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。这些信息以小电信号的形式编码，在被称为神经元的特殊细胞之间传递，神经元连接在一起形成电路。信息从一个神经元传递到另一个神经元的方式受到严格控制，但也是可调节的，这些特性构成了大脑计算能力的基础。目前，我们对传播只有基本的了解；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（一个信号神经元和一个接收神经元）靠近的特殊接触点。信号神经元有一簇叫做囊泡的球形结构，每个囊泡都包含一种化学信号。电活动触发该神经元动员其囊泡，并将其化学成分释放到属于接收神经元的突触部分。这种目标结构具有特殊的受体，这些受体对化学物质作出反应，并引起神经元电活动的变化。通过这种方式，信息从一个神经元传递到下一个神经元。为什么这个过程涉及到中间的化学传递步骤？这使得突触可以控制和调整传递的信息。这样的特征将神经元网络与计算机中的数字电路区分开来，允许它们适应操作需求的变化，甚至“重新布线”自己以支持学习。这种传播的灵活性是如何实现的？这是神经科学中的一个关键问题。一种可能性是含有传递素的囊泡本身也参与其中。大多数哺乳动物的突触有大约250个囊泡。令人惊讶的是，其中只有一小部分可以释放化学信号。如果每个突触释放的囊泡数量可以改变，这将提供一种简单的机制来调整传递的信息。另外，突触中囊泡的物理位置——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。这项奖助金的目的是探索这些关键思想。我们将利用荧光成像方法直接观察大鼠脑组织中单个突触的动态特性。此外，使用一种新颖的方法，我们将以超微结构分辨率独特地观察单个可释放的囊泡。我们将描述大型神经网络中不同突触中功能性囊泡的数量，并确定哪些分子途径和电路的其他特性设置了这些参数。通过在突触中构建囊泡群的三维重建，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。我们还将定义由视觉输入驱动的突触中囊泡组织的特征，使我们能够探索与真实感觉信号相关的突触特性。此外，我们将测试与神经元在学习过程中所经历的活动相对应的电输入形式是否会导致囊泡组织性质的变化。解决这些问题是理解大脑基本功能的重要一步。此外，突触传递是阿尔茨海默病等神经系统疾病的主要靶点。囊泡群代表了突触失败的潜在底物；在未来，表征调节其功能的机制可能为疾病相关药物治疗提供有希望的新策略。

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.