All ETDs from UAB

Advisory Committee Chair

Rita M Cowell

Advisory Committee Members

Scott W Ballinger

Karen L Gamble

Matthew S Goldberg

Darren L Moore

Document Type


Date of Award


Degree Name by School

Doctor of Philosophy (PhD) Heersink School of Medicine


Parkinson’s disease (PD) is a severe, progressive neurodegenerative movement disorder. Currently, there is no cure, nor effective treatments for prevention or modification of disease progression. Thus, novel approaches are required to understand etiological contributors to cell dysfunction and death in PD to enable rational drug design. Recently, huge advancements have been made in the technology involved in collection, storage, and processing of high-volume genetic information. Genome-wide association studies (GWAS) have been integral in identifying genomic loci with common, disease risk-associated variation. However, annotation and prioritization of GWAS-identified risk variants is far from straightforward, and the gap between the identification of risk-associated variants and defining etiological mechanisms of disease is vast. Bridging this gap will require a richer understanding of where risk-associated mutations initiate dysfunction: what cell types are most affected by disease-associated mutations? Development of single-cell sequencing techniques has revealed unexplored levels of cellular heterogeneity within tissues once thought to be genetically uniform. This dissertation presents several approaches for contextualizing risk-associated genetic variation within a framework of cellular heterogeneity to understand cell-type-specific iv etiology. First, we focus on a single cell type vulnerable to dysfunction and loss in PD: the midbrain dopaminergic neuron. Multiplex fluorescent in situ hybridization was used to neuroanatomically localize dopaminergic subpopulations markers identified in a mouse brain single-cell sequencing database and to assess nigrostriatal dopaminergic subpopulations in two rodent models of sporadic PD. Next, we identify patterns of risk-associated genes across multiple cell types by bioinformatically clustering transcripts by cell-type-specific expression. We follow up on these analyses with empirical validation in multiple model systems and use gene ontology and protein-protein-interaction modeling to further explore cell-type-specific etiology. Altogether, this dissertation investigates how to leverage single-cell transcriptomic data to best predict cell-type-specific vulnerability to PD pathogenesis across diverse cell types in the brain. Future studies can use this information to construct cellular models to identify convergent targets for drug discovery efforts and determine the cell types most likely affected based on an individual’s genetic makeup.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.