High-Throughput and Integrated CRISPR/Cas12a-Based Molecular Diagnosis Using a Deep Learning Enabled Microfluidic System.

Journal: ACS nano
PMID:

Abstract

CRISPR/Cas-based molecular diagnosis demonstrates potent potential for sensitive and rapid pathogen detection, notably in SARS-CoV-2 diagnosis and mutation tracking. Yet, a major hurdle hindering widespread practical use is its restricted throughput, limited integration, and complex reagent preparation. Here, a system, icrofluidic multiplate-based ltrahigh hroughput nalysis of ARS-CoV-2 variants of concern using CRISPR/s12a and onextraction RT-LAMP (mutaSCAN), is proposed for rapid detection of SARS-CoV-2 and its variants with limited resource requirements. With the aid of the self-developed reagents and deep-learning enabled prototype device, our mutaSCAN system can detect SARS-CoV-2 in mock swab samples below 30 min as low as 250 copies/mL with the throughput up to 96 per round. Clinical specimens were tested with this system, the accuracy for routine and mutation testing (22 wildtype samples, 26 mutational samples) was 98% and 100%, respectively. No false-positive results were found for negative ( = 24) samples.

Authors

  • Li Zhang
    Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
  • Huili Wang
    Guangxi Forestry Research Institute, Key Laboratory of Central South Fast-Growing Timber Cultivation of Forestry Ministry of China, Nanning, China.
  • Sheng Yang
    Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, China.
  • Jiajia Liu
    College of Science, North China University of Science and Technology, Tangshan 063210, China.
  • Jie Li
    Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence Application Technology Research Institute, Shenzhen Polytechnic University, Shenzhen, China.
  • Ying Lu
    Department of Endocrinology, Institute of Translational Medicine, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, Guangdong, China.
  • Jing Cheng
    Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
  • Youchun Xu
    School of Biomedical Engineering, Tsinghua University, Beijing 100084, China.