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Eigenfeld M, Schwaminger SP. Cellular variability as a driver for bioprocess innovation and optimization. Biotechnol Adv 2025; 79:108528. [PMID: 39914686 DOI: 10.1016/j.biotechadv.2025.108528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2024] [Revised: 12/29/2024] [Accepted: 02/03/2025] [Indexed: 02/11/2025]
Abstract
Cellular heterogeneity plays a crucial role in biotechnological processes, significantly influencing metabolic activity, product yield, and process consistency. This review explores the different dimensions of cellular heterogeneity, focusing on its manifestation at both single-cell and population levels. The study examines how factors such as asymmetric cell division, age, and environmental conditions contribute to functional diversity within cell populations, with an emphasis on microorganisms like yeast. Age-related cellular heterogeneity, in particular, is highlighted for its impact on metabolic pathways, mitochondrial function, and secondary metabolite production, which directly affect bioprocess outcomes. Furthermore, the review discusses advanced techniques for detecting and managing heterogeneity, including surface marker-based approaches, which utilize proteins, polysaccharides, and lipids, and label-free methods that leverage cellular volume and physical properties for separation. Understanding and controlling cellular heterogeneity is essential for optimizing industrial bioprocesses, improving yield, and ensuring product quality. The review also underscores the potential of emerging biotechnological tools, such as real-time single-cell analysis and microfluidic devices, in enhancing separation techniques and managing cellular diversity for better process efficiency and robustness.
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Affiliation(s)
- M Eigenfeld
- Medical University of Graz, Otto Loewi Research Center, Division of Medicinal Chemistry, NanoLab Graz, Neue Stiftingtalstraße 6, 8010 Graz, Austria; BioTechMed-Graz, Mozartgasse 12/II, 8010 Graz, Austria.
| | - S P Schwaminger
- Medical University of Graz, Otto Loewi Research Center, Division of Medicinal Chemistry, NanoLab Graz, Neue Stiftingtalstraße 6, 8010 Graz, Austria; BioTechMed-Graz, Mozartgasse 12/II, 8010 Graz, Austria.
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2
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Sheneman L, Stephanopoulos G, Vasdekis AE. Deep learning classification of lipid droplets in quantitative phase images. PLoS One 2021; 16:e0249196. [PMID: 33819277 PMCID: PMC8021159 DOI: 10.1371/journal.pone.0249196] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 03/12/2021] [Indexed: 12/15/2022] Open
Abstract
We report the application of supervised machine learning to the automated classification of lipid droplets in label-free, quantitative-phase images. By comparing various machine learning methods commonly used in biomedical imaging and remote sensing, we found convolutional neural networks to outperform others, both quantitatively and qualitatively. We describe our imaging approach, all implemented machine learning methods, and their performance with respect to computational efficiency, required training resources, and relative method performance measured across multiple metrics. Overall, our results indicate that quantitative-phase imaging coupled to machine learning enables accurate lipid droplet classification in single living cells. As such, the present paradigm presents an excellent alternative of the more common fluorescent and Raman imaging modalities by enabling label-free, ultra-low phototoxicity, and deeper insight into the thermodynamics of metabolism of single cells.
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Affiliation(s)
- Luke Sheneman
- Northwest Knowledge Network, University of Idaho, Moscow, Idaho, United States of America
| | - Gregory Stephanopoulos
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Andreas E. Vasdekis
- Department of Physics, University of Idaho, Moscow, Idaho, United States of America
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3
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Vasdekis AE, Singh A. Microbial metabolic noise. WIREs Mech Dis 2020; 13:e1512. [PMID: 33225608 DOI: 10.1002/wsbm.1512] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 09/23/2020] [Accepted: 10/26/2020] [Indexed: 11/06/2022]
Abstract
From the time a cell was first placed under the microscope, it became apparent that identifying two clonal cells that "look" identical is extremely challenging. Since then, cell-to-cell differences in shape, size, and protein content have been carefully examined, informing us of the ultimate limits that hinder two cells from occupying an identical phenotypic state. Here, we present recent experimental and computational evidence that similar limits emerge also in cellular metabolism. These limits pertain to stochastic metabolic dynamics and, thus, cell-to-cell metabolic variability, including the resulting adapting benefits. We review these phenomena with a focus on microbial metabolism and conclude with a brief outlook on the potential relationship between metabolic noise and adaptive evolution. This article is categorized under: Metabolic Diseases > Computational Models Metabolic Diseases > Biomedical Engineering.
