Rapid Bacteria Identification and Antimicrobial Susceptibility Testing via 5-Min Fluorescence In Situ Hybridization.

Area of Science:

• Microbiology
• Medical Diagnostics
• Biotechnology

Background:

• Accurate infection diagnosis and antibiotic susceptibility testing (AST) are crucial for effective disease management and combating antibiotic resistance.
• Conventional methods for bacterial identification and AST are often time-consuming, delaying appropriate treatment.
• There is a need for rapid, sensitive, and accurate diagnostic tools in clinical microbiology.

Purpose of the Study:

• To introduce SWIFT-FISH, a novel diagnostic platform for rapid bacterial identification and AST.
• To demonstrate the efficiency and accuracy of SWIFT-FISH in a clinical microbiology setting.
• To highlight the potential of SWIFT-FISH in improving antibiotic stewardship and patient outcomes.

Main Methods:

• Developed SWIFT-FISH (Sensitive, Wash-free, Immediate, microFluidic, Thermal permeabilization-facilitated Fluorescence In Situ Hybridization) utilizing transient thermal permeabilization for rapid hybridization of fluorescent peptide nucleic acid (PNA) probes to bacterial 16S ribosomal RNA (rRNA).
• Integrated SWIFT-FISH into a continuous-flow microfluidic platform with laser-induced fluorescence (LIF) detection for in-solution, single-cell analysis without a washing step.
• Employed machine learning-based analysis with broad-based probes for scalable bacterial species identification and quantified bacterial proliferation for rapid phenotypic AST.

Main Results:

• Achieved bacterial identification in approximately 5 minutes with ≈99% accuracy for 6 bacterial species.
• Enabled rapid phenotypic antibiotic susceptibility testing (AST) with a turnaround time of approximately 1 hour.
• Demonstrated enhanced sensitivity and reduced background noise through wash-free, continuous-flow detection.

Conclusions:

• SWIFT-FISH is a simple, rapid, sensitive, and versatile diagnostic technology for bacterial identification and AST.
• The platform significantly accelerates conventional FISH workflows, offering a substantial improvement for clinical microbiology.
• SWIFT-FISH has significant potential to enhance clinical decision-making, improve antibiotic stewardship, and combat antibiotic resistance.