Single cell mass spectrometry and the effects of protein expression variation and correlation on pathways
Abstract/Contents
- Abstract
- Chapter I: Due to noise in the synthesis and degradation of proteins, the concentrations of individual vertebrate signaling proteins were estimated to vary with a coefficient of variation (CV) of approximately 25\% between cells. Such high variation is beneficial for population-level regulation of cell functions but abolishes accurate single-cell signal transmission. Here, we measure cell-to-cell variability of relative protein abundance using quantitative proteomics of individual \enph{Xenopus laevis} eggs and cultured human cells and show that variation is typically much lower, in the range of $5-15\%$, compatible with accurate single-cell transmission. Focusing on bimodal ERK signaling, we show that variation and covariation in MEK and ERK expression improves controllability of the percentage of activated cells, demonstrating how variation and covariation in expression enables population-level control of binary cell-fate decisions. Together, our study argues for a control principle whereby low expression variation enables accurate control of analog single-cell signaling, while increased variation, covariation, and numbers of pathway components are required to widen the stimulus range over which external inputs regulate binary cell activation to enable precise control of the fraction of activated cells in a population. Chapter II: Protein expression variation leads to phenotypic variance between cells which, for example, in cell signaling and differentiation decisions, can lead to differences in cell fate. Targeted assays have shown correlation in expression of proteins that are part of larger assemblies or regulatory modules. However, there has not been a direct measurement of correlation within and between all protein modules in a single cell. Here we measure variation and correlation of over 1000 proteins in individual cells using quantitative proteomics of individual \emph{Xenopus laevis} eggs and found that proteins involved in the same metabolic module tend to have low levels of expression variance, as well as high correlation. Markedly, we also identified a meta-logic of correlation that reflects the connection between metabolic modules. Molecular modeling showed that low variance and high correlation within metabolic modules results in higher efficiency of metabolic pathways. Additionally, the meta-analysis shows that related metabolic modules such as oxidative phosphorylation and lipid metabolism have high levels of correlation with one another, likely further maximizing overall efficiency. Our study argues for a control principle whereby coordinated variance and correlation within and between metabolic modules helps cells to increase metabolic efficiency.
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 | Kovary, Kyle Murphy |
|---|---|
| Degree supervisor | Ferrell, James Ellsworth |
| Degree supervisor | Teruel, Mary |
| Thesis advisor | Ferrell, James Ellsworth |
| Thesis advisor | Teruel, Mary |
| Thesis advisor | Chen, James Kenneth |
| Thesis advisor | Mallick, Parag, 1976- |
| Degree committee member | Chen, James Kenneth |
| Degree committee member | Mallick, Parag, 1976- |
| Associated with | Stanford University, Department of Chemical and Systems Biology |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Kyle M. Kovary. |
|---|---|
| Note | Submitted to the Department of Chemical and Systems Biology. |
| Thesis | Thesis Ph.D. Stanford University 2020. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Kyle Murphy Kovary
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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