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REVIEW article
Front. Microbiol.
Sec. Antimicrobials, Resistance and Chemotherapy
Volume 16 - 2025 |
doi: 10.3389/fmicb.2025.1536131
Advancing Antibiotic Discovery with Bacterial Cytological Profiling: A High-Throughput Solution to Antimicrobial Resistance
Provisionally accepted- School of Biological and Behavioural Sciences, Faculty of Science and Engineering, Queen Mary University of London, London, England, United Kingdom
Developing new antibiotics poses a significant challenge in the fight against antimicrobial resistance (AMR), a critical global health threat responsible for approximately 5 million deaths annually. Finding new classes of antibiotics that are safe, have acceptable pharmacokinetic properties, and are appropriately active against pathogens is a lengthy and expensive process. Therefore, high-throughput platforms are needed to screen large libraries of synthetic and natural compounds. In this review, we present bacterial cytological profiling (BCP) as a rapid, scalable, and cost-effective method for identifying antibiotic mechanisms of action. Notably, BCP has proven its potential in drug discovery, demonstrated by the identification of the cellular target of spirohexenolide A against methicillin-resistant Staphylococcus aureus 1 . We present the application of BCP for different bacterial organisms and different classes of antibiotics and discuss BCP's advantages, limitations, and potential improvements. Furthermore, we highlight the studies that have utilized BCP to investigate pathogens listed in the Bacterial Priority Pathogens List 2024 and we identify the pathogens whose cytological profiles are missing. We also explore the most recent artificial intelligence and deep learning techniques that could enhance the analysis of data generated by BCP, potentially advancing our understanding of antibiotic resistance mechanisms and the discovery of novel druggable pathways.
Keywords: Antibiotic resistance;, bacterial cytological profiling, high-throuput screening, Antibiotic mechanism of action, bacterial priority pathogen list, Cell segmentation, machine learning, deep learning
Received: 29 Nov 2024; Accepted: 29 Jan 2025.
Copyright: © 2025 Salgado, Rayner and Ojkic. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Nikola Ojkic, School of Biological and Behavioural Sciences, Faculty of Science and Engineering, Queen Mary University of London, London, E1 4NS, England, United Kingdom
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