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Abdel-Rahman Al-Absi

Evaluation of semi-automatic 3D reconstruction for studying geometry of dendritic spines

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Background: Spine geometry is considered to reflect synapse function. An accurate and fast method for 3D reconstruction of spines is considered a valuable tool for the purpose of studying spine geometry. Currently, most studies employ manual or automatic reconstruction methods, which still suffer from either poor accuracy or extreme time-consumption. The semi-automatic reconstruction method has previously been described as a time-economic and accurate tool for spine number counting. The purpose of this study is to further validate the semi-automatic method with regards to spine geometry investigation, by comparing it with the manual method as well as with the automatic method. Methods: In this study, dendritic trees of six pyramidal neurons that belong to layers II/III of mouse frontal cortex are stained using the Golgi method. Thereafter, spines from 42 dendritic branches are 3D reconstructed by manual, semi-automatic and automatic methods using Imaris software. Spine features, including spine volume, spine area, spine length and spine neck length, and the relative distribution of classified stubby, mushroom and thin spines are compared between the semi-automatic method and the two other methods. Results: Results from the semi-automatic and the manual reconstruction methods are in line with respect to all measured spine geometric features as well as spine classes. However, significant difference has been detected between the two methods and the automatic method in spine length, spine neck length and spine volume. Compared to the manual method, both the semi-automatic and the automatic methods have significantly reduced the spine reconstruction time. Conclusion: These findings suggest that the semi-automatic method may represent both a time-economic and reliable option for the purpose of studying spine geometry.

TidsskriftJournal of Chemical Neuroanatomy
Sider (fra-til)119-124
Antal sider6
StatusUdgivet - 1 dec. 2018

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