Millifluidic culture improves human midbrain organoid vitality and differentiation

Emanuel Berger(University of Luxembourg), Chiara Magliaro(University of Pisa), Nicole Paczia(University of Luxembourg), Anna S. Monzel(University of Luxembourg), Paul Antony(University of Luxembourg), Carole L. Linster(University of Luxembourg), Silvia Bolognin(University of Luxembourg), Arti Ahluwalia(University of Pisa), Jens C. Schwamborn(University of Luxembourg)
Lab on a Chip
January 1, 2018
Cited by 155Open Access
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Abstract

Human midbrain-specific organoids (hMOs) serve as an experimental in vitro model for studying the pathogenesis of Parkinson's disease (PD). In hMOs, neuroepithelial stem cells (NESCs) give rise to functional midbrain dopaminergic (mDA) neurons that are selectively degenerating during PD. A limitation of the hMO model is an under-supply of oxygen and nutrients to the densely packed core region, which leads eventually to a "dead core". To reduce this phenomenon, we applied a millifluidic culture system that ensures media supply by continuous laminar flow. We developed a computational model of oxygen transport and consumption in order to predict oxygen levels within the hMOs. The modelling predicts higher oxygen levels in the hMO core region under millifluidic conditions. In agreement with the computational model, a significantly smaller "dead core" was observed in hMOs cultured in a bioreactor system compared to those ones kept under conventional shaking conditions. Comparing the necrotic core regions in the organoids with those obtained from the model allowed an estimation of the critical oxygen concentration necessary for ensuring cell vitality. Besides the reduced "dead core" size, the differentiation efficiency from NESCs to mDA neurons was elevated in hMOs exposed to medium flow. Increased differentiation involved a metabolic maturation process that was further developed in the millifluidic culture. Overall, bioreactor conditions that improve hMO quality are worth considering in the context of advanced PD modelling.


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