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.

中文翻译：

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细胞迁移耦合细胞和核轨迹的贝叶斯分析

细胞迁移是细胞从一个位置移动到另一个位置的过程，在许多生物事件中起着至关重要的作用。虽然很多研究都致力于理解这个过程，但大多数统计细胞迁移模型依赖于单独使用来自细胞轨迹的延时显微镜数据。然而，细胞及其相关的细胞核协同工作以协调细胞运动，这激发了对耦合的细胞 - 细胞核轨迹的联合分析。在本文中，我们提出了用于分析细胞迁移的贝叶斯分层模型。我们采用双变量角分布来处理耦合的细胞核轨迹，并引入潜在运动状态指标以将细胞运动建模为时间依赖性特征。提供马尔可夫链蒙特卡罗算法用于我们模型的实际实现，它用于来自 MDA-MB-231 和 NIH 3T3 细胞的真实实验数据。通过拟合模型，可以更深入地了解这些实验细胞群的迁移模式，并量化它们的差异。