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使用自动化方法对挫伤、脱位和牵张性脊髓损伤后存活神经元进行定量分析。

Quantification of surviving neurons after contusion, dislocation, and distraction spinal cord injuries using automated methods.

作者信息

Wang Jingchao, Zhang Meiyan, Guo Yue, Hu Hai, Chen Kinon

机构信息

School of Biological Science and Medical Engineering, Beihang University (BUAA)-Yifu Science Hall, Beijing, China.

Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University (BUAA), Beijing, China.

出版信息

J Exp Neurosci. 2019 Aug 19;13:1179069519869617. doi: 10.1177/1179069519869617. eCollection 2019.

Abstract

This study proposes and validates an automated method for counting neurons in spinal cord injury (SCI) and then uses it to examine and compare the surviving cells in common types of SCI mechanisms. Moderate contusion, dislocation, and distraction SCIs were surgically induced in Sprague Dawley male rats (n = 6 for each type of injury). Their spinal cords were harvested 8 weeks post injury with 5 normal weight-matched rats. The spinal cords were cut, stained with anti-NeuN antibody and fluorescent Nissl, and imaged in the dorsal and ventral horns at various distances to the epicenter. Neurons in the images were automatically counted using an algorithm that was designed to filter non-soma-like objects based on morphological characteristics (size, solidity, circular pattern) and check the remaining objects for the double-stained nucleus/cell body features (brightness variation, brightness distribution, color). To validate the automated method, some of the images were randomly selected for manual counting. The number of surviving cells that were automatically measured by the algorithm was found to be correlated with the values that were manually measured by 2 observers ( < .001) with similar differences ( > .05). Neurons in the dorsal and ventral horns were reduced after the SCIs ( < .05). Dislocation and distraction, respectively, caused the most severe damage to the ventral horn neurons especially near the epicenter and the most extensive and uniform damage to the dorsal horn neurons ( < .05). Our method was proved to be reliable, which is suitable for studying different types of SCI.

摘要

本研究提出并验证了一种用于脊髓损伤(SCI)中神经元计数的自动化方法,然后使用该方法检查和比较常见类型SCI机制中的存活细胞。对雄性Sprague Dawley大鼠进行手术诱导中度挫伤、脱位和牵张性脊髓损伤(每种损伤类型n = 6)。在损伤后8周,连同5只体重匹配的正常大鼠一起收获它们的脊髓。将脊髓切开,用抗NeuN抗体和荧光尼氏染色,并在距震中不同距离的背角和腹角成像。使用一种算法自动计数图像中的神经元,该算法旨在根据形态特征(大小、坚实度、圆形模式)过滤非体细胞样物体,并检查剩余物体是否具有双重染色的细胞核/细胞体特征(亮度变化、亮度分布、颜色)。为了验证该自动化方法,随机选择一些图像进行人工计数。发现该算法自动测量的存活细胞数量与两名观察者人工测量的值相关(<0.001),差异相似(>0.05)。脊髓损伤后背角和腹角的神经元减少(<0.05)。脱位和牵张分别对腹角神经元造成最严重的损伤,尤其是在震中附近,对背角神经元造成最广泛和均匀的损伤(<0.05)。我们的方法被证明是可靠的,适用于研究不同类型的脊髓损伤。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a855/6702772/1ff6e5150b90/10.1177_1179069519869617-fig1.jpg

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