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

Author

Listed:
  • Tuan-Anh Tran

    (University of Cambridge
    Oxford University Clinical Research Unit
    University of Oxford)

  • Sushmita Sridhar

    (University of Cambridge
    Hinxton)

  • Stephen T. Reece

    (University of Cambridge
    Babraham Research Campus)

  • Octavie Lunguya

    (Institut National de Recherche Biomédicale
    Cliniques Universitaires de Kinshasa)

  • Jan Jacobs

    (Immunology and Transplantation, KU Leuven
    Institute of Tropical Medicine)

  • Sandra Puyvelde

    (University of Cambridge
    University of Antwerp)

  • Florian Marks

    (University of Cambridge
    Gwanak-gu
    University of Heidelberg
    University of Antananarivo)

  • Gordon Dougan

    (University of Cambridge)

  • Nicholas R. Thomson

    (Hinxton
    London School of Hygiene and Tropical Medicine)

  • Binh T. Nguyen

    (University of Science, Vietnam National University Ho Chi Minh City)

  • Pham The Bao

    (Saigon University)

  • Stephen Baker

    (University of Cambridge
    Chelsea and Westminster Hospital)

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.

Suggested Citation

  • 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 & Stephen Bak, 2024. "Combining machine learning with high-content imaging to infer ciprofloxacin susceptibility in isolates of Salmonella Typhimurium," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49433-4
    DOI: 10.1038/s41467-024-49433-4
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    References listed on IDEAS

    as
    1. Qingyuan Zhao & Trevor Hastie, 2021. "Causal Interpretations of Black-Box Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 272-281, January.
    2. Sandra Van Puyvelde & Derek Pickard & Koen Vandelannoote & Eva Heinz & Barbara Barbé & Tessa de Block & Simon Clare & Eve L. Coomber & Katherine Harcourt & Sushmita Sridhar & Emily A. Lees & Nicole E., 2019. "An African Salmonella Typhimurium ST313 sublineage with extensive drug-resistance and signatures of host adaptation," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
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