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

Zhuo-Fan Zhang, Wen-Zhen Gui, Yi-Fan Tang, Hui-Qi Gan, Xi-Le Hu, Tony D James, Shengbing Yang, Qian Liu, Xiao-Peng He

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Article in Chemical science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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9 authors.

Zhuo-Fan ZhangKey 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 xlhu@ecust.edu.cn xphe@ecust.edu.cn.

Wen-Zhen GuiKey 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 xlhu@ecust.edu.cn xphe@ecust.edu.cn.

Yi-Fan TangAcademy of Pharmacy Xi'an-Jiaotong Liverpool University Suzhou 215123 China.

Hui-Qi GanKey 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 xlhu@ecust.edu.cn xphe@ecust.edu.cn.

Xi-Le HuKey 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 xlhu@ecust.edu.cn xphe@ecust.edu.cn.

Tony D JamesDepartment of Chemistry, University of Bath Bath BA2 7AY UK t.d.james@bath.ac.uk.ORCID https://orcid.org/0000-0002-4095-2191

Shengbing YangShanghai Key Laboratory of Orthopedic Implants, Department of Orthopedic Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine Shanghai 200011 China shengbingyang@shusmu.edu.cn.

Qian LiuDepartment of Laboratory Medicine, Ren Ji Hospital 160 Pujian Road Shanghai 200127 China liuqian_rj@shsmu.edu.cn.

Xiao-Peng HeKey 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 xlhu@ecust.edu.cn xphe@ecust.edu.cn.ORCID https://orcid.org/0000-0002-8736-3511

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6 · The paper itself

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

Identifiers

PMID42164444

DOI10.1039/d6sc00084c

PMCPMC13186398

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