Title: Automated high content image analysis of dendritic arborization in primary mouse hippocampal and rat cortical neurons in culture.
Authors: Schmuck, Martin R; Keil, Kimberly P; Sethi, Sunjay; Morgan, Rhianna K; Lein, Pamela J
Published In J Neurosci Methods, (2020 Jul 15)
Abstract: BACKGROUND: 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. NEW METHOD: 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. RESULTS: 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. CONCLUSION: These results demonstrate the importance of using unbiased automated methods to mitigate experimenter-dependent bias in analyzing dendritic morphology.
PubMed ID: 32461071
MeSH Terms: No MeSH terms associated with this publication