1
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Zhang J, Sarollahi M, Luckhart S, Harrison MJ, Vasdekis AE. Quantitative phase imaging by gradient retardance optical microscopy. Sci Rep 2024; 14:9754. [PMID: 38679622 PMCID: PMC11056386 DOI: 10.1038/s41598-024-60057-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 04/18/2024] [Indexed: 05/01/2024] Open
Abstract
Quantitative phase imaging (QPI) has become a vital tool in bioimaging, offering precise measurements of wavefront distortion and, thus, of key cellular metabolism metrics, such as dry mass and density. However, only a few QPI applications have been demonstrated in optically thick specimens, where scattering increases background and reduces contrast. Building upon the concept of structured illumination interferometry, we introduce Gradient Retardance Optical Microscopy (GROM) for QPI of both thin and thick samples. GROM transforms any standard Differential Interference Contrast (DIC) microscope into a QPI platform by incorporating a liquid crystal retarder into the illumination path, enabling independent phase-shifting of the DIC microscope's sheared beams. GROM greatly simplifies related configurations, reduces costs, and eradicates energy losses in parallel imaging modalities, such as fluorescence. We successfully tested GROM on a diverse range of specimens, from microbes and red blood cells to optically thick (~ 300 μm) plant roots without fixation or clearing.
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Affiliation(s)
- Jinming Zhang
- Department of Physics, University of Idaho, 875 Perimeter Drive, Moscow, ID, 83844, USA
| | - Mirsaeid Sarollahi
- Department of Physics, University of Idaho, 875 Perimeter Drive, Moscow, ID, 83844, USA
| | - Shirley Luckhart
- Department of Entomology, Plant Pathology and Nematology, University of Idaho, 875 Perimeter Drive, Moscow, ID, 83844, USA
- Department of Biological Sciences, University of Idaho, 875 Perimeter Drive, Moscow, ID, 83844, USA
| | | | - Andreas E Vasdekis
- Department of Physics, University of Idaho, 875 Perimeter Drive, Moscow, ID, 83844, USA.
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2
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Dadole I, Blaha D, Personnic N. The macrophage-bacterium mismatch in persister formation. Trends Microbiol 2024:S0966-842X(24)00049-0. [PMID: 38443279 DOI: 10.1016/j.tim.2024.02.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 02/12/2024] [Accepted: 02/13/2024] [Indexed: 03/07/2024]
Abstract
Many pathogens are hard to eradicate, even in the absence of genetically detectable antimicrobial resistance mechanisms and despite proven antibiotic susceptibility. The fraction of clonal bacteria that temporarily elude effective antibiotic treatments is commonly known as 'antibiotic persisters.' Over the past decade, there has been a growing body of research highlighting the pivotal role played by the cellular host in the development of persisters. In parallel, this research has also sought to elucidate the molecular mechanisms underlying the formation of intracellular antibiotic persisters and has demonstrated a prominent role for the bacterial stress response. However, questions remain regarding the conditions leading to the formation of stress-induced persisters among a clonal population of intracellular bacteria and despite an ostensibly uniform environment. In this opinion, following a brief review of the current state of knowledge regarding intracellular antibiotic persisters, we explore the ways in which macrophage functional heterogeneity and bacterial phenotypic heterogeneity may contribute to the emergence of these persisters. We propose that the degree of mismatch between the macrophage permissiveness and the bacterial preparedness to invade and thrive intracellularly may explain the formation of stress-induced nonreplicating intracellular persisters.
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Affiliation(s)
- Iris Dadole
- CIRI - Centre International de Recherche en Infectiologie, CNRS, INSERM, Ecole Normale Supérieure de Lyon, Université Claude Bernard Lyon 1, Lyon, France; Group Persistence and single-cell dynamics of respiratory pathogens, CIRI, Lyon, France
| | - Didier Blaha
- CIRI - Centre International de Recherche en Infectiologie, CNRS, INSERM, Ecole Normale Supérieure de Lyon, Université Claude Bernard Lyon 1, Lyon, France; Group Persistence and single-cell dynamics of respiratory pathogens, CIRI, Lyon, France
| | - Nicolas Personnic
- CIRI - Centre International de Recherche en Infectiologie, CNRS, INSERM, Ecole Normale Supérieure de Lyon, Université Claude Bernard Lyon 1, Lyon, France; Group Persistence and single-cell dynamics of respiratory pathogens, CIRI, Lyon, France.
