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.

中文翻译：

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使用拓扑数据分析和基于代理的建模量化间充质细胞群中的集体运动模式

已知汇合单层中的成纤维细胞采用细长的形态，其中细胞与相邻细胞平行定向。我们收集并分析了新的显微镜图像，以表明汇合的成纤维细胞是活动的，并且邻近的细胞经常以反平行方向移动，这是一种集体运动现象，我们称之为细胞群的“流态化”。我们使用机器学习对每部电影进行单元跟踪，然后利用拓扑数据分析（TDA）来显示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，即反平行单个相邻细胞和相邻细胞组的长距离移动。然后，我们利用从每部电影中提取的 TDA 摘要对 D'Orsgona 模型执行贝叶斯参数估计，这是一种基于代理的模型 (ABM)，已知会产生各种不同的模式，包括与流化性质相似的模式。尽管 D'Orsgona ABM 是一个仅描述细胞间吸引力和排斥力的现象学模型，但 D'Orsogna 模型参数空间的估计区域在所有电影中都是一致的，这表明近距离的细胞间排斥力存在特定水平可能是一种有助于驱动汇合间充质细胞群流化模式的机制。