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Affiliation(s)
| | - Abhyudai Singh
- Department of Electrical and Computer Engineering, University of Delaware, Newark, Delaware, USA
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4
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Lee JA, Riazi S, Nemati S, Bazurto JV, Vasdekis AE, Ridenhour BJ, Remien CH, Marx CJ. Microbial phenotypic heterogeneity in response to a metabolic toxin: Continuous, dynamically shifting distribution of formaldehyde tolerance in Methylobacterium extorquens populations. PLoS Genet 2019; 15:e1008458. [PMID: 31710603 PMCID: PMC6858071 DOI: 10.1371/journal.pgen.1008458] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 11/15/2019] [Accepted: 10/04/2019] [Indexed: 12/31/2022] Open
Abstract
While microbiologists often make the simplifying assumption that genotype determines phenotype in a given environment, it is becoming increasingly apparent that phenotypic heterogeneity (in which one genotype generates multiple phenotypes simultaneously even in a uniform environment) is common in many microbial populations. The importance of phenotypic heterogeneity has been demonstrated in a number of model systems involving binary phenotypic states (e.g., growth/non-growth); however, less is known about systems involving phenotype distributions that are continuous across an environmental gradient, and how those distributions change when the environment changes. Here, we describe a novel instance of phenotypic diversity in tolerance to a metabolic toxin within wild-type populations of Methylobacterium extorquens, a ubiquitous phyllosphere methylotroph capable of growing on the methanol periodically released from plant leaves. The first intermediate in methanol metabolism is formaldehyde, a potent cellular toxin that is lethal in high concentrations. We have found that at moderate concentrations, formaldehyde tolerance in M. extorquens is heterogeneous, with a cell's minimum tolerance level ranging between 0 mM and 8 mM. Tolerant cells have a distinct gene expression profile from non-tolerant cells. This form of heterogeneity is continuous in terms of threshold (the formaldehyde concentration where growth ceases), yet binary in outcome (at a given formaldehyde concentration, cells either grow normally or die, with no intermediate phenotype), and it is not associated with any detectable genetic mutations. Moreover, tolerance distributions within the population are dynamic, changing over time in response to growth conditions. We characterized this phenomenon using bulk liquid culture experiments, colony growth tracking, flow cytometry, single-cell time-lapse microscopy, transcriptomics, and genome resequencing. Finally, we used mathematical modeling to better understand the processes by which cells change phenotype, and found evidence for both stochastic, bidirectional phenotypic diversification and responsive, directed phenotypic shifts, depending on the growth substrate and the presence of toxin. Scientists tend to appreciate microbes for their simplicity and predictability: a population of genetically identical cells inhabiting a uniform environment is expected to behave in a uniform way. However, counter-examples to this assumption are frequently being discovered, forcing a re-examination of the relationship between genotype and phenotype. In most such examples, bacterial cells are found to split into two discrete populations, for instance growing and non-growing. Here, we report the discovery of a novel example of microbial phenotypic heterogeneity in which cells are distributed along a gradient of phenotypes, ranging from low to high tolerance of a toxic chemical. Furthermore, we demonstrate that the distribution of phenotypes changes in different growth conditions, and we use mathematical modeling to show that cells may change their phenotype either randomly or in a particular direction in response to the environment. Our work expands our understanding of how a bacterial cell's genome, family history, and environment all contribute to its behavior, with implications for the diverse situations in which we care to understand the growth of any single-celled populations.