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3
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Park J, Bai B, Ryu D, Liu T, Lee C, Luo Y, Lee MJ, Huang L, Shin J, Zhang Y, Ryu D, Li Y, Kim G, Min HS, Ozcan A, Park Y. Artificial intelligence-enabled quantitative phase imaging methods for life sciences. Nat Methods 2023; 20:1645-1660. [PMID: 37872244 DOI: 10.1038/s41592-023-02041-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 09/11/2023] [Indexed: 10/25/2023]
Abstract
Quantitative phase imaging, integrated with artificial intelligence, allows for the rapid and label-free investigation of the physiology and pathology of biological systems. This review presents the principles of various two-dimensional and three-dimensional label-free phase imaging techniques that exploit refractive index as an intrinsic optical imaging contrast. In particular, we discuss artificial intelligence-based analysis methodologies for biomedical studies including image enhancement, segmentation of cellular or subcellular structures, classification of types of biological samples and image translation to furnish subcellular and histochemical information from label-free phase images. We also discuss the advantages and challenges of artificial intelligence-enabled quantitative phase imaging analyses, summarize recent notable applications in the life sciences, and cover the potential of this field for basic and industrial research in the life sciences.
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Affiliation(s)
- Juyeon Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
| | - Bijie Bai
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
- Bioengineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - DongHun Ryu
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tairan Liu
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Chungha Lee
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
| | - Yi Luo
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Mahn Jae Lee
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Luzhe Huang
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jeongwon Shin
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Yijie Zhang
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Yuzhu Li
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Geon Kim
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
| | | | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
- Bioengineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
| | - YongKeun Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea.
- Tomocube, Daejeon, Republic of Korea.
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4
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Wehrens M, Krah LHJ, Towbin BD, Hermsen R, Tans SJ. The interplay between metabolic stochasticity and cAMP-CRP regulation in single E. coli cells. Cell Rep 2023; 42:113284. [PMID: 37864793 DOI: 10.1016/j.celrep.2023.113284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 07/17/2023] [Accepted: 09/29/2023] [Indexed: 10/23/2023] Open
Abstract
The inherent stochasticity of metabolism raises a critical question for understanding homeostasis: are cellular processes regulated in response to internal fluctuations? Here, we show that, in E. coli cells under constant external conditions, catabolic enzyme expression continuously responds to metabolic fluctuations. The underlying regulatory feedback is enabled by the cyclic AMP (cAMP) and cAMP receptor protein (CRP) system, which controls catabolic enzyme expression based on metabolite concentrations. Using single-cell microscopy, genetic constructs in which this feedback is disabled, and mathematical modeling, we show how fluctuations circulate through the metabolic and genetic network at sub-cell-cycle timescales. Modeling identifies four noise propagation modes, including one specific to CRP regulation. Together, these modes correctly predict noise circulation at perturbed cAMP levels. The cAMP-CRP system may thus have evolved to control internal metabolic fluctuations in addition to external growth conditions. We conjecture that second messengers may more broadly function to achieve cellular homeostasis.
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Affiliation(s)
- Martijn Wehrens
- AMOLF, 1098 XG Amsterdam, the Netherlands; Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences (KNAW) and University Medical Center, 3584 CT Utrecht, the Netherlands
| | - Laurens H J Krah
- Theoretical Biology Group, Biology Department, Utrecht University, 3584 CH Utrecht, the Netherlands; Centre for Complex Systems Studies, Utrecht University, 3584 CE Utrecht, the Netherlands
| | - Benjamin D Towbin
- Institute of Cell Biology, University of Bern, 3012 Bern, Switzerland
| | - Rutger Hermsen
- Theoretical Biology Group, Biology Department, Utrecht University, 3584 CH Utrecht, the Netherlands; Centre for Complex Systems Studies, Utrecht University, 3584 CE Utrecht, the Netherlands
| | - Sander J Tans
- AMOLF, 1098 XG Amsterdam, the Netherlands; Department of Bionanoscience, Kavli Institute of Nanoscience, Delft University of Technology, 2629 HZ Delft, the Netherlands.
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5
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Subedi NR, Stolyar S, Tuson SJ, Marx CJ, Vasdekis AE. Scattered-light-sheet microscopy with sub-cellular resolving power. JOURNAL OF BIOPHOTONICS 2023; 16:e202300068. [PMID: 37287076 DOI: 10.1002/jbio.202300068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 05/26/2023] [Accepted: 05/30/2023] [Indexed: 06/09/2023]
Abstract
Since its first demonstration over 100 years ago, scattering-based light-sheet microscopy has recently re-emerged as a key modality in label-free tissue imaging and cellular morphometry; however, scattering-based light-sheet imaging with subcellular resolution remains an unmet target. This is because related approaches inevitably superimpose speckle or granular intensity modulation on to the native subcellular features. Here, we addressed this challenge by deploying a time-averaged pseudo-thermalized light-sheet illumination. While this approach increased the lateral dimensions of the illumination sheet, we achieved subcellular resolving power after image deconvolution. We validated this approach by imaging cytosolic carbon depots in yeast and bacteria with increased specificity, no staining, and ultralow irradiance levels. Overall, we expect this scattering-based light-sheet microscopy approach will advance single, live cell imaging by conferring low-irradiance and label-free operation towards eradicating phototoxicity.
