Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article

Degree

Doctor of Philosophy

Program

Neuroscience

Supervisor

Hebb, Matthew

2nd Supervisor

Schmid, Susanne

Co-Supervisor

Abstract

Background: Parkinson's disease (PD) is a neurodegenerative disorder characterized by the progressive loss of dopaminergic neurons in the brain. To date, no disease-modifying treatments for PD are available and novel insights and therapeutics are essential. This thesis explores a novel substrate for cell-based therapies for PD, brain-derived progenitor cells (BDPCs), derived from living PD brain samples obtained during deep-brain stimulation surgery, while also utilizing this unique tissue source to gain important insights into the molecular underpinnings of the disease. Methods: In Chapter 2, human and rat BDPCs were compared, and the latter engineered for longitudinal tracking in vivo using bioluminescence imaging(BLI). The engineered cells were implanted in a rodent syngeneic graft model to evaluate BDPCs survival and integration after transplantation, mimicking an autologous therapeutic application. Chapters 3 and 4 utilized RNA sequencing to analyze gene expression and alternative splicing changes in the living PD frontal cortex. A random forest classifier was then trained on a 370-gene signature to discern PD samples from healthy controls. Results: Rodent BDPCs exhibited qualities analogous to their human counterparts, including expression and secretion of neurotrophic factors BDNF and GDNF. The rodent BDPC grafts could be tracked effectively with BLI, and showed survival and engraftment in the host brain. Transcriptomic profiling of the living PD brain revealed dysregulation of genes involved in trophic factor signaling, apoptosis, inflammation, and other key pathways. Numerous alternative splicing events were also found to be altered in PD. Building on these findings, a machine learning classifier was developed that could accurately distinguish PD samples from controls using the PD-associated gene expression signature, with potential applications for early diagnosis. Conclusions: This thesis establishes a preclinical platform to evaluate autologous BDPCs as a cell-based therapy for PD and provides unprecedented insights into the molecular landscape of the living PD brain. The identification of novel dysregulated pathways and splicing events, as well as the development of a diagnostic classifier, opens new avenues for understanding disease mechanisms and developing targeted (disease-modifying) interventions. The ability to directly access and interrogate the living PD brain represents a powerful approach to advance Parkinson's disease research.

Summary for Lay Audience

Parkinson's disease (PD) is a brain disorder that causes problems with movement, balance, and other functions. People with PD gradually lose important brain cells that produce a chemical called dopamine, which is crucial for coordinating movement. Medications can help manage the symptoms for a while, but there is currently no cure. One promising approach being explored to treat PD is cell therapy, where healthy cells are transplanted into the brain to replace or repair the ones that have been lost or support the remaining cells. Unfortunately, researchers have not found suitable conditions and cell types to successfully treat PD in this way. We have discovered a new source of stem-like cells, called brain-derived progenitor cells (BDPCs) that can be isolated from the brain of patients living with PD while they are undergoing surgery. These BDPCs have qualities that make them excellent candidates for cell therapies. In animal studies, we show that these BDPCs can survive and integrate when transplanted into the brain, suggesting they might be useful as a treatment. We also used advanced genetic analysis techniques to examine the activity of genes in the Parkinson's brain tissue. Many genes involved in supporting brain cell health, reducing inflammation, and other key processes had different levels of expression in the Parkinson's samples compared to healthy brains. Interestingly, we also found many changes in how certain genes were being assembled, or "spliced," which can impact their function. Using the data from our genetic analyses, we developed a computer algorithm that could accurately identify Parkinson’s patients by looking at the activity of a unique set of genes in brain and even blood samples. This strategy could lead to earlier and more reliable diagnosis of PD in the future. Overall, this work gives us new information about how PD happens and gives us ideas for developing new tools and treatments for PD. It also shows how much valuable information can be gained by studying the living Parkinson’s brain.

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

Download

Share

COinS