A membrane-impermeant nucleic acid dye converts bacteriophage plaque assays into a machine-readable format for automated counting

Abstract

Plaque assays remain the gold standard for bacteriophage quantification, but routine plaque counting is labor-intensive, time-consuming, and poorly suited to large experiments or automated workflows. Conventional plaque images also often provide insufficient contrast for simple software-based counting, especially when plaques are small, faint, or heterogeneous. Here we show that a membrane-impermeant nucleic acid dye can convert standard bacteriophage plaque assays into a high-contrast, machine-readable format compatible with simple automated counting. In a soft-agar overlay workflow, fluorescent labeling enabled plaque detection and automated enumeration using an open-source ImageJ pipeline based on Find Maxima, without phage engineering, machine learning, or custom software. Because the method improves the image contrast of the assay itself, it may also provide improved input for future machine-learning or other advanced automated counting workflows. The method was evaluated across diverse phage-host systems spanning dsDNA, ssRNA, filamentous, and enveloped phages, including T7, MS2, M13, and phi6. In lytic systems, fluorescent signal emerged prior to or alongside conventional plaque visibility and yielded automated counts that agreed closely with manual counting. M13 exhibited delayed fluorescence consistent with its chronic, nonlytic lifestyle, yet remained machine-countable at the conventional next-day endpoint. A Gram-positive Leo2-Bacillus safensis system revealed an important compatibility limit: dye incorporation at plating inhibited plaque formation, but a post-labeling workflow restored detectability and automated counting. Together, these results show that membrane-impermeant dye labeling can make plaque assays more computationally tractable while preserving the accessibility of standard phage methods. This approach provides a practical path toward higher-throughput, statistically rigorous phage biology in both low-resource and automation-oriented laboratories.