The brain is the most complex organ of the human body. During the last two decades, a new generation of scientists, between a microfabrication engineer and a cell biologist, started to develop engineered microenvironments trying to recapitulate the complexity of neural networks. In such context, brain white matter has a central role in motor coordination and cognitive functions. White matter abnormalities are detrimental and significant in many neurological disorders, including autism spectrum disorders (ASD). Most of the current approaches for the study of cells residing in the white matter, rely on the use of strategies that are not able to realistically represent the three dimensional (3D) spatial configuration in our brain, or are far from the human white matter complexity. Representative systems are of utmost importance to understand how physical and mechanical cues in the 3rd dimension, i.e. mechanobiology, affect neural cell differentiation and fate in health and disease. In the context of a NWO funded project in collaboration with Vrije Universiteit of Amsterdam (VU), you will design, fabricate and characterize 3D micro- and meso-scale environments to: investigate the mechanobiology and foster/accelerate the maturation of iPSC-derived neural cells from both healthy and ASD patients; study properties of and changes in white matter structures within long-term diffusible neural tissue-like architectures (i.e. spheroids/organoids). This integrated approach can be potentially used to ‘diagnose’ patient cell phenotypes and, in future, be employed to screen (lead) compounds for the treatment of ASD. The investigation will be conducted synergistically with another PhD student specialized in stem cell biology and simultaneously appointed at VU. The preferred starting date will be the 1st of November 2021.
If you are passionate about pursuing a PhD project at the interface between Mechanical Engineering, Microfabrication, Materials Science and Neuroscience, then we would welcome your application to join our group. The successful candidate will:
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