Reelin is a large, extracellular glycoprotein that binds to several membrane receptors on neural stem cells (HNSCs), neural progenitor cells (NPCs), and neuroblasts of mammals to direct their migration. Previously, our lab established the presence of Reelin increased migration of wild-type fetal-derived HNSC's, both in vitro and in vivo. In addition, we demonstrated that Reelin protein treatment also increases the formation of radial glia via Notch-1 signaling, in vitro. Radial glia are precursors to NPCs, as well as a scaffold for neuroblast migration during cortical lamination. Reelin has long been associated with Schizophrenia (SZ). Because post-mortem brains are limited to describing the end-point of the disease, heterozygous haplodeficient Reelin knock-out (Reeler) mice are used to model developmental aspects of SZ in vivo. However, SZ is a complex, polyfactoral disease with a myriad of dysfunctional pathways that may have unforeseen effects on Reelin signaling. K. Brennand et al. (2014) reported low Reelin mRNA expression and cellular characteristics mirroring the Reeler mouse in induced pluripotent stem (iPS) cell-derived NPCs and neurons from living SZ patients. Building upon this and our work with stem cells, here we consider Reelin's effects on migration of Reelin-deficient iPS cell-derived NPCs. Reelin treatment of consists of secreted Reelin from transfected human embryonic kidney 293 cells (HEK 293) with the pCRL RELN gene-containing plasmid created by G. D'Arcangelo (1997) and given to us by T. Curran. Using the metric of cellular migration, this is the first time it have been shown that SZ iNPCs are capable of receiving and reacting to extracellular Reelin. Due to our validation of this model, further work using iPS cell-derived neural cells can confidently be used for future disease modeling and drug discovery of Reelin-deficient SZ.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-7196 |
| Date | 01 January 2018 |
| Creators | Roberts, Nicole |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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