Joint estimation of motion model, model parameters, and particle trajectories in single particle tracking

项目概要： 单粒子跟踪（SPT）是一种强大的技术，用于了解生物分子的运动， 在质膜、细胞质和细胞核的拥挤环境中的亚细胞水平。基本 一种方案是获取图像序列，通常通过宽场荧光成像，产生轨迹 并最终通过使用工具从轨迹估计运动参数 如曲线拟合到均方位移（MSD）曲线。这个方法非常有效 用于研究在固定模型下在平面内运动的单个粒子。SPT将有一个变革性的 一旦它能够研究三维运动的生物大分子， 复杂的运动模式，在粒子经历的单次运行期间在不同模型之间切换， 例如，内化，回收和细胞之间的运输。在3D环境中， 简单和鲁棒的标准方法都不再适用，并且诸如运动模糊、特别选择 拟合参数对结果的准确性有很大的影响， 数据排除了在单个轨迹中的模式切换的分析， 轨迹从运动参数估计，并缺乏建模的影响，非高斯噪声必须 解决和克服，使SPT有效的三维，因为它已经在研究平面运动。 拟议项目包括三个具体目标。第一个重点是创造技术， 使用允许以下的框架从SPT数据集估计粒子轨迹和运动参数： 复杂的运动和观察模型，包括相机特定的描述，深度相关的点扩散 功能和在不同模型之间切换的动态。由此产生的方法将大大改善 SPT在三维环境中的准确性和适用性。第二个目标是数据采集，使用共焦- 基于跟踪计划的启发，非线性，随机极值搜索控制。共焦模态 提供了更好的信噪比，固有的3-D能力，最重要的是，一个极快的采样率，以miti， 门效应的运动模糊。所提出的方法，实现标准的共焦仪器，是可调的 在不同的实验环境中获得最佳性能，并在高 需要单个粒子的时间分辨率。最后，第三个目标是验证所提出的技术- 在两个实验系统中。第一个是在水凝胶内跟踪量子点的简单设置。 这些基于聚合物的系统广泛用于许多生物医学应用，包括组织 工程、药物输送和免疫隔离。第二个设置是跟踪个人，标记为 AMPA受体在大鼠海马神经元，提供了一个生物学环境的验证和演示。

PROJECT SUMMARY: Single particle tracking (SPT) is a powerful class of techniques for understanding biomolecular motion at the subcellular level in the crowded environments of the plasma membrane, cytoplasm, and nucleus. The basic scheme is to acquire image sequences, typically through wide-field fluorescence imaging, produce trajectories from these images, and finally to estimate motion parameters from the trajectories through the use of tools such as curve-fitting to the mean-square displacement (MSD) curve. The method has been extremely effective for the study of single particles moving in the plane under a fixed model. SPT will have a transformative impact once it is capable of studying biological macromolecules moving in three dimensions and undergoing complex modes of motion that switch between different models during a single run as particles undergo, for example, internalization, recycling, and trafficking between cells. In the 3-D setting, the assumptions that make the standard methods both simple and robust no longer hold and issues such as motion blur, ad hoc choices of fitting parameters that have a large impact on the accuracy of results, an assumption of stationarity in the data which precludes analysis of mode switching in a single trajectory, separation of the analysis of particle trajectory from motion parameter estimation, and lack of modeling of effects of non-Gaussian noise must be addressed and overcome to make SPT as effective in 3-D as it has been in studying planar motion. The proposed project consists of three specific aims. The first is focused on creating techniques for jointly estimating particle trajectory and motion parameters from SPT data sets using a framework that allows for complex motion and observation models, including camera-specific descriptions, depth-dependent point spread functions, and dynamics that switch between different models. The resulting method will greatly improve the accuracy and applicability of SPT in the 3-D setting. The second aim targets data acquisition, using a confocal- based tracking scheme inspired by nonlinear, stochastic extremum-seeking control. The confocal modality provides a better SNR, innate 3-D capability and, most significantly, an extremely fast sampling rate to miti- gate effects of motion blur. The proposed method, implementable on standard confocal instruments, is tunable for optimal performance at different experimental settings and complements wide-field techniques when high temporal resolution of a single particle is needed. Finally, the third aim seeks to validate the proposed tech- niques in two experimental systems. The first is a simple setting of tracking quantum dots inside hydrogels. These polymer-based systems are extensively used in a number of biomedical applications, including tissue engineering, drug delivery, and immunoisolation. The second setting is that of tracking individual, labeled AMPA receptors in rat hippocampal neurons, providing a biological setting for validation and demonstration.