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通过将重组酶聚合酶扩增-成簇规律间隔短回文重复序列/Cas12a-表面等离子体激元共振与深度学习相结合检测蛙病毒3

Detection of Frog Virus 3 by Integrating RPA-CRISPR/Cas12a-SPM with Deep Learning.

作者信息

Lei Zhengyang, Lian Lijin, Zhang Likun, Liu Changyue, Zhai Shiyao, Yuan Xi, Wei Jiazhang, Liu Hong, Liu Ying, Du Zhicheng, Gul Ijaz, Zhang Haihui, Qin Zhifeng, Zeng Shaoling, Jia Peng, Du Ke, Deng Lin, Yu Dongmei, He Qian, Qin Peiwu

机构信息

Center of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong Province 518055, China.

Tsinghua Shenzhen International Graduate School, Institute of Biopharmaceutics and Health Engineering, Shenzhen, Guangdong Province 518055, China.

出版信息

ACS Omega. 2023 Aug 25;8(36):32555-32564. doi: 10.1021/acsomega.3c02929. eCollection 2023 Sep 12.

Abstract

A fast, easy-to-implement, highly sensitive, and point-of-care (POC) detection system for frog virus 3 (FV3) is proposed. Combining recombinase polymerase amplification (RPA) and CRISPR/Cas12a, a limit of detection (LoD) of 100 aM (60.2 copies/μL) is achieved by optimizing RPA primers and CRISPR RNAs (crRNAs). For POC detection, smartphone microscopy is implemented, and an LoD of 10 aM is achieved in 40 min. The proposed system detects four positive animal-derived samples with a quantitation cycle (Cq) value of quantitative PCR (qPCR) in the range of 13 to 32. In addition, deep learning models are deployed for binary classification (positive or negative samples) and multiclass classification (different concentrations of FV3 and negative samples), achieving 100 and 98.75% accuracy, respectively. Without temperature regulation and expensive equipment, the proposed RPA-CRISPR/Cas12a combined with smartphone readouts and artificial-intelligence-assisted classification showcases the great potential for FV3 detection, specifically POC detection of DNA virus.

摘要

本文提出了一种用于蛙病毒3(FV3)的快速、易于实施、高灵敏度的即时检测(POC)系统。通过将重组酶聚合酶扩增(RPA)与CRISPR/Cas12a相结合,优化RPA引物和CRISPR RNA(crRNA)后,检测限(LoD)可达100 aM(60.2拷贝/μL)。对于即时检测,采用了智能手机显微镜,40分钟内检测限可达10 aM。该系统检测了四个阳性动物来源样本,定量PCR(qPCR)的定量循环(Cq)值在13至32之间。此外,还部署了深度学习模型用于二元分类(阳性或阴性样本)和多类分类(不同浓度的FV3和阴性样本),准确率分别达到100%和98.75%。该RPA-CRISPR/Cas12a系统无需温度调节和昂贵设备,结合智能手机读数和人工智能辅助分类,在FV3检测,特别是DNA病毒的即时检测方面展现出巨大潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97ca/10500685/201e9137561f/ao3c02929_0001.jpg

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