PDF(Pubmed)
Abstract：
Fibroblasts in a confluent monolayer are known to adopt elongated morphologies in which cells are oriented parallel to their neighbors. We collected and analyzed new microscopy movies to show that confluent fibroblasts are motile and that neighboring cells often move in anti-parallel directions in a collective motion phenomenon we refer to as \"fluidization\" of the cell population. We used machine learning to perform cell tracking for each movie and then leveraged topological data analysis (TDA) to show that time-varying point-clouds generated by the tracks contain significant topological information content that is driven by fluidization, i.e., the anti-parallel movement of individual neighboring cells and neighboring groups of cells over long distances. We then utilized the TDA summaries extracted from each movie to perform Bayesian parameter estimation for the D\'Orsgona model, an agent-based model (ABM) known to produce a wide array of different patterns, including patterns that are qualitatively similar to fluidization. Although the D\'Orsgona ABM is a phenomenological model that only describes inter-cellular attraction and repulsion, the estimated region of D\'Orsogna model parameter space was consistent across all movies, suggesting that a specific level of inter-cellular repulsion force at close range may be a mechanism that helps drive fluidization patterns in confluent mesenchymal cell populations.
摘要：
已知汇合单层中的成纤维细胞采用细长形态,其中细胞平行于其邻居取向。我们收集并分析了新的显微镜电影，以显示融合的成纤维细胞是活动的，并且相邻细胞经常在我们称为细胞群的“流化”的集体运动现象中反平行方向移动。我们使用机器学习对每部电影执行细胞跟踪，然后利用拓扑数据分析（TDA）来显示由轨道生成的时变点云包含由流化驱动的重要拓扑信息内容。即，单个相邻细胞和相邻细胞组在长距离上的反平行运动。然后，我们利用从每部电影中提取的TDA摘要对D'Orsgona模型进行贝叶斯参数估计，一种基于代理的模型(ABM)，已知会产生各种各样的不同模式，包括在质量上类似于流化的模式。虽然D'OrsgonaABM是一个现象学模型，只描述细胞间的吸引和排斥，D'Orsogna模型参数空间的估计区域在所有电影中都是一致的，这表明近距离特定水平的细胞间斥力可能是一种有助于驱动融合间充质细胞群体流化模式的机制。
