Tissue engineers – in order to develop therapies for the treatment of complex neurological injuries and diseases – attempt to recreate elaborate developmental mechanisms in vitro. Neuronal precursor cells are excellent candidates for the study of developmental operations such as cell adhesion, differentiation, and axonal pathfinding. Hyaluronan (HA) is a common polysaccharide that is found extensively throughout the neuronal extracellular matrix (ECM), and can be functionalised and crosslinked to form stable hydrogels that support growing neuronal cells. Hyaluronan hydrogels can be modified chemically and mechanically to mimic the ECM of the developing brain, awarding control over mechanisms such as differentiation and axonal pathfinding. This thesis is concerned with the functionalisation and characterisation of HA hydrogels, ultimately in order to simulate vital properties of the developing brain. Here we show that HA hydrogels can be finely tuned mechanically (by modulating stiffness and viscosity), and chemically, by the conjugation of peptides that mimic the neural cell adhesion molecule (NCAM). NCAM mimics and novel mimics of sialylated NCAM significantly influence the differentiation of NSPCs in 2D and 3D. HA hydrogels successfully support long term culture of neural cells in 3D, and encourage the formation and extension of neurites of several cell types including human, mouse and rat neuronal precursor and stem cells. These results demonstrate for the first time that novel NCAM mimicking peptides can be conjugated to well defined hydrogel matrices that influence intricate developmental behaviours in 3D. Understanding how neural cells form functional networks is essential for the development of clinical approaches that attempt to address the injuries and diseases that affect these systems.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:635310 |
| Date | January 2015 |
| Creators | Putter, Phillipus Johannes |
| Contributors | Cui, Zhanfeng |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://ora.ox.ac.uk/objects/uuid:cd043ef4-a7bd-44f4-a9bf-4055e3d5ac13 |
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