A 3D particle tracking tool for next generation neuroscience microscopy

项目摘要 功能性神经网络的延时3D成像，由许多神经元通过一个 复杂的突触网络，是一个有前途的方法，为深入了解如何中央 中枢神经系统（CNS）工作。使用高速共聚焦和荧光显微镜， 常规获得，以阐明功能电路的发展，以及分子动力学， 相互作用，驱动中枢神经系统的发展或病理变性。现在可以想象更多 在中枢神经系统原位具有高横向和轴向分辨率的复杂和完整的神经回路。这些图像 新的模型系统可以激发新一代的科学探索，从而带来新的发现 和治疗。 然而，3D神经元序列具有较低的信噪比（SNR），而粒子的复杂性 运动被更生理化的环境加剧。因此，粒子动力学的量化 由于当前3D图像的限制，这些复杂模型中的分子相互作用是困难的 分析工具。特别是，当前的粒子跟踪工具难以解决这两个挑战。这 构成了生物机制定量分析的关键瓶颈和限速步骤 神经发育和疾病的基础 我们开发了一种高性能和可配置的跟踪工具，非常适合广泛的2D 粒子跟踪应用程序，这是现在正在商业化的尼康公司在一项基准研究 覆盖广泛的颗粒跟踪应用，该跟踪工具实现了比 一些商业和学术工具（表1.I）。我们在哈佛医学院的合作者是 神经发育和突触形态发生领域的领导者。它们通常获得高分辨率， 3D共聚焦，体外成像数据显示微管动力学和神经元过程形态测量， 脊椎动物和无脊椎动物的细胞。这为下一代3D提供了一个很好的测试平台 追踪工具 本阶段I提案的目标是开发和验证针对3D优化的信息学工具 亚细胞追踪应用通用工具将解决检测和 在函数神经网络中跟踪具有非均匀运动的运动粒子。这些类型的复杂 实验制剂越来越多地被采用，并引起人们对 新一代的追踪工具。该工具的主要创新包括：1）动态模型， 自适应控制，表示动态对象状态和转换，并执行状态相关粒子 检测和跟踪方法; 2）有效状态转换的自调节和使用运动的轨迹匹配 能量（对匹配结果的独立检查）。我们将在第一阶段使用完整的 果蝇和非洲爪蟾的制备物以及模拟数据。在第二阶段，我们将处理更广泛的问题， 的3D粒子跟踪应用，并广泛满足市场对3D动态 显微镜信息学，包括3D动力学事件表征和筛选。具体目标是： 目标1：使用模拟3D图像创建并验证3D异构跟踪工具 目标2：在广泛的荧光3D动力学显微镜应用中使用该工具 目标3：在函数神经网络的+TIP跟踪中执行原理验证实验

Project Summary Time-lapse, 3D imaging of functional neural networks, composed of many neurons connected through a complex web of synapses, is a promising approach for gaining in-depth understanding of how the central nervous system (CNS) works. Using high speed confocal and fluorescence microscopy, 3D sequences are routinely acquired to elucidate the development of functional circuits, as well as the molecular kinetics and interactions that drive CNS development or pathological degeneration. It is now possible to image more complex and intact neural circuits in the CNS in situ with high lateral and axial resolution. Imaging of these new model systems could unleash a new generation of scientific inquiries that would lead to new discoveries and therapies. However, 3D neuronal sequences have a lower signal to noise ratio (SNR) while the complexity of particle motion is exacerbated by the more physiological environment. Therefore, quantification of particle dynamics and molecular interactions in these complex models is difficult due to the limitations of the current 3D image analysis tools. In particular, current particle tracking tools struggle to address these twin challenges. This constitutes a critical bottleneck and rate-limiting step for quantitative analysis of the biological mechanisms that underlie neural development and disease. We have developed a high performance and configurable tracking tool, well suited for a broad range of 2D particle tracking applications, which is now being commercialized by Nikon Corp. In a benchmark study covering broad particle tracking applications this tracking tool achieved significantly better performance than several commercial and academic tools (Table 1.I). Our collaborators at the Harvard Medical School are leaders in the field of neural development and synaptic morphogenesis. They routinely acquire high resolution, 3D confocal, in vitro imaging data showing microtubule dynamics and neuronal process morphometry using both vertebrate and invertebrate cells. This provide an excellent test platform for the next generation 3D tracking tool. The objective of this Phase I proposal is to develop and validate an informatics tool optimized for 3D subcellular tracking applications. The general purpose tool would address the challenge of detecting and tracking moving particles with heterogeneous motion in functional neural networks. These types of complex experimental preparations are increasingly being adopted and are drawing attention to the limitations of the current generation of tracking tools. The key innovations of the proposed tool include: 1) a Dynamic model and adaptive control that represents dynamic object states and transitions, and executes state-dependent particle detection and tracking methods; 2) Self-regulation of valid state transitions and track matching using motion energy (an independent check on the matching outcomes). We'll prove the feasibility in Phase I using intact preparations from Drosophila and Xenopus as well as simulated data. In Phase II we will tackle a broader set of 3D particle tracking applications, and also broadly address the market requirement for 3D kinetic microscopy informatics including 3D kinetic event characterization and screening. The specific aims are: Aim 1: Create and validate the 3D heterogeneous tracking tool using simulated 3D images Aim 2: Validate the tool in broad fluorescence 3D kinetic microscopy applications Aim 3: Execute a proof-of-principle experiment in +TIP tracking for functional neural networks