High-throughput virus quantification using cytopathic effect area analysis.

Authors

Michael J Murphy, Steven Mazur, Elena N Postnikova, Brett P Eaton, Gregory A Kocher, Winston Chu, Matthew G Lackemeyer, Syed Qasim Gilani, Jens H Kuhn, Michael R Holbrook, C Paul Morris

Published in

PLoS computational biology. Volume 22. Issue 9. Pages e1013511. Sep 16, 2026. Epub Sep 16, 2026.

Abstract

Traditional infectivity-based virion quantification methods, such as 50% tissue culture infectious dose (TCID50) and plaque assays, are typically performed in 6-, 12-, 24-, or 48-well cell culture plates and require manual work and analysis based on legacy protocols. Adaptation of these methods to high-throughput formats (96-, 384- or 1,536-well plates) is challenging due to both assay automation constraints and the lack of well surface area available for reliable analysis. Here, we present a scalable alternative to traditional methods that uses whole-well image thresholding to quantify infectious virions by measuring virus-induced cytopathic effect (CPE) via cell lysis and detachment. The CPE area assay is best positioned as a practical high-throughput preliminary screening tool, effectively quantifying samples within the assay range and flagging samples above or below the concentration thresholds. To improve analysis efficiency and reduce user bias in CPE area selection, we evaluated an nnU-Net model for automated image segmentation against images segmented by manually defined brightness thresholds. The model achieved strong correlation with manual thresholding (R2 = 1.00), showing minimal differences in the identified CPE area and thus demonstrating that nnU-Net accurately reproduces manual analysis. This approach provides two complementary pipelines: manual thresholding, which tolerates adjustments of hyperparameters, and fully automated segmentation via nnU-Net, which streamlines analysis and enhances throughput. This flexible CPE area assay enables accurate and automated quantification in high-throughput screening formats, thereby greatly accelerating a routine laboratory task while decreasing subjectivity and bias.

PMID:
42748173
Bibliographic data and abstract were imported from PubMed on 17 Sep 2026.

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