Bayesian analysis of coupled cellular and nuclear trajectories for cell migration

• Published in: Biometrics Vol. 78; no. 3; pp. 1209 - 1220
• Main Authors: Chakraborty, Saptarshi, Lan, Tian, Tseng, Yiider, Wong, Samuel W.K.
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01-09-2022
• Subjects: Algorithms; Angular distribution; Bayesian analysis; Bayesian hierarchical models; Bivariate analysis; bivariate angular data; Cell adhesion & migration; Cell culture; Cell migration; directional statistics; Markov chain Monte Carlo; Markov chains; Mathematical models; Motility; Nuclei (cytology); Statistical analysis; Time dependence; Trajectories; von Mises cosine distribution; cell migration; von Mises cosine distribution; Bayesian hierarchical models; directional statistics; Markov chain Monte Carlo; bivariate angular data
• ISSN: 0006-341X; 1541-0420
• DOI: 10.1111/biom.13468

Cell migration, the process by which cells move from one location to another, plays crucial roles in many biological events. While much research has been devoted to understand the process, most statistical cell migration models rely on using time‐lapse microscopy data from cell trajectories alone. However, the cell and its associated nucleus work together to orchestrate cell movement, which motivates a joint analysis of coupled cell–nucleus trajectories. In this paper, we propose a Bayesian hierarchical model for analyzing cell migration. We incorporate a bivariate angular distribution to handle the coupled cell–nucleus trajectories and introduce latent motility status indicators to model a cell's motility as a time‐dependent characteristic. A Markov chain Monte Carlo algorithm is provided for practical implementation of our model, which is used on real experimental data from MDA‐MB‐231 and NIH 3T3 cells. Through the fitted models, deeper insights into the migratory patterns of these experimental cell populations are gained and their differences are quantified.