or fungi per ddPCR reaction. In total, 45 blood samples were collected from patients, of which 15 (33%) presented with positive blood cultures. The overall sensitivity of ddPCR was 80% (95% CI 52-96) and specificity 87% (95% CI 69-96). In conclusion, the ddPCR technique has considerable potential and is able to detect very low amounts of pathogen DNA in whole blood within 4 h. Currently, ddPCR has a reasonable sensitivity and specificity, but requires further optimization to make it more useful for clinical practice.

SUBMITTER: Wouters Y

PROVIDER: S-EPMC7111091 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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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Droplet digital polymerase chain reaction for rapid broad-spectrum detection of bloodstream infections.

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