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

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dc.citation.firstpage1954en_US
dc.citation.issueNumber9en_US
dc.citation.journalTitleMicroorganismsen_US
dc.citation.volumeNumber9en_US
dc.contributor.authorZhang, Jiangguoen_US
dc.contributor.authorComstock, Jessica A.en_US
dc.contributor.authorCotter, Christopher R.en_US
dc.contributor.authorMurphy, Patrick A.en_US
dc.contributor.authorNie, Weilien_US
dc.contributor.authorWelch, Roy D.en_US
dc.contributor.authorPatel, Ankit B.en_US
dc.contributor.authorIgoshin, Oleg A.en_US
dc.date.accessioned2021-09-23T17:11:34Zen_US
dc.date.available2021-09-23T17:11:34Zen_US
dc.date.issued2021en_US
dc.description.abstractMyxococcus 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 cells. To combine the advantages of both imaging methodologies, we develop a generative adversarial network that converts phase-contrast into synthesized fluorescent images. By including an additional histogram-equalized output to the state-of-the-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.en_US
dc.identifier.citationZhang, Jiangguo, Comstock, Jessica A., Cotter, Christopher R., et al.. "Quantification of Myxococcus xanthus Aggregation and Rippling Behaviors: Deep-Learning Transformation of Phase-Contrast into Fluorescence Microscopy Images." <i>Microorganisms,</i> 9, no. 9 (2021) MDPI: 1954. https://doi.org/10.3390/microorganisms9091954.en_US
dc.identifier.digitalmicroorganisms-09-01954-v2en_US
dc.identifier.doihttps://doi.org/10.3390/microorganisms9091954en_US
dc.identifier.urihttps://hdl.handle.net/1911/111403en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsThis is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly citeden_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleQuantification of Myxococcus xanthus Aggregation and Rippling Behaviors: Deep-Learning Transformation of Phase-Contrast into Fluorescence Microscopy Imagesen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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