A random-sampling approach to track cell divisions in time-lapse fluorescence microscopy

Area of Science:

• Cell and developmental biology
• Biophysics
• Microscopy image analysis

Background:

• 3D particle tracking is crucial for analyzing dynamical processes in time-lapse imaging, especially in live cell and developmental biology.
• Standard tracking algorithms struggle with transient signals and objects with constrained movements within tissues.
• Existing methods fail to provide robust tracking under these challenging conditions.

Purpose of the Study:

• To develop an optimized 3D tracking algorithm for scenarios with transient signals and constrained object movement.
• To address the limitations of conventional tracking methods in complex biological imaging.
• To provide a robust solution for identity management in particle tracking.

Main Methods:

• Developed a novel registration algorithm merging registration and tracking tasks.
• Integrated random sampling to solve the identity management problem.
• Applied and validated the algorithm on 4D light-sheet fluorescence microscopy data of Arabidopsis thaliana roots.

Main Results:

• The algorithm demonstrates robust tracking performance in challenging datasets with transient signals.
• Successfully tracked mitotic events in growing Arabidopsis thaliana roots.
• Validated against surrogate data and manual tracking, confirming method efficacy.

Conclusions:

• The developed method effectively tracks mitotic events in challenging datasets with transient fluorescent markers.
• Fills a critical gap in existing particle tracking techniques for unregistered images.
• Provides an open-source software implementation for broader scientific application.