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Affiliation(s)
- Nava R Subedi
- Department of Physics, University of Idaho, Moscow, Idaho, USA
| | - Sergey Stolyar
- Department of Biological Sciences, University of Idaho, Moscow, Idaho, USA
| | - Sabrina J Tuson
- Department of Physics, University of Idaho, Moscow, Idaho, USA
| | - Christopher J Marx
- Department of Biological Sciences, University of Idaho, Moscow, Idaho, USA
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6
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Dunn L, Luo H, Subedi NR, Kasu R, McDonald AG, Christodoulides DN, Vasdekis AE. Video-rate Raman-based metabolic imaging by Airy light-sheet illumination and photon-sparse detection. Proc Natl Acad Sci U S A 2023; 120:e2210037120. [PMID: 36812197 PMCID: PMC9992822 DOI: 10.1073/pnas.2210037120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 11/15/2022] [Indexed: 02/24/2023] Open
Abstract
Despite its massive potential, Raman imaging represents just a modest fraction of all research and clinical microscopy to date. This is due to the ultralow Raman scattering cross-sections of most biomolecules that impose low-light or photon-sparse conditions. Bioimaging under such conditions is suboptimal, as it either results in ultralow frame rates or requires increased levels of irradiance. Here, we overcome this tradeoff by introducing Raman imaging that operates at both video rates and 1,000-fold lower irradiance than state-of-the-art methods. To accomplish this, we deployed a judicially designed Airy light-sheet microscope to efficiently image large specimen regions. Further, we implemented subphoton per pixel image acquisition and reconstruction to confront issues arising from photon sparsity at just millisecond integrations. We demonstrate the versatility of our approach by imaging a variety of samples, including the three-dimensional (3D) metabolic activity of single microbial cells and the underlying cell-to-cell variability. To image such small-scale targets, we again harnessed photon sparsity to increase magnification without a field-of-view penalty, thus, overcoming another key limitation in modern light-sheet microscopy.
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Affiliation(s)
- Lochlann Dunn
- Department of Physics, University of Idaho, Moscow, ID83844-0903
| | - Haokun Luo
- The College of Optics and Photonics, University of Central Florida, Orlando, FL32816-2700
| | - Nava R. Subedi
- Department of Physics, University of Idaho, Moscow, ID83844-0903
| | | | - Armando G. McDonald
- Department of Forest, Rangeland, and Fire Sciences, University of Idaho, Moscow, ID83844-1132
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7
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Lee D, Lee M, Kwak H, Kim YS, Shim J, Jung JH, Park WS, Park JH, Lee S, Park Y. High-fidelity optical diffraction tomography of live organisms using iodixanol refractive index matching. BIOMEDICAL OPTICS EXPRESS 2022; 13:6404-6415. [PMID: 36589574 PMCID: PMC9774853 DOI: 10.1364/boe.465066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 10/25/2022] [Accepted: 10/25/2022] [Indexed: 06/17/2023]
Abstract
Optical diffraction tomography (ODT) enables the three-dimensional (3D) refractive index (RI) reconstruction. However, when the RI difference between a sample and a medium increases, the effects of light scattering become significant, preventing the acquisition of high-quality and accurate RI reconstructions. Herein, we present a method for high-fidelity ODT by introducing non-toxic RI matching media. Optimally reducing the RI contrast enhances the fidelity and accuracy of 3D RI reconstruction, enabling visualization of the morphology and intra-organization of live biological samples without producing toxic effects. We validate our method using various biological organisms, including C. albicans and C. elegans.
