Artificial intelligence-assisted phenotyping of drug-resistant bacteria using a monosaccharide-based fluorescent sensor array

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Chem. Sci., 2026, Advance ArticleDOI: 10.1039/D6SC00084C, Edge Article Open Access &nbsp This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.Zhuo-Fan Zhang, Wen-Zhen Gui, Yi-Fan Tang, Hui-Qi Gan, Xi-Le Hu, Tony D. James, Shengbi…

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Artificial intelligence-assisted phenotyping of drug-resistant bacteria using a monosaccharide-based fluorescent sensor array

* Corresponding authors

a Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, Feringa Nobel Prize Scientist Joint Research Centre, School of Chemistry and Molecular Engineering. East China University of Science & Technology, Shanghai 200237, China
E-mail: [email protected], [email protected]

b National Center for Liver Cancer, The International Cooperation Laboratory on Signal Transduction. Eastern Hepatobiliary Surgery Hospital, Shanghai 200438, China

c Academy of Pharmacy Xi'an-Jiaotong Liverpool University, Suzhou, China

d Department of Laboratory Medicine, Ren Ji Hospital, 160 Pujian Road, Shanghai 200127, China
E-mail: [email protected]

e Shanghai Key Laboratory of Orthopedic Implants, Department of Orthopedic Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
E-mail: [email protected]

f Department of Chemistry, University of Bath, Bath BA2 7AY, UK
E-mail: [email protected]

g School of Chemistry and Chemical Engineering, Henan Normal University, Xinxiang 453007, China

Abstract

Chemical tools capable of effectively phenotyping drug-resistant bacteria can help improve therapeutic efficacy toward bacterial infections. While conventional techniques rely on labor-intensive procedures for the determination of bacterial susceptibility to antibiotics, here we developed a sensor array based on fluorogen-labelled monosaccharides to accurately phenotype drug-resistant bacteria with the assistance of artificial intelligence (AI). D-Glucose, D-galactose, L-fucose and D-mannose, which are common monomeric building blocks of natural glycans, were labelled with a “conformationally-adaptive” fluorophore (DPAC) with two different linkers, giving rise to a sensor array that consists of eight fluorescent glycoprobes. Using homogeneous high-throughput screening, we found that all the glycoprobes exhibited sensitive ratiometric fluorescence changes in the presence of Pseudomonas aeruginosa (P. aeruginosa) expressing bacterial lectins (LecA and LecB) selective for D-galactose, L-fucose and D-mannose. However, minimal fluorescence changes were seen when the glycoprobes were incubated with other bacterial strains lacking lectin expression. The use of ensemble learning to process the acquired sensing signals further enabled the accurate discrimination of clinically isolated, drug-resistant P. aeruginosa from drug-sensitive strains. Interestingly, using AI-assisted array sensing, we also achieved the phenotyping of P. aeruginosa after long-term exposure to mechanistically different antibiotics, thus highlighting the effectiveness of this approach for precision medicine.

• This article is part of the themed collection: 2026 Chemical Science HOT Article Collection