Researcher ORCID Identifier

0009-0005-2181-1309

Graduation Year

2023

Date of Submission

5-2023

Document Type

Campus Only Senior Thesis

Degree Name

Bachelor of Arts

Department

Neuroscience

Reader 1

Brian Duistermars

Reader 2

Bruce M. Wang

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Terms of Use for work posted in Scholarship@Claremont.

Rights Information

© 2023 Matthew Choi

Abstract

Single-cell RNA sequencing (scRNA-seq) is a powerful method to obtain high dimensional information pertaining to the differential gene expression of cells in a specific tissue. While it may require meticulous preparations, high technical prowess, and high operation costs, this method can help identify novel cell populations, in addition to identifying similar and different injury responses. To test the capabilities of scRNA-seq on AD pathology research, a theoretical experiment was proposed comparing the differential gene expression of traumatic brain injury (TBI), chronic cerebral hypoperfusion (CCH), and transgenic mice model expressing genes such as amyloid precursor protein (APP) and presenilin 1 (PS1). The mouse models were analyzed to answer questions about new cell types that can be discovered when analyzing the scRNA-seq data, potential common injury responses in the various models, and injury-specific responses in the four models.” To illustrate the scRNA-seq analysis pipeline, the Seurat package was utilized to preform quality control measures, standardized, normalized, and visualization creation such as: violin plots, UMAPs, DEG dot plots, and ration contribution bar plots. After processing the data and analyzing it, previously conducted experiments were referenced to predict potential results of the experiment. Due to the gene expression profiles of the various injury models, it was expected that amyloid precursor protein, presenilin-1, tau protein (MAPT), and apolipoprotein E (APOE) would be expressed similarly in the models. The models were also expected to uniquely express genes, such as the 3xTg-AD mice models expressing the genes Cst7, Ctss, Tyrobp, Gfap, and Trem2. While these methods may provide invaluable information regarding AD pathology research, machine learning algorithms have been developed to study multi-omics tissue data to not only study the gene expressions, but also the functions of the various proteins in samples.

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