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A standardized and automated method of perineuronal net analysis using Wisteria floribunda agglutinin staining intensity

机译: Wisteria floribunda 凝集素染色强度进行标准化的自动神经周围神经网络分析方法

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Perineuronal nets (PNNs) are aggregations of extracellular matrix molecules that are critical for plasticity. Their altered development or changes during adulthood appear to contribute to a wide range of diseases/disorders of the brain. An increasing number of studies examining the contribution of PNN to various behaviors and types of plasticity have analyzed the fluorescence intensity of Wisteria floribunda agglutinin (WFA) as an indirect measure of the maturity of PNNs, with brighter WFA staining corresponding to a more mature PNN and dim WFA staining corresponding to an immature PNN. However, a clearly-defined and unified method for assessing the intensity of PNNs is critical to allow us to make comparisons across studies and to advance our understanding of how PNN plasticity contributes to normal brain function and brain disease states. Here we examined methods of PNN intensity quantification and demonstrate that creating a region of interest around each PNN and subtracting appropriate background is a viable method for PNN intensity quantification that can be automated. This method produces less variability and bias across experiments compared to other published analyses, and this method increases reproducibility and reliability of PNN intensity measures, which is critical for comparisons across studies in this emerging field. Highlights ? PNN intensity quantification varies across the field. ? The “ROI” method decreases variability among researchers. ? The “ROI” method can be automated.
机译:神经周围神经网(PNN)是细胞外基质分子的聚集体,对可塑性至关重要。它们在成年期发生的发育或改变发生改变,可能导致多种脑部疾病/失调。越来越多的研究PNN对各种行为和可塑性的贡献的研究已经分析了紫藤凝集素(WFA)的荧光强度,作为间接检测PNN成熟度的方法,WFA染色较亮则对应于更成熟的PNN和暗淡的WFA染色对应于未成熟的PNN。但是,明确定义和统一的评估PNN强度的方法至关重要,这使我们能够在各个研究之间进行比较,并加深我们对PNN可塑性如何促进正常脑功能和脑部疾病状态的理解。在这里,我们检查了PNN强度量化的方法,并证明在每个PNN周围创建一个感兴趣的区域并减去适当的背景是可行的PNN强度量化方法,该方法可以自动化。与其他已发表的分析相比,该方法在整个实验中产生的变异性和偏差较小,并且该方法提高了PNN强度测量值的可重复性和可靠性,这对于在此新兴领域进行的研究进行比较至关重要。强调 ? PNN强度量化在整个领域有所不同。 ? “ ROI”方法减少了研究人员之间的差异。 ? “ ROI”方法可以自动化。

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