Automated characterization and parameter-free classification of cell tracks based on local migration behavior

Area of Science:

• Cell biology
• Biophysics
• Computational biology

Background:

• Cell migration is crucial for biological processes, but traditional population-level analysis of microscopy data misses single-cell dynamics.
• Image-based systems biology aims to quantitatively analyze biological processes using image data.

Purpose of the Study:

• To develop a fully automated, parameter-free method for characterizing and classifying cell track data.
• To fully exploit information from microscopy image data at the single-cell level.

Main Methods:

• Combined single-cell track measures (confinement ratio, asphericity) computed in a staggered fashion.
• Applied agglomerative hierarchical clustering in a parameter space of these measures.
• Validated on synthetic and in vitro neutrophil tracks.

Main Results:

• The method fully exploits track data information without prior knowledge, ensuring unbiased and general analysis.
• Successfully identified distinct cell migration behaviors within populations.
• Demonstrated effectiveness on both synthetic and real experimental data.

Conclusions:

• The developed approach enables comprehensive, automated analysis of cell migration dynamics.
• Facilitates unbiased classification of cell behaviors for deeper biological insights.
• Advances the field of image-based systems biology by unlocking single-cell track data potential.