CRISPR‐based diagnostics detects invasive insect pests

Rapid identification of organisms is essential for many biological and medical disciplines, from understanding basic ecosystem processes, disease diagnosis, to the detection of invasive pests. CRISPR‐based diagnostics offers a novel and rapid alternative to other identification methods and can revol...

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Veröffentlicht in:Molecular ecology resources 2024-01, Vol.24 (1), p.e13881-n/a
Hauptverfasser: Shashank, Pathour R., Parker, Brandon M., Rananaware, Santosh R., Plotkin, David, Couch, Christian, Yang, Lilia G., Nguyen, Long T., Prasannakumar, N. R., Braswell, W. Evan, Jain, Piyush K., Kawahara, Akito Y.
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Sprache:eng
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Zusammenfassung:Rapid identification of organisms is essential for many biological and medical disciplines, from understanding basic ecosystem processes, disease diagnosis, to the detection of invasive pests. CRISPR‐based diagnostics offers a novel and rapid alternative to other identification methods and can revolutionize our ability to detect organisms with high accuracy. Here we describe a CRISPR‐based diagnostic developed with the universal cytochrome‐oxidase 1 gene (CO1). The CO1 gene is the most sequenced gene among Animalia, and therefore our approach can be adopted to detect nearly any animal. We tested the approach on three difficult‐to‐identify moth species (Keiferia lycopersicella, Phthorimaea absoluta and Scrobipalpa atriplicella) that are major invasive pests globally. We designed an assay that combines recombinase polymerase amplification (RPA) with CRISPR for signal generation. Our approach has a much higher sensitivity than real‐time PCR assays and achieved 100% accuracy for identification of all three species, with a detection limit of up to 120 fM for P. absoluta and 400 fM for the other two species. Our approach does not require a sophisticated laboratory, reduces the risk of cross‐contamination, and can be completed in less than 1 h. This work serves as a proof of concept that has the potential to revolutionize animal detection and monitoring.
ISSN:1755-098X
1755-0998
DOI:10.1111/1755-0998.13881