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Affiliation(s)
- Dohyeon Lee
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon 34141, Republic of Korea
| | - Moosung Lee
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon 34141, Republic of Korea
| | - Haechan Kwak
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon 34141, Republic of Korea
| | - Young Seo Kim
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon 34141, Republic of Korea
| | - Jaehyu Shim
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon 34141, Republic of Korea
| | - Jik Han Jung
- KAIST Institute for Health Science and Technology, KAIST, Daejeon 34141, Republic of Korea
- Department of Bio and Brain Engineering, KAIST, Daejeon 34141, Republic of Korea
| | - Wei-sun Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon 34141, Republic of Korea
| | - Ji-Ho Park
- KAIST Institute for Health Science and Technology, KAIST, Daejeon 34141, Republic of Korea
- Department of Bio and Brain Engineering, KAIST, Daejeon 34141, Republic of Korea
| | - Sumin Lee
- Tomocube Inc., Daejeon 34109, Republic of Korea
| | - YongKeun Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon 34141, Republic of Korea
- Tomocube Inc., Daejeon 34109, Republic of Korea
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8
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Cai Y, Chen H, Tang X, Zhao J, Zhang H, Chen YQ, Chen W. The relationship between amino acid and lipid metabolism in oleaginous eukaryotic microorganism. Appl Microbiol Biotechnol 2022; 106:3405-3417. [PMID: 35503470 DOI: 10.1007/s00253-022-11931-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 04/19/2022] [Accepted: 04/20/2022] [Indexed: 11/30/2022]
Abstract
Amino acids are the building blocks of protein, promoting the balance between growth and lipid synthesis. However, the accumulation of microbial lipids involves multiple pathways, which requires the analysis of the global cellular metabolic network in which amino acid metabolism is involved. This review illustrates the dependence patterns of intracellular amino acids and lipids of oleaginous eukaryotic microorganisms in different environments and points out the contribution of amino acid metabolic precursors to the de novo synthesis of fatty acids. We emphasized the key role of amino acid metabolism in lipid remodeling and autophagy behavior and highlighted the regulatory effects of amino acids and their secondary metabolites as signal factors for microbial lipid synthesis. The application prospects of omics technology and genetic engineering technology in the field of microbial lipids are described. KEY POINTS: • Overview of microbial lipid synthesis mediated by amino acid metabolism • Insight into metabolic mechanisms founding multiple regulatory networks is provided • Description of microbial lipid homeostasis mediated by amino acid excitation signal.
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Affiliation(s)
- Yibo Cai
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, People's Republic of China.,School of Food Science and Technology, Jiangnan University, Wuxi, 214122, People's Republic of China
| | - Haiqin Chen
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, People's Republic of China. .,School of Food Science and Technology, Jiangnan University, Wuxi, 214122, People's Republic of China. .,National Engineering Research Center for Functional Food, Jiangnan University, Wuxi, 214122, People's Republic of China.
| | - Xin Tang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, People's Republic of China.,School of Food Science and Technology, Jiangnan University, Wuxi, 214122, People's Republic of China
| | - Jianxin Zhao
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, People's Republic of China.,School of Food Science and Technology, Jiangnan University, Wuxi, 214122, People's Republic of China
| | - Hao Zhang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, People's Republic of China.,School of Food Science and Technology, Jiangnan University, Wuxi, 214122, People's Republic of China.,National Engineering Research Center for Functional Food, Jiangnan University, Wuxi, 214122, People's Republic of China.,Wuxi Translational Medicine Research Center and Jiangsu Translational Medicine Research Institute Wuxi Branch, Wuxi, 214122, People's Republic of China
| | - Yong Q Chen
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, People's Republic of China.,School of Food Science and Technology, Jiangnan University, Wuxi, 214122, People's Republic of China.,National Engineering Research Center for Functional Food, Jiangnan University, Wuxi, 214122, People's Republic of China.,Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC 5: , 27127, USA
| | - Wei Chen
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, People's Republic of China.,School of Food Science and Technology, Jiangnan University, Wuxi, 214122, People's Republic of China.,National Engineering Research Center for Functional Food, Jiangnan University, Wuxi, 214122, People's Republic of China
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9
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Density fluctuations, homeostasis, and reproduction effects in bacteria. Commun Biol 2022; 5:397. [PMID: 35484403 PMCID: PMC9050864 DOI: 10.1038/s42003-022-03348-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 04/10/2022] [Indexed: 12/02/2022] Open
Abstract
Single-cells grow by increasing their biomass and size. Here, we report that while mass and size accumulation rates of single Escherichia coli cells are exponential, their density and, thus, the levels of macromolecular crowding fluctuate during growth. As such, the average rates of mass and size accumulation of a single cell are generally not the same, but rather cells differentiate into increasing one rate with respect to the other. This differentiation yields a density homeostasis mechanism that we support mathematically. Further, we observe that density fluctuations can affect the reproduction rates of single cells, suggesting a link between the levels of macromolecular crowding with metabolism and overall population fitness. We detail our experimental approach and the “invisible” microfluidic arrays that enabled increased precision and throughput. Infections and natural communities start from a few cells, thus, emphasizing the significance of density-fluctuations when taking non-genetic variability into consideration. Quantitative imaging, invisible microfluidics, and mathematical models demonstrate how the density of single E. coli cells fluctuates during the cell cycle, unmasking key homeostasis and population fitness effects.
