Bayesian analysis of coupled cellular and nuclear trajectories for cell migration

Area of Science:

• * Cell Biology
• * Biophysics
• * Statistical Modeling

Background:

• * Cell migration is vital for biological processes but often modeled using only cell trajectories.
• * Existing models do not account for the coupled movement of the cell and its nucleus.
• * Understanding cell-nucleus coordination is key to accurate migration analysis.

Purpose of the Study:

• * To develop a novel Bayesian hierarchical model for analyzing coupled cell-nucleus trajectories in cell migration.
• * To incorporate latent motility status indicators for time-dependent motility characterization.
• * To provide a computational framework for analyzing real experimental cell migration data.

Main Methods:

• * Developed a Bayesian hierarchical model incorporating a bivariate angular distribution for coupled trajectories.
• * Introduced latent motility status indicators to model time-varying cell motility.
• * Implemented the model using a Markov chain Monte Carlo algorithm.

Main Results:

• * Applied the model to experimental data from MDA-MB-231 and NIH 3T3 cells.
• * Gained deeper insights into the migratory patterns of these cell populations.
• * Quantified the differences in migratory behaviors between the studied cell types.

Conclusions:

• * The proposed model offers a more comprehensive approach to cell migration analysis by considering cell-nucleus coupling.
• * Latent motility indicators enhance the understanding of dynamic cellular behavior.
• * The model provides valuable quantitative insights into cell migration patterns and differences, applicable to various cell types.