Network Analysis of Developing Neural Progenitor Cells

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dc.contributor.advisorQutub, Amina Aen_US
dc.creatorMahadevan, Arunen_US
dc.date.accessioned2019-05-17T15:57:25Zen_US
dc.date.available2019-05-17T15:57:25Zen_US
dc.date.created2018-08en_US
dc.date.issued2018-08-01en_US
dc.date.submittedAugust 2018en_US
dc.date.updated2019-05-17T15:57:25Zen_US
dc.description.abstractThe architecture of the mammalian brain has been characterized through decades of innovation in the field of network neuroscience. However, the assembly of the brain from neural progenitor cells (NPCs) during embryonic development is an immensely complex process that has yet to be well characterized. The complex interplay between ‘programmed’ genetic behavior of individual cells and cell-cell interactions among NPCs shapes how neural circuits self-assemble. A lack of tractable experimental methods and accompanying analytical techniques have hindered understanding of this process. To address these challenges, I designed, developed and implemented two new technologies: (1) cytoNet – a computational platform to enable quantitative characterization of cell-cell interactions and (2) Living Neural Networks – an experimental-computational assay to capture the development of neural network formation from NPCs. The first technology, cytoNet, is a software tool designed to characterize multicellular topology from microscopy images. Accessible through a web-based interface, cytoNet quantifies the spatial relationships in cell communities using principles of graph theory, and evaluates the effect of cell-cell interactions on individual cell phenotypes. cytoNet’s capabilities are demonstrated in two applications relevant to regenerative medicine: quantifying the morphological response of endothelial cells to neurotrophic factors present in the brain after injury, and characterizing cell cycle dynamics of differentiating neural progenitor cells. For development of the Living Neural Networks technology, I leveraged the cytoNet technique to characterize the evolution of spatial and functional network features in human NPCs during the formation of neural networks in vitro. Results from the Living Neural Networks analysis show that the rise and fall in spatial network efficiency is a characteristic feature of the transition from immature NPC networks to mature neural networks. Furthermore, networks at intermediate stages of differentiation that display high spatial network efficiency also show high levels of network-wide spontaneous electrical activity. These results support the view that network-wide signaling in immature progenitor cells gives way to a hierarchical form of communication in mature neural networks. In addition to identifying global trends in neural network formation, I also leveraged graph theory to study the spatial features of individual cell types in developing cultures, uncovering spatial features of polarized neuroepithelium. Finally, I employed the method to uncover aberrant network features in a neurodevelopmental disorder using induced pluripotent stem cell (iPSC) models. The techniques introduced by my thesis work bridge the gap between developmental neurobiology and network neuroscience, and offers insight into the relationship between developing and mature neural networks in health and disease.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationMahadevan, Arun. "Network Analysis of Developing Neural Progenitor Cells." (2018) Diss., Rice University. <a href="https://hdl.handle.net/1911/105823">https://hdl.handle.net/1911/105823</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/105823en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectgraph theoryen_US
dc.subjectneural progenitor cellsen_US
dc.subjectneural networksen_US
dc.subjectbioimage analysisen_US
dc.titleNetwork Analysis of Developing Neural Progenitor Cellsen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentBioengineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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