Identifying antibiotic-resistant strains via cell sorting and elastic-light-scatter phenotyping

Area of Science:

• Microbiology
• Biotechnology
• Analytical Chemistry

Background:

• Antimicrobial resistance (AMR) is a growing global threat, driven by antibiotic misuse.
• Current detection methods like agar plating are slow, while genotypic methods require pure samples and cannot distinguish live from dead bacteria.

Purpose of the Study:

• To develop a rapid, accurate method for detecting and identifying antibiotic-resistant bacterial strains.
• To overcome limitations of existing phenotypic and genotypic detection techniques.

Main Methods:

• A cell sorter precisely deposits individual bacterial cells onto antibiotic-gradient agar plates.
• Elastic-light scattering is employed to analyze unique colony scatter patterns.
• Machine learning algorithms classify bacterial strains based on scatter patterns for rapid identification.

Main Results:

• The cell sorter and gradient plate method reduced subculturing steps for direct detection of resistant strains.
• Elastic-light scattering provided rapid, label-free, non-destructive classification of bacterial colonies.
• Scatter patterns correlated with antimicrobial resistance phenotypes.

Conclusions:

• This combined approach offers a faster, more efficient way to identify antibiotic-resistant bacteria.
• The method enables direct qualitative binary detection of resistance.
• Elastic-light scattering technology is a promising tool for real-time pathogen classification.