A Propagated Skeleton Approach to High Throughput Screening of Neurite Outgrowth for In Vitro Parkinson’s Disease Modelling

Abstract

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 (R2-value 0.9077 for SH-SY5Y cells and R2-value 0.9297 for MDN). Pooled linear regressions of results from SH-SY5Y cell image data could be integrated into an equation formula (y=0.5410·x+1792; y=0.8789·x+0.09191 for normalized results) with y depicting automated and 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.