发表机构
University of Geneva; CNRS and Université Claude Bernard Lyon 1（日内瓦大学; 法国国家科学研究中心和里昂第一大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
NEMO是一个Python框架，通过三角化网格重建和切平面向列张量分析，实现弯曲及多层生物表面深度分辨的向列序量化、拓扑缺陷识别与形态测量，并在囊泡和水螅数据上验证。
AI 中文摘要
在从细胞骨架网络到整个组织的生物尺度上，取向序和拓扑缺陷出现在复杂的三维几何结构中。然而，量化具有头尾对称性的向列取向序仍然具有挑战性：二维投影会引入几何畸变，而当前的三维方法往往难以解析弯曲或多层结构内不同的向列场。在此，我们介绍NEMO，一个模块化的Python框架，用于深度分辨的切向向列序和表面形态的量化。通过将生物表面重建为三角化网格，NEMO投影弯曲的强度层，提取局部向列指向矢，并在切平面内计算局部平均的向列张量。该流程识别拓扑缺陷，并通过考虑底层表面的高斯曲率来计算其拓扑电荷。此外，NEMO通过表面间距离和表面拟合的高斯曲率与平均曲率估计来量化组织形态。我们使用囊泡上的合成向列薄膜和水螅中的实验性肌动蛋白组织展示了该框架的能力。通过将可定制的投影与表面约束分析相结合，NEMO提供了一个统一的框架，用于跨尺度量化取向序与几何之间的相互作用。
英文摘要
Across biological scales, from cytoskeletal networks to whole tissues, orientational order and topological defects arise within complex three-dimensional geometries. However, quantifying nematic order orientational order with head-to-tail symmetry remains challenging: 2D projections introduce geometric distortions, while current 3D methods often struggle to resolve distinct nematic fields within curved or multilayer structures. Here, we introduce NEMO, a modular Python framework for the depth-resolved quantification of tangential nematic order and surface morphology. By reconstructing biological surfaces as triangulated meshes, NEMO projects curved intensity layers, extracts local nematic directors, and computes locally averaged nematic tensors within the tangent plane. The pipeline identifies topological defects and computes their topological charge by accounting for the Gaussian curvature of the underlying surface. Furthermore, NEMO quantifies tissue morphology through inter-surface distance and surface-fitted estimates of Gaussian and mean curvatures. We show the capacities of this framework using a synthetic nematic film on a vesicle and experimental actin organisation in Hydra. By combining customisable projections with surface-constrained analysis, NEMO provides a unified framework for quantifying the interplay between orientational order and geometry across scales.
