Schmuck Martin R, Keil Kimberly P, Sethi Sunjay, Morgan Rhianna K, Lein Pamela J
Department of Molecular Biosciences, School of Veterinary Medicine, University of California, Davis, CA, USA.
J Neurosci Methods. 2020 Jul 15;341:108793. doi: 10.1016/j.jneumeth.2020.108793. Epub 2020 May 24.
Primary neuronal cell cultures are useful for studying mechanisms that influence dendritic morphology during normal development and in response to various stressors. However, analyzing dendritic morphology is challenging, particularly in cultures with high cell density, and manual methods of selecting neurons and tracing dendritic arbors can introduce significant bias, and are labor-intensive. To overcome these challenges, semi-automated and automated methods are being developed, with most software solutions requiring computer-assisted dendrite tracing with subsequent quantification of various parameters of dendritic morphology, such as Sholl analysis. However fully automated approaches for classic Sholl analysis of dendritic complexity are not currently available.
The previously described Omnisphero software, was extended by adding new functions to automatically assess dendritic mass, total length of the dendritic arbor and the number of primary dendrites, branch points, and terminal tips, and to perform Sholl analysis.
The new functions for assessing dendritic morphology were validated using primary mouse hippocampal and rat cortical neurons transfected with a fluorescently tagged MAP2 cDNA construct. These functions allow users to select specific populations of neurons as a training set for subsequent automated selection of labeled neurons in high-density cultures.
COMPARISON WITH EXISTING SEMI-AUTOMATED METHODS: Compared to manual or semi-automated analyses of dendritic arborization, the new functions increase throughput while significantly decreasing researcher bias associated with neuron selection, tracing, and thresholding.
These results demonstrate the importance of using unbiased automated methods to mitigate experimenter-dependent bias in analyzing dendritic morphology.
原代神经元细胞培养对于研究正常发育过程中以及对各种应激源反应时影响树突形态的机制很有用。然而，分析树突形态具有挑战性，特别是在细胞密度高的培养物中，手动选择神经元和追踪树突分支的方法可能会引入显著偏差，并且劳动强度大。为了克服这些挑战，正在开发半自动和自动方法，大多数软件解决方案需要计算机辅助的树突追踪以及随后对树突形态的各种参数进行量化，例如Sholl分析。然而，目前尚无用于经典树突复杂性Sholl分析的全自动方法。
通过添加新功能扩展了先前描述的Omnisphero软件，以自动评估树突质量、树突分支的总长度以及初级树突、分支点和末端的数量，并进行Sholl分析。
使用转染了荧光标记的MAP2 cDNA构建体的原代小鼠海马神经元和大鼠皮质神经元验证了评估树突形态的新功能。这些功能允许用户选择特定的神经元群体作为训练集，以便随后在高密度培养物中自动选择标记的神经元。
与手动或半自动分析树突分支相比，新功能提高了通量，同时显著降低了与神经元选择、追踪和阈值设定相关的研究者偏差。
这些结果证明了使用无偏差自动方法来减轻分析树突形态时实验者依赖偏差的重要性。
