Cochet-Escartin Olivier, Locke Tiffany T, Shi Winnie H, Steele Robert E, Collins Eva-Maria S
Department of Physics, University of California, San Diego, La Jolla, California.
Division of Biological Sciences, University of California, San Diego, La Jolla, California.
Biophys J. 2017 Dec 19;113(12):2827-2841. doi: 10.1016/j.bpj.2017.10.045.
Cell sorting, whereby a heterogeneous cell mixture organizes into distinct tissues, is a fundamental patterning process in development. Hydra is a powerful model system for carrying out studies of cell sorting in three dimensions, because of its unique ability to regenerate after complete dissociation into individual cells. The physicists Alfred Gierer and Hans Meinhardt recognized Hydra's self-organizing properties more than 40 years ago. However, what drives cell sorting during regeneration of Hydra from cell aggregates is still debated. Differential motility and differential adhesion have been proposed as driving mechanisms, but the available experimental data are insufficient to distinguish between these two. Here, we answer this longstanding question by using transgenic Hydra expressing fluorescent proteins and a multiscale experimental and numerical approach. By quantifying the kinematics of single cell and whole aggregate behaviors, we show that no differences in cell motility exist among cell types and that sorting dynamics follow a power law with an exponent of ∼0.5. Additionally, we measure the physical properties of separated tissues and quantify their viscosities and surface tensions. Based on our experimental results and numerical simulations, we conclude that tissue interfacial tensions are sufficient to explain cell sorting in aggregates of Hydra cells. Furthermore, we demonstrate that the aggregate's geometry during sorting is key to understanding the sorting dynamics and explains the exponent of the power law behavior. Our results answer the long standing question of the physical mechanisms driving cell sorting in Hydra cell aggregates. In addition, they demonstrate how powerful this organism is for biophysical studies of self-organization and pattern formation.
细胞分选是发育过程中的一个基本模式形成过程,通过该过程,异质细胞混合物组织成不同的组织。水螅是用于在三维空间中进行细胞分选研究的强大模型系统,因为它具有在完全解离成单个细胞后再生的独特能力。物理学家阿尔弗雷德·吉勒和汉斯·迈因哈特在40多年前就认识到了水螅的自组织特性。然而,从细胞聚集体再生水螅的过程中驱动细胞分选的因素仍存在争议。不同的运动性和不同的黏附力已被提出作为驱动机制,但现有的实验数据不足以区分这两者。在这里,我们通过使用表达荧光蛋白的转基因水螅以及多尺度实验和数值方法来回答这个长期存在的问题。通过量化单细胞和整个聚集体行为的运动学,我们表明细胞类型之间不存在细胞运动性差异,并且分选动力学遵循幂律,指数约为0.5。此外,我们测量了分离组织的物理性质,并量化了它们的粘度和表面张力。基于我们的实验结果和数值模拟,我们得出结论,组织界面张力足以解释水螅细胞聚集体中的细胞分选。此外,我们证明了分选过程中聚集体的几何形状对于理解分选动力学至关重要,并解释了幂律行为的指数。我们的结果回答了驱动水螅细胞聚集体中细胞分选的物理机制这一长期存在的问题。此外,它们展示了这种生物体在自组织和模式形成的生物物理研究中的强大作用。