Use of high-content analysis and machine learning to characterize complex microbial samples via morphological analysis.

High Content Analysis (HCA) has become a cornerstone of cellular analysis within the drug discovery industry. To expand the capabilities of HCA, we have applied the same analysis methods, validated in numerous mammalian cell models, to microbiology methodology. Image acquisition and analysis of various microbial samples, ranging from pure cultures to culture mixtures containing up to three different bacterial species, were quantified and identified using various machine learning processes. These HCA techniques allow for faster cell enumeration than standard agar-plating methods, identification of "viable but not plate culturable" microbe phenotype, classification of antibiotic treatment effects, and identification of individual microbial strains in mixed cultures. These methods greatly expand the utility of HCA methods and automate tedious and low-throughput standard microbiological methods.

High-throughput screening of a collection of known pharmacologically active small compounds for identification of Candida albicans biofilm inhibitors.

Candida albicans is the most common etiologic agent of systemic fungal infections with unacceptably high mortality rates. The existing arsenal of antifungal drugs is very limited and is particularly ineffective against C. albicans biofilms. To address the unmet need for novel antifungals, particularly those active against biofilms, we have screened a small molecule library consisting of 1,200 off-patent drugs already approved by the Food and Drug Administration (FDA), the Prestwick Chemical Library, to identify inhibitors of C. albicans biofilm formation. According to their pharmacological applications that are currently known, we classified these bioactive compounds as antifungal drugs, as antimicrobials/antiseptics, or as miscellaneous drugs, which we considered to be drugs with no previously characterized antifungal activity. Using a 96-well microtiter plate-based high-content screening assay, we identified 38 pharmacologically active agents that inhibit C. albicans biofilm formation. These drugs were subsequently tested for their potency and efficacy against preformed biofilms, and we identified three drugs with novel antifungal activity. Thus, repurposing FDA-approved drugs opens up a valuable new avenue for identification and potentially rapid development of antifungal agents, which are urgently needed.