Myelin is critical for the operation of a functional vertebrate nervous system, allowing for rapid saltatory conduction and providing trophic support to axons. In multiple sclerosis (MS), the immune system attacks myelin sheaths, leading to de-myelination of axons. De-myelinated axons not only lose their ability to conduct rapid nerve impulses, but are themselves susceptible to damage and loss. Long term demyelination leads to neuronal loss and the devastating symptoms of secondary stages of MS. One therapeutic approach which has been suggested is to improve the ability of oligodendrocyte precursor cells (OPCs) to differentiate into mature re-myelinating oligodendrocytes. This process is known to occur in vivo, however, the myelin produced appears reduced and the efficiency with which OPCs differentiate into myelinating oligodendrocytes (OLs) varies greatly. For example, the ability of OPCs from older mice to differentiate is reduced compared to those from young mice. This fact taken alongside the presence of many OPCs in some MS lesions which have failed to re-myelinate makes identifying compounds which can increase OPC differentiation into OLs a key goal for MS drug development. In this work, I use OPC to OL differentiation during zebrafish development as a model for differentiation of OLs more generally. Zebrafish are widely used for chemical screening, with recent developments in genetic manipulation, such as CRISPR/Cas9 gene editing and Tol2 transgenesis, allowing for production of targeted mutations and fluorescent reporter lines respectively. Retinoid X receptor-γ (RXRγ) has previously been identified as being transcriptionally upregulated during re-myelination. Moreover, treatment with 9-cis-retinoic acid, an agonist for the receptor, has been shown to improve remyelination in vivo in rats with toxin induced focal demyelination. I first present a manual chemical screen of a library of compounds designed to target RXRγ, from which I identify several compounds which reproducibly increase OLs in zebrafish. In order to assess whether any of the hit compounds could be acting as agonists for RXRγ, I have created a double knockout zebrafish lacking both genes coding for the zebrafish RXRγ homologues (rxrga and rxrgb). This line has been used to test the activity of hit compounds in a RXRγ loss of function background. Following this first chemical screen, it was clear that great improvements could be made to both throughput and robustness if the screen was automated. Using a commercially available fish handling robot which automates the imaging of plates of zebrafish embryos, known as the VAST BioImager, the throughput of our assay was increased from ~40 fish per day to up to around 300 to 400. We combined the VAST BioImager with a state of the art spinning disk confocal microscope, giving us (to the best of our knowledge) the world's fastest in vivo vertebrate screening system capable of orienting fish and imaging at sub-cellular resolution. This significant increase in rate of fish processing led to the need for an increase in the rate of image analysis. Much of the gains in throughput would be lost to time counting cells, and so I developed software to automate the image processing and analysis. The software developed is shown to closely match the abilities of a human to identify compounds which give significant increases in OLs, with very little human intervention required. In the final section of this work I present an example screen performed using the VAST BioImager in combination with the automated cell counting software, which I developed. The hits from this screen highlight our ability to automatically identify compounds that increase the number of OLs in the developing zebrafish. This method is broadly applicable to other central nervous system cell types and other methods of analysis can be integrated into the presented screening software.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:735609 |
| Date | January 2016 |
| Creators | Early, Jason John |
| Contributors | Lyons, David ; Patton, Elizabeth |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/25882 |
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