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Justus Schikora, Nina Kiwatrowski, Nils Förster, Leonie Selbach, Friederike Ostendorf, Frida Pallapies, Britta Hasse, Judith Metzdorf, Ralf Gold, Axel Mosig, Lars Tönges

• Neuronal models of neurodegenerative diseases such as Parkinson’s Disease (PD) 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 an autonomous computational assay for a 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 and primary mesencephalic dopaminergic neurons (MDN) treated with the neurotoxic lesioning compound Rotenone. Results of manual annotation using NeuronJ and automated data were shown to correlate strongly (\(R^{2}\)-value 0.9077 for SH-SY5Y cells and \(R^{2}\)-value 0.9297 for MDN). Pooled linear regressions of results from SH-SY5Y cell image data could be integrated into an equation formula (\(\it y\) = 0.5410 ⋅ \(\it x\) + 1792; \(\it y\) = 0.8789 ⋅ \(\it x\) + 0.09191 for normalized results) with \(\it y\) depicting automated and \(\it x\) depicting manual data. This automated neurite length algorithm constitutes a valuable tool for modelling of neurite outgrowth that can be easily applied to evaluate therapeutic compounds with high throughput approaches.