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

Area of Science:

• Infectious diseases
• Microbiology
• Zebrafish models

Background:

• Zebrafish larvae are valuable models for studying bacterial pathogenesis.
• Quantifying bacterial burden in vivo using fluorescent pixel counts (FPC) offers advantages over traditional methods.
• Manual image processing for FPC measurement is labor-intensive and time-consuming.

Purpose of the Study:

• To develop an automated method for delineating zebrafish larvae in images.
• To improve the efficiency and accuracy of quantifying bacterial burdens in zebrafish models.

Main Methods:

• Development of an automated ImageJ/Fiji-based macro.
• Utilizing microscopy to capture images of Mycobacterium marinum-infected zebrafish larvae.
• Image processing to automatically detect larval borders.

Main Results:

• The automated macro accurately detects the outside borders of zebrafish larvae.
• This automation significantly reduces the manual effort required for image processing.
• Enables more efficient quantification of fluorescent bacterial burdens in vivo.

Conclusions:

• An automated ImageJ/Fiji macro provides an efficient and accurate solution for analyzing bacterial infections in zebrafish larvae.
• This tool facilitates high-throughput studies of bacterial pathogenesis using zebrafish models.