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10
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Robustness: linking strain design to viable bioprocesses. Trends Biotechnol 2022; 40:918-931. [PMID: 35120750 DOI: 10.1016/j.tibtech.2022.01.004] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 01/05/2022] [Accepted: 01/05/2022] [Indexed: 12/18/2022]
Abstract
Microbial cell factories are becoming increasingly popular for the sustainable production of various chemicals. Metabolic engineering has led to the design of advanced cell factories; however, their long-term yield, titer, and productivity falter when scaled up and subjected to industrial conditions. This limitation arises from a lack of robustness - the ability to maintain a constant phenotype despite the perturbations of such processes. This review describes predictable and stochastic industrial perturbations as well as state-of-the-art technologies to counter process variability. Moreover, we distinguish robustness from tolerance and discuss the potential of single-cell studies for improving system robustness. Finally, we highlight ways of achieving consistent and comparable quantification of robustness that can guide the selection of strains for industrial bioprocesses.
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11
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Hu C, Kandel ME, Lee YJ, Popescu G. Synthetic aperture interference light (SAIL) microscopy for high-throughput label-free imaging. APPLIED PHYSICS LETTERS 2021; 119:233701. [PMID: 34924588 PMCID: PMC8660142 DOI: 10.1063/5.0065628] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 11/29/2021] [Indexed: 05/07/2023]
Abstract
Quantitative phase imaging (QPI) is a valuable label-free modality that has gained significant interest due to its wide potentials, from basic biology to clinical applications. Most existing QPI systems measure microscopic objects via interferometry or nonlinear iterative phase reconstructions from intensity measurements. However, all imaging systems compromise spatial resolution for the field of view and vice versa, i.e., suffer from a limited space bandwidth product. Current solutions to this problem involve computational phase retrieval algorithms, which are time-consuming and often suffer from convergence problems. In this article, we presented synthetic aperture interference light (SAIL) microscopy as a solution for high-resolution, wide field of view QPI. The proposed approach employs low-coherence interferometry to directly measure the optical phase delay under different illumination angles and produces large space-bandwidth product label-free imaging. We validate the performance of SAIL on standard samples and illustrate the biomedical applications on various specimens: pathology slides, entire insects, and dynamic live cells in large cultures. The reconstructed images have a synthetic numeric aperture of 0.45 and a field of view of 2.6 × 2.6 mm2. Due to its direct measurement of the phase information, SAIL microscopy does not require long computational time, eliminates data redundancy, and always converges.
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12
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Lv X, Jin K, Sun G, Ledesma-Amaro R, Liu L. Microscopy imaging of living cells in metabolic engineering. Trends Biotechnol 2021; 40:752-765. [PMID: 34799183 DOI: 10.1016/j.tibtech.2021.10.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/25/2021] [Accepted: 10/25/2021] [Indexed: 01/23/2023]
Abstract
Microscopy imaging of living cells is becoming a pivotal, noninvasive, and highly specific tool in metabolic engineering to visualize molecular dynamics in industrial microorganisms. This review describes the different microscopy methods, from fluorescence to super resolution, with application in microbial bioengineering. Firstly, the role and importance of microscopy imaging is analyzed in the context of strain design. Then, the advantages and disadvantages of different microscopy technologies are discussed, including confocal laser scanning microscopy (CLSM), spatial light interference microscopy (SLIM), and super-resolution microscopy, followed by their applications in synthetic biology. Finally, the future perspectives of live-cell imaging and their potential to transform microbial systems are analyzed. This review provides theoretical guidance and highlights the importance of microscopy in understanding and engineering microbial metabolism.
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Affiliation(s)
- Xueqin Lv
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Ke Jin
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Guoyun Sun
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
| | - Rodrigo Ledesma-Amaro
- Department of Bioengineering and Imperial College Centre for Synthetic Biology, Imperial College London, London SW72AZ, UK
| | - Long Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, China.
