Automated Analysis of a Diverse Synapse Population
Automated Analysis of a Diverse Synapse Population
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DOI：
10.1371/journal.pcbi.1002976
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发表时间：
2013-03-01
影响因子：
4.3
通讯作者：
Smith, Stephen
中科院分区：
文献类型：
--
作者：
Busse, Brad;Smith, Stephen
哺乳动物中枢神经系统的突触在功能和分子组成上高度多样化。突触多样性本身可能对大脑功能至关重要，因为记忆和自我平衡机制被认为主要源于单个突触特定子集的活动依赖性可塑性变化。不幸的是，突触多样性的测量受到能够在单个突触水平上测量突触特性的方法的限制。阵列断层扫描是一种新的高分辨率，高通量的蛋白质组成像方法，有可能推进测量单位水平的突触多样性在大型和多样化的突触群体。在这里，我们提出了一个自动化的特征提取和分类算法，旨在量化从高维阵列断层扫描数据过于庞大的人工分析突触。我们证明了使用这种方法来量化小鼠体感皮层中突触的层状分布，并通过检测已知但不常见的蛋白质组学特征来验证分类过程。这种分类和量化将是非常有用的，在识别特定的亚群突触表现出可塑性，从环境或感觉周边的扰动作出反应。
Synapses of the mammalian central nervous system are highly diverse in function and molecular composition. Synapse diversity per se may be critical to brain function, since memory and homeostatic mechanisms are thought to be rooted primarily in activity-dependent plastic changes in specific subsets of individual synapses. Unfortunately, the measurement of synapse diversity has been restricted by the limitations of methods capable of measuring synapse properties at the level of individual synapses. Array tomography is a new high-resolution, high-throughput proteomic imaging method that has the potential to advance the measurement of unit-level synapse diversity across large and diverse synapse populations. Here we present an automated feature extraction and classification algorithm designed to quantify synapses from high-dimensional array tomographic data too voluminous for manual analysis. We demonstrate the use of this method to quantify laminar distributions of synapses in mouse somatosensory cortex and validate the classification process by detecting the presence of known but uncommon proteomic profiles. Such classification and quantification will be highly useful in identifying specific subpopulations of synapses exhibiting plasticity in response to perturbations from the environment or the sensory periphery.