A versatile automated pipeline for quantifying virus infectivity by label-free light microscopy and artificial intelligence

Anthony Petkidis, Vardan Andriasyan, Luca Murer, Romain Volle and Urs F. Greber ()
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Anthony Petkidis: Winterthurerstrasse 190
Vardan Andriasyan: Winterthurerstrasse 190
Luca Murer: Winterthurerstrasse 190
Romain Volle: Winterthurerstrasse 190
Urs F. Greber: Winterthurerstrasse 190

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract Virus infectivity is traditionally determined by endpoint titration in cell cultures, and requires complex processing steps and human annotation. Here we developed an artificial intelligence (AI)-powered automated framework for ready detection of virus-induced cytopathic effect (DVICE). DVICE uses the convolutional neural network EfficientNet-B0 and transmitted light microscopy images of infected cell cultures, including coronavirus, influenza virus, rhinovirus, herpes simplex virus, vaccinia virus, and adenovirus. DVICE robustly measures virus-induced cytopathic effects (CPE), as shown by class activation mapping. Leave-one-out cross-validation in different cell types demonstrates high accuracy for different viruses, including SARS-CoV-2 in human saliva. Strikingly, DVICE exhibits virus class specificity, as shown with adenovirus, herpesvirus, rhinovirus, vaccinia virus, and SARS-CoV-2. In sum, DVICE provides unbiased infectivity scores of infectious agents causing CPE, and can be adapted to laboratory diagnostics, drug screening, serum neutralization or clinical samples.

Date: 2024
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DOI: 10.1038/s41467-024-49444-1

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