FLIMJ

FLIMJ performs quantitative analysis of fluorescence lifetime imaging microscopy (FLIM) data to extract fluorophore lifetimes and infer chemical microenvironment characteristics.

Key Features:

• FLIM fitting routines: Implements decay curve fitting routines built on the FLIMLib decay curve fitting library to compute fluorophore lifetimes.
• ImageJ Ops integration: Integrates with the ImageJ Ops framework to enable composition and extension of image-analysis pipelines.
• Lifetime parameter extraction: Extracts lifetime-derived parameters relevant to fluorophore proximity, pH, and hydrophobic microenvironments.
• Validation: Fitting routines have been validated against industry-standard FLIM analysis protocols.
• Programming environment compatibility: Provides interoperability with Jupyter notebooks and language bindings for Python and Groovy.
• Analytical capabilities: Supports lifetime-based image segmentation and image colocalization analyses.

Scientific Applications:

• Metabolic measurements: Enables FLIM-based assessment of cellular metabolism through lifetime-derived metrics.
• Protein distance quantification: Supports quantification of distances between proteins via lifetime changes indicative of proximity.
• Cellular imaging studies: Facilitates investigation of cellular microenvironments by mapping lifetime variations associated with pH and hydrophobic regions.
• Lifetime-based image segmentation: Uses lifetime contrasts to segment images based on fluorophore environment or state.
• Image colocalization: Employs lifetime information to assess spatial correlation between fluorophores or labeled structures.

Methodology:

Performs decay curve fitting using the FLIMLib decay curve fitting library within the ImageJ Ops framework and validates fitting results against industry-standard FLIM analysis protocols.