Development and application of quantitative methods for clinical high-throughput proteomics
Abstract/Contents
- Abstract
- The ability to generate robust quantitative high-throughput protein abundance estimates directly from clinical samples would be useful for translational biomedical research. Knowing the protein content and abundance of a clinical sample would enable, for example, effective biomarker discovery, risk stratification of patient outcomes, and the ability to better understand disease mechanisms. Mass spectrometry has emerged as a powerful tool in the high-throughput measurement of proteins from human tissues and fluids. We are now beginning to have access to proteomics data of the coverage (hundreds to thousands of proteins), scale (tens to hundreds of samples), and throughput (a few days to weeks per experiment) to bring mass spectrometry proteomics to the study of clinical questions. In this work, we discuss methods for quantitation of peptide and protein abundance from mass spectrometry proteomics data. We apply our approaches to identify key proteins that are differentially expressed in the urine of kidney transplant patients with acute rejection. We also investigate the response of the human plasma proteome to severe burn injury. We characterize the protein composition of T-cells, monocytes, and neutrophils of trauma patients, healthy controls, and stimulated cells, and compare information content of proteomics data from these cell populations with gene expression data from similar samples. Finally, we discuss a novel metric for scoring normalization algorithms from mixture titration quality control experiments, and we apply this metric to proteomics and transcriptomics data. Through the methods for quantitation of peptides and proteins, application of proteomics to different clinical questions, and integration of proteomics with other high-throughput data, we demonstrate the utility of mass spectrometry based proteomics to the study of human biofluids in human disease research.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2013 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Kaushal, Amit |
|---|---|
| Associated with | Stanford University, Program in Biomedical Informatics. |
| Primary advisor | Davis, Ronald W. (Ronald Wayne), 1941- |
| Thesis advisor | Davis, Ronald W. (Ronald Wayne), 1941- |
| Thesis advisor | Altman, Russ |
| Thesis advisor | Xiao, Wenzhong, (Geneticist) |
| Advisor | Altman, Russ |
| Advisor | Xiao, Wenzhong, (Geneticist) |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Amit Kaushal. |
|---|---|
| Note | Submitted to the Program in Biomedical Informatics. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2013. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2013 by Amit Kaushal
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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