Dissertation
Integrating multi-disciplinary approaches in guiding neurite growth: engineering micropatterned cues, Ca2+ signaling mechanisms, and automated image analysis
University of Iowa
Doctor of Philosophy (PhD), University of Iowa
Spring 2025
DOI: 10.25820/etd.007881
Abstract
The intricate, tonotopic organization of afferent innervation in the cochlea enables the effective processing of complex auditory stimuli. To create this precisely organized architecture, the neurites of spiral ganglion neurons (SGNs) navigate through a complex environment of cells, extracellular matrix, biophysical features, and biochemical gradients to establish precise connections with their targets in the organ of Corti and cochlear nuclei. This process, known as pathfinding, involves the growth cone of SGN neurites sensing, turning, and growing toward specific targets in response to biophysical and biochemical cues. In a related context, neural electrode devices, such as cochlear implants (CIs), are utilized to stimulate neural pathways for auditory sensation. However, limitations arise due to the distance between the electrode array and target neurons. Thus, many aspire to improve CIs by inducing SGN neurites to grow into close proximity to the CI. Therefore, here we study the factors governing SGN neurite growth and their pathfinding responses to diverse cues in order to better understand and engineer this process.
Here, we use engineering to investigate biophysical and biochemical factors that direct sensory neuron neurite growth and to probe the pathways these neurons use in pathfinding in response to these cues. In particular, we demonstrate that topographical feature geometry (amplitude and angle) determine neurite turning efficiency. More specifically we show that increasing feature amplitude promotes neurite turning to increasing angle turn challenges in a dose response manner. Key signaling elements, such as inositol triphosphate (IP3) and ryanodine-sensitive receptors (RyR), are found to be essential for SGNs to sense and respond to these biophysical and biochemical cues. Then in related work, we developed a machine learning image analysis tool, NeuriteNet, to study this neurite growth in an automated and unbiased manner. In this we demonstrate that NeuriteNet focuses on and is sensitivity to quantifiable traits of the neuron that represent the differences distinguishing relevant treatment groups.
In conclusion, this multidisciplinary research sheds light on the intricate processes of neurite growth, pathfinding, and alignment in response to diverse cues. The investigation reveals the significance of topographical feature geometry and the involvement of signaling pathways like IP3 and ryanodine receptor RyR in regulating these responses. The findings contribute to the fundamental understanding of critical features of neurite guidance cues and how neurites sense and respond to these cues; it also offers insights into the translation of these systems into potential clinical applications such as guiding SGN neurite growth for improved neural prostheses, including CIs.
Details
- Title: Subtitle
- Integrating multi-disciplinary approaches in guiding neurite growth: engineering micropatterned cues, Ca2+ signaling mechanisms, and automated image analysis
- Creators
- Joseph Vecchi
- Contributors
- Marlan R Hansen (Advisor)Christopher Ahern (Committee Member)Steven Green (Committee Member)Charles Harata (Committee Member)Amy Lee (Committee Member)Kristan Worthington (Committee Member)
- Resource Type
- Dissertation
- Degree Awarded
- Doctor of Philosophy (PhD), University of Iowa
- Degree in
- Biomedical Science (Molecular Physiology and Biophysics)
- Date degree season
- Spring 2025
- DOI
- 10.25820/etd.007881
- Publisher
- University of Iowa
- Number of pages
- xix, 160 pages
- Copyright
- Copyright 2025 Joseph Vecchi
- Grant note
- This work was supported by the following funding sources: NIDCD R01-DC012578 to the Hansen and Guymon Laboratories; NIDCD F31-DC020371 for my predoctoral fellowship; NIGMS T32-GM007337 to the Iowa MSTP; Chateaubriand Fellowship for supporting my research at the Université de Montpellier
- Language
- English
- Description illustrations
- illustrations, tables, graphs
- Description bibliographic
- Includes bibliographical references.
- Public Abstract (ETD)
- Neural electrode devices, including cochlear implant (CI), are implanted electrical devices that directly stimulate neural pathways to replace or enhance neural pathways that are diminished or absent due to disease, trauma, or aging. CIs replace the mechanosensory transduction of sound by directly stimulating spiral ganglion neurons (SGNs) to provide auditory sensation. CIs do not fully emulate native neural pathways and a limitation is the large distance between the electrode array and their target neurons. There is interest to improve the stimulation precision of CIs by inducing SGN neurites to grow into close proximity to the CI. The work here informs the foundational engineering and biology required for this endeavor. First, we engineered a system to study neurite turning in response to topographical cues. The geometry of the topographical turns determines the neuron’s ability to navigate these cues and the growth cones drive this pathfinding by changing their morphology and behavior. Second, we study how the neurite senses and pathfinds in response to various cues by using Ca2+ signaling in the growth cone. Then in related work, we developed a machine learning image analysis tool, NeuriteNet, to study neurite growth in an automated and unbiased manner. NeuriteNet focuses on and is sensitivity to quantifiable traits of the neuron that represent the differences distinguishing those groups. In total, this work offers multidisciplinary scientific findings and innovative methodologies to field of neurite guidance and regeneration. These same principles and techniques will need to be integrated in translating this work to guide SGN neurite outgrowth in vivo.
- Academic Unit
- Biomedical Science Program
- Record Identifier
- 9984830730102771
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