SpotitPy

SpotitPy performs object-based co-localization analysis of fluorescent labels in microscopy images to quantify spatial overlap and interactions between molecular markers.

Key Features:

• Semi-automated workflow: Streamlines identification and quantification of spatial overlaps between fluorescent labels in microscopy images.
• Advanced pre-processing (de-noising): Applies pre-processing manipulations to de-noise images and enhance image quality prior to analysis.
• Robust segmentation capabilities: Performs semi-automated segmentation to delineate regions of interest across distinct cell types for improved specificity.
• Quantitative co-localization analysis: Quantifies co-localized fluorescent signals across two channels to measure spatial overlap between markers.
• High-throughput dataset handling: Processes large image datasets to support scalable quantitative analyses.

Scientific Applications:

• Analysis of cytoplasmic ribonucleoprotein granules (e.g., P bodies): Validated for assessing processing (P) bodies in fluorescence microscopy images.
• Studies under challenged mRNA translation: Applied to evaluate co-localization changes in conditions that challenge mRNA translation.
• Large-scale quantitative image analysis: Enables semi-automatic quantification of molecular interactions across extensive microscopy datasets.

Methodology:

Selection of fluorescent labels; semi-automated segmentation to delineate regions of interest; advanced pre-processing steps for image denoising; quantitative analysis of co-localized signals across distinct channels; handling of large image datasets.