Eliciting the impacts of cellular noise on metabolic trade-offs by quantitative mass imaging
Abstract
Optimal metabolic trade-offs between growth and productivity are key constraints in strain optimization by metabolic engineering; however, how cellular noise impacts these trade-offs and drives the emergence of subpopulations with distinct resource allocation strategies, remains largely unknown. Here, we introduce a single-cell strategy for quantifying the trade-offs between triacylglycerol production and growth in the oleaginous microorganism Yarrowia lipolytica. The strategy relies on high-throughput quantitative-phase imaging and, enabled by nanoscale secondary ion mass spectrometry analyses and dedicated image processing, allows us to image how resources are partitioned between growth and productivity. Enhanced precision over population-averaging biotechnologies and conventional microscopy demonstrates how cellular noise impacts growth and productivity differently. As such, subpopulations with distinct metabolic trade-offs emerge, with notable impacts on strain performance and robustness. By quantifying the self-degradation of cytosolic macromolecules under nutrient-limiting conditions, we discover the cell-to-cell heterogeneity in protein and fatty-acid recycling, unmasking a potential bet-hedging strategy under starvation.
- Authors:
-
- Univ. of Idaho, Moscow, ID (United States)
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Publication Date:
- Research Org.:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Univ. of Idaho, Moscow, ID (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1507525
- Alternate Identifier(s):
- OSTI ID: 1594160
- Report Number(s):
- PNNL-SA-141117
Journal ID: ISSN 2041-1723
- Grant/Contract Number:
- AC05-76RL01830; SC0019249; P20GM104420; SC0008744; 49084
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nature Communications
- Additional Journal Information:
- Journal Volume: 10; Journal Issue: 1; Journal ID: ISSN 2041-1723
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; lipids; Yarrowia lipolytica; triacylglycerol
Citation Formats
Vasdekis, Andreas E., Alanazi, H., Silverman, Andrew M., Williams, C. J., Canul, Amrah J., Cliff, John B., Dohnalkova, Alice C., and Stephanopoulos, Gregory. Eliciting the impacts of cellular noise on metabolic trade-offs by quantitative mass imaging. United States: N. p., 2019.
Web. doi:10.1038/s41467-019-08717-w.
Vasdekis, Andreas E., Alanazi, H., Silverman, Andrew M., Williams, C. J., Canul, Amrah J., Cliff, John B., Dohnalkova, Alice C., & Stephanopoulos, Gregory. Eliciting the impacts of cellular noise on metabolic trade-offs by quantitative mass imaging. United States. https://doi.org/10.1038/s41467-019-08717-w
Vasdekis, Andreas E., Alanazi, H., Silverman, Andrew M., Williams, C. J., Canul, Amrah J., Cliff, John B., Dohnalkova, Alice C., and Stephanopoulos, Gregory. Tue .
"Eliciting the impacts of cellular noise on metabolic trade-offs by quantitative mass imaging". United States. https://doi.org/10.1038/s41467-019-08717-w. https://www.osti.gov/servlets/purl/1507525.
@article{osti_1507525,
title = {Eliciting the impacts of cellular noise on metabolic trade-offs by quantitative mass imaging},
author = {Vasdekis, Andreas E. and Alanazi, H. and Silverman, Andrew M. and Williams, C. J. and Canul, Amrah J. and Cliff, John B. and Dohnalkova, Alice C. and Stephanopoulos, Gregory},
abstractNote = {Optimal metabolic trade-offs between growth and productivity are key constraints in strain optimization by metabolic engineering; however, how cellular noise impacts these trade-offs and drives the emergence of subpopulations with distinct resource allocation strategies, remains largely unknown. Here, we introduce a single-cell strategy for quantifying the trade-offs between triacylglycerol production and growth in the oleaginous microorganism Yarrowia lipolytica. The strategy relies on high-throughput quantitative-phase imaging and, enabled by nanoscale secondary ion mass spectrometry analyses and dedicated image processing, allows us to image how resources are partitioned between growth and productivity. Enhanced precision over population-averaging biotechnologies and conventional microscopy demonstrates how cellular noise impacts growth and productivity differently. As such, subpopulations with distinct metabolic trade-offs emerge, with notable impacts on strain performance and robustness. By quantifying the self-degradation of cytosolic macromolecules under nutrient-limiting conditions, we discover the cell-to-cell heterogeneity in protein and fatty-acid recycling, unmasking a potential bet-hedging strategy under starvation.},
doi = {10.1038/s41467-019-08717-w},
journal = {Nature Communications},
number = 1,
volume = 10,
place = {United States},
year = {Tue Feb 19 00:00:00 EST 2019},
month = {Tue Feb 19 00:00:00 EST 2019}
}
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Figure 1: Metabolic trade-offs by quantitative mass imaging. a Schematic illustrating substrate uptake and resource partitioning to growth and production, as well as the underlying trade-offs between these two metabolic objectives. b Quantitative phase-imaging (QPI) enables the independent localization (x,y,z) and phase-delay quantification of the cell cytosol (ΔΦcytosol) and TAGmore »
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Regulation of amino-acid metabolism controls flux to lipid accumulation in Yarrowia lipolytica
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Direct chemical evidence for sphingolipid domains in the plasma membranes of fibroblasts
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Analysis of optimality in natural and perturbed metabolic networks
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Alternative routes of acetyl-CoA synthesis identified by comparative genomic analysis: involvement in the lipid production of oleaginous yeast and fungi
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Stochastic Gene Expression in a Single Cell
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Effects of substrate patterning on cellular spheroid growth and dynamics measured by gradient light interference microscopy (GLIM)
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