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Reconstructing 3D deformation dynamics for curved epithelial sheet morphogenesis from positional data of sparsely-labeled cells

机译:从稀疏标记细胞的位置数据重建曲面上皮片形态发生的3D变形动力学

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摘要

Quantifying global tissue deformation patterns is essential for understanding how organ-specific morphology is generated during development and regeneration. However, due to imaging difficulties and complex morphology, little is known about deformation dynamics for most vertebrate organs such as the brain and heart. To better understand these dynamics, we propose a method to precisely reconstruct global deformation patterns for three-dimensional morphogenesis of curved epithelial sheets using positional data from labeled cells representing only 1–10% of the entire tissue with limited resolution. By combining differential-geometrical and Bayesian frameworks, the method is applicable to any morphology described with arbitrary coordinates, and ensures the feasibility of analyzing many vertebrate organs. Application to data from chick forebrain morphogenesis demonstrates that our method provides not only a quantitative description of tissue deformation dynamics but also predictions of the mechanisms that determine organ-specific morphology, which could form the basis for the multi-scale understanding of organ morphogenesis.
机译:量化整体组织变形模式对于了解在发育和再生过程中如何生成器官特定形态至关重要。但是,由于成像困难和复杂的形态,对于大多数脊椎动物器官(如大脑和心脏)的变形动力学知之甚少。为了更好地理解这些动力学,我们提出了一种方法,该方法使用来自标记细胞的位置数据(仅代表整个组织的1-10%)以有限的分辨率精确地重建弯曲上皮片层的三维形态发生的整体变形模式。通过组合差分几何和贝叶斯框架,该方法适用于用任意坐标描述的任何形态,并确保分析许多脊椎动物器官的可行性。应用于鸡前脑形态发生数据的研究表明,我们的方法不仅提供了组织变形动力学的定量描述,而且还预测了决定器官特异性形态的机制,这可以为对器官形态发生的多尺度理解提供基础。

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