Shiaelis, Nicolas, Tometzki, Alexander, Peto, Leon, McMahon, Andrew, Hepp, Christof, Bickerton, Erica Jane, Favard, Cyril, Muriaux, Delphine, Andersson, Monique, Oakley, Sarah J., Vaughan, Ali, Matthews, Philippa C., Stoesser, Nicole, Crook, Derrick W., Kapanidis, Achillefs N. and Robb, Nicole C. (2023) Virus detection and identification in minutes using single-particle imaging and deep learning. ACS Nano . doi:10.1021/acsnano.2c10159 ISSN 1936-0851.
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Abstract
The increasing frequency and magnitude of viral outbreaks in recent decades, epitomized by the COVID-19 pandemic, has resulted in an urgent need for rapid and sensitive diagnostic methods. Here, we present a methodology for virus detection and identification that uses a convolutional neural network to distinguish between microscopy images of fluorescently labeled intact particles of different viruses. Our assay achieves labeling, imaging, and virus identification in less than 5 min and does not require any lysis, purification, or amplification steps. The trained neural network was able to differentiate SARS-CoV-2 from negative clinical samples, as well as from other common respiratory pathogens such as influenza and seasonal human coronaviruses. We were also able to differentiate closely related strains of influenza, as well as SARS-CoV-2 variants. Additional and novel pathogens can easily be incorporated into the test through software updates, offering the potential to rapidly utilize the technology in future infectious disease outbreaks or pandemics. Single-particle imaging combined with deep learning therefore offers a promising alternative to traditional viral diagnostic and genomic sequencing methods and has the potential for significant impact.
| Item Type: | Journal Article |
|---|---|
| Subjects: | R Medicine > RA Public aspects of medicine |
| Divisions: | Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Biomedical Sciences Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School |
| SWORD Depositor: | Library Publications Router |
| Library of Congress Subject Headings (LCSH): | COVID-19 (Disease), Influenza -- Transmission, Fluorescence microscopy, Machine learning, Pandemics |
| Journal or Publication Title: | ACS Nano |
| Publisher: | American Chemical Society |
| ISSN: | 1936-0851 |
| Official Date: | 10 January 2023 |
| Dates: | Date Event 10 January 2023 Published 21 December 2022 Available 14 December 2022 Accepted |
| DOI: | 10.1021/acsnano.2c10159 |
| Status: | Peer Reviewed |
| Publication Status: | Published |
| Access rights to Published version: | Open Access (Creative Commons open licence) |
| Date of first compliant deposit: | 10 July 2023 |
| Date of first compliant Open Access: | 11 July 2023 |
| RIOXX Funder/Project Grant: | Project/Grant ID RIOXX Funder Name Funder ID BB/V001868/1 [BBSRC] Biotechnology and Biological Sciences Research Council Studentship [BBSRC] Biotechnology and Biological Sciences Research Council |
| URI: | https://wrap.warwick.ac.uk/172255/ |
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