Neurotechnology design features' impact on the function and identity of reactive astrocytes
Implantable neurotechnology offers substantial promise to improve the condition of many neurodegenerative diseases. Microelectrode arrays implanted in the brain have the capability to stimulate or record electrical activity from neighboring cells. However, shortly after implantation, a foreign body response occurs, which is what researchers believe decreases the electrical recording stability and longevity of signal detection of these devices. Established biomarkers such as astrogliosis, and stimuli such as the mechanical mismatch at the device-tissue interface, have been studied to understand the tissue response to the devices. However, the relationship of these factors with device performance is not well understood. Astrocytes play an important role in the brain's immune system and recently, RNA analysis has confirmed transcriptional profiles of reactive astrocytes which are associated with specific injury states and neurodegenerative diseases. In this dissertation, I have investigated new biomarkers of astroglial reactivity at the electrode interface and characterized the surface topography and bending stiffness of devices. I induced two types of inflammatory astrocytic cell culture models, and I characterize each model's reactivity in comparison to gene expression surrounding electrodes implanted in rat tissue. Atomic microscope microscopy (AFM) techniques were also used to measure surface roughness and bending stiffness as it may predict cellular adhesion and device performance. I aim to elucidate pathways in the neurological foreign body response which will give researchers new potential biomarkers to target to improve recording performance, motivating improved designs for implantable neurotechnology. The research presented in this dissertation investigates how design features influence the tissue interface and asks questions about possible ways to mitigate tissue response: (1) by exploring and summarizing the design space as a whole, suggesting ways to characterize designs and evaluating each designs' successes and limitations (2) using a cutting edge imaging technique to image and measure material properties of three commonly used materials, (3) and creating a reactive tissue culture model, comparing its proteomic and genetic expression to the established rat model. Chapter 2 describes surface characterization techniques that could be used to better classify device features to predict performance and explores next generation probes from a design and performance standpoint. Chapter 3 uses atomic force microscopy to image and measure surface roughness on device surfaces while also measuring the bending stiffness to help determine possible micromotion in the brain. Here, we speculate what these findings mean for the performance and longevity of current probe design. Chapter 4 develops an astroglial culture model to mimic foreign body response in the brain and compare the genomic results to tissue culture near and far from the implanted device. Here, we report the transcriptomic results of the model in comparison to brain transcriptomic results, and what these biomarkers may implicate regarding tissue response and neurodegenerative signaling. This body of work uncovers knowledge recapitulating important factors of device features that affects tissue signaling at the tissue device interface, and biomarkers that play a role and cell signaling. Future directions aim at developing a more physiologically relevant tissue culture model that can predict clinical outcomes, and use high throughput screening techniques to help researchers address the challenge of long term suboptimal device performance.
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- In Collections
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Electronic Theses & Dissertations
- Copyright Status
- Attribution-NonCommercial-NoDerivatives 4.0 International
- Material Type
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Theses
- Authors
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Riggins, Ti'Air
- Thesis Advisors
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Purcell, Erin
- Committee Members
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Chan, Christina
Li, Wen
Harada, Masako
- Date
- 2021
- Subjects
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Neurosciences
Biomedical engineering
- Program of Study
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Biomedical Engineering - Doctor of Philosophy
- Degree Level
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Doctoral
- Language
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English
- Pages
- 91 pages
- ISBN
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9798762100946
- Permalink
- https://doi.org/doi:10.25335/csfc-ks82