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Affiliation(s)
- Jessica A. Lee
- Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America
- Center for Modeling Complex Interactions, University of Idaho, Moscow, Idaho, United States of America
- Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, Idaho, United States of America
- Global Viral, San Francisco, California, United States of America
- * E-mail: (JAL); (CJM)
| | - Siavash Riazi
- Center for Modeling Complex Interactions, University of Idaho, Moscow, Idaho, United States of America
- Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, Idaho, United States of America
- Bioinformatics and Computational Biology Graduate Program, University of Idaho, Moscow, Idaho, United States of America
| | - Shahla Nemati
- Center for Modeling Complex Interactions, University of Idaho, Moscow, Idaho, United States of America
- Department of Physics, University of Idaho, Moscow, Idaho, United States of America
| | - Jannell V. Bazurto
- Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America
- Center for Modeling Complex Interactions, University of Idaho, Moscow, Idaho, United States of America
- Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, Idaho, United States of America
- Department of Plant and Microbial Biology, University of Minnesota, Twin Cities, Minnesota, United States of America
- Microbial and Plant Genomics Institute, University of Minnesota, Twin Cities, Minnesota, United States of America
| | - Andreas E. Vasdekis
- Center for Modeling Complex Interactions, University of Idaho, Moscow, Idaho, United States of America
- Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, Idaho, United States of America
- Department of Physics, University of Idaho, Moscow, Idaho, United States of America
| | - Benjamin J. Ridenhour
- Center for Modeling Complex Interactions, University of Idaho, Moscow, Idaho, United States of America
- Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, Idaho, United States of America
- Department of Mathematics, University of Idaho, Moscow, Idaho, United States of America
| | - Christopher H. Remien
- Center for Modeling Complex Interactions, University of Idaho, Moscow, Idaho, United States of America
- Department of Mathematics, University of Idaho, Moscow, Idaho, United States of America
| | - Christopher J. Marx
- Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America
- Center for Modeling Complex Interactions, University of Idaho, Moscow, Idaho, United States of America
- Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, Idaho, United States of America
- * E-mail: (JAL); (CJM)
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5
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Vasdekis AE, Alanazi H, Silverman AM, Williams CJ, Canul AJ, Cliff JB, Dohnalkova AC, Stephanopoulos G. Eliciting the impacts of cellular noise on metabolic trade-offs by quantitative mass imaging. Nat Commun 2019; 10:848. [PMID: 30783105 PMCID: PMC6381102 DOI: 10.1038/s41467-019-08717-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2018] [Accepted: 01/26/2019] [Indexed: 02/06/2023] Open
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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Affiliation(s)
- A E Vasdekis
- Department of Physics, University of Idaho, Moscow, ID, 83844, USA.
| | - H Alanazi
- Department of Physics, University of Idaho, Moscow, ID, 83844, USA
| | - A M Silverman
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - C J Williams
- Department of Statistical Science, University of Idaho, Moscow, ID, 83844, USA
| | - A J Canul
- Department of Physics, University of Idaho, Moscow, ID, 83844, USA
| | - J B Cliff
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - A C Dohnalkova
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - G Stephanopoulos
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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6
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Coradetti ST, Pinel D, Geiselman GM, Ito M, Mondo SJ, Reilly MC, Cheng YF, Bauer S, Grigoriev IV, Gladden JM, Simmons BA, Brem RB, Arkin AP, Skerker JM. Functional genomics of lipid metabolism in the oleaginous yeast Rhodosporidium toruloides. eLife 2018. [PMID: 29521624 PMCID: PMC5922974 DOI: 10.7554/elife.32110] [Citation(s) in RCA: 69] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
The basidiomycete yeast Rhodosporidium toruloides (also known as Rhodotorula toruloides) accumulates high concentrations of lipids and carotenoids from diverse carbon sources. It has great potential as a model for the cellular biology of lipid droplets and for sustainable chemical production. We developed a method for high-throughput genetics (RB-TDNAseq), using sequence-barcoded Agrobacterium tumefaciens T-DNA insertions. We identified 1,337 putative essential genes with low T-DNA insertion rates. We functionally profiled genes required for fatty acid catabolism and lipid accumulation, validating results with 35 targeted deletion strains. We identified a high-confidence set of 150 genes affecting lipid accumulation, including genes with predicted function in signaling cascades, gene expression, protein modification and vesicular trafficking, autophagy, amino acid synthesis and tRNA modification, and genes of unknown function. These results greatly advance our understanding of lipid metabolism in this oleaginous species and demonstrate a general approach for barcoded mutagenesis that should enable functional genomics in diverse fungi. The fungus Rhodosporidium toruloides can grow on substances extracted from plant matter that is inedible to humans such as corn stalks, wood pulp, and grasses. Under some growth conditions, the fungus can accumulate massive