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State of the art in CRISPR/Cas system-based signal conversion and amplification applied in the field of food analysis

清脆的 工具箱 生化工程 生物技术 食品安全 风险分析(工程) 计算机科学 纳米技术 计算生物学 系统工程 数据科学 生物 工程类 遗传学 业务 材料科学 程序设计语言 基因 食品科学
作者
Bingqian Lu,Zhiru Guo,Kunli Zhong,Tolbert Osire,Yang Sun,Ling Jiang
出处
期刊:Trends in Food Science and Technology [Elsevier]
卷期号:135: 174-189 被引量:32
标识
DOI:10.1016/j.tifs.2023.04.002
摘要

Food safety is a major global public health issue. The majority of traditional culture-based methods have lengthy detection times, require sophisticated sample pretreatment, preparation of high-quality primers, and trained personnel. As these are not always available, there is an urgent need for rapid, sensitive, portable, and specific detection technology. This review introduces new tools in the field of molecular diagnostics based on the CRISPR/Cas system. It gives a detailed overview of different proteins, recognition elements and signal amplification methods. CRISPR systems have been fused with different technologies to create detection platforms that can detect and amplify very weak signals, increase the detection efficiency, and create new channels for effective rapid food analysis. It can detect a wide range of targets, including bacteria, fungi, pesticides, metal ions, genetically engineered crops, adulterated food, etc. Finally, we discuss the benefits and future challenges of these new detection tools, as well as the prospects of combining them with other techniques. Food safety is a global concern. In recent years, the newly developed CRISPR/Cas toolbox has shown good prospects in the field of molecular detection. The system is first coupled with various components for signal conversion, and then combined with nucleic acid amplification, allowing the development of a new generation of detection and sensing platforms. Nevertheless, CRISPR/Cas technology still faces various challenges on the way to fully mature development despite its outstanding advantages in detection. Today, the development of CRISPR/Cas-based detection tools aims at reducing the number of steps, using multiple pathways, increasing speed and efficiency, as well as integrating intelligent genetic circuits, which have great potential for advancing the diagnosis and prevention of foodborne diseases and food safety.
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