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13
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Modi S, Dey S, Singh A. Noise suppression in stochastic genetic circuits using PID controllers. PLoS Comput Biol 2021; 17:e1009249. [PMID: 34319990 PMCID: PMC8360635 DOI: 10.1371/journal.pcbi.1009249] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 08/12/2021] [Accepted: 07/05/2021] [Indexed: 01/01/2023] Open
Abstract
Inside individual cells, protein population counts are subject to molecular noise due to low copy numbers and the inherent probabilistic nature of biochemical processes. We investigate the effectiveness of proportional, integral and derivative (PID) based feedback controllers to suppress protein count fluctuations originating from two noise sources: bursty expression of the protein, and external disturbance in protein synthesis. Designs of biochemical reactions that function as PID controllers are discussed, with particular focus on individual controllers separately, and the corresponding closed-loop system is analyzed for stochastic controller realizations. Our results show that proportional controllers are effective in buffering protein copy number fluctuations from both noise sources, but this noise suppression comes at the cost of reduced static sensitivity of the output to the input signal. In contrast, integral feedback has no effect on the protein noise level from stochastic expression, but significantly minimizes the impact of external disturbances, particularly when the disturbance comes at low frequencies. Counter-intuitively, integral feedback is found to amplify external disturbances at intermediate frequencies. Next, we discuss the design of a coupled feedforward-feedback biochemical circuit that approximately functions as a derivate controller. Analysis using both analytical methods and Monte Carlo simulations reveals that this derivative controller effectively buffers output fluctuations from bursty stochastic expression, while maintaining the static input-output sensitivity of the open-loop system. In summary, this study provides a systematic stochastic analysis of biochemical controllers, and paves the way for their synthetic design and implementation to minimize deleterious fluctuations in gene product levels. In the noisy cellular environment, biochemical species such as genes, RNAs and proteins that often occur at low molecular counts, are subject to considerable stochastic fluctuations in copy numbers over time. How cellular biochemical processes function reliably in the face of such randomness is an intriguing fundamental problem. Increasing evidence suggests that random fluctuations (noise) in protein copy numbers play important functional roles, such as driving genetically identical cells to different cell fates. Moreover, many disease states have been attributed to elevated noise levels in specific proteins. Here we systematically investigate design of biochemical systems that function as proportional, integral and derivative-based feedback controllers to suppress protein count fluctuations arising from bursty expression of the protein and external disturbance in protein synthesis. Our results show that different controllers are effective in buffering different noise components, and identify ranges of feedback gain for minimizing deleterious fluctuations in protein levels.
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Affiliation(s)
- Saurabh Modi
- Department of Biomedical Engineering, University of Delaware, Newark, Delaware, United States of America
| | - Supravat Dey
- Department of Electrical and Computer Engineering, University of Delaware, Newark, Delaware, United States of America
| | - Abhyudai Singh
- Department of Biomedical Engineering, University of Delaware, Newark, Delaware, United States of America
- Department of Electrical and Computer Engineering, University of Delaware, Newark, Delaware, United States of America
- * E-mail:
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14
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Chen X, Kandel ME, Popescu G. Spatial light interference microscopy: principle and applications to biomedicine. ADVANCES IN OPTICS AND PHOTONICS 2021; 13:353-425. [PMID: 35494404 PMCID: PMC9048520 DOI: 10.1364/aop.417837] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
In this paper, we review spatial light interference microscopy (SLIM), a common-path, phase-shifting interferometer, built onto a phase-contrast microscope, with white-light illumination. As one of the most sensitive quantitative phase imaging (QPI) methods, SLIM allows for speckle-free phase reconstruction with sub-nanometer path-length stability. We first review image formation in QPI, scattering, and full-field methods. Then, we outline SLIM imaging from theory and instrumentation to diffraction tomography. Zernike's phase-contrast microscopy, phase retrieval in SLIM, and halo removal algorithms are discussed. Next, we discuss the requirements for operation, with a focus on software developed in-house for SLIM that enables high-throughput acquisition, whole slide scanning, mosaic tile registration, and imaging with a color camera. We introduce two methods for solving the inverse problem using SLIM, white-light tomography, and Wolf phase tomography. Lastly, we review the applications of SLIM in basic science and clinical studies. SLIM can study cell dynamics, cell growth and proliferation, cell migration, mass transport, etc. In clinical settings, SLIM can assist with cancer studies, reproductive technology, blood testing, etc. Finally, we review an emerging trend, where SLIM imaging in conjunction with artificial intelligence brings computational specificity and, in turn, offers new solutions to outstanding challenges in cell biology and pathology.
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15
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Fuentes DAF, Manfredi P, Jenal U, Zampieri M. Pareto optimality between growth-rate and lag-time couples metabolic noise to phenotypic heterogeneity in Escherichia coli. Nat Commun 2021; 12:3204. [PMID: 34050162 PMCID: PMC8163773 DOI: 10.1038/s41467-021-23522-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 05/04/2021] [Indexed: 02/04/2023] Open
Abstract
Despite mounting evidence that in clonal bacterial populations, phenotypic variability originates from stochasticity in gene expression, little is known about noise-shaping evolutionary forces and how expression noise translates to phenotypic differences. Here we developed a high-throughput assay that uses a redox-sensitive dye to couple growth of thousands of bacterial colonies to their respiratory activity and show that in Escherichia coli, noisy regulation of lower glycolysis and citric acid cycle is responsible for large variations in respiratory metabolism. We found that these variations are Pareto optimal to maximization of growth rate and minimization of lag time, two objectives competing between fermentative and respiratory metabolism. Metabolome-based analysis revealed the role of respiratory metabolism in preventing the accumulation of toxic intermediates of branched chain amino acid biosynthesis, thereby supporting early onset of cell growth after carbon starvation. We propose that optimal metabolic tradeoffs play a key role in shaping and preserving phenotypic heterogeneity and adaptation to fluctuating environments.