stores of hydrocarbon-rich fats and pigments. A community of scientists and engineers has begun genetically modifying R. toruloides to convert these naturally produced fats and pigments into fuels, chemicals and medicines. These could form sustainable replacements for products made from petroleum or harvested from threatened animal and plant species. Fungi, plants, animals and other eukaryotes store fat in specialized compartments called lipid droplets. The genes that control the metabolism – the production, use and storage – of fat in lipid bodies have been studied in certain eukaryotes, including species of yeast. However, R. toruloides is only distantly related to the most well-studied of these species. This means that we cannot be certain that a gene will play the same role in R. toruloides as in those species. To assemble the most comprehensive list possible of the genes in R. toruloides that affect the production, use, or storage of fat in lipid bodies, Coradetti, Pinel et al. constructed a population of hundreds of thousands of mutant fungal strains, each with its own unique DNA ‘barcode’. The effects that mutations in over 6,000 genes had on growth and fat accumulation in these fungi were measured simultaneously in several experiments. This general approach is not new, but technical limitations had, until now, restricted its use in fungi to a few species. Coradetti, Pinel et al. identified hundreds of genes that affected the ability of R. toruloides to metabolise fat. Many of these genes were related to genes with known roles in fat metabolism in other eukaryotes. Other genes are involved in different cell processes, such as the recycling of waste products in the cell. Their identification adds weight to the view that the links between these cellular processes and fat metabolism are deep and widespread amongst eukaryotes. Finally, some of the genes identified by Coradetti, Pinel et al. are not closely related to any well-studied genes. Further study of these genes could help us to understand why R. toruloides can accumulate much larger amounts of fat than most other fungi. The methods developed by Coradetti, Pinel et al. should be possible to implement in many species of fungi. As a result these techniques may eventually contribute to the development of new treatments for human fungal diseases, the protection of important food crops, and a deeper understanding of the roles various fungi play in the broader ecosystem.
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Affiliation(s)
| | - Dominic Pinel
- Energy Biosciences Institute, Berkeley, United States
| | | | - Masakazu Ito
- Energy Biosciences Institute, Berkeley, United States
| | - Stephen J Mondo
- United States Department of Energy Joint Genome Institute, Walnut Creek, United States
| | - Morgann C Reilly
- Joint BioEnergy Institute, Emeryville, United States.,Chemical and Biological Processes Development Group, Pacific Northwest National Laboratory, Richland, United States
| | - Ya-Fang Cheng
- Energy Biosciences Institute, Berkeley, United States
| | - Stefan Bauer
- Energy Biosciences Institute, Berkeley, United States
| | - Igor V Grigoriev
- United States Department of Energy Joint Genome Institute, Walnut Creek, United States.,Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley, United States.,Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, United States
| | | | - Blake A Simmons
- Joint BioEnergy Institute, Emeryville, United States.,Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, United States
| | - Rachel B Brem
- The Buck Institute for Research on Aging, Novato, United States.,Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley, United States
| | - Adam P Arkin
- Energy Biosciences Institute, Berkeley, United States.,Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, United States.,Department of Bioengineering, University of California, Berkeley, Berkeley, United States
| | - Jeffrey M Skerker
- Energy Biosciences Institute, Berkeley, United States.,Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, United States.,Department of Bioengineering, University of California, Berkeley, Berkeley, United States
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7
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González-Cabaleiro R, Mitchell AM, Smith W, Wipat A, Ofiţeru ID. Heterogeneity in Pure Microbial Systems: Experimental Measurements and Modeling. Front Microbiol 2017; 8:1813. [PMID: 28970826 PMCID: PMC5609101 DOI: 10.3389/fmicb.2017.01813] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Accepted: 09/05/2017] [Indexed: 01/02/2023] Open
Abstract
Cellular heterogeneity influences bioprocess performance in ways that until date are not completely elucidated. In order to account for this phenomenon in the design and operation of bioprocesses, reliable analytical and mathematical descriptions are required. We present an overview of the single cell analysis, and the mathematical modeling frameworks that have potential to be used in bioprocess control and optimization, in particular for microbial processes. In order to be suitable for bioprocess monitoring, experimental methods need to be high throughput and to require relatively short processing time. One such method used successfully under dynamic conditions is flow cytometry. Population balance and individual based models are suitable modeling options, the latter one having in particular a good potential to integrate the various data collected through experimentation. This will be highly beneficial for appropriate process design and scale up as a more rigorous approach may prevent a priori unwanted performance losses. It will also help progressing synthetic biology applications to industrial scale.
