Phage phenotyping by measuring plaque expansion dynamics
- 1Vrije Universiteit Amsterdam
Abstract
Bacteriophages exhibit staggering genomic diversity, yet phenotypic characterization remains bottlenecked by assays that generally yield simple binary infection outcomes. Quantitative traits can be extracted from standard liquid cultures, but many phages fail to produce a measurable effect in these settings, necessitating an improved quantitative approach on semi-solid media. While end-point plaque size is commonly used for quantitative phenotyping, it only provides a static snapshot that fails to disentangle phage performance across changing bacterial growth phases. Here, we report a cost-effective, scalable method to quantify the bacterial killing rates of diverse phages by continuously tracking plaque development using a consumer-grade flatbed scanner and custom open-source computer vision software. By fitting a minimal, five-parameter phenomenological model to the diverse BASEL collection of Escherichia coli phages, we accurately captured plaque expansion dynamics across all bacterial growth phases. We discovered that plaque expansion parameters vary significantly across individual phages and taxonomic families, with the initial expansion rate demonstrating an inverse correlation with viral genome size. Notably, these plaque expansion dynamics did not correlate with bacterial collapse times in liquid culture, indicating that they represent distinct physiological and kinetic processes. Finally, we demonstrated that phage taxonomic family can be predicted with 72.1% accuracy solely from dynamic plaque parameters using a random forest classifier, whereas conventional endpoint plaque sizes held no predictive power. This time-lapse phenotyping approach yields robust kinetic data superior to end-point measurements, establishing a scalable platform to accelerate phage discovery and genotype-phenotype mapping.
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BibTeX
@misc{costa2026phage,
title = {{Phage phenotyping by measuring plaque expansion dynamics}},
author = {J. Costa and B. A. van der Steen and Y. Mulla},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.07.757331},
url = {https://www.biorxiv.org/content/10.64898/2026.10.07.757331v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Phage phenotyping by measuring plaque expansion dynamics
AU - J. Costa
AU - B. A. van der Steen
AU - Y. Mulla
PY - 2026
DA - 2026/10/07
PB - bioRxiv
DO - 10.64898/2026.10.07.757331
UR - https://www.biorxiv.org/content/10.64898/2026.10.07.757331v1
AB - Bacteriophages exhibit staggering genomic diversity, yet phenotypic characterization remains bottlenecked by assays that generally yield simple binary infection outcomes. Quantitative traits can be extracted from standard liquid cultures, but many phages fail to produce a measurable effect in these settings, necessitating an improved quantitative approach on semi-solid media. While end-point plaque size is commonly used for quantitative phenotyping, it only provides a static snapshot that fails to disentangle phage performance across changing bacterial growth phases. Here, we report a cost-effective, scalable method to quantify the bacterial killing rates of diverse phages by continuously tracking plaque development using a consumer-grade flatbed scanner and custom open-source computer vision software. By fitting a minimal, five-parameter phenomenological model to the diverse BASEL collection of Escherichia coli phages, we accurately captured plaque expansion dynamics across all bacterial growth phases. We discovered that plaque expansion parameters vary significantly across individual phages and taxonomic families, with the initial expansion rate demonstrating an inverse correlation with viral genome size. Notably, these plaque expansion dynamics did not correlate with bacterial collapse times in liquid culture, indicating that they represent distinct physiological and kinetic processes. Finally, we demonstrated that phage taxonomic family can be predicted with 72.1% accuracy solely from dynamic plaque parameters using a random forest classifier, whereas conventional endpoint plaque sizes held no predictive power. This time-lapse phenotyping approach yields robust kinetic data superior to end-point measurements, establishing a scalable platform to accelerate phage discovery and genotype-phenotype mapping.
ER -