FilamentSensor 2.0: An open-source modular toolbox for 2D/3D cytoskeletal filament tracking.

Area of Science:

• Cell Biology
• Biophysics
• Bioimaging

Background:

• Cytoskeletal dynamics are crucial for cellular functions and homeostasis.
• Quantitative analysis of cytoskeletal structures and dynamics provides critical insights.
• Current methods lack robust, automated workflows for detailed cytoskeletal analysis.

Purpose of the Study:

• To develop an automated, quantitative analysis workflow for cytoskeletal structures and dynamics.
• To enhance existing fiber detection algorithms for improved performance.
• To enable real-time tracking and cell event monitoring of fibrous elements.

Main Methods:

• Upgraded and expanded the FilamentSensor (FS) algorithm to FilamentSensor 2.0 (FS2.0).
• Implemented automatic detection and segmentation of fibrous structures.
• Developed real-time data extraction (center of mass, length, width, orientation, curvature) and object tracking.

Main Results:

• FS2.0 provides an unbiased, quantitative, and robust analysis workflow.
• The toolbox allows for automatic segmentation and characterization of cytoskeletal fibers.
• Enables frame-to-frame tracking of fibrous structures and monitoring of cellular events.

Conclusions:

• FS2.0 offers a highly automated solution for analyzing cytoskeletal dynamics.
• This tool facilitates detailed studies of cellular homeostasis and pathology.
• FS2.0 advances high-content live cell imaging analysis for cytoskeletal research.