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Affiliation(s)
- Rebeca González-Cabaleiro
- School of Engineering, Chemical Engineering, Newcastle UniversityNewcastle upon Tyne, United Kingdom
| | - Anca M Mitchell
- School of Engineering, Chemical Engineering, Newcastle UniversityNewcastle upon Tyne, United Kingdom
| | - Wendy Smith
- Interdisciplinary Computing and Complex BioSystems (ICOS), School of ComputingNewcastle University, Newcastle upon Tyne, United Kingdom
| | - Anil Wipat
- Interdisciplinary Computing and Complex BioSystems (ICOS), School of ComputingNewcastle University, Newcastle upon Tyne, United Kingdom
| | - Irina D Ofiţeru
- School of Engineering, Chemical Engineering, Newcastle UniversityNewcastle upon Tyne, United Kingdom
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8
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Czajka J, Wang Q, Wang Y, Tang YJ. Synthetic biology for manufacturing chemicals: constraints drive the use of non-conventional microbial platforms. Appl Microbiol Biotechnol 2017; 101:7427-7434. [PMID: 28884354 DOI: 10.1007/s00253-017-8489-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Revised: 08/16/2017] [Accepted: 08/19/2017] [Indexed: 11/25/2022]
Abstract
Genetically modified microbes have had much industrial success producing protein-based products (such as antibodies and enzymes). However, engineering microbial workhorses for biomanufacturing of commodity compounds remains challenging. First, microbes cannot afford burdens with both overexpression of multiple enzymes and metabolite drainage for product synthesis. Second, synthetic circuits and introduced heterologous pathways are not yet as "robust and reliable" as native pathways due to hosts' innate regulations, especially under suboptimal fermentation conditions. Third, engineered enzymes may lack channeling capabilities for cascade-like transport of metabolites to overcome diffusion barriers or to avoid intermediate toxicity in the cytoplasmic environment. Fourth, moving engineered hosts from laboratory to industry is unreliable because genetic mutations and non-genetic cell-to-cell variations impair the large-scale fermentation outcomes. Therefore, synthetic biology strains often have unsatisfactory industrial performance (titer/yield/productivity). To overcome these problems, many different species are being explored for their metabolic strengths that can be leveraged to synthesize specific compounds. Here, we provide examples of non-conventional and genetically amenable species for industrial manufacturing, including the following: Corynebacterium glutamicum for its TCA cycle-derived biosynthesis, Yarrowia lipolytica for its biosynthesis of fatty acids and carotenoids, cyanobacteria for photosynthetic production from its sugar phosphate pathways, and Rhodococcus for its ability to biotransform recalcitrant feedstock. Finally, we discuss emerging technologies (e.g., genome-to-phenome mapping, single cell methods, and knowledge engineering) that may facilitate the development of novel cell factories.
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Affiliation(s)
- Jeffrey Czajka
- Department of Energy, Environmental and Chemical Engineering, Washington University, Saint Louis, MO, 63130, USA
| | - Qinhong Wang
- CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences (CAS), Tianjin, 300308, China
| | - Yechun Wang
- Arch Innotek, LLC, 4320 Forest Park Ave, St Louis, MO, 63108, USA.
| | - Yinjie J Tang
- Department of Energy, Environmental and Chemical Engineering, Washington University, Saint Louis, MO, 63130, USA.
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9
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Binder D, Drepper T, Jaeger KE, Delvigne F, Wiechert W, Kohlheyer D, Grünberger A. Homogenizing bacterial cell factories: Analysis and engineering of phenotypic heterogeneity. Metab Eng 2017. [DOI: 10.1016/j.ymben.2017.06.009] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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10
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Alanazi H, Canul AJ, Garman A, Quimby J, Vasdekis AE. Robust microbial cell segmentation by optical-phase thresholding with minimal processing requirements. Cytometry A 2017; 91:443-449. [PMID: 28371011 PMCID: PMC6585648 DOI: 10.1002/cyto.a.23099] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
High-throughput imaging with single-cell resolution has enabled remarkable discoveries in cell physiology and Systems Biology investigations. A common, and often the most challenging step in all such imaging implementations, is the ability to segment multiple images to regions that correspond to individual cells. Here, a robust segmentation strategy for microbial cells using Quantitative Phase Imaging is reported. The proposed method enables a greater than 99% yeast cell segmentation success rate, without any computationally-intensive, post-acquisition processing. We also detail how the method can be expanded to bacterial cell segmentation with 98% success rates with substantially reduced processing requirements in comparison to existing methods. We attribute this improved performance to the remarkably uniform background, elimination of cell-to-cell and intracellular optical artifacts, and enhanced signal-to-background ratio-all innate properties of imaging in the optical-phase domain. © 2017 International Society for Advancement of Cytometry.
