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

单粒子跟踪中运动模型、模型参数和粒子轨迹的联合估计

基本信息

批准号：
10245111
负责人：
Sean B. Andersson
金额：
$ 33.41万
依托单位：
BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-20 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10245111
关键词：
3-Dimensional AMPA Receptors Address Algorithms Biological Biology Biomedical Engineering Boston Cell Nucleus Cell membrane Cells Collaborations Complement Complex Crowding Cytoplasm Data Data Analyses Data Set Development Disease Drug Delivery Systems Engineering Environment Glutamate Receptor Hippocampus (Brain)Hydrogels Image Individual Joints Label Laws Lead Mathematics Measurement Methods Microscope Modality Modeling Motion Neurons Noise Non-linear Models Pathologic Performance Physiological Polymers Positioning Attribute Quantum Dots Rattus Recycling Running Sampling Scheme Signal Transduction Speed Synaptic Receptors System Techniques Three-dimensional analysis Tissue Engineering Tweens Universities Validation base curve fitting data acquisition experimental study fluorescence imaging improved insight instrument interest macromolecule man non-Gaussian model particle signal processing temporal measurement tool trafficking

项目摘要

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.

项目摘要：单个粒子跟踪（SPT）是一种强大的技术，用于理解生物分子运动质膜，细胞质和核的拥挤环境中的亚细胞水平。基本方案是通过广泛的荧光成像获取图像序列，产生轨迹从这些图像中，最终通过使用工具来估算轨迹的运动参数例如曲线拟合到均方位移（MSD）曲线。该方法非常有效用于研究在固定模型下在平面中移动的单个颗粒。 SPT将具有变革性一旦能够研究生物学大分子在三维中移动并进行的影响复杂的运动模式，随着粒子的经历，单个运行期间不同模型之间切换例如，细胞之间的内在化，回收和流动。在3-D设置中，假设的假设标准方法简单又强大不再存在，以及运动模糊，临时选择等问题拟合参数对结果的准确性有很大影响，这是一个固定性的假设排除单个轨迹中模式切换分析的数据，粒子分析的分离运动参数估计的轨迹以及缺乏非高斯噪声效应的建模必须是解决并克服以使SPT在3D中与研究平面运动一样有效。拟议的项目由三个特定目标组成。首先专注于创建共同的技术使用一个允许的框架从SPT数据集中估算粒子轨迹和运动参数复杂的运动和观察模型，包括摄像头描述，深度依赖点扩散功能和在不同模型之间切换的动力学。最终的方法将大大改善 SPT在3-D设置中的精度和适用性。第二个目标是使用共焦 - 基于非线性，随机的超级寻求控制的基础跟踪方案。共聚焦方式提供更好的SNR，先天的3D功能，最重要的是，对Miti-的采样率极为快。运动模糊的栅极效应。可以在标准共聚焦仪器上实施的提出的方法是可调的在不同的实验设置和完成范围内的最佳性能时需要单个粒子的临时分辨率。最后，第三个目标旨在验证拟议的技术 - 两个实验系统中的有线。首先是跟踪水凝胶中量子点的简单设置。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，药物输送和免疫分散。第二个设置是跟踪个人，标记的设置大鼠海马神经元中的AMPA受体，为验证和演示提供了生物学设置。