Droplet volume variability as a critical factor for accuracy of absolute quantification using droplet digital PCR.

Accurate and precise nucleic-acid quantification is crucial for clinical and diagnostic decisions, as overestimation or underestimation can lead to misguided treatment of a disease or incorrect labelling of the products. Digital PCR is one of the best tools for absolute nucleic-acid copy-number determination. However, digital PCR needs to be well characterised in terms of accuracy and sources of uncertainty. With droplet digital PCR, discrepancies between the droplet volume assigned by the manufacturer and measured by independent laboratories have already been shown in previous studies. In the present study, we report on the results of an inter-laboratory comparison of different methods for droplet volume determination that is based on optical microscopy imaging and is traceable to the International System of Units. This comparison was conducted on the same DNA material, with the examination of the influence of parameters such as droplet generators, supermixes, operators, inter-cartridge and intra-cartridge variability, and droplet measuring protocol. The mean droplet volume was measured using a QX200™ AutoDG™ Droplet Digital™ PCR system and two QX100™ Droplet Digital™ PCR systems. The data show significant volume differences between these two systems, as well as significant differences in volume when different supermixes are used. We also show that both of these droplet generator systems produce droplets with significantly lower droplet volumes (13.1%, 15.9%, respectively) than stated by the manufacturer and previously measured by other laboratories. This indicates that to ensure precise quantification, the droplet volumes should be assessed for each system.

Author Correction: Development and inter-laboratory assessment of droplet digital PCR assays for multiplex quantification of 15 genetically modified soybean lines.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

Author Correction: ALF: a strategy for identification of unauthorized GMOs in complex mixtures by a GW-NGS method and dedicated bioinformatics analysis.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Development and inter-laboratory assessment of droplet digital PCR assays for multiplex quantification of 15 genetically modified soybean lines.

Quantification of genetically modified organisms (GMOs) in food and feed products is often required for their labelling or for tolerance thresholds. Standard-curve-based simplex quantitative polymerase chain reaction (qPCR) is the prevailing technology, which is often combined with screening analysis. With the rapidly growing number of GMOs on the world market, qPCR analysis becomes laborious and expensive. Innovative cost-effective approaches are therefore urgently needed. Here, we report the development and inter-laboratory assessment of multiplex assays to quantify GMO soybean using droplet digital PCR (ddPCR). The assays were developed to facilitate testing of foods and feed for compliance with current GMO regulations in the European Union (EU). Within the EU, the threshold for labelling is 0.9% for authorised GMOs per ingredient. Furthermore, the EU has set a technical zero tolerance limit of 0.1% for certain unauthorised GMOs. The novel multiplex ddPCR assays developed target 11 GMO soybean lines that are currently authorised, and four that are tolerated, pending authorisation in the EU. Potential significant improvements in cost efficiency are demonstrated. Performance was assessed for the critical parameters, including limits of detection and quantification, and trueness, repeatability, and robustness. Inter-laboratory performance was also determined on a number of proficiency programme and real-life samples.

ALF: a strategy for identification of unauthorized GMOs in complex mixtures by a GW-NGS method and dedicated bioinformatics analysis.

The majority of feed products in industrialised countries contains materials derived from genetically modified organisms (GMOs). In parallel, the number of reports of unauthorised GMOs (UGMOs) is gradually increasing. There is a lack of specific detection methods for UGMOs, due to the absence of detailed sequence information and reference materials. In this research, an adapted genome walking approach was developed, called ALF: Amplification of Linearly-enriched Fragments. Coupling of ALF to NGS aims for simultaneous detection and identification of all GMOs, including UGMOs, in one sample, in a single analysis. The ALF approach was assessed on a mixture made of DNA extracts from four reference materials, in an uneven distribution, mimicking a real life situation. The complete insert and genomic flanking regions were known for three of the included GMO events, while for MON15985 only partial sequence information was available. Combined with a known organisation of elements, this GMO served as a model for a UGMO. We successfully identified sequences matching with this organisation of elements serving as proof of principle for ALF as new UGMO detection strategy. Additionally, this study provides a first outline of an automated, web-based analysis pipeline for identification of UGMOs containing known GM elements.