ORCID ID

0000-0003-3500-2025

Publication Date

2021-11-22

Document Type

Doctoral Dissertation

Academic Program

Bioinformatics and Computational Biology

Department

Bioinformatics and Integrative Biology

First Thesis Advisor

Manuel Garber

Keywords

scRNA-seq, Autoimmunity, Vitiligo

Abstract

The advent of scRNA-seq has rapidly advanced our understanding of complex systems by enabling the researcher to look at the full transcriptional profile within each cell, with the potential to reveal intercellular communications within a tissue. To map these communications, I created SignallingSingleCell, an R package that provides an end-to-end approach for the analysis of scRNA-seq data, with a particular focus on building ligand and receptor signaling networks. Using these powerful techniques, we sought to dissect the heterogenous population of cells recently reported within the BMDC culture system. From this data we were able to determine the cell type composition, identify the different myeloid responses to similar stimuli, and unify recent conflicting studies about the populations within this system.

We then applied these tools to study vitiligo, an autoimmune disease of the skin, to answer fundamental questions about the initiation and progression of disease. We found signatures of increased antigen presentation through MHC-I, loss of immunotolerance cytokines such as TGFB1 and IL-10, and changes in the complex chemokine circuits that influence T cell localization, including an essential role for CCR5 in Treg function. In order to identify and characterize the autoreactive T cells that are responsible for the targeted destruction of melanocytes, we then paired scRNA-seq with TCR-seq and MHC-II complexes loaded with melanocyte antigen. From this data we contrast the transcriptional state of melanocyte specific T cells to bystanders found within the skin and circulation.

DOI

10.13028/p53z-t156

Rights and Permissions

Licensed under a Creative Commons license

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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