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Affiliation(s)
- H. Alanazi
- Department of PhysicsUniversity of IdahoMoscowIdaho83844
| | - A. J. Canul
- Department of PhysicsUniversity of IdahoMoscowIdaho83844
| | - A. Garman
- Department of PhysicsUniversity of IdahoMoscowIdaho83844
| | - J. Quimby
- Department of PhysicsUniversity of IdahoMoscowIdaho83844
| | - A. E. Vasdekis
- Department of PhysicsUniversity of IdahoMoscowIdaho83844
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11
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Vasdekis AE, Silverman AM, Stephanopoulos G. Exploiting Bioprocessing Fluctuations to Elicit the Mechanistics of De Novo Lipogenesis in Yarrowia lipolytica. PLoS One 2017; 12:e0168889. [PMID: 28052085 PMCID: PMC5215641 DOI: 10.1371/journal.pone.0168889] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 12/07/2016] [Indexed: 01/14/2023] Open
Abstract
Despite substantial achievements in elucidating the metabolic pathways of lipogenesis, a mechanistic representation of lipid accumulation and degradation has not been fully attained to-date. Recent evidence suggests that lipid accumulation can occur through increases of either the cytosolic copy-number of lipid droplets (LDs), or the LDs size. However, the prevailing phenotype, or how such mechanisms pertain to lipid degradation remain poorly understood. To address this shortcoming, we employed the-recently discovered-innate bioprocessing fluctuations in Yarrowia lipolytica, and performed single-cell fluctuation analysis using optical microscopy and microfluidics that generate a quasi-time invariant microenvironment. We report that lipid accumulation at early stationary phase in rich medium is substantially more likely to occur through variations in the LDs copy-number, rather than the LDs size. Critically, these mechanistics are also preserved during lipid degradation, as well as upon exposure to a protein translation inhibitor. The latter condition additionally induced a lipid accumulation phase, accompanied by the downregulation of lipid catabolism. Our results enable an in-depth mechanistic understanding of lipid biogenesis, and expand longitudinal single-cell fluctuation analyses from gene regulation to metabolism.
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Affiliation(s)
- Andreas E. Vasdekis
- Department of Physics, University of Idaho, Moscow, ID, United States of America
- Environmental and Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, United States of America
- * E-mail: (AEV); (GS)
| | - Andrew M. Silverman
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States of America
| | - Gregory Stephanopoulos
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States of America
- * E-mail: (AEV); (GS)
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12
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Oliveira AF, Pessoa ACSN, Bastos RG, de la Torre LG. Microfluidic tools toward industrial biotechnology. Biotechnol Prog 2016; 32:1372-1389. [DOI: 10.1002/btpr.2350] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2016] [Revised: 08/15/2016] [Indexed: 01/29/2023]
Affiliation(s)
- Aline F. Oliveira
- Department of Bioprocesses and Materials Engineering, School of Chemical Engineering, University of Campinas; 500 Albert Einstein avenue Campinas P.O. Box 6066
| | - Amanda C. S. N. Pessoa
- Department of Bioprocesses and Materials Engineering, School of Chemical Engineering, University of Campinas; 500 Albert Einstein avenue Campinas P.O. Box 6066
| | - Reinaldo G. Bastos
- Department of Agroindustrial Technology and Rural Socioeconomy, Center of Agricultural Sciences, Federal University of São Carlos; Km 174 Anhanguera Highway Araras P.O. Box 153
| | - Lucimara G. de la Torre
- Department of Bioprocesses and Materials Engineering, School of Chemical Engineering, University of Campinas; 500 Albert Einstein avenue Campinas P.O. Box 6066
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