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  1. Article ; Online: Quantitative phase imaging by gradient retardance optical microscopy.

    Zhang, Jinming / Sarollahi, Mirsaeid / Luckhart, Shirley / Harrison, Maria J / Vasdekis, Andreas E

    Scientific reports

    2024  Volume 14, Issue 1, Page(s) 9754

    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 ... ...

    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.
    MeSH term(s) Humans ; Microscopy/methods ; Erythrocytes ; Microscopy, Phase-Contrast/methods ; Plant Roots ; Quantitative Phase Imaging
    Language English
    Publishing date 2024-04-29
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-024-60057-y
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  2. Article ; Online: Microbial metabolic noise.

    Vasdekis, Andreas E / Singh, Abhyudai

    WIREs mechanisms of disease

    2020  Volume 13, Issue 3, Page(s) e1512

    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 ... ...

    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.
    MeSH term(s) Biochemical Phenomena ; Proteins
    Chemical Substances Proteins
    Language English
    Publishing date 2020-11-23
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S. ; Review
    ISSN 2692-9368
    ISSN (online) 2692-9368
    DOI 10.1002/wsbm.1512
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  3. Article ; Online: Scattered-light-sheet microscopy with sub-cellular resolving power.

    Subedi, Nava R / Stolyar, Sergey / Tuson, Sabrina J / Marx, Christopher J / Vasdekis, Andreas E

    Journal of biophotonics

    2023  Volume 16, Issue 9, Page(s) e202300068

    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 ... ...

    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.
    MeSH term(s) Microscopy, Fluorescence/methods ; Cytosol
    Language English
    Publishing date 2023-06-20
    Publishing country Germany
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2390063-5
    ISSN 1864-0648 ; 1864-063X
    ISSN (online) 1864-0648
    ISSN 1864-063X
    DOI 10.1002/jbio.202300068
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    Zs.A 6723: Show issues Location:
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  4. Article ; Online: Deep learning classification of lipid droplets in quantitative phase images.

    Sheneman, Luke / Stephanopoulos, Gregory / Vasdekis, Andreas E

    PloS one

    2021  Volume 16, Issue 4, Page(s) e0249196

    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 ... ...

    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.
    MeSH term(s) Deep Learning ; Image Processing, Computer-Assisted/methods ; Lipid Droplets/classification ; Microscopy, Phase-Contrast/methods ; Yarrowia/cytology
    Language English
    Publishing date 2021-04-05
    Publishing country United States
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0249196
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  5. Article ; Online: Correction: Microbial phenotypic heterogeneity in response to a metabolic toxin: Continuous, dynamically shifting distribution of formaldehyde tolerance in Methylobacterium extorquens populations.

    Lee, Jessica A / Riazi, Siavash / Nemati, Shahla / Bazurto, Jannell V / Vasdekis, Andreas E / Ridenhour, Benjamin J / Remien, Christopher H / Marx, Christopher J

    PLoS genetics

    2023  Volume 19, Issue 4, Page(s) e1010714

    Abstract: This corrects the article DOI: 10.1371/journal.pgen.1008458.]. ...

    Abstract [This corrects the article DOI: 10.1371/journal.pgen.1008458.].
    Language English
    Publishing date 2023-04-05
    Publishing country United States
    Document type Published Erratum
    ZDB-ID 2186725-2
    ISSN 1553-7404 ; 1553-7390
    ISSN (online) 1553-7404
    ISSN 1553-7390
    DOI 10.1371/journal.pgen.1010714
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  6. Article ; Online: Deep learning classification of lipid droplets in quantitative phase images.

    Luke Sheneman / Gregory Stephanopoulos / Andreas E Vasdekis

    PLoS ONE, Vol 16, Iss 4, p e

    2021  Volume 0249196

    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 ... ...

    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.
    Keywords Medicine ; R ; Science ; Q
    Subject code 006
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Video-rate Raman-based metabolic imaging by Airy light-sheet illumination and photon-sparse detection.

    Dunn, Lochlann / Luo, Haokun / Subedi, Nava R / Kasu, Ramachandran / McDonald, Armando G / Christodoulides, Demetrios N / Vasdekis, Andreas E

    Proceedings of the National Academy of Sciences of the United States of America

    2023  Volume 120, Issue 9, Page(s) e2210037120

    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 ... ...

