Metabolism-Driven Colorimetric "Read-to-Answer" Sensor Array for Bacterial Discrimination and Antimicrobial Susceptibility Testing

Authors

Xiaodong Lin, Department of Bioengineering, University of California Riverside, Riverside, California92521, United States.
Kairui Zhai, Department of Bioengineering, University of California Riverside, Riverside, California92521, United States.
Benjamin M. Liu, Division of Pathology and Laboratory Medicine, Children's National Hospital, Washington, D.C. 20010, United States.
Juhong Chen, Department of Bioengineering, University of California Riverside, Riverside, California92521, United States.

Document Type

Journal Article

Publication Date

7-30-2025

Journal

Analytical chemistry

DOI

10.1021/acs.analchem.5c02762

Abstract

Due to the complexity of clinical samples, rapid and reliable bacterial identification and antimicrobial susceptibility testing (AST) remain challenging. To address these challenges, we developed a colorimetric sensing platform for bacterial identification and AST in clinical samples based on bacterial metabolism-driven synthesis of gold nanoparticles (AuNPs) via hydrogen peroxide (HO) mediation. In this strategy, bacteria metabolic differences among bacterial species were converted into distinct colorimetric signals. Integrated with linear discriminant analysis (LDA), our developed sensing system enables automated and high-resolution profiling of bacterial species and strains. We achieved 100% classification accuracy for seven bacterial species in serum and urine and successfully differentiated nine Escherichia coli strains. For AST, the system correctly assessed antibiotic resistance profiles in six clinical isolates, reaching an overall accuracy of 97.62%. Unlike the conventional AuNP-based aggregation sensors, our approach is more user-friendly, robust against environmental variability, and directly reflects bacterial metabolic activities. By directly converting metabolic signatures to diagnostic outcomes, this "read-to-answer" sensor array offers a powerful and accessible solution for bacterial identification and AST, with broad applicability in clinical and field settings.

APA Citation

Lin, Xiaodong; Zhai, Kairui; Liu, Benjamin M.; and Chen, Juhong, "Metabolism-Driven Colorimetric "Read-to-Answer" Sensor Array for Bacterial Discrimination and Antimicrobial Susceptibility Testing" (2025). GW Authored Works. Paper 7534.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/7534

Department

Pathology