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Affiliation(s)
| | | | - Urs Jenal
- Biozentrum, University of Basel, Basel, Switzerland
| | - Mattia Zampieri
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland.
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16
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Kandel ME, Kim E, Lee YJ, Tracy G, Chung HJ, Popescu G. Multiscale Assay of Unlabeled Neurite Dynamics Using Phase Imaging with Computational Specificity. ACS Sens 2021; 6:1864-1874. [PMID: 33882232 PMCID: PMC8815662 DOI: 10.1021/acssensors.1c00100] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Primary neuronal cultures have been widely used to study neuronal morphology, neurophysiology, neurodegenerative processes, and molecular mechanism of synaptic plasticity underlying learning and memory. However, the unique behavioral properties of neurons make them challenging to study, with phenotypic differences expressed as subtle changes in neuronal arborization rather than easy-to-assay features such as cell count. The need to analyze morphology, growth, and intracellular transport has motivated the development of increasingly sophisticated microscopes and image analysis techniques. Due to its high-contrast, high-specificity output, many assays rely on confocal fluorescence microscopy, genetic methods, or antibody staining techniques. These approaches often limit the ability to measure quantitatively dynamic activity such as intracellular transport and growth. In this work, we describe a method for label-free live-cell cell imaging with antibody staining specificity by estimating the associated fluorescence signals via quantitative phase imaging and deep convolutional neural networks. This computationally inferred fluorescence image is then used to generate a semantic segmentation map, annotating subcellular compartments of live unlabeled neural cultures. These synthetic fluorescence maps were further applied to study the time-lapse development of hippocampal neurons, highlighting the relationships between the cellular dry mass production and the dynamic transport activity within the nucleus and neurites. Our implementation provides a high-throughput strategy to analyze neural network arborization dynamically, with high specificity and without the typical phototoxicity and photobleaching limitations associated with fluorescent markers.
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Affiliation(s)
- Mikhail E Kandel
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Eunjae Kim
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Young Jae Lee
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Gregory Tracy
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana Champaign, Urbana, Illinois 61801, United States
| | - Hee Jung Chung
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana Champaign, Urbana, Illinois 61801, United States
| | - Gabriel Popescu
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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17
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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: 4] [Impact Index Per Article: 1.3] [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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18
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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: 1.0] [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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19
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Subedi NR, Jung PS, Bredeweg EL, Nemati S, Baker SE, Christodoulides DN, Vasdekis AE. Integrative quantitative-phase and airy light-sheet imaging. Sci Rep 2020; 10:20150. [PMID: 33214600 PMCID: PMC7678854 DOI: 10.1038/s41598-020-76730-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 10/30/2020] [Indexed: 01/05/2023] Open
Abstract
Light-sheet microscopy enables considerable speed and phototoxicity gains, while quantitative-phase imaging confers label-free recognition of cells and organelles, and quantifies their number-density that, thermodynamically, is more representative of metabolism than size. Here, we report the fusion of these two imaging modalities onto a standard inverted microscope that retains compatibility with microfluidics and open-source software for image acquisition and processing. An accelerating Airy-beam light-sheet critically enabled imaging areas that were greater by more than one order of magnitude than a Gaussian beam illumination and matched exactly those of quantitative-phase imaging. Using this integrative imaging system, we performed a demonstrative multivariate investigation of live-cells in microfluidics that unmasked that cellular noise can affect the compartmental localization of metabolic reactions. We detail the design, assembly, and performance of the integrative imaging system, and discuss potential applications in biotechnology and evolutionary biology.
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Affiliation(s)
- N R Subedi
- Department of Physics, University of Idaho, Moscow, ID, 83844, USA
| | - P S Jung
- CREOL-The College of Optics and Photonics, University of Central Florida, Orlando, FL, 32816-2700, USA
- Faculty of Physics, Warsaw University of Technology, Warsaw, Poland
| | - E L Bredeweg
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - S Nemati
- Department of Physics, University of Idaho, Moscow, ID, 83844, USA
| | - S E Baker
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - D N Christodoulides
- CREOL-The College of Optics and Photonics, University of Central Florida, Orlando, FL, 32816-2700, USA
| | - A E Vasdekis
- Department of Physics, University of Idaho, Moscow, ID, 83844, USA.
