Joint modeling of cell and nuclear shape variation

Area of Science:

• Cell Biology
• Biophysics
• Computational Biology

Background:

• Modeling cell shape variation is crucial for understanding cell biology.
• Nonrigid image registration enables nonparametric nuclear shape modeling and analysis.
• Previous methods focused on individual cell or nuclear shapes.

Purpose of the Study:

• To explore the relationship between cell and nuclear shape.
• To develop combined cell and nuclear shape space models for joint variation analysis.
• To create a dynamics model for predicting shape changes and assessing molecular effects.

Main Methods:

• Nonrigid image registration for nonparametric shape modeling.
• Construction of low-dimensional shape spaces from pairwise deformation distances.
• Development of first-order dynamics models for shape prediction.
• Application of models to analyze effects of protein tags (e.g., C1QBP) and drugs on cell lines.

Main Results:

• Cell and nuclear shapes are frequently dependent on each other.
• Developed models capture joint shape variation in 3D cellular structures.
• The dynamics model predicts shape changes over time.
• Tagged C1QBP was shown to reduce cell-nuclear shape correlation.
• Shape spaces can be reconstructed efficiently using a fraction of pairwise distances.

Conclusions:

• Combined cell and nuclear shape models offer deeper insights into cellular organization.
• The developed open-source tools facilitate future research on the molecular basis of cell shape.
• Understanding coupled shape dynamics is key to cell biology and disease research.