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

Author

Listed:
  • Anthony Petkidis

    (Winterthurerstrasse 190
    ETH and University of Zürich)

  • Vardan Andriasyan

    (Winterthurerstrasse 190)

  • Luca Murer

    (Winterthurerstrasse 190
    Roche Diagnostics)

  • Romain Volle

    (Winterthurerstrasse 190)

  • Urs F. Greber

    (Winterthurerstrasse 190)

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.

Suggested Citation

  • Anthony Petkidis & Vardan Andriasyan & Luca Murer & Romain Volle & Urs F. Greber, 2024. "A versatile automated pipeline for quantifying virus infectivity by label-free light microscopy and artificial intelligence," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49444-1
    DOI: 10.1038/s41467-024-49444-1
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    1. G. Tuba Barut & Nico Joel Halwe & Adriano Taddeo & Jenna N. Kelly & Jacob Schön & Nadine Ebert & Lorenz Ulrich & Christelle Devisme & Silvio Steiner & Bettina Salome Trüeb & Bernd Hoffmann & Inês Bere, 2022. "The spike gene is a major determinant for the SARS-CoV-2 Omicron-BA.1 phenotype," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
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