A Propagated Skeleton Approach to High ThroughputScreening of Neurite Outgrowth forin vitro Parkinson’s Disease Modelling
Scale Bar = 500 μm.
Neuronal in vitro models of neurodegenerative diseases such as Parkinson’s Disease are extensively studied in pathological and therapeutical research with neurite outgrowth being a core feature. Screening of neurite outgrowth enables characterization of various stimuli and therapeutic effects after lesion. In this study, we describe a computational assay for high throughput skeletonization approach allowing for quantification of neurite outgrowth in large data sets from fluorescence microscopic imaging. Development and validation of the assay was conducted with differentiated SH-SY5Y cells treated with the neurotoxic lesioning compound Rotenone reducing neurite outgrowth in a concentration-dependent manner up to 70.7%. Results of manual annotation using NeuronJ and automated data were shown to correlate strongly. This automated neurite length algorithm constitutes a valuable tool for in vitro modelling of neurite outgrowth that can be easily applied to evaluate therapeutic compounds with high throughput approaches.
This pipeline is a joint project of the Ruhr Universität in Bochum, Germany and St. Josef-Hospital in Bochum, Germany.
Prof. Dr. Axel Mosig: Bioinformatics, Center for Protein Diagnostics (ProDi), Ruhr-University Bochum, 44780 Bochum, Germany
Prof. Dr. Lars Tönges: Department of Neurology, St. Josef-Hostipal, Ruhr-University Bochum, 44780 Bochum, Germany
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This software is an implementation of the Compact Skeletonization method by Durix et al.
One-step compact skeletonization, Durix B., Morin G., Chambon S., Mari J.-L. and Leonard K., Eurographics 2019
http://durix.perso.enseeiht.fr/
License: https://github.com/RUB-Bioinf/HT-PropagatedNeuriteSkeletonization/blob/master/durix_license.txt
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Keywords: Parkinson’s Disease; Neurodegeneration; Neurotoxicity; Neurite Outgrowth; Neuronal Morphology;13High Throughput Screening; Skeletonization
Funding: This research received no external funding.
Acknowledgments: We acknowledge support by the Open Access Publication funds of the Ruhr-Universität Bochum.
Conflicts of Interest: The authors declare no conflict of interest