Full metadata
Title
Unveiling Cellular Heterogeneity, Genetic Regulation, and Protein Trafficking Dynamics Via Novel Integrative Multi-Omic Approaches
Description
Advancements in high-throughput biotechnologies have generated large-scale multi-omics datasets encompassing diverse dimensions such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics, and phenomics. Traditionally, statistical and machine learning-based approaches utilize single-omics data sources to uncover molecular signatures, dissect complicated cellular mechanisms, and predict clinical results. However, to capture the multifaceted pathological mechanisms, integrative multi-omics analysis is needed that can provide a comprehensive picture of the disease. Here, I present three novel approaches to multi-omics integrative analysis. I introduce a single-cell integrative clustering method, which leverages multi-omics to enhance the resolution of cell subpopulations. Applied to a Cellular Indexing of Transcriptomes and Epitopes (CITE-Seq) dataset from human Acute Myeloid Lymphoma (AML) and control samples, this approach unveiled nuanced cell populations that otherwise remain elusive. I then shift the focus to a computational framework to discover transcriptional regulatory trios in which a transcription factor binds to a regulatory element harboring a genetic variant and subsequently differentially regulates the transcription level of a target gene. Applied to whole-exome, whole-genome, and transcriptome data of multiple myeloma samples, this approach discovered synergetic cis-acting and trans-acting regulatory elements associated with tumorigenesis. The next part of this work introduces a novel methodology that leverages the transcriptome and surface protein data at the single-cell level produced by CITE-Seq to model the intracellular protein trafficking process. Applied to COVID-19 samples, this approach revealed dysregulated protein trafficking associated with the severity of the infection.
Date Created
2023
Contributors
- Mudappathi, Rekha (Author)
- Liu, Li (Thesis advisor)
- Dinu, Valentin (Committee member)
- Sun, Zhifu (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
218 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.190974
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: Ph.D., Arizona State University, 2023
Field of study: Biomedical Informatics
System Created
- 2023-12-14 02:02:23
System Modified
- 2023-12-14 02:02:29
- 11 months 1 week ago
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