转角分析揭示活细胞中分子反常扩散的隐藏各向异性
Turning angle analysis reveals hidden anisotropies in the anomalous diffusion of molecules in live cells
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中文总结 AI 辅助
该研究提出转角统计方法，通过理论与模拟证明其可揭示活细胞内分子反常扩散的隐藏各向异性，应用于单粒子追踪数据验证了量子点等的各向异性，为检测复杂环境组织提供了新策略。
中文摘要 AI 辅助
活细胞内的分子运动为理解细胞内组织与运输的物理原理提供了窗口，但存在一个根本局限：测得的轨迹通常较短、噪声大且取向随机，导致传统分析无法获取空间各向异性。我们证明转角统计是揭示反常扩散中各向异性的可靠方法。采用具有随机取向的二维各向异性分数布朗运动模型，我们从理论和模拟两方面证明转角分布保留各向异性的特征。我们将该方法应用于单粒子追踪数据，包括HeLa细胞细胞质中的量子点和海马神经元中的膜蛋白。转角揭示了量子点、Nav 1.6通道以及糖蛋白CD4特定动力学状态运动中的隐藏各向异性。这些结果确立转角分析作为检测复杂环境中组织的强大策略，并表明各向异性反常扩散是细胞内动力学被忽视的特征。
英文摘要
Molecular motion within living cells provides a window into the physical principles underlying intracellular organization and transport. Yet a fundamental limitation remains: measured trajectories are often short, noisy, and randomly oriented, rendering spatial anisotropies inaccessible to conventional analyses. We show that turning-angle statistics provide a robust approach for uncovering anisotropies in anomalous diffusion. Using a two-dimensional anisotropic fractional Brownian motion model with random orientations, we show theoretically and through simulations that turning-angle distributions preserve signatures of anisotropy. We apply this approach to single-particle tracking data, including quantum dots in the cytoplasm of HeLa cells and membrane proteins in hippocampal neurons. Turning angles reveal hidden anisotropies in the motion of quantum dots and Nav 1.6 channels, and in specific dynamical states of glycoprotein CD4. These results establish turning-angle analysis as a powerful strategy for detecting organization in complex environments and reveal that anisotropic anomalous diffusion is an overlooked feature of intracellular dynamics.
