Multi-scenario surveillance of respiratory viruses in aerosols with sub-single-copy spatial resolution

Highly sensitive airborne virus monitoring is critical for preventing and containing epidemics. However, the detection of airborne viruses at ultra-low concentrations remains challenging due to the lack of ultra-sensitive methods and easy-to-deployment equipment. Here, we present an integrated microfluidic cartridge that can accurately detect SARS-COV-2, Influenza A, B, and respiratory syncytial virus with a sensitivity of 10 copies/mL. When integrated with a high-flow aerosol sampler, our microdevice can achieve a sub-single-copy spatial resolution of 0.83 copies/m<sup>3</sup> for airborne virus surveillance with an air flow rate of 400 L/min and a sampling time of 30 minutes. We then designed a series of virus-in-aerosols monitoring systems (RIAMs), including versions of a multi-site sampling RIAMs (M-RIAMs), a stationary real-time RIAMs (S-RIAMs), and a roaming real-time RIAMs (R-RIAMs) for different application scenarios. Using M-RIAMs, we performed a comprehensive evaluation of 210 environmental samples from COVID-19 patient wards, including 30 aerosol samples. The highest positive detection rate of aerosol samples (60%) proved the aerosol-based SARS-CoV-2 monitoring represents an effective method for spatial risk assessment. The detection of 78 aerosol samples in real-world settings via S-RIAMs confirmed its reliability for ultra-sensitive and continuous airborne virus monitoring. Therefore, RIAMs shows the potential as an effective solution for mitigating the risk of airborne virus transmission.

Authors

Institutions

Publication Details

Journal
Nature Communications
Published
2024-10-09
DOI
https://doi.org/10.1038/s41467-024-53059-x
Citations
22
Primary Topic
Infection Control and Ventilation
Type
article
Field-Weighted Citation Impact
9.22

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Multi-scenario surveillance of respiratory viruses in aerosols with sub-single-copy spatial resolution

Wu Zeng, Yin Gu, Guangxin Xiang, Xiaoqun Zheng et al.
22 citations
Nature Communications
Infection Control and Ventilation
9.22
article

Multi-scenario surveillance of respiratory viruses in aerosols with sub-single-copy spatial resolution

Wu Zeng, Yin Gu, Guangxin Xiang, Xiaoqun Zheng, Chang Liu, Hao Cheng, Peng Liu, Bao Li, Ye Shi, Baobao Lin, Guiqiang Wang, Yulan Zhao, Hui Gao, Yan Wang
article en
22 citations

Abstract

Highly sensitive airborne virus monitoring is critical for preventing and containing epidemics. However, the detection of airborne viruses at ultra-low concentrations remains challenging due to the lack of ultra-sensitive methods and easy-to-deployment equipment. Here, we present an integrated microfluidic cartridge that can accurately detect SARS-COV-2, Influenza A, B, and respiratory syncytial virus with a sensitivity of 10 copies/mL. When integrated with a high-flow aerosol sampler, our microdevice can achieve a sub-single-copy spatial resolution of 0.83 copies/m<sup>3</sup> for airborne virus surveillance with an air flow rate of 400 L/min and a sampling time of 30 minutes. We then designed a series of virus-in-aerosols monitoring systems (RIAMs), including versions of a multi-site sampling RIAMs (M-RIAMs), a stationary real-time RIAMs (S-RIAMs), and a roaming real-time RIAMs (R-RIAMs) for different application scenarios. Using M-RIAMs, we performed a comprehensive evaluation of 210 environmental samples from COVID-19 patient wards, including 30 aerosol samples. The highest positive detection rate of aerosol samples (60%) proved the aerosol-based SARS-CoV-2 monitoring represents an effective method for spatial risk assessment. The detection of 78 aerosol samples in real-world settings via S-RIAMs confirmed its reliability for ultra-sensitive and continuous airborne virus monitoring. Therefore, RIAMs shows the potential as an effective solution for mitigating the risk of airborne virus transmission.

Nature CommunicationsVol. 15(1)
Peking University (CN), Wenzhou Medical University (CN), 302 Military Hospital of China (CN), Peking University First Hospital (CN), Peking University International Hospital (CN), Beijing Tsinghua Chang Gung Hospital (CN), China Astronaut Research and Training Center (CN), Tsinghua University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 1%
Infection Control and Ventilation
9.22
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.