TRAIT2D: a Software for Quantitative Analysis of Single Particle Diffusion Data.
TRAIT2D: a Software for Quantitative Analysis of Single Particle Diffusion Data.
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DOI：
10.12688/f1000research.54788.2
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Eggeling C
中科院分区：
文献类型：
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作者：
Reina F;Wigg JMA;Dmitrieva M;Vogler B;Lefebvre J;Rittscher J;Eggeling C
单粒子跟踪（SPT）是光学显微镜中最广泛使用的工具之一，用于评估各种情况下的粒子迁移率，包括细胞和模型膜动力学。最近的技术发展，如干涉散射显微镜，允许记录长，不间断的单粒子轨迹在千赫兹的帧速率。由此产生的数据，其中粒子被连续检测到，并且在观测之间没有太大的位移，因此不需要复杂的链接算法。此外，虽然这些测量提供了跟踪粒子的短期扩散行为的更多细节，但它们也受到局部化不确定性的影响，而这些不确定性通常被传统的分析管道低估。因此，我们开发了一个Python库，名为TRAIT2D（跟踪分析2D版本），以便以高采样率跟踪粒子扩散，并以创新的方法分析产生的轨迹。引入的数据分析管道更具本地化不确定性意识，并且还为基于统计提供的数据选择最合适的扩散模型。轨迹模拟平台还允许用户方便地生成轨迹，甚至合成时间间隔，以测试替代跟踪算法和数据分析方法。分析管道的高度定制，例如引入不同的扩散模式，可以从源代码开始。最后，图形用户界面的出现降低了几乎没有编程经验的用户的访问障碍。
Single particle tracking (SPT) is one of the most widely used tools in optical microscopy to evaluate particle mobility in a variety of situations, including cellular and model membrane dynamics. Recent technological developments, such as Interferometric Scattering microscopy, have allowed recording of long, uninterrupted single particle trajectories at kilohertz framerates. The resulting data, where particles are continuously detected and do not displace much between observations, thereby do not require complex linking algorithms. Moreover, while these measurements offer more details into the short-term diffusion behaviour of the tracked particles, they are also subject to the influence of localisation uncertainties, which are often underestimated by conventional analysis pipelines. we thus developed a Python library, under the name of TRAIT2D (Tracking Analysis Toolbox – 2D version), in order to track particle diffusion at high sampling rates, and analyse the resulting trajectories with an innovative approach. The data analysis pipeline introduced is more localisation-uncertainty aware, and also selects the most appropriate diffusion model for the data provided on a statistical basis. A trajectory simulation platform also allows the user to handily generate trajectories and even synthetic time-lapses to test alternative tracking algorithms and data analysis approaches. A high degree of customisation for the analysis pipeline, for example with the introduction of different diffusion modes, is possible from the source code. Finally, the presence of graphical user interfaces lowers the access barrier for users with little to no programming experience.