Automated image analysis for quantification of filamentous bacteria

Area of Science:

• Microbiology
• Bacterial Morphology
• Antimicrobial Resistance

Background:

• Beta-lactam antibiotics can alter bacterial cell wall shape, causing filamentation.
• Bacterial filamentation can complicate antimicrobial susceptibility testing (AST) by mimicking growth.
• Automated image analysis offers a potential solution for accurate filamentation quantification.

Purpose of the Study:

• To evaluate a novel automated image analysis algorithm for quantifying bacterial filamentation.
• To assess the algorithm's performance using a 3D digital microscopy system (oCelloScope).

Main Methods:

• Utilized three E. coli strains with varying resistance profiles.
• Analyzed 12 beta-lactam antibiotics and combinations for filamentation induction.
• Employed a 3D digital microscopy imaging system for data acquisition.

Main Results:

• The algorithm successfully quantified bacterial length and filamentation.
• Filamentation induced by beta-lactams peaked around 120 minutes.
• Average cell length reached approximately 30 μm during peak filamentation.

Conclusions:

• The automated algorithm effectively detects and quantifies beta-lactam-induced filamentation in E. coli.
• Rapid detection of morphological changes can advance the development of fast AST systems.
• Early detection of beta-lactam effects has significant clinical implications for antimicrobial therapy.