Authors
Pembery, A., Nadir, H. H., MacDonald, C., Leake, M. C.
Abstract
Quantification of microbial growth inhibition is central to assays ranging from antibiotic susceptibility of bacteria to sensitivity of yeasts to antifungal therapeutics. Classical analysis approaches derive from zone-of-inhibition (termed halo) formats using filter paper discs, spanning methods from laser detection to machine learning. However, these tools struggle with non-uniform halos, fail to account for lawn density variability despite its experimental influence, and lack accessible, reproducible code. Here, we present Halo Unbiased Measurement of growth Inhibition (HaloUMI); an open-source Python graphical user interface for automated, high-throughput analysis of lawn-based microbial assays. HaloUMI integrates robust image processing with physics-informed models to quantify inhibition zones irrespective of shape, enabling accurate segmentation of uniform and irregular halo phenotypes. This analysis pipeline incorporates the critical correction for spatial heterogeneity in lawn density, improving reproducibility across experimental conditions. The software enhances usability without sacrificing precision, allowing rapid batch processing and intuitive parameter control. HaloUMI can be applied to multiple assay types, including yeast toxin halo, microbial mating, and conventional filter paper disc assays. It yields high-precision measurement of halo size and morphology, with improved consistency compared to standard thresholding and circular fitting. By combining accessibility, flexibility, and biophysical modelling, HaloUMI provides a quantitative framework for irregularly shaped halos of lawns of varying growth potential, enabling generalisable analysis of broad microbial interactions.
Preprint server:
bioRxiv
The authors list and abstract were imported from bioRxiv on 14 Aug 2026.
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