Cytometric and genomic dissection of normal and neoplastic stem cell hierarchies
Abstract/Contents
- Abstract
- Multicellular tissues are hierarchically organized into distinct cell types with intrinsic differences in function and developmental potential. Residing at the apex of this complex organization is the stem cell—the master orchestrator of tissue development, maintenance, and regeneration. Stem cells carry the unique property to indefinitely divide and differentiate into specialized cells. As best demonstrated in the blood system, a single hematopoietic (blood-forming) stem cell (HSC) can repopulate all the blood and immune cells of an organism. The regenerative potential of stem cells and their ability to become any cell type in the body has inspired numerous biological studies on how cell decisions are made and tissues are organized. These insights have influenced clinical applications in cell replacement therapy, disease modeling, and even the treatment of cancer, where stem cells that promote tumor growth, metastasis, and recurrence can be targeted. Here, I describe the identification of stem cell populations in various normal and neoplastic tissues, including the mouse bone marrow, human skeleton, and human breast cancer. In the first part, I find a new marker expressed on the surface of mouse HSCs, Neogenin-1 (Neo1), that stratifies HSCs into two subpopulations—one that is more active and biased towards producing myeloid cells and another that is more dormant and capable of producing all blood lineages equally. Next, I demonstrate how analysis of single cell RNA-sequencing data from the human fetal growth plate enabled me to identify surface markers to isolate a highly regenerative population of human skeletal stem cells. Finally, I show how I leveraged a simple measure of transcriptional diversity in single-cell RNA-sequencing data to build a computational tool, CytoTRACE, that predicts the ordering of cells based on differentiation status and reveals genes associated with candidate human breast cancer stem cells. In summary, these studies highlight both functional and computational approaches for the identification of novel stem cell populations
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Gulati, Gunsagar Singh |
|---|---|
| Degree supervisor | Newman, Aaron, (Biomedical data scientist) |
| Degree supervisor | Weissman, Irving L |
| Thesis advisor | Newman, Aaron, (Biomedical data scientist) |
| Thesis advisor | Weissman, Irving L |
| Thesis advisor | Chan, Charles K. F. (Charles Kwok Fai), 1975-2024 |
| Degree committee member | Chan, Charles K. F. (Charles Kwok Fai), 1975-2024 |
| Associated with | Stanford University, Cancer Biology Program |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Gunsagar Singh Gulati |
|---|---|
| Note | Submitted to the Cancer Biology Program |
| Thesis | Thesis Ph.D. Stanford University 2020 |
| Location | https://purl.stanford.edu/ch048fm1896 |
Access conditions
- Copyright
- © 2020 by Gunsagar Singh Gulati
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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