Quantitative Association of Anatomical and Functional Classes of Olfactory Bulb Neurons.
Quantitative Association of Anatomical and Functional Classes of Olfactory Bulb Neurons.
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DOI：
10.1523/jneurosci.0303-18.2018
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发表时间：
2018-08-15
期刊：
影响因子：
--
通讯作者：
Kollo M
中科院分区：
文献类型：
--
作者：
Tavakoli A;Schmaltz A;Schwarz D;Margrie TW;Schaefer AT;Kollo M
嗅球肾小球层（GL）的近球细胞（JGC）在嗅觉信息处理中起着重要作用。它们在形态学、生理学和连接性上的变化表明了不同的功能。然而，对种群形态和生理特性的定量理解以及基于定量参数的全面分类仍然缺乏，阻碍了微电路的分析。在这里，我们提供了95个在体外采样的细胞从GL的小鼠（雄性或雌性C57 BL/6）OB的多变量聚类和进行详细的形态和生理特性的七个计算的JGC类型。使用基于参数子集的分类器，我们识别了配对记录中的神经元类型，以表征其功能连接。我们发现，7个集群中的4个符合GL细胞类型的流行概念，而其他3个代表自己不同的实体。我们将这些实体标记为水平表面簇状细胞（hSTC），垂直表面簇状细胞和微肾小球细胞（MGC）：hSTC是具有侧树突的簇状细胞，非常像二尖瓣细胞，簇状细胞从外部簇状细胞接收兴奋性输入，但同样作为肾小球中间神经元的兴奋性元件。另一方面，垂直表面簇状细胞代表具有垂直突出的基树突的簇状细胞类型。我们进一步定义了MGC，其特征在于一个小的树突树和高原动作电位。除了嗅觉神经驱动和外部簇状细胞驱动的中间神经元之外，这些MGCs代表第三种功能不同的类型，即hSTC驱动的中间神经元。所提出的相关分析有助于弥合分支模式和细胞功能特性之间的差距，允许从体内记录，先进的形态学工具和连接组学的结果的整合。神经元特性的变化是哺乳动物大脑回路的一个特征，有助于信号处理并增加网络的计算鲁棒性。在嗅球的肾小球层中尤其明显，这是嗅觉信息处理的第一个站点。我们提供了第一个无偏的群体明智的多变量分析相关的肾小球细胞的形态和生理参数。我们确定了七种细胞类型，包括四个以前描述的神经元类型，并确定进一步三个不同的类。所提出的形态和生理参数的相关分析提供了一个机会，从生理测量或从形态神经元的功能特性来预测形态类，并开辟了整合体内记录，先进的形态学工具和连接组学的结果的途径。
Juxtaglomerular cells (JGCs) of the olfactory bulb (OB) glomerular layer (GL) play a fundamental role in olfactory information processing. Their variability in morphology, physiology, and connectivity suggests distinct functions. The quantitative understanding of population-wise morphological and physiological properties and a comprehensive classification based on quantitative parameters, however, is still lacking, impeding the analysis of microcircuits. Here, we provide multivariate clustering of 95 in vitro sampled cells from the GL of the mouse (male or female C57BL/6) OB and perform detailed morphological and physiological characterization for the seven computed JGC types. Using a classifier based on a subselection of parameters, we identified the neuron types in paired recordings to characterize their functional connectivity. We found that 4 of the 7 clusters comply with prevailing concepts of GL cell types, whereas the other 3 represent own distinct entities. We have labeled these entities horizontal superficial tufted cell (hSTC), vertical superficial tufted cell, and microglomerular cell (MGC): The hSTC is a tufted cell with a lateral dendrite that much like mitral cells and tufted cells receives excitatory inputs from the external tufted cell but likewise serves as an excitatory element for glomerular interneurons. The vertical superficial tufted cell, on the other hand, represents a tufted cell type with vertically projecting basal dendrites. We further define the MGC, characterized by a small dendritic tree and plateau action potentials. In addition to olfactory nerve-driven and external tufted cell driven interneurons, these MGCs represent a third functionally distinct type, the hSTC-driven interneurons. The presented correlative analysis helps to bridge the gap between branching patterns and cellular functional properties, permitting the integration of results from in vivo recordings, advanced morphological tools, and connectomics. SIGNIFICANCE STATEMENT The variance of neuron properties is a feature across mammalian cerebral circuits, contributing to signal processing and adding computational robustness to the networks. It is particularly noticeable in the glomerular layer of the olfactory bulb, the first site of olfactory information processing. We provide the first unbiased population-wise multivariate analysis to correlate morphological and physiological parameters of juxtaglomerular cells. We identify seven cell types, including four previously described neuron types, and identify further three distinct classes. The presented correlative analysis of morphological and physiological parameters gives an opportunity to predict morphological classes from physiological measurements or the functional properties of neurons from morphology and opens the way to integrate results from in vivo recordings, advanced morphological tools, and connectomics.
DOI：
10.1111/j.1460-9568.2011.07978.x
发表时间：
2012-02
期刊：
The European journal of neuroscience
影响因子：
--
作者：
Masurkar AV;Chen WR
通讯作者：
Chen WR