Combining machine learning with high-content imaging to infer ciprofloxacin susceptibility in clinical isolates of Salmonella Typhimurium

Abstract

Abstract Antimicrobial resistance (AMR) is a growing public health crisis that requires innovative solutions. Presently we rely on exposing single organisms to an antimicrobial and growth to determine susceptibility; throughput and interpretation hinder our ability to rapidly distinguish between antimicrobial-susceptible and -resistant organisms isolated from clinical samples. Salmonella Typhimurium (S. Typhimurium) is an enteric pathogen responsible for severe gastrointestinal illness in immunocompetent individuals and can also cause invasive disease in immunocompromised people. Despite widespread resistance, ciprofloxacin remains a common treatment, particularly in lower-resource settings, where the drug is given empirically. Here, we exploited high-content imaging to generate deep phenotyping of various S. Typhimurium isolates longitudinally exposed to increasing concentrations of ciprofloxacin. We applied machine learning algorithms to the resulting imaging data and demonstrated that individual isolates display distinct growth and morphological characteristics that clustered by time point and susceptibility to ciprofloxacin, which occurred independently of ciprofloxacin exposure. We used a further set of S. Typhimurium clinical isolates to test the ability of our algorithm to distinguish between ciprofloxacin-susceptible and -resistant isolates. We found that a random forest classifier could accurately predict how the organism would respond to ciprofloxacin without exposure to it or any prior knowledge of ciprofloxacin susceptibility. These results provide the first proof-of-principle for the use of high-content imaging with machine learning algorithms to predict drug susceptibility of clinical bacterial isolates. This technique can be exploited to identify drug-resistant bacteria more rapidly and accurately and may be an important tool in understanding the phenotypic impact of antimicrobials on the bacterial cell in order to identify drugs with new modes of action.