ColonyPy: a Python-based image analysis pipeline to quantify tolerance and heteroresistance in Cryptococcus neoformans

Conventional drug susceptibility testing may overlook subpopulations that survive above the minimum inhibitory concentration (MIC). Fluconazole (FLC), a fungistatic drug central to cryptococcal meningitis therapy, selects for tolerance (slow-growing cells that persist above the MIC), heteroresistance (~1% of cells growing above the MIC but revert without drug), and subpopulation resistance (emerging genetically stable resistant cells). To measure these drug subpopulation phenotypes simultaneously, we developed ColonyPy, a Python-based image analysis pipeline that quantifies colony number and size within the zone of inhibition (ZOI) in disk diffusion assays. Using ColonyPy, we assessed tolerance and heteroresistance/subpopulation resistance in Cryptococcus neoformans clinical isolates grown on Sabouraud dextrose agar (SDA), yeast peptone dextrose agar (YPD), and Mueller–Hinton agar (MHA) with 2% glucose. Our results show that (i) clinical isolates vary widely in tolerance and heteroresistance/subpopulation resistance; (ii) these phenotypes differ significantly across media types; (iii) YPD supports the highest levels of heteroresistance/subpopulation resistance; and (iv) heteroresistance, but not subpopulation resistance, is observed in all the susceptible clinical isolates tested. Together, these findings demonstrate that tolerance and heteroresistance are independent phenotypes that are influenced by nutrient conditions. Our data show the presence of adaptive subpopulations that are able to grow above MIC fluconazole drug concentrations within clinical C. neoformans isolates classified as sensitive in traditional MIC assays that could influence treatment outcomes. IMPORTANCE Cryptococcus neoformans is the leading cause of fungal meningitis in people living with HIV, and fluconazole remains a cornerstone of treatment. Despite its widespread clinical use, fluconazole treatment failure is poorly understood, and conventional susceptibility testing methods fail to capture the full complexity of growth phenotypes that are not cleared by antifungals, including tolerance, heteroresistance, and subpopulation resistance. This study demonstrates that C. neoformans clinical isolates can simultaneously express two distinct drug-survival phenotypes—tolerance and heteroresistance. The balance between these phenotypes is isolate specific and shaped by environmental nutrient conditions. To enable objective, simultaneous quantification of both antifungal-survival phenotypes, we developed ColonyPy—an automated Python-based image analysis pipeline that classifies and quantifies tolerant and heteroresistant/subpopulation-resistant colonies from disk diffusion assay images. Our findings challenge the adequacy of single-metric susceptibility testing and provide a practical tool for investigating how phenotypic subpopulation heterogeneity contributes to fluconazole treatment failure in cryptococcal meningitis.