Estimation of diffusion constants from single molecular measurement without explicit tracking.
Estimation of diffusion constants from single molecular measurement without explicit tracking.
复制标题
DOI：
10.1186/s12918-018-0526-5
复制
发表时间：
2018-04-11
影响因子：
--
通讯作者：
Kumagai Y
中科院分区：
文献类型：
--
作者：
Teraguchi S;Kumagai Y
细胞表面单个分子的时程测量提供了有关分子动力学的详细信息，否则这些信息将无法获得。为了提取定量信息，通常执行单粒子跟踪（SPT）。然而，当单个分子的扩散速度与粒子密度的尺度相比很高时，SPT提取的轨迹不可避免地存在连接误差。为了避免这个问题，我们开发了一个算法来估计扩散常数，而不依赖于SPT。该算法是基于概率模型的距离最近的点在后续帧。该概率模型在平均场近似下，将孤立环境下的单粒子布朗运动模型推广到多粒子环境中。我们表明，该算法提供了合理的估计扩散常数，即使当其他方法遭受由于高粒子密度或不均匀的粒子分布。此外，我们的算法可以用于可视化的时间过程数据从单分子测量。所提出的算法的基础上的概率模型的不可区分的布朗粒子提供准确的估计扩散常数，即使在政权的传统SPT方法低估他们由于链接错误。本文的在线版本（10.1186/s12918-018-0526-5）包含补充材料，可供授权用户使用。
Time course measurement of single molecules on a cell surface provides detailed information about the dynamics of the molecules that would otherwise be inaccessible. To extract the quantitative information, single particle tracking (SPT) is typically performed. However, trajectories extracted by SPT inevitably have linking errors when the diffusion speed of single molecules is high compared to the scale of the particle density. To circumvent this problem, we develop an algorithm to estimate diffusion constants without relying on SPT. The proposed algorithm is based on a probabilistic model of the distance to the nearest point in subsequent frames. This probabilistic model generalizes the model of single particle Brownian motion under an isolated environment into the one surrounded by indistinguishable multiple particles, with a mean field approximation. We demonstrate that the proposed algorithm provides reasonable estimation of diffusion constants, even when other methods suffer due to high particle density or inhomogeneous particle distribution. In addition, our algorithm can be used for visualization of time course data from single molecular measurements. The proposed algorithm based on the probabilistic model of indistinguishable Brownian particles provide accurate estimation of diffusion constants even in the regime where the traditional SPT methods underestimate them due to linking errors. The online version of this article (10.1186/s12918-018-0526-5) contains supplementary material, which is available to authorized users.
登录
查看更多内容
影响因子：
3.2
作者：
Pandzic, Elvis;Rossy, Jeremie;Gaus, Katharina
通讯作者：
Gaus, Katharina
影响因子：
48
作者：
de Chaumont, Fabrice;Dallongeville, Stephane;Olivo-Marin, Jean-Christophe
通讯作者：
Olivo-Marin, Jean-Christophe
GPI锚定的受体簇瞬时募集lyn和g alpha进行临时簇固定和LYN激活：单分子跟踪研究1。
影响因子：
7.8
作者：
Suzuki, Kenichi G N;Fujiwara, Takahiro K;Sanematsu, Fumiyuki;Iino, Ryota;Edidin, Michael;Kusumi, Akihiro
通讯作者：
Kusumi, Akihiro
影响因子：
3.4
作者：
Kolin, DL;Costantino, S;Wiseman, PW
通讯作者：
Wiseman, PW
平面六方 B36 作为扩展单原子层硼片的潜在基础
影响因子：
16.6
作者：
Piazza, Zachary A.;Hu, Han-Shi;Wang, Lai-Sheng
通讯作者：
Wang, Lai-Sheng