Researchers from Sciensano, partner of the DARWIN project, have published a new paper in npj Science of Food addressing one of the key scientific and regulatory challenges linked to genome-edited (GE) organisms, their reliable detection and identification in complex food mixtures.
In the European Union, GMOs in the food chain are currently regulated under Regulations (EC) No. 1829/2003 and No. 1830/2003 to ensure safety, traceability, and consumer freedom of choice. Since 2018, genome-edited organisms have also fallen under this legislation. However, detecting and unambiguously identifying these organisms remains particularly challenging, as they can differ from their wild-type counterparts by only one or a few single-nucleotide variations (SNVs).
The publication investigates for the first time the use of high-throughput sequencing combined with adaptive sampling (AS) to selectively enrich a target species of interest in food mixtures. This approach aims to reduce matrix complexity and subsequently facilitate the detection and identification of GE lines.
As a proof-of-concept, the researchers analyzed soybean mixtures containing trace levels of genome-edited or wild-type rice using three sequencing modes: standard sequencing, adaptive sampling enriching rice, and adaptive sampling depleting soybean. The resulting sequencing data were then used to evaluate the effectiveness of, on the one hand, rice enrichment in food mixtures and, on the other hand, reliable identification of each tested rice line through the detection of their respective genetic fingerprints.
The study represents a promising first step toward facilitating the detection and identification of genome-edited organisms in the food chain, supporting future traceability and regulatory compliance efforts within the European Union framework.
More information
Arno Stuyts et al, Enhancing low-level genome-edited crop detection and identification in food mixtures using nanopore adaptive sampling: Rice-Soybean mixture as proof-of-concept, npj Science of Food (2026). DOI: 10.1038/s41538-026-00846-z
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Advancing detection of genome-edited crops in food mixtures (2026, May 27)
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