Functional Genomics, Center for Neurogenomics and Cognitive Research (CNCR), VU University Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands.
J Neurosci Methods. 2011 Feb 15;195(2):185-93. doi: 10.1016/j.jneumeth.2010.12.011. Epub 2010 Dec 15.
The shape, structure and connectivity of nerve cells are important aspects of neuronal function. Genetic and epigenetic factors that alter neuronal morphology or synaptic localization of pre- and post-synaptic proteins contribute significantly to neuronal output and may underlie clinical states. To assess the impact of individual genes and disease-causing mutations on neuronal morphology, reliable methods are needed. Unfortunately, manual analysis of immuno-fluorescence images of neurons to quantify neuronal shape and synapse number, size and distribution is labor-intensive, time-consuming and subject to human bias and error. We have developed an automated image analysis routine using steerable filters and deconvolutions to automatically analyze dendrite and synapse characteristics in immuno-fluorescence images. Our approach reports dendrite morphology, synapse size and number but also synaptic vesicle density and synaptic accumulation of proteins as a function of distance from the soma as consistent as expert observers while reducing analysis time considerably. In addition, the routine can be used to detect and quantify a wide range of neuronal organelles and is capable of batch analysis of a large number of images enabling high-throughput analysis.
神经细胞的形状、结构和连接性是神经元功能的重要方面。改变神经元形态或突触前和突触后蛋白突触定位的遗传和表观遗传因素对神经元输出有重要贡献，并可能是临床状态的基础。为了评估单个基因和致病突变对神经元形态的影响，需要可靠的方法。不幸的是，手动分析神经元免疫荧光图像以定量神经元形状和突触数量、大小和分布既费力、耗时，又容易受到人为偏见和误差的影响。我们已经开发了一种使用可引导滤波器和反卷积的自动图像分析例程，以自动分析免疫荧光图像中的树突和突触特征。我们的方法报告树突形态、突触大小和数量，但也报告突触囊泡密度和蛋白质在从胞体的距离上的突触积累，与专家观察者一样一致，同时大大减少了分析时间。此外，该例程可用于检测和量化广泛的神经元细胞器，并且能够对大量图像进行批处理分析，从而实现高通量分析。
