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A New Fiji-Based Algorithm That Systematically Quantifies Nine Synaptic Parameters Provides Insights into Drosophila NMJ Morphometry

机译:一种新的基于斐济的算法可系统地量化九个突触参数可洞察果蝇NMJ形态

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摘要

The morphology of synapses is of central interest in neuroscience because of the intimate relation with synaptic efficacy. Two decades of gene manipulation studies in different animal models have revealed a repertoire of molecules that contribute to synapse development. However, since such studies often assessed only one, or at best a few, morphological features at a given synapse, it remained unaddressed how different structural aspects relate to one another. Furthermore, such focused and sometimes only qualitative approaches likely left many of the more subtle players unnoticed. Here, we present the image analysis algorithm ‘Drosophila_NMJ_Morphometrics’, available as a Fiji-compatible macro, for quantitative, accurate and objective synapse morphometry of the Drosophila larval neuromuscular junction (NMJ), a well-established glutamatergic model synapse. We developed this methodology for semi-automated multiparametric analyses of NMJ terminals immunolabeled for the commonly used markers Dlg1 and Brp and showed that it also works for Hrp, Csp and Syt. We demonstrate that gender, genetic background and identity of abdominal body segment consistently and significantly contribute to variability in our data, suggesting that controlling for these parameters is important to minimize variability in quantitative analyses. Correlation and principal component analyses (PCA) were performed to investigate which morphometric parameters are inter-dependent and which ones are regulated rather independently. Based on nine acquired parameters, we identified five morphometric groups: NMJ size, geometry, muscle size, number of NMJ islands and number of active zones. Based on our finding that the parameters of the first two principal components hardly correlated with each other, we suggest that different molecular processes underlie these two morphometric groups. Our study sets the stage for systems morphometry approaches at the well-studied Drosophila NMJ.
机译:突触的形态在神经科学中具有重要意义,因为它与突触功效密切相关。在不同动物模型中进行的二十年基因操纵研究已经揭示了一系列有助于突触形成的分子。但是,由于此类研究通常仅评估给定突触的一个或最多几个形态特征,因此仍未解决不同结构方面如何相互关联的问题。此外,这种集中的,有时只是定性的方法可能使许多更细微的参与者没有被注意到。在这里,我们介绍图像分析算法“ Drosophila_NMJ_Morphometrics”(可作为斐济兼容宏使用),用于对果蝇幼虫神经肌肉接头(NMJ)(一种完善的谷氨酸能模型突触)进行定量,准确和客观的突触形态测量。我们开发了这种方法,用于对常用标记Dlg1和Brp进行免疫标记的NMJ终端进行半自动多参数分析,并显示它也适用于Hrp,Csp和Syt。我们证明性别,遗传背景和腹腔段身份始终如一且显着有助于我们数据的变异性,这表明控制这些参数对于最小化定量分析的变异性很重要。进行了相关和主成分分析(PCA),以研究哪些形态计量学参数是相互依赖的,哪些是相当独立地调节的。基于九个获取的参数,我们确定了五个形态组:NMJ大小,几何形状,肌肉大小,NMJ岛数和活动区域数。基于我们的发现,前两个主要成分的参数几乎不相互关联,我们建议这两个形态计量基团是不同的分子过程。我们的研究为研究果蝇NMJ的系统形态学方法奠定了基础。

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