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20
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Environmental drivers of metabolic heterogeneity in clonal microbial populations. Curr Opin Biotechnol 2020; 62:202-211. [DOI: 10.1016/j.copbio.2019.11.018] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 11/15/2019] [Accepted: 11/22/2019] [Indexed: 02/06/2023]
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21
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Allen DK, Young JD. Tracing metabolic flux through time and space with isotope labeling experiments. Curr Opin Biotechnol 2019; 64:92-100. [PMID: 31864070 PMCID: PMC7302994 DOI: 10.1016/j.copbio.2019.11.003] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 11/02/2019] [Accepted: 11/04/2019] [Indexed: 12/11/2022]
Abstract
Metabolism is dynamic and must function in context-specific ways to adjust to changes in the surrounding cellular and ecological environment. When isotopic tracers are used, metabolite flow (i.e. metabolic flux) can be quantified through biochemical networks to assess metabolic pathway operation. The cellular activities considered across multiple tissues and organs result in the observed phenotype and can be analyzed to discover emergent, whole-system properties of biology and elucidate misconceptions about network operation. However, temporal and spatial challenges remain significant hurdles and require novel approaches and creative solutions. We survey current investigations in higher plant and animal systems focused on dynamic isotope labeling experiments, spatially resolved measurement strategies, and observations from re-analysis of our own studies that suggest prospects for future work. Related discoveries will be necessary to push the frontier of our understanding of metabolism to suggest novel solutions to cure disease and feed a growing future world population.
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Affiliation(s)
- Doug K Allen
- United States Department of Agriculture-Agricultural Research Service, Plant Genetics Research Unit, 975 North Warson Road, St. Louis, MO 63132, United States; Donald Danforth Plant Science Center, 975 North Warson Road, St. Louis, MO 63132, United States.
| | - Jamey D Young
- Department of Chemical & Biomolecular Engineering, Vanderbilt University, PMB 351604, 2301 Vanderbilt Place, Nashville, TN 37235, United States; Department of Molecular Physiology & Biophysics, Vanderbilt University, PMB 351604, 2301 Vanderbilt Place, Nashville, TN 37235, United States.
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22
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Fanous MJ, Li Y, Kandel ME, Abdeen AA, Kilian KA, Popescu G. Effects of substrate patterning on cellular spheroid growth and dynamics measured by gradient light interference microscopy (GLIM). JOURNAL OF BIOPHOTONICS 2019; 12:e201900178. [PMID: 31400294 PMCID: PMC7716417 DOI: 10.1002/jbio.201900178] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 07/05/2019] [Accepted: 08/07/2019] [Indexed: 05/12/2023]
Abstract
The development of three-dimensional (3D) cellular architectures during development and pathological processes involves intricate migratory patterns that are modulated by genetics and the surrounding microenvironment. The substrate composition of cell cultures has been demonstrated to influence growth, proliferation and migration in 2D. Here, we study the growth and dynamics of mouse embryonic fibroblast cultures patterned in a tissue sheet which then exhibits 3D growth. Using gradient light interference microscopy (GLIM), a label-free quantitative phase imaging approach, we explored the influence of geometry on cell growth patterns and rotational dynamics. We apply, for the first time to our knowledge, dispersion-relation phase spectroscopy (DPS) in polar coordinates to generate the radial and rotational cell mass-transport. Our data show that cells cultured on engineered substrates undergo rotational transport in a radially independent manner and exhibit faster vertical growth than the control, unpatterned cells. The use of GLIM and polar DPS provides a novel quantitative approach to studying the effects of spatially patterned substrates on cell motility and growth.
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Affiliation(s)
- Michael J. Fanous
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois
- Quantitative Light Imaging Laboratory, Department of Electrical and Computer Engineering, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois
| | - Yanfen Li
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois
- Department of Biomedical Engineering, University of Massachusetts Lowell, Lowell, Massachusetts
| | - Mikhail E. Kandel
- Quantitative Light Imaging Laboratory, Department of Electrical and Computer Engineering, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois
| | - Amr A. Abdeen
- Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois
| | - Kristopher A. Kilian
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois
- School of Chemistry, Australian Centre for NanoMedicine, University of New South Wales, Sydney, Australia
- School of Materials Science and Engineering, University of New South Wales, Sydney, Australia
| | - Gabriel Popescu
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois
- Quantitative Light Imaging Laboratory, Department of Electrical and Computer Engineering, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois
- Correspondence: Gabriel Popescu, Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL.
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23
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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: 3.0] [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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