4.8 Article

Sensitive and Rapid Diagnosis of Respiratory Virus Coinfection Using a Microfluidic Chip-Powered CRISPR/Cas12a System

Journal

SMALL
Volume 18, Issue 26, Pages -

Publisher

WILEY-V C H VERLAG GMBH
DOI: 10.1002/smll.202200854

Keywords

multiplex diagnosis; point-of-care testing; respiratory disease; COVID-19

Funding

  1. National Natural Science Foundation of China [31870853, 82161138004]
  2. Guangzhou Institute of Respiratory Health Open Project
  3. China Evergrande Group [2020GIRHHMS02]
  4. Tsinghua University Spring Breeze Fund [20201080532]
  5. Beijing Nova Program
  6. Beijing Lab Foundation
  7. Tsinghua University Initiative Scientific Research Program [20211080003]

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The ongoing COVID-19 pandemic has had a profound impact on the global healthcare system and daily lives of people. This has resulted in insufficient surveillance of coinfection or resurgence of other critical respiratory epidemics. To address this issue, a microfluidic system (MAPnavi) has been developed for rapid and sensitive diagnosis of multiple respiratory viruses, showing promising results in detecting coinfections among patients with COVID-19.
The ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 is profoundly influencing the global healthcare system and people's daily lives. The high resource consumption of coronavirus disease 2019 (COVID-19) is resulting in insufficient surveillance of coinfection or resurgence of other critical respiratory epidemics, which is of public concern. To facilitate evaluation of the current coinfection situation, a microfluidic system (MAPnavi) is developed for the rapid (<40 min) and sensitive diagnosis of multiple respiratory viruses from swab samples in a fully sealed and automated manner, in which a nested-recombinase polymerase amplification and the CRISPR-based amplification system is first proposed to ensure the sensitivity and specificity. This novel system has a remarkably low limit of detection (50-200 copies mL(-1)) and is successfully applied to detect 171 clinical samples (98.5% positive predictive agreement; 100% negative predictive agreement), and the results identify 45.6% coinfection among clinical samples from patients with COVID-19. This approach has the potential to shift diagnostic and surveillance efforts from targeted testing for a high-priority virus to comprehensive testing of multiple virus sets and to greatly benefit the implementation of decentralized testing.

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