Spatio-temporal cell cycle phase analysis using level sets and fast marching methods.

Area of Science:

• Cell Biology
• Biophysics
• Computational Biology

Background:

• Live cell imaging with novel molecular markers allows monitoring of cellular functions.
• High-throughput and high-content analysis requires automated image processing for live cell assays.

Purpose of the Study:

• To develop and validate automated image analysis tools for simultaneous tracking of cell cycle phase and cell motion in live cells.
• To enable high-content screening of compounds targeting cell cycle progression in cancer research.

Main Methods:

• A model-based approach using a new cell cycle marker for automated phase analysis (G1, S, G2, M).
• Spatio-temporal volume segmentation treating time as the z-axis.
• Level sets with shape/size constraints for G2/S phase segmentation.
• A novel speed function and fast marching method for G1 phase tracking based on nuclear appearance changes.

Main Results:

• Simultaneous tracking of cell cycle phase and cell motion at the single-cell level was achieved.
• Quantitative results demonstrated the approach's viability on control and inhibitor-treated cells.
• The developed tools accurately characterize the four phases of the cell cycle.

Conclusions:

• The developed image analysis tools enable robust, automated monitoring of cell cycle dynamics in live cells.
• This approach facilitates the study of cell cycle inhibitors and their effects on cancer cell replication.
• The method supports high-content screening for drug discovery targeting cell cycle regulation.