Profiling human immune variation at the systems-level in clinical contexts and at steady-state
Abstract/Contents
- Abstract
- The immune system consists of a multitude of interacting cells that together execute coor- dinated responses against threats. However, most studies thus far have focused on studying single cell types in isolation. Furthermore, much of this research has been performed in mice rather than in humans. The work presented here combines the high-dimensional single-cell technology of mass cytometry with analysis techniques from statistics and machine learning to study the hu- man immune system as a system. We provide the first demonstration of the clinical utility of mass cytometry as an immune monitoring tool. We present a pipeline for using mass cytometry on clinical samples to relate patient immune responses with clinical outcomes, specifically recovery from surgery. We follow up this work with an assay that tests a pa- tient's pre-surgical immune state to predict recovery. We further use our mass cytometry pipeline to identify system-wide differences in maternal and neonatal immune systems. In a finaly study, we present a developed and curated data set of healthy individuals for use as a reference tool for studies of the immune system that use mass cytometry in different clinical contexts. We measured cell type-specific signaling responses to a variety of immune modulators, generating a set of reference ranges of healthy immune variation. Both the raw and analyzed data are available for public use. We further exploited the high- dimensional nature of the dataset to identify sets of coordinated immune features that, when grouped as modules, improve predictive performance in modeling immune differences in our demographic data. This work contributes to the growing efforts in using systems-immunology for human immune monitoring. Future work is needed to apply this framework to other immune perturbations, to integrate this data with the the healthy reference, and to translate these results from the research setting to personalized diagnostics of immune health in the clinic.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Fragiadakis, Gabriela K | |
|---|---|---|
| Associated with | Stanford University, Department of Microbiology and Immunology. | |
| Primary advisor | Nolan, Garry P | |
| Thesis advisor | Nolan, Garry P | |
| Thesis advisor | Angst, Martin S | |
| Thesis advisor | Davis, Mark | |
| Thesis advisor | Sonnenburg, Justin, 1973- | |
| Thesis advisor | Tibshirani, Robert | |
| Advisor | Angst, Martin S | |
| Advisor | Davis, Mark | |
| Advisor | Sonnenburg, Justin, 1973- | |
| Advisor | Tibshirani, Robert |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Gabriela K. Fragiadakis. |
|---|---|
| Note | Submitted to the Department of Microbiology and Immunology. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Gabriela Krista Fragiadakis
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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