Ultrastructure-function properties of recycling vesicle pools in native central synapses

大脑的基本功能是处理信息：接收感官输入，产生适当的反应和学习/记忆。这些信息以微小的电信号的形式编码，这些电信号在被称为神经元的专门细胞之间传递，这些细胞连接在一起形成电路。信息在神经元之间的传递方式受到严格控制，但也是可调节的，这些特性是大脑计算能力的基础。目前，我们对信息传递只有一个基本的理解;确定神经元如何调节和改变信息流是理解大脑如何工作的核心，也是神经科学研究的一个主要目标。大多数信息传递发生在化学突触上，这是两个神经元（信号神经元和接收神经元）靠近的专门接触点。信号神经元有一簇称为囊泡的球形结构，每个囊泡都含有化学信号。电活动触发该神经元移动其囊泡并向属于接收神经元的突触部分释放其化学成分。这种靶结构具有特殊的受体，它们对化学物质作出反应，并引起神经元电活动的变化。信息就是这样从一个神经元传递到另一个神经元的，为什么这个过程要经过一个中间的化学传递步骤？这使得突触能够控制和调整传递的信息。这种特性使神经元网络与计算机中的数字电路区别开来，使它们能够适应操作需求的变化，甚至“重新布线”以支持学习。这种灵活的传输特性是如何实现的？这是神经科学中的一个关键问题。一种可能性是含有递质的囊泡本身也参与其中。大多数哺乳动物的突触有约250个囊泡。令人惊讶的是，只有一小部分可以释放它们的化学信号。如果每个突触上可释放的囊泡的数量可以改变，这将提供一个简单的机制来调整传递的信息。或者，囊泡在突触中的物理位置--允许它们或多或少有效地释放--可能是一个重要因素。这些想法一直很难解决，特别是在真实的大脑回路中，因为在监测小突触和纳米尺度的囊泡方面存在技术挑战。我们将使用荧光成像方法直接可视化大鼠脑组织中单个突触的动态特性。此外，使用一种新的方法，我们将独特地查看个人可释放囊泡的超微结构分辨率。我们将描述大型神经元网络中不同突触中功能囊泡的数量，并确定是什么分子通路和电路的其他特性设置了这些参数。通过建立突触处囊泡群体的三维重建，我们还将研究可释放囊泡是如何排列的，并研究这些特性如何影响突触的性能。我们还将定义在行为动物的视觉输入驱动的突触囊泡组织的特点，使我们能够探索突触特性与真实的感觉信号。此外，我们将测试是否形式的电输入，对应于神经元在学习过程中经历的活动，带来囊泡组织的性质的变化。解决这些问题是了解大脑基本功能的重要一步。此外，突触传递是神经系统疾病（如阿尔茨海默氏症）的主要靶点。囊泡群体代表了明显的潜在底物，可能是突触失败的基础;在未来，表征调节其功能的机制可以为疾病相关的药物治疗提供有前途的新策略。

The basic function of the brain is to process information: receiving sensory input, generating appropriate responses and learning/remembering. The information is encoded in the form of small electrical signals, which are passed between specialized cells called neurons wired up together to form circuits. The way the information transfers from neuron to neuron is closely controlled but also adjustable, and these properties underlie the computational power of the brain. Currently, we only have a basic comprehension of transmission; determining how neurons regulate and vary information flow is central to understanding how the brain works and is a major goal in neuroscience research.Most information transmission occurs at chemical synapses, specialized contact points where two neurons - a signalling and receiving neuron - come close together. The signalling neuron has a cluster of spherical structures called vesicles, each containing a chemical signal. Electrical activity triggers this neuron to mobilize its vesicles and release their chemical contents towards the part of the synapse belonging to the receiving neuron. This target structure has special receptors which respond to the chemical and bring about a change in the electrical activity of the neuron. In this way, information is moved from one neuron to the next.Why does the process involve an intermediate chemical transmission step? This allows synapses to control and adjust the information transferred. Such a feature sets networks of neurons apart from digital circuits in computers, allowing them to adapt to changes in operational demand or even 'rewire' themselves to support learning.How is this flexible nature of transmission achieved? This is a key issue in neuroscience. One possibility is that transmitter-containing vesicles themselves are involved. Most mammalian synapses have ~250 vesicles. Surprisingly, only a fraction of these are available to release their chemical signal. If the number of releasable vesicles could be varied at each synapse, this would offer a simple mechanism to allow adjustments in the information transferred. Alternatively, the physical positioning of vesicles in synapses - allowing them to be released more or less efficiently - could be an important factor. These ideas have been difficult to address, particularly in real brain circuits, because of the technical challenges in monitoring small synapses and the nanometre scale of the vesicles.The objective of this grant is to explore these key ideas. We will use fluorescence imaging methods to directly visualize the dynamic properties of single synapses in rat brain tissue. Also, using a novel approach we will uniquely view individual releasable vesicles with ultrastructural resolution. We will characterize the numbers of functional vesicles in different synapses in large neuronal networks, and determine what molecular pathways and other properties of circuits set these parameters. By building 3d reconstructions of vesicle populations at a synapse, we will also investigate how releasable vesicles are arranged, and examine how these properties influence synaptic performance. We will also define the characteristics of vesicle organization in synapses driven by visual input in behaving animals, allowing us to explore synaptic properties in relation to real sensory signals. Additionally, we will test whether forms of electrical input, corresponding to activity experienced by neurons during learning, bring about changes in the properties of vesicle organization. Addressing these questions is a major step in understanding fundamental brain function. Moreover, synaptic transmission is a major target for neurological diseases, such as Alzheimer's. Vesicle populations represent obvious potential substrates that could underlie synaptic failure; in the future, characterizing the mechanisms that regulate their function could offer promising new strategies for disease-related drug therapies.