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

Tuan-Anh Tran, Sushmita Sridhar, Stephen T. Reece, Octavie Lunguya, Jan Jacobs, Sandra Puyvelde, Florian Marks, Gordon Dougan, Nicholas R. Thomson, Binh T. Nguyen, Pham The Bao and Stephen Baker ()
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Tuan-Anh Tran: University of Cambridge
Sushmita Sridhar: University of Cambridge
Stephen T. Reece: University of Cambridge
Octavie Lunguya: Institut National de Recherche Biomédicale
Jan Jacobs: Immunology and Transplantation, KU Leuven
Sandra Puyvelde: University of Cambridge
Florian Marks: University of Cambridge
Gordon Dougan: University of Cambridge
Nicholas R. Thomson: Hinxton
Binh T. Nguyen: University of Science, Vietnam National University Ho Chi Minh City
Pham The Bao: Saigon University
Stephen Baker: University of Cambridge

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: Abstract Antimicrobial resistance (AMR) is a growing public health crisis that requires innovative solutions. Current susceptibility testing approaches limit our ability to rapidly distinguish between antimicrobial-susceptible and -resistant organisms. Salmonella Typhimurium (S. Typhimurium) is an enteric pathogen responsible for severe gastrointestinal illness and invasive disease. Despite widespread resistance, ciprofloxacin remains a common treatment for Salmonella infections, particularly in lower-resource settings, where the drug is given empirically. Here, we exploit high-content imaging to generate deep phenotyping of S. Typhimurium isolates longitudinally exposed to increasing concentrations of ciprofloxacin. We apply machine learning algorithms to the imaging data and demonstrate that individual isolates display distinct growth and morphological characteristics that cluster by time point and susceptibility to ciprofloxacin, which occur independently of ciprofloxacin exposure. Using a further set of S. Typhimurium clinical isolates, we find that machine learning classifiers can accurately predict ciprofloxacin susceptibility without exposure to it or any prior knowledge of resistance phenotype. These results demonstrate the principle of using high-content imaging with machine learning algorithms to predict drug susceptibility of clinical bacterial isolates. This technique may be an important tool in understanding the morphological impact of antimicrobials on the bacterial cell to identify drugs with new modes of action.

Date: 2024
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DOI: 10.1038/s41467-024-49433-4

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