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

  • Cells. 2021 Apr 17;10(4):931. doi: 10.3390/cells10040931.
Justus Schikora  1  2 ,  Nina Kiwatrowski  3  4 ,  Nils Förster  3  4 ,  Leonie Selbach  3  4 ,  Friederike Ostendorf  1 ,  Frida Pallapies  4 ,  Britta Hasse  1 ,  Judith Metzdorf  1  2 ,  Ralf Gold  1  2 ,  Axel Mosig  3  4 ,  Lars Tönges  1  2
Affiliations
  • 1. Department of Neurology, St. Josef-Hospital, Ruhr-University Bochum, 44803 Bochum, Germany.
  • 2. Experimental Neurology, Center for Protein Diagnostics (ProDi), Ruhr-University Bochum, 44780 Bochum, Germany.
  • 3. Bioinformatics Group, Department of Biology and Biotechnology, Ruhr-University Bochum, 44780 Bochum, Germany.
  • 4. Bioinformatics, Center for Protein Diagnostics (ProDi), Ruhr-University Bochum, 44780 Bochum, Germany.
PMID: 33920556 DOI: 10.3390/cells10040931
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.

Keywords
Parkinson’s Disease; high throughput screening; neurite outgrowth; neurodegeneration; neuronal morphology; neurotoxicity; skeletonization.