Departments of Bioinformatics and Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX.
Department of Cell Biology, Harvard Medical School, Boston, MA.
J Cell Biol. 2019 Jan 7;218(1):350-379. doi: 10.1083/jcb.201711023. Epub 2018 Dec 6.
Growth cones are complex, motile structures at the tip of an outgrowing neurite. They often exhibit a high density of filopodia (thin actin bundles), which complicates the unbiased quantification of their morphologies by software. Contemporary image processing methods require extensive tuning of segmentation parameters, require significant manual curation, and are often not sufficiently adaptable to capture morphology changes associated with switches in regulatory signals. To overcome these limitations, we developed Growth Cone Analyzer (GCA). GCA is designed to quantify growth cone morphodynamics from time-lapse sequences imaged both in vitro and in vivo, but is sufficiently generic that it may be applied to nonneuronal cellular structures. We demonstrate the adaptability of GCA through the analysis of growth cone morphological variation and its relation to motility in both an unperturbed system and in the context of modified Rho GTPase signaling. We find that perturbations inducing similar changes in neurite length exhibit underappreciated phenotypic nuance at the scale of the growth cone.
生长锥是一个伸出的神经突尖端的复杂的、可移动的结构。它们通常表现出很高密度的丝状伪足（细的肌动蛋白束），这使得软件对其形态进行无偏量化变得复杂。当代图像处理方法需要对分割参数进行广泛的调整，需要大量的手动管理，并且往往不够灵活，无法捕捉到与调节信号转换相关的形态变化。为了克服这些限制，我们开发了生长锥分析器（GCA）。GCA 旨在从体外和体内成像的延时序列中定量测量生长锥形态动力学，但它足够通用，可以应用于非神经元细胞结构。我们通过分析生长锥形态变化及其与运动的关系，证明了 GCA 的适应性，无论是在未受干扰的系统中还是在改变 Rho GTPase 信号的情况下。我们发现，在诱导神经突长度相似变化的情况下，生长锥在尺度上表现出被低估的表型细微差别。