    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.
    MeSH term(s) Lighting ; Microscopy/methods ; Photons ; Imaging, Three-Dimensional/methods
    Language English
    Publishing date 2023-02-22
    Publishing country United States
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.2210037120
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1148: Show issues Location:
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    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
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  8. Article ; Online: Density fluctuations, homeostasis, and reproduction effects in bacteria.

    Nemati, Shahla / Singh, Abhyudai / Dhuey, Scott D / McDonald, Armando / Weinreich, Daniel M / Vasdekis, Andreas E

    Communications biology

    2022  Volume 5, Issue 1, Page(s) 397

    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. ... ...

    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.
    MeSH term(s) Escherichia coli/metabolism ; Homeostasis ; Macromolecular Substances/metabolism ; Reproduction
    Chemical Substances Macromolecular Substances
    Language English
    Publishing date 2022-04-28
    Publishing country England
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
    ISSN 2399-3642
    ISSN (online) 2399-3642
    DOI 10.1038/s42003-022-03348-2
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  9. Article: Raman-probes for monitoring metabolites and nutrient fate in Yarrowia lipolytica using deuterated glucose

    Kukal, Gurkeerat / Vasdekis, Andreas E. / McDonald, Armando G.

    Biocatalysis and agricultural biotechnology. 2022 Jan., v. 39

    2022  

    Abstract: The aim of this study was to explore the application of deuterium (D) labeled glucose-D₇ as Raman-tag in Yarrowia lipolytica to assess triacyl glycerides (TAG) biosynthesis. Cells were grown in N depleted media in: (i) glucose in H₂O (control), (ii) ... ...

    Abstract The aim of this study was to explore the application of deuterium (D) labeled glucose-D₇ as Raman-tag in Yarrowia lipolytica to assess triacyl glycerides (TAG) biosynthesis. Cells were grown in N depleted media in: (i) glucose in H₂O (control), (ii) glucose-D₇ in H₂O and (iii) glucose-D₇ in D₂O, and the resulting biomass and lipids quantified. Infrared and Raman spectroscopies showed the incorporation of D with the presence of a C-D stretching bands in the silent region (1800–2600 cm⁻¹) for cells grown in glucose-D₇ in H₂O and glucose-D₇ in D₂O as compared to the control. Fatty acid methyl ester analysis of cells grown in the three media showed significant differences in lipid profiles. Cells grown on glucose-D₇ in H₂O and glucose-D₇ in D₂O showed unequivocally D incorporation in fatty acids and TAG by mass spectrometry. Higher D incorporation was observed when cells were grown in D₂O than H₂O. Deuterium was also incorporated into the cell wall polysaccharides.
    Keywords Yarrowia lipolytica ; agricultural biotechnology ; biocatalysis ; biomass ; biosynthesis ; cell walls ; deuterium ; fatty acid methyl esters ; glucose ; mass spectrometry ; metabolites ; polysaccharides
    Language English
    Dates of publication 2022-01
    Publishing place Elsevier Ltd
    Document type Article
    ZDB-ID 2642052-1
    ISSN 1878-8181
    ISSN 1878-8181
    DOI 10.1016/j.bcab.2021.102241
    Database NAL-Catalogue (AGRICOLA)

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  10. Article: Single microbe trap and release in sub-microfluidics

    Vasdekis, Andreas E

    RSC advances. 2013 Apr. 16, v. 3, no. 18

    2013  

    Abstract: ... described, as well as the trap and release of single E. coli. The release time from the trap is found ...

    Abstract Life on Earth is comprised mostly of microbes with significant implications in disease and carbon cycling. However, their dimensions and mobility make microbes challenging to analyse on-chip. A sub-micron resolution microfluidic system (sub-microfluidics) capable of trapping and releasing single Escherichia coli bacteria is presented. The fabrication method based on electron-beam and cast molding lithography is described, as well as the trap and release of single E. coli. The release time from the trap is found to depend on cell morphology.
    Keywords bacteria ; carbon cycle ; cell structures ; Escherichia coli ; organ-on-a-chip
    Language English
    Dates of publication 2013-0416
    Size p. 6343-6346.
    Publishing place The Royal Society of Chemistry
    Document type Article
    ISSN 2046-2069
    DOI 10.1039/c3ra40369f
    Database NAL-Catalogue (AGRICOLA)

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