Reconstruction & Imaging of Living Nerve Cells

描述(申请人提供)：大脑功能是基于神经细胞内和神经细胞之间同时发生的计算。在单个神经细胞的水平上，来自数千个突触输入的信息在树突棘接收，在那里由树突的特定形态和电压门控离子通道的分布来处理。我们的两个参与实验室一直在研究这种神经元计算的各个方面，包括突触电位的非线性求和和树突动作电位的信号功能。为了理解这些树突功能，分析复杂的结构和功能的相互作用是绝对必要的。这种分析需要神经元的计算模型，其中包含有关神经元结构及其离子通道分布的信息。建造这样的模型一直很困难，因为技术考虑决定了结构和功能必须分开获得。光学成像技术的最新进展，现在使我们能够获得活的神经细胞的结构，并在单个实验中进行神经元功能的多点记录。然而，功能成像的地点仍然必须做出选择。我们建议基于对正在研究的神经细胞的在线模拟来选择这些最佳记录位置。这样的模拟需要在短时间内获取结构信息，进行形态重建，并构建神经元的隔室模型。模拟的输出将通过确定将产生关于正在研究的过程的最多信息的位置来指导功能成像。最后，获取的功能成像数据将被合并到计算模型中进行进一步的实验。 该项目的目标是开发一种计算和实验框架，以允许在非常有限的急性实验持续时间内实时映射或功能成像数据(例如，树突电压或细胞内离子的时空模式)到神经元结构。 为了实现这一目标，本提案的研究目标如下： 。开发理论框架和计算技术，用于从使用非侵入性结构成像获得的光学切片堆叠中在线、稳健和准确地重建荧光标记的活神经细胞。 。使用重构的形态和包含在隔室模型中的关于离子通道分布的先验知识在线预测神经细胞的行为。 。根据模型的预测和重建的形态来指导功能成像。 。通过最小化预测和在功能成像过程中获得的数据之间的误差来优化神经元的计算模型。 拟议项目的影响在于它加强了数据采集过程，特别是优化了多点光学记录的价值，并将数据集中和定向地纳入神经细胞的定量计算模型。我们的计算和实验框架将指导有效的实验设计和新假说的生成，这些假说可以帮助我们和其他研究人员揭示神经系统正常和疾病状态下的功能机制。这项拟议研究的成功完成需要神经科学、生物成像、生物物理学和计算机科学的投入。因此，我们的项目需要协作和互补的专业知识才能成功-我们的团队非常适合完成这一挑战，并吸引和培养优秀的学生。

DESCRIPTION (provided by applicant): Brain function is based on the computation that occurs concurrently within and among nerve cells. At the level of the single nerve cell, information from thousands of synaptic inputs is received at the dendritic spines, where it is processed by the dendrites' specific morphology and distribution of voltage-gated ion channels. Two of our participating labs have been investigating various aspects of this Neuronal Computation, including nonlinear summation of synaptic potentials and signaling functions of dendritic action potentials. To understand these dendritic functions, it is absolutely necessary to analyze the complex interplay of structure and function. Such analysis requires computational models of the neuron incorporating both information about the neuron's structure and its distribution of ion channels. Building such models has been difficult because technical considerations have dictated that structure and function be acquired separately. Recent advances in optical imaging techniques, now allow us to acquire the structure of living nerve cells and perform multi-site recording of neuronal function during a single experiment. However, choices as of the sites for functional imaging must still be made. We propose to choose these optimal recording sites based on an on-line simulation of the nerve cell under study. Such a simulation requires that structural information be acquired, a morphological reconstruction be performed, and a compartmental model of the neuron be constructed, during the short time frame of an acute experiment. The output of the simulation will guide the functional imaging by identifying sites that will yield the most information about the process under study. Finally, the acquired functional imaging data would be incorporated into the computational model for further experiments. The goal of this project is the development of a computational and experimental framework to allow real-time mapping or functional imaging data (e.g., spatio-temporal patterns of dendritic voltages or intracellular ion) to neuronal structure, during the very limited duration of an acute experiment. In order to accomplish this goal, the research objectives of this proposal are the following: . To develop the theoretical framework and computational techniques for on-line, robust, and accurate morphological reconstruction of a fluorescently labeled live nerve cell from stack of optical sections obtained using non-invasive structural imaging. . To predict on-line a nerve cell's behavior using the reconstructed morphology and a priori knowledge regarding the distribution of ion channels embodied in a compartmental model. . To guide functional imaging based on predictions of the model and the reconstructed morphology. . To optimize the computational model of the neuron by minimizing error between the predictions and the data acquired during functional imaging. The impact of the proposed project is in its enhancement of the data acquisition process, particularly in optimizing the value of multi-site optical recordings, and in the focused and directed incorporation of data into quantitative computational models of nerve cells. Our computational and experimental framework will guide the efficient design of experiments and the generation of new hypotheses that can help reveal functional mechanisms underlying both normal and diseased states of the nervous system, both for us and for other researchers. The successful completion of the proposed research requires input from neuroscience, bio-imaging, biophysics, and computer science. Thus, our project demands collaboration and complementary expertise for its success - our team is uniquely suited to accomplish this challenge and also to attract and train excellent students.