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

Area of Science:

• Biotechnology
• Nanotechnology
• Clinical Diagnostics

Background:

• Rapid and reliable bacterial identification and antimicrobial susceptibility testing (AST) are crucial for effective clinical treatment but remain challenging due to sample complexity.
• Current methods often lack speed, user-friendliness, or direct correlation with bacterial metabolic activity.

Purpose of the Study:

• To develop a colorimetric sensing platform for simultaneous bacterial identification and AST in clinical samples.
• To leverage bacterial metabolism-driven gold nanoparticle (AuNP) synthesis for generating distinct colorimetric signals.
• To create a user-friendly, robust, and accessible diagnostic tool for clinical and field settings.

Main Methods:

• Developed a sensing platform based on bacterial metabolism-driven synthesis of gold nanoparticles (AuNPs) mediated by hydrogen peroxide (H2O2).
• Utilized differences in bacterial metabolic activity to generate unique colorimetric signals.
• Integrated the sensing system with linear discriminant analysis (LDA) for automated data interpretation and high-resolution profiling.

Main Results:

• Achieved 100% classification accuracy for seven bacterial species in serum and urine samples.
• Successfully differentiated nine strains of *Escherichia coli*.
• Demonstrated high accuracy (97.62%) in assessing antibiotic resistance profiles for six clinical isolates.

Conclusions:

• The developed colorimetric sensing platform accurately identifies bacterial species and determines antimicrobial susceptibility by converting metabolic signatures into diagnostic outcomes.
• This "read-to-answer" sensor array provides a user-friendly, robust, and rapid alternative to conventional methods.
• The technology holds significant potential for broad applicability in clinical diagnostics and field-based microbial analysis.