Eliciting the impacts of cellular noise on metabolic trade-offs by quantitative mass imaging

A. E. Vasdekis; H. Alanazi; A. M. Silverman; C. J. Williams; A. J. Canul; J. B. Cliff; A. C. Dohnalkova; G. Stephanopoulos

doi:10.1038/s41467-019-08717-w

Eliciting the impacts of cellular noise on metabolic trade-offs by quantitative mass imaging

A. E. Vasdekis, H. Alanazi, A. M. Silverman, C. J. Williams, A. J. Canul, J. B. Cliff, A. C. Dohnalkova, G. Stephanopoulos

Research output: Contribution to journal › Article › peer-review

27 Scopus citations

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.

Original language	English
Article number	848
Journal	Nature Communications
Volume	10
Issue number	1
DOIs	https://doi.org/10.1038/s41467-019-08717-w
State	Published - Dec 1 2019
Externally published	Yes

Funding

A.E.V. acknowledges support from the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (DE-SC0019249) for the acquisition/ analysis of the data presented in this work, the Leonard Halland Fund for equipment acquisition and support during preliminary investigations, as well as partial support from the National Institutes of Health (P20GM104420) during preliminary investigations. G.S. acknowledges support from the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (DE-SC0008744). Part of the research was performed using EMSL at Pacific Northwest National Laboratory (Proposal ID 49084), a DOE Office of Science Use Facility sponsored by the Office of Biological and Environmental Research. Publication of this article was partially funded by the University of Idaho Open Access Publishing Fund.

Funders	Funder number
DOE Office of Science
Leonard Halland Fund
Office of Biological and Environmental Research	DE-SC0019249
University of Idaho Open Access Publishing Fund
National Institutes of Health	DE-SC0008744
U.S. Department of Energy
National Institute of General Medical Sciences	P20GM104420
Office of Science
Pacific Northwest National Laboratory	49084

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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.",

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Eliciting the impacts of cellular noise on metabolic trade-offs by quantitative mass imaging

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