Lymphocyte differentiation trajectories in human health and cancer
Abstract/Contents
- Abstract
- Cellular differentiation is a continuous and coordinated process that integrates outputs from complex cell signaling networks to make decisions about cell identity, proliferation, and death. Traditionally, cellular differentiation has been described as a series of discrete populations. More recently, emerging technologies for deep single-cell phenotyping, such as mass cytometry by time-of-flight (CyTOF), have enabled us to describe cellular differentiation as a continuous process, where individual cells are aligned onto a trajectory based on their phenotypes. In Chapter 2, I build on prior work from our and other labs to develop experimental and computational methods in order to: (1) build a single-cell map of human naïve T-cell differentiation during expansion for adoptive cell transfer therapy for cancer as a model system; (2) discern division state-dependent from time-dependent processes on this map; (3) examine regulatory signaling on this map to rationally define a strategy to steer differentiation towards a clinically desirable T stem cell memory subset. In Chapter 3, I demonstrate the clinical utility of a known cell differentiation trajectory. Specifically, by aligning single leukemic cells onto a scaffold of human B-cell development, in collaboration with Profs. Kara L. Davis and Robert Tibshirani, we identify a signaling state at a developmental transition from late pro-B to early pre-B cells that predicts relapse based on a diagnostic bone marrow biopsy. Notably, such signaling behavior pre-exists at diagnosis and persists at relapse. In Chapters 4 and 5, in collaboration with Dr. Nikolay Samusik, we perform a comparative study and develop novel software for single-cell embedding, clustering, and visualization. Chapter 6 contains concluding remarks and future direction. Further, in collaboration with Prof. Purvesh Khatri, Prof. Mark M. Davis, and other Computational and Systems Immunology PhD students, we share advice on equipping a modern immunology graduate student with the computational skills they may need on their journey towards a career as a scientist.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Good, Zinaida |
|---|---|
| Associated with | Stanford University, Program in Immunology. |
| Primary advisor | Bendall, Sean, 1979- |
| Primary advisor | Nolan, Garry P |
| Thesis advisor | Bendall, Sean, 1979- |
| Thesis advisor | Nolan, Garry P |
| Thesis advisor | Cleary, Michael L |
| Thesis advisor | Plevritis, Sylvia |
| Thesis advisor | Wernig, Marius |
| Advisor | Cleary, Michael L |
| Advisor | Plevritis, Sylvia |
| Advisor | Wernig, Marius |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Zinaida Good. |
|---|---|
| Note | Submitted to the Program in Immunology. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by Zinaida Good
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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