[Zurück]


Zeitschriftenartikel:

C. Zanetti, S. Spitz, E. Berger, S. Bolognin, L. Smits, P. Crepaz, M. Rothbauer, J. Rosser, M. Marchetti-Deschmann, J. Schwamborn, P. Ertl:
"Monitoring the neurotransmitter release of human midbrain organoids using a redox cycling microsensor as a novel tool for personalized Parkinson's disease modelling and drug screening";
Analyst, 146 (2021), 7; 10 S.



Kurzfassung englisch:
In this study, we have aimed at developing a novel electrochemical sensing approach capable of detecting dopamine, the main biomarker in Parkinson's disease, within the highly complex cell culture matrix of human midbrain organoids in a non-invasive and label-free manner. With its ability to generate organotypic structures in vitro, induced pluripotent stem cell technology has provided the basis for the development of advanced patient-derived disease models. These include models of the human midbrain, the affected region in the neurodegenerative disorder Parkinson's disease. Up to now, however, the analysis of so-called human midbrain organoids has relied on time-consuming and invasive strategies, incapable of monitoring organoid development. Using a redox-cycling approach in combination with a 3-mercaptopropionic acid self-assembled monolayer modification enabled the increase of sensor selectivity and sensitivity towards dopamine, while simultaneously reducing matrix-mediated interferences. In this work, we demonstrate the ability to detect and monitor even small differences in dopamine release between healthy and Parkinson`s disease-specific midbrain organoids over prolonged cultivation periods, which was additionally verified using liquid chromatography-multiple reaction monitoring mass spectrometry. Furthermore, the detection of a phenotypic rescue in midbrain organoids carrying a pathogenic mutation in leucine-rich repeat kinase 2, upon treatment with the leucine-rich repeat kinase 2 inhibitor II underlines the practical implementability of our sensing approach for drug screening applications as well as personalized disease modelling.

Schlagworte:
Parkinson Disease


"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.1039/d0an02206c

Elektronische Version der Publikation:
https://publik.tuwien.ac.at/files/publik_299280.pdf


Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.