Microscopy deep learning predicts virus infections and reveals mechanics of lytic-infected cells.
Machine learning
Virology
Journal
iScience
ISSN: 2589-0042
Titre abrégé: iScience
Pays: United States
ID NLM: 101724038
Informations de publication
Date de publication:
25 Jun 2021
25 Jun 2021
Historique:
received:
02
10
2020
revised:
07
01
2021
accepted:
12
05
2021
entrez:
21
6
2021
pubmed:
22
6
2021
medline:
22
6
2021
Statut:
epublish
Résumé
Imaging across scales reveals disease mechanisms in organisms, tissues, and cells. Yet, particular infection phenotypes, such as virus-induced cell lysis, have remained difficult to study. Here, we developed imaging modalities and deep learning procedures to identify herpesvirus and adenovirus (AdV) infected cells without virus-specific stainings. Fluorescence microscopy of vital DNA-dyes and live-cell imaging revealed learnable virus-specific nuclear patterns transferable to related viruses of the same family. Deep learning predicted two major AdV infection outcomes, non-lytic (nonspreading) and lytic (spreading) infections, up to about 20 hr prior to cell lysis. Using these predictive algorithms, lytic and non-lytic nuclei had the same levels of green fluorescent protein (GFP)-tagged virion proteins but lytic nuclei enriched the virion proteins faster, and collapsed more extensively upon laser-rupture than non-lytic nuclei, revealing impaired mechanical properties of lytic nuclei. Our algorithms may be used to infer infection phenotypes of emerging viruses, enhance single cell biology, and facilitate differential diagnosis of non-lytic and lytic infections.
Identifiants
pubmed: 34151222
doi: 10.1016/j.isci.2021.102543
pii: S2589-0042(21)00511-3
pmc: PMC8192562
doi:
Types de publication
Journal Article
Langues
eng
Pagination
102543Informations de copyright
© 2021 The Author(s).
Déclaration de conflit d'intérêts
The authors declare no competing interests.
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