IJOQ

IJOQ quantifies intercellular junction organization in fluorescence microscopy images to assess adherens and tight junction integrity and epithelial barrier disruption during respiratory infections such as Streptococcus pneumoniae.

Key Features:

• Automated analysis: A Python script that processes fluorescence microscopy images to measure intercellular junction integrity, including adherens and tight junctions.
• Handling variability: Robust algorithms that manage sample-to-sample staining variability to produce consistent quantification across diverse images.
• Validation and accuracy: Validated in silico with computer-generated images containing simulated intercellular junction disruptions and confirmed on images from in vitro and in vivo bacterial infection models, showing superior performance to existing semi-automated scripts.

Scientific Applications:

• Respiratory pathogen research: Quantitative analysis of junction organization to study how pathogens such as Streptococcus pneumoniae disrupt epithelial barriers.
• Pathogen-induced barrier dysfunction: Measurement of adherens and tight junction disruptions to investigate mechanisms underlying pneumonia, septicemia, and meningitis caused by S. pneumoniae.
• High-throughput and variable-staining studies: Application to datasets with high staining variability and diverse experimental setups for large-scale fluorescence microscopy analyses.

Methodology:

Automated image analysis implemented in Python that processes fluorescence microscopy images, identifies and measures disruptions in adherens and tight junctions, employs algorithms robust to staining variability, and was validated in silico using computer-generated images with simulated junction disruptions and on in vitro and in vivo bacterial infection images.