An Image Processing Tool for Automated Quantification of Bacterial Burdens in Zebrafish Larvae

Area of Science:

• Microbiology
• Infectious Disease Modeling
• Zebrafish Research

Background:

• Zebrafish larvae serve as a model organism for studying bacterial pathogenesis.
• Quantifying bacterial burden in vivo using fluorescent pixel counts (FPC) offers advantages over traditional plating methods.
• Manual image processing for accurate FPC measurements is time-consuming and labor-intensive.

Purpose of the Study:

• To develop an automated ImageJ/Fiji-based macro for accurate detection of zebrafish larval borders.
• To streamline the quantification of fluorescent bacterial burdens in Mycobacterium marinum-infected zebrafish larvae.
• To replace laborious manual image processing with an efficient, automated workflow.

Main Methods:

• Development of an automated macro for ImageJ/Fiji software.
• Utilizing image processing techniques to accurately detect the external borders of zebrafish larvae.
• Application of the macro to Mycobacterium marinum-infected zebrafish larvae.

Main Results:

• The automated macro accurately detects the outside borders of zebrafish larvae.
• This automation facilitates precise delineation of bacteria within larvae from the surrounding medium.
• The developed macro significantly reduces the time and effort required for image analysis.

Conclusions:

• An automated ImageJ/Fiji macro provides an efficient and accurate method for quantifying bacterial burdens in zebrafish larvae.
• This tool enhances the utility of zebrafish as a model for infectious disease research.
• The automated approach improves the reproducibility and throughput of bacterial pathogenesis studies.