Correlating anomalous diffusion with lipid bilayer membrane structure using single molecule tracking and atomic force microscopy.
Correlating anomalous diffusion with lipid bilayer membrane structure using single molecule tracking and atomic force microscopy.
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DOI：
10.1063/1.3596377
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发表时间：
2011-06
期刊：
影响因子：
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通讯作者：
Michael J. Skaug;R. Faller;M. Longo
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文献类型：
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作者：
Michael J. Skaug;R. Faller;M. Longo
在生物细胞的质膜中大量观察到异常扩散，但其潜在机制仍不清楚。一般来说，无法直接对膜中扩散的障碍进行成像，这些障碍被认为是骨架结合蛋白、蛋白质聚集体和脂质结构域，因此利用扩散颗粒的动力学来推断障碍特征。我们提出了一种支持的脂质双层系统，其中我们使用单分子追踪来表征脂质分子的异常扩散，同时使用原子力显微镜对扩散障碍进行成像。为了解释我们的实验结果，我们在存在实验确定的障碍物配置的情况下对示踪剂扩散进行了晶格蒙特卡罗模拟。我们将观察到的异常扩散与障碍物面积分数、分形维数和相关长度相关联。为了准确测量异常扩散指数，我们推导了一个表达式来解释所有单分子跟踪实验固有的时间平均。我们表明，单分子轨迹的长度对于异常扩散指数的确定至关重要。我们在限制模型和生成随机过程的背景下进一步讨论我们的结果。
Anomalous diffusion has been observed abundantly in the plasma membrane of biological cells, but the underlying mechanisms are still unclear. In general, it has not been possible to directly image the obstacles to diffusion in membranes, which are thought to be skeleton bound proteins, protein aggregates, and lipid domains, so the dynamics of diffusing particles is used to deduce the obstacle characteristics. We present a supported lipid bilayer system in which we characterized the anomalous diffusion of lipid molecules using single molecule tracking, while at the same time imaging the obstacles to diffusion with atomic force microscopy. To explain our experimental results, we performed lattice Monte Carlo simulations of tracer diffusion in the presence of the experimentally determined obstacle configurations. We correlate the observed anomalous diffusion with obstacle area fraction, fractal dimension, and correlation length. To accurately measure an anomalous diffusion exponent, we derived an expression to account for the time-averaging inherent to all single molecule tracking experiments. We show that the length of the single molecule trajectories is critical to the determination of the anomalous diffusion exponent. We further discuss our results in the context of confinement models and the generating stochastic process.