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对一个不断生长和分支的网络的时空结构进行全面识别。

Full identification of a growing and branching network's spatio-temporal structures.

作者信息

Chassereau Thibault, Chapeland-Leclerc Florence, Herbert Éric

机构信息

Université Paris Cité, CNRS, UMR 8236-LIED, Paris, France.

Université Paris Cité, CNRS, UMR 8236-LIED, Paris, France.

出版信息

Biophys J. 2025 Jan 21;124(2):284-296. doi: 10.1016/j.bpj.2024.12.002. Epub 2024 Dec 5.

Abstract

Experimentally monitoring the kinematics of branching network growth is a tricky task, given the complexity of the structures generated in three dimensions. One option is to drive the network in such a way as to obtain two-dimensional growth, enabling a collection of independent images to be obtained. The density of the network generates ambiguous structures, such as overlaps and meetings, which hinder the reconstruction of the chronology of connections. In this paper, we propose a general method for global network reconstruction. Each network connection is defined by a unique label, enabling it to be tracked in time and space. In this work, we distinguish between lateral and apical branches on the one hand, and extremities on the other. Finally, we reconstruct the network after identifying and eliminating overlaps. This method is then applied to the model filamentous fungus Podospora anserina to reconstruct its growing thallus. We derive criteria for differentiating between apical and lateral branches. We find that the outer ring is favorably composed of apical branches, while densification within the network comes from lateral branches. From this, we derive the specific dynamics of each of the two types. Finally, in the absence of any latency phase during growth initiation, we can reconstruct a time based on the equality of apical and lateral branching collections. This makes it possible to directly compare the growth dynamics of different thalli.

摘要

鉴于三维空间中生成的结构的复杂性,通过实验监测分支网络生长的运动学是一项棘手的任务。一种选择是以某种方式驱动网络以获得二维生长,从而能够获取一系列独立的图像。网络的密度会产生模糊的结构,例如重叠和交汇,这会阻碍连接时间顺序的重建。在本文中,我们提出了一种用于全局网络重建的通用方法。每个网络连接都由一个唯一的标签定义,从而能够在时间和空间上对其进行跟踪。在这项工作中,一方面,我们区分侧向分支和顶端分支,另一方面,区分末端分支。最后,在识别并消除重叠之后,我们重建网络。然后将该方法应用于丝状真菌模式生物——栗疫霉,以重建其生长中的菌体。我们推导了区分顶端分支和侧向分支的标准。我们发现外环主要由顶端分支组成,而网络内部的致密化来自侧向分支。据此,我们推导了这两种类型各自的特定动态。最后,在生长起始阶段不存在任何潜伏期的情况下,我们可以基于顶端分支和侧向分支集合的相等性来重建一个时间。这使得直接比较不同菌体的生长动态成为可能。

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