An open-source pipeline for longitudinal single-cell tracking and cell-cycle/migration coupling analysis for...

Area of Science:

• Cell Biology
• Bioimaging
• Computational Biology

Background:

• High-throughput time-lapse imaging is crucial for neuroprotection, drug discovery, and neurorepair studies.
• Analyzing long-term datasets is challenging due to segmentation noise, photobleaching, and tracking errors.
• Fluorescent ubiquitination-based cell cycle indicator (FUCCI) reporters visualize cell-cycle phases but have limited integration with migration behavior analysis.

Purpose of the Study:

• To develop an open, integrated workflow for tracking cell-cycle progression and cell motility simultaneously.
• To overcome limitations in analyzing complex live-cell imaging datasets.
• To enable high-throughput analysis of cell behavior in various biological contexts.

Main Methods:

• Combined Fiji/ImageJ preprocessing, Cellpose deep learning segmentation, and TrackMate cell tracking.
• Retrained Cellpose models using manually corrected masks for improved segmentation accuracy.
• Optimized TrackMate parameters for enhanced trajectory continuity.

Main Results:

• Iterative retraining significantly improved segmentation accuracy in both brightfield and fluorescent images.
• Optimized tracking parameters enhanced trajectory continuity, particularly for frame-to-frame linking.
• The workflow enabled detailed analysis of cell migration, morphology, division, and cell-cycle progression.
• Observed heterogeneous cell motility, with reduced displacement during S/G2/M phases compared to G1 phase.

Conclusions:

• The developed workflow successfully links cell-cycle state with migration dynamics.
• This adaptable workflow can be applied to neuroprotection screening, neural cultures, organoids, and co-culture studies.
• Facilitates advanced single-cell and population-level analysis of cellular behaviors.