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Quantification of Myxococcus xanthus Aggregation and Rippling Behaviors: Deep-learning Transformation of Phase-contrast into Fluorescence Microscopy Images

View ORCID ProfileJiangguo Zhang, Jessica A. Comstock, Christopher R. Cotter, Patrick A. Murphy, Weili Nie, Roy D. Welch, View ORCID ProfileAnkit B. Patel, View ORCID ProfileOleg A. Igoshin
doi: https://doi.org/10.1101/2021.07.16.452736
Jiangguo Zhang
1Department of Bioengineering, Rice University, Houston, TX 77005, USA
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Jessica A. Comstock
2Department of Biology, Syracuse University, Syracuse, NY 13244, USA
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Christopher R. Cotter
1Department of Bioengineering, Rice University, Houston, TX 77005, USA
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Patrick A. Murphy
1Department of Bioengineering, Rice University, Houston, TX 77005, USA
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Weili Nie
3Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
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Roy D. Welch
2Department of Biology, Syracuse University, Syracuse, NY 13244, USA
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Ankit B. Patel
3Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
4Department of Neuroscience, Baylor College of Medicine, Houston, TX 77005, USA
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Oleg A. Igoshin
1Department of Bioengineering, Rice University, Houston, TX 77005, USA
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  • ORCID record for Oleg A. Igoshin
  • For correspondence: igoshin@rice.edu
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Abstract

Myxococcus xanthus bacteria are a model system for understanding pattern formation and collective cell behaviors. When starving, cells aggregate into fruiting bodies to form metabolically inert spores. During predation, cells self-organize into traveling cell-density waves termed ripples. Both phase-contrast and fluorescence microscopy are used to observe these patterns but each has its limitations. Phase-contrast images have higher contrast, but the resulting image intensities lose their correlation with cell density. The intensities of fluorescence microscopy images, on the other hand, are well correlated with cell density, enabling better segmentation of aggregates and better visualization of streaming patterns in between aggregates. However, fluorescence microscopy requires the engineering of cells to express fluorescent proteins and can be phototoxic to the cells. To combine the advantages of both imaging methodologies, we develop a generative adversarial network that converts phase-contrast into fluorescent images. By including an additional histogram-equalized output to the state-of-art pix2pixHD algorithm, our model generates accurate images of aggregates and streams, enabling the estimation of aggregate positions and sizes, but with small shifts of their boundaries. Further training on ripple patterns enables accurate estimation of the rippling wavelength. Our methods are thus applicable for many other phenotypic behaviors and pattern formation studies.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/IgoshinLab/pix2pixHD-HE.git

  • Abbreviations

    MSE
    Mean Square Error
    SSIM
    Structural Similarity Index Measure
    HE
    Histogram Equalization
    CLAHE
    Contrast Limited Adaptive Histogram Equalization
    DFT
    Discrete Fourier Transformation
    GAN
    Generative Adversarial Network
    GFP
    Green Fluorescent Protein
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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    Posted July 17, 2021.
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    Quantification of Myxococcus xanthus Aggregation and Rippling Behaviors: Deep-learning Transformation of Phase-contrast into Fluorescence Microscopy Images
    Jiangguo Zhang, Jessica A. Comstock, Christopher R. Cotter, Patrick A. Murphy, Weili Nie, Roy D. Welch, Ankit B. Patel, Oleg A. Igoshin
    bioRxiv 2021.07.16.452736; doi: https://doi.org/10.1101/2021.07.16.452736
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    Quantification of Myxococcus xanthus Aggregation and Rippling Behaviors: Deep-learning Transformation of Phase-contrast into Fluorescence Microscopy Images
    Jiangguo Zhang, Jessica A. Comstock, Christopher R. Cotter, Patrick A. Murphy, Weili Nie, Roy D. Welch, Ankit B. Patel, Oleg A. Igoshin
    bioRxiv 2021.07.16.452736; doi: https://doi.org/10.1101/2021.07.16.452736

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