Shape matters: Inferring the motility of confluent cells from static images

Quirine J. S. Braat, Giulia Janzen, Bas C. Jansen, Vincent E. Debets, Simone Ciarella, Liesbeth M. C. Janssen
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Abstract

Cell motility in dense cell collectives is pivotal in various diseases like cancer metastasis and asthma. A central aspect in these phenomena is the heterogeneity in cell motility, but identifying the motility of individual cells is challenging. Previous work has established the importance of the average cell shape in predicting cell dynamics. Here, we aim to identify the importance of individual cell shape features, rather than collective features, to distinguish between high-motility (active) and low-motility (passive) cells in heterogeneous cell layers. Employing the Cellular Potts Model, we generate simulation snapshots and extract static features as inputs for a simple machine-learning model. Our results show that when the passive cells are non-motile, this machine-learning model can accurately predict whether a cell is passive or active using only single-cell shape features. Furthermore, we explore scenarios where passive cells also exhibit some degree of motility, albeit less than active cells. In such cases, our findings indicate that a neural network trained on shape features can accurately classify cell motility, particularly when the number of active cells is low, and the motility of active cells is significantly higher compared to passive cells. This work offers potential for physics-inspired predictions of single-cell properties with implications for inferring cell dynamics from static histological images.
形状很重要从静态图像推断汇合细胞的运动性
密集细胞群中的细胞运动在癌症转移和哮喘等多种疾病中至关重要。这些现象的核心是细胞运动的异质性,但识别单个细胞的运动具有挑战性。之前的研究已经确定了细胞平均形状在预测细胞动态中的重要性。在这里,我们旨在确定单个细胞形状特征的重要性,而不是集体特征,以区分异质细胞层中的高运动性(主动)和低运动性(被动)细胞。我们利用细胞波特斯模型生成模拟快照,并提取静态特征作为简单机器学习模型的输入。结果表明,当被动细胞不运动时,该机器学习模型仅利用单细胞形状特征就能准确预测细胞是被动还是主动。此外,我们还探索了被动细胞也表现出一定程度运动性的情况,尽管运动性低于主动细胞。在这种情况下,我们的研究结果表明,根据形状特征训练的神经网络可以准确地对细胞运动进行分类,尤其是当活跃细胞的数量较少,而活跃细胞的运动能力明显高于被动细胞时。这项工作为受物理学启发的单细胞特性预测提供了可能性,对从静态组织学图像推断细胞动态具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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