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In this study, the applicability of droplet digital PCR (ddPCR) for routine analysis in food and feed samples was demonstrated with the quantification of genetically modified organisms (GMOs). Real-time quantitative polymerase chain reaction (qPCR) is currently used for quantitative molecular analysis of the presence of GMOs in products. However, its use is limited for detecting and quantifying very small numbers of DNA targets, as in some complex food and feed matrices. Using ddPCR duplex assay, we have measured the absolute numbers of MON810 transgene and hmg maize reference gene copies in DNA samples. Key performance parameters of the assay were determined. The ddPCR system is shown to offer precise absolute and relative quantification of targets, without the need for calibration curves. The sensitivity (five target DNA copies) of the ddPCR assay compares well with those of individual qPCR assays and of the chamber digital PCR (cdPCR) approach. It offers a dynamic range over four orders of magnitude, greater than that of cdPCR. Moreover, when compared to qPCR, the ddPCR assay showed better repeatability at low target concentrations and a greater tolerance to inhibitors. Finally, ddPCR throughput and cost are advantageous relative to those of qPCR for routine GMO quantification. It is thus concluded that ddPCR technology can be applied for routine quantification of GMOs, or any other domain where quantitative analysis of food and feed samples is needed.

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Genetically modified plants, in the following referred to as genetically modified organisms or GMOs, have been commercially grown for almost two decades. In 2010 approximately 10% of the total global crop acreage was planted with GMOs (James, 2011). More than 30 countries have been growing commercial GMOs, and many more have performed field trials. Although the majority of commercial GMOs both in terms of acreage and specific events belong to the four species: soybean, maize, cotton and rapeseed, there are another 20+ species where GMOs are commercialized or in the pipeline for commercialization. The number of GMOs cultivated in field trials or for commercial production has constantly increased during this time period. So have the number of species, the number of countries involved, the diversity of novel (added) genetic elements and the global trade. All of these factors contribute to the increasing complexity of detecting and correctly identifying GMO derived material. Many jurisdictions, including the European Union (EU), legally distinguish between authorized (and therefore legal) and un-authorized (and therefore illegal) GMOs. Information about the developments, field trials, authorizations, cultivation, trade and observations made in the official GMO control laboratories in different countries around the world is often limited, despite several attempts such as the OECD BioTrack for voluntary dissemination of data. This lack of information inevitably makes it challenging to detect and identify GMOs, especially the un-authorized GMOs. The present paper reviews the state of the art technologies and approaches in light of coverage, practicability, sensitivity and limitations. Emphasis is put on exemplifying practical detection of un-authorized GMOs. Although this paper has a European (EU) bias when examples are given, the contents have global relevance.

Detection methods and data from molecular characterization of genetically modified (GM) events are needed by stakeholders of public risk assessors and regulators. Generally, the molecular characteristics of GM events are incomprehensively revealed by current approaches and biased towards detecting transformation vector derived sequences. GM events are classified based on available knowledge of the sequences of vectors and inserts (insert knowledge). Herein we present three insert knowledge-adapted approaches for characterization GM events (TT51-1 and T1c-19 rice as examples) based on paired-end re-sequencing with the advantages of comprehensiveness, accuracy, and automation. The comprehensive molecular characteristics of two rice events were revealed with additional unintended insertions comparing with the results from PCR and Southern blotting. Comprehensive transgene characterization of TT51-1 and T1c-19 is shown to be independent of a priori knowledge of the insert and vector sequences employing the developed approaches. This provides an opportunity to identify and characterize also unknown GM events.