Francesca Macchi1, Angélique Deleersnijder1, Chris Van den Haute2, Sebastian Munck3, Hans Pottel4, Annelies Michiels2, Zeger Debyser5, Melanie Gerard6, Veerle Baekelandt7. 1. KU Leuven, Laboratory for Neurobiology and Gene Therapy, Kapucijnenvoer 33, Leuven B-3000, Flanders, Belgium. 2. KU Leuven, Laboratory for Neurobiology and Gene Therapy, Kapucijnenvoer 33, Leuven B-3000, Flanders, Belgium; Leuven Viral Vector Core, KU Leuven, Leuven B-3000, Flanders, Belgium. 3. KU Leuven, Department of Human Genetics, Flanders Interuniversity Institute of Biotechnology, Kapucijnenvoer 33, Leuven B-3000, Flanders, Belgium. 4. KU Leuven Campus Kulak Kortrijk, Public Health and Primary Care, Interdisciplinary Research Facility Life Sciences, Etienne Sabbelaan 53, Kortrijk B-8500, Flanders, Belgium. 5. Laboratory for Molecular Virology and Gene Therapy, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven B-3000, Flanders, Belgium. 6. KU Leuven, Laboratory for Neurobiology and Gene Therapy, Kapucijnenvoer 33, Leuven B-3000, Flanders, Belgium; KU Leuven campus Kulak Kortrijk, Laboratory of Biochemistry, Interdisciplinary Research Facility Life Sciences, Etienne Sabbelaan 53, Kortrijk B-8500, Flanders, Belgium. 7. KU Leuven, Laboratory for Neurobiology and Gene Therapy, Kapucijnenvoer 33, Leuven B-3000, Flanders, Belgium. Electronic address: veerle.baekelandt@med.kuleuven.be.
Abstract
BACKGROUND: Alpha-synuclein (α-SYN) aggregates represent a key feature of Parkinson's disease, but the exact relationship between α-SYN aggregation and neurodegeneration remains incompletely understood. Therefore, the availability of a cellular assay that allows medium-throughput analysis of α-SYN-linked pathology will be of great value for studying the aggregation process and for advancing α-SYN-based therapies. NEW METHOD: Here we describe a high-content neuronal cell assay that simultaneously measures oxidative stress-induced α-SYN aggregation and apoptosis. RESULTS: We optimized an automated and reproducible assay to quantify both α-SYN aggregation and cell death in human SH-SY5Y neuroblastoma cells. COMPARISON WITH EXISTING METHODS: Quantification of α-SYN aggregates in cells has typically relied on manual imaging and counting or cell-free assays, which are time consuming and do not allow a concurrent analysis of cell viability. Our high-content analysis method for quantification of α-SYN aggregation allows simultaneous measurements of multiple cell parameters at a single-cell level in a fast, objective and automated manner. CONCLUSIONS: The presented analysis approach offers a rapid, objective and multiparametric approach for the screening of compounds and genes that might alter α-SYN aggregation and/or toxicity.
BACKGROUND:Alpha-synuclein (α-SYN) aggregates represent a key feature of Parkinson's disease, but the exact relationship between α-SYN aggregation and neurodegeneration remains incompletely understood. Therefore, the availability of a cellular assay that allows medium-throughput analysis of α-SYN-linked pathology will be of great value for studying the aggregation process and for advancing α-SYN-based therapies. NEW METHOD: Here we describe a high-content neuronal cell assay that simultaneously measures oxidative stress-induced α-SYN aggregation and apoptosis. RESULTS: We optimized an automated and reproducible assay to quantify both α-SYN aggregation and cell death in human SH-SY5Y neuroblastoma cells. COMPARISON WITH EXISTING METHODS: Quantification of α-SYN aggregates in cells has typically relied on manual imaging and counting or cell-free assays, which are time consuming and do not allow a concurrent analysis of cell viability. Our high-content analysis method for quantification of α-SYN aggregation allows simultaneous measurements of multiple cell parameters at a single-cell level in a fast, objective and automated manner. CONCLUSIONS: The presented analysis approach offers a rapid, objective and multiparametric approach for the screening of compounds and genes that might alter α-SYN aggregation and/or toxicity.
Authors: Jianmin Si; Chris Van den Haute; Evy Lobbestael; Shaun Martin; Sarah van Veen; Peter Vangheluwe; Veerle Baekelandt Journal: Int J Mol Sci Date: 2021-03-07 Impact factor: 5.923
Authors: David Barata; Giulia Spennati; Cristina Correia; Nelson Ribeiro; Björn Harink; Clemens van Blitterswijk; Pamela Habibovic; Sabine van Rijt Journal: Biomed Microdevices Date: 2017-09-07 Impact factor: 2.838