Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America.
PLoS One. 2014 Apr 16;9(4):e94766. doi: 10.1371/journal.pone.0094766. eCollection 2014.
Cytoskeletal polymers play a fundamental role in the responses of cells to both external and internal stresses. Quantitative knowledge of the mechanical properties of those polymers is essential for developing predictive models of cell mechanics and mechano-sensing. Linear cytoskeletal polymers, such as actin filaments and microtubules, can grow to cellular length scales at which they behave as semiflexible polymers that undergo thermally-driven shape deformations. Bending deformations are often modeled using the wormlike chain model. A quantitative metric of a polymer's resistance to bending is the persistence length, the fundamental parameter of that model. A polymer's bending persistence length is extracted from its shape as visualized using various imaging techniques. However, the analysis methodologies required for determining the persistence length are often not readily within reach of most biological researchers or educators. Motivated by that limitation, we developed user-friendly, multi-platform compatible software to determine the bending persistence length from images of surface-adsorbed or freely fluctuating polymers. Three different types of analysis are available (cosine correlation, end-to-end and bending-mode analyses), allowing for rigorous cross-checking of analysis results. The software is freely available and we provide sample data of adsorbed and fluctuating filaments and expected analysis results for educational and tutorial purposes.
细胞骨架聚合物在细胞对外界和内部压力的响应中起着基本作用。定量了解这些聚合物的力学性能对于开发细胞力学和机械传感的预测模型至关重要。线性细胞骨架聚合物,如肌动蛋白丝和微管,可以生长到细胞长度尺度,在这个尺度上,它们表现为经历热驱动形状变形的半柔性聚合物。弯曲变形通常使用蠕虫链模型进行建模。聚合物抵抗弯曲的定量度量标准是持久长度,这是该模型的基本参数。聚合物的弯曲持久长度可以通过使用各种成像技术可视化其形状来提取。然而,用于确定持久长度的分析方法通常不容易被大多数生物研究人员或教育工作者所掌握。出于这个限制,我们开发了用户友好的、多平台兼容的软件,以便从表面吸附或自由波动的聚合物的图像中确定弯曲持久长度。有三种不同类型的分析(余弦相关、端到端和弯曲模式分析),允许对分析结果进行严格的交叉检查。该软件是免费提供的,我们提供了吸附和波动丝的示例数据以及用于教育和教程目的的预期分析结果。