2. Academic Validation
  3. Metabolism-Driven Colorimetric "Read-to-Answer" Sensor Array for Bacterial Discrimination and Antimicrobial Susceptibility Testing

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

  • Anal Chem. 2025 Aug 12;97(31):17040-17049. doi: 10.1021/acs.analchem.5c02762.
Xiaodong Lin 1,  Kairui Zhai 1,  Benjamin M Liu 2 3 4 5 6,  Juhong Chen 1
Affiliations

Affiliations

  • 1 Department of Bioengineering, University of California Riverside, Riverside, California92521, United States.
  • 2 Division of Pathology and Laboratory Medicine, Children's National Hospital, Washington, D.C. 20010, United States.
  • 3 Department of Pediatrics, George Washington University School of Medicine and Health Sciences, Washington, D.C. 20010, United States.
  • 4 Department of Pathology, George Washington University School of Medicine and Health Sciences, Washington, D.C. 20037, United States.
  • 5 Department of Microbiology, Immunology & Tropical Medicine, George Washington University School of Medicine and Health Sciences, Washington, D.C. 20037, United States.
  • 6 Children's National Research Institute, Washington, D.C. 20012, United States.
PMID: 40737437 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 (H2O2) 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.

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