A Computational Pipeline for Quantifying Kinetochore Morphological Changes in Live Cells.

Area of Science:

• Cell Biology
• Biophysics
• Computational Biology

Background:

• Kinetochores are crucial for chromosome segregation, requiring resistance and deformation under spindle forces.
• Understanding kinetochore morphology provides insights into their structure and function.
• Existing methods struggle to measure dynamic kinetochore shapes in live cells due to diffraction limits and irregular forms.

Purpose of the Study:

• To develop and validate a computational pipeline for quantifying kinetochore morphology in live mammalian cells.
• To provide robust metrics for analyzing complex, non-Gaussian shape changes in kinetochores.
• To offer a framework for studying dynamic cellular structures.

Main Methods:

• A computational pipeline was developed to track, pair, and rotate kinetochores relative to their load-bearing axis.
• Kinetochore signals were segmented, and frames with overlapping signals were excluded.
• Novel metrics were introduced, including a non-parametric size metric, classification of morphological patterns (asymmetry, tails, multimodality), and a protein-to-protein vector for structural rearrangements.

Main Results:

• The pipeline successfully quantifies kinetochore morphology in live mammalian cells with fluorescently tagged proteins.
• The proposed non-parametric size metric is more robust than the traditional full-width-at-half-maximum (FWHM).
• The metrics effectively capture complex kinetochore size and shape changes, outperforming FWHM.

Conclusions:

• The developed computational pipeline offers a robust framework for analyzing complex kinetochore shape changes in live cells.
• This method enhances our understanding of kinetochore structure, function, and dynamics during chromosome segregation.
• The pipeline has potential applications for studying other small, dynamic cellular structures.