AFid

AFid identifies and tags autofluorescent regions in multi-channel fluorescence microscopy images to enable their exclusion from quantitative image analysis.

Key Features:

• Automated Identification: Automates identification of autofluorescent pixels within multi-channel fluorescence microscopy images post-acquisition.
• Discrete Object Tagging: Labels autofluorescent regions as discrete objects for precise exclusion from downstream analysis.
• Integration with Existing Workflows: Provides implementations for ImageJ, Matlab, and R to incorporate autofluorescence identification into existing image analysis workflows.
• Versatility Across Samples and Methods: Applicable to various sample types, staining panels, and image acquisition methods.

Scientific Applications:

• Validation on FFPE samples: Validated on formalin-fixed paraffin-embedded (FFPE) human colorectal tissue samples stained for common immune markers.
• Quantitative image analysis: Facilitates accurate quantitative analysis in complex biological studies by removing autofluorescence confounders.
• HIV transmission studies: Applied to measure HIV-Dendritic cell interactions within a colorectal explant model of HIV transmission.

Methodology:

Analyzes multi-channel fluorescence microscopy images to identify regions exhibiting autofluorescent characteristics, processes those regions as discrete objects, and enables their exclusion from further analysis in a post-acquisition workflow.