PunctaSpecks

PunctaSpecks extracts and quantifies punctate fluorescent signals from time-lapse fluorescence microscopy to detect, track, and analyze dynamic subcellular processes.

Key Features:

• Automated detection and tracking: Automatically detects and tracks fluorescently labeled biomolecules, including diffusing entities such as moving mitochondria.
• Quantitative analysis: Calculates quantitative parameters of fluorescence signals including number, areas, lifetimes, and amplitudes.
• Overlap probability determination: Determines overlap probability between two processes or organelles imaged with indicator dyes of different colors for co-localization and interaction analysis.
• Robustness testing: Validated on synthetic time-lapse movies simulating mobile fluorescence objects of various sizes with varying noise levels and patterns of appearance, disappearance, and movement.
• Application to biological processes: Applied to characterize protein-protein interactions in store-operated calcium entry, the evolution of IP₃-induced calcium signals in neurons, and activity of Alzheimer's disease-associated β-amyloid pores.
• Extensibility: Implemented as an open-source algorithm that can be customized to analyze more than two types of biomolecules visualized with different colored markers.

Scientific Applications:

• Store-operated calcium entry: Characterizes protein-protein interactions involved in store-operated calcium entry using punctate fluorescence analysis.
• IP₃-induced calcium signaling in neurons: Analyzes the evolution of IP₃-induced calcium signals in neuronal time-lapse recordings.
• Alzheimer's β-amyloid pore activity: Quantifies activity of Alzheimer's disease-associated β-amyloid pores from fluorescence imaging data.
• Co-localization and interaction studies: Measures overlap probabilities between differently colored indicator dyes to assess organelle and process interactions.
• Dynamic organelle tracking: Tracks and quantifies dynamics of diffusing organelles such as mitochondria in live-cell imaging.

Methodology:

Computational steps explicitly include automated detection and tracking of fluorescent puncta, calculation of number, areas, lifetimes, and amplitudes, computation of overlap probabilities between two channels, and robustness validation using synthetic time-lapse movies that simulate mobile fluorescence objects with varying sizes, noise levels, and dynamic appearance/disappearance.