Authors
Wang, I-HaoFaculty Advisor
Paul GreerAcademic Program
Interdisciplinary Graduate ProgramUMass Chan Affiliations
Morningside Graduate School of Biomedical SciencesProgram in Molecular Medicine
Document Type
Doctoral DissertationPublication Date
2024-05-20
Metadata
Show full item recordAbstract
The olfactory system is crucial for animals in tasks such as foraging, mate selection, and predator avoidance due to its ability to detect and distinguish a vast array of environmental chemicals. Mice detect these chemicals via olfactory receptor (OR) proteins, which are uniquely expressed by olfactory sensory neurons (OSNs); each OSN expresses only one OR type. OSNs with the same OR converge their axons to a specific location in the olfactory bulb (OB), forming a structure known as a glomerulus. This precise organization ensures a consistent, spatially invariant pattern of glomerular activation for each odorant, playing a likely role in the brain's decoding of odor identities. Nevertheless, the exact locations of most glomeruli are unknown, and the mechanisms that create consistent glomerular maps across different animals are not fully understood. In this study, we leveraged spatial transcriptomics and machine learning to map the majority of glomerular positions within the mouse OB. Furthermore, single-cell RNA sequencing revealed distinct transcriptional profiles for each OSN type, characterized not only by their OR gene but also by a unique set of axon guidance genes. These profiles can predict the eventual location of each OSN's glomerulus within the olfactory bulb. We also identified a correlation between the spatial distribution of glomeruli and the characteristics of their corresponding ORs, suggesting a chemotopic arrangement in the mouse olfactory system. Additionally, we probed the complexity of the OB by creating a spatially resolved cell atlas through spatial single-cell transcriptomics, revealing the identity and distribution of neuron subtypes that contribute to odor perception.DOI
10.13028/v7k4-cb06Permanent Link to this Item
http://hdl.handle.net/20.500.14038/53392Rights
Copyright © 2024 I-Hao WangDistribution License
https://creativecommons.org/licenses/by/4.0/ae974a485f413a2113503eed53cd6c53
10.13028/v7k4-cb06