Department of Biomedicine, Faculty of Health Sciences, Aarhus University, Aarhus C, Denmark.
J Neurosci Methods. 2012 Jul 15;208(2):128-33. doi: 10.1016/j.jneumeth.2012.05.009. Epub 2012 May 15.
Estimation of spine number and spine density by manual counting under the assumption that all dendrite protrusions equal spines are often used in studies on neuroplasticity occurring during health, brain diseases, and different experimental paradigms. Manual spine counting is, however, time consuming and biased by inter-observer variation. We present accordingly a quick, reproducible and simple non-stereological semi-automatic spine density estimation method based on the irregularity of the dendrite surface. Using the freeware ImageJ program, microphotographs of Golgi impregnated hippocampal dendrites derived from a previously performed study on the impact of chronic restrained stress were binarized, skeletonized, and the skeleton endings assumed to represent spine positions were counted and the spine densities calculated. The results based on 754 dendrite fragments were compared to manual spine counting of the same dendrite fragments using the Bland-Altman method. The results from both methods were correlated (r=0.79, p<0.0001), The semi-automatic counting method gave a statistically higher (approx. 4%) spine density number, but both counting methods showed similar significant differences between the groups in the CA1 area, and no differences between the groups in the CA3 area. In conclusion, the presented semi-automatic spine density estimation method yields consistently a higher spine density number than manual counting resulting in similar significance between groups. The proposed method may therefore be a reproducible time saving and useful non-stereological approach to spine counting in neuroplasticity studies requiring analysis of hundreds of dendrites.
通过假设所有树突突起都等于棘突来手动计数棘突数量和棘突密度,这种方法常用于研究健康、脑部疾病和不同实验范式下发生的神经可塑性。然而,手动棘突计数既耗时又容易受到观察者间差异的影响。因此,我们提出了一种快速、可重复且简单的非立体计量学半自动化棘突密度估计方法,该方法基于树突表面的不规则性。使用免费的 ImageJ 程序,对先前进行的慢性束缚应激对海马树突影响研究中获得的高尔基染色海马树突的显微照片进行二值化、骨架化,并假设骨架末端代表棘突位置进行计数,计算棘突密度。基于 754 个树突片段的结果与使用 Bland-Altman 方法对相同树突片段进行手动棘突计数的结果进行比较。两种方法的结果均相关(r=0.79,p<0.0001)。半自动计数方法给出的棘突密度数值统计上更高(约 4%),但两种计数方法在 CA1 区的组间均显示出显著差异,而在 CA3 区的组间无差异。总之,所提出的半自动棘突密度估计方法产生的棘突密度数值始终高于手动计数,导致组间的统计学意义相似。因此,对于需要分析数百个树突的神经可塑性研究来说,该方法可能是一种可重复、节省时间且有用的非立体计量学棘突计数方法。