Ecological Stability Properties of Microbial Communities Assessed by Flow Cytometry
ABSTRACT Natural microbial communities affect human life in countless ways, ranging from global biogeochemical cycles to the treatment of wastewater and health via the human microbiome. In order to probe, monitor, and eventually control these communities, fast detection and evaluation methods are re...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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American Society for Microbiology
2018-02-01
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| Series: | mSphere |
| Subjects: | |
| Online Access: | https://journals.asm.org/doi/10.1128/mSphere.00564-17 |
| _version_ | 1818836542418845696 |
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| author | Zishu Liu Nicolas Cichocki Fabian Bonk Susanne Günther Florian Schattenberg Hauke Harms Florian Centler Susann Müller |
| author_facet | Zishu Liu Nicolas Cichocki Fabian Bonk Susanne Günther Florian Schattenberg Hauke Harms Florian Centler Susann Müller |
| author_sort | Zishu Liu |
| collection | DOAJ |
| description | ABSTRACT Natural microbial communities affect human life in countless ways, ranging from global biogeochemical cycles to the treatment of wastewater and health via the human microbiome. In order to probe, monitor, and eventually control these communities, fast detection and evaluation methods are required. In order to facilitate rapid community analysis and monitor a community’s dynamic behavior with high resolution, we here apply community flow cytometry, which provides single-cell-based high-dimensional data characterizing communities with high acuity over time. To interpret time series data, we draw inspiration from macroecology, in which a rich set of concepts has been developed for describing population dynamics. We focus on the stability paradigm as a promising candidate to interpret such data in an intuitive and actionable way and present a rapid workflow to monitor stability properties of complex microbial ecosystems. Based on single-cell data, we compute the stability properties resistance, resilience, displacement speed, and elasticity. For resilience, we also introduce a method which can be implemented for continuous online community monitoring. The proposed workflow was tested in a long-term continuous reactor experiment employing both an artificial and a complex microbial community, which were exposed to identical short-term disturbances. The computed stability properties uncovered the superior stability of the complex community and demonstrated the global applicability of the protocol to any microbiome. The workflow is able to support high temporal sample densities below bacterial generation times. This may provide new opportunities to unravel unknown ecological paradigms of natural microbial communities, with applications to environmental, biotechnological, and health-related microbiomes. IMPORTANCE Microbial communities drive many processes which affect human well-being directly, as in the human microbiome, or indirectly, as in natural environments or in biotechnological applications. Due to their complexity, their dynamics over time is difficult to monitor, and current sequence-based approaches are limited with respect to the temporal resolution. However, in order to eventually control microbial community dynamics, monitoring schemes of high temporal resolution are required. Flow cytometry provides single-cell-based data in the required temporal resolution, and we here use such data to compute stability properties as easy to interpret univariate indicators of microbial community dynamics. Such monitoring tools will allow for a fast, continuous, and cost-effective screening of stability states of microbiomes. Applicable to various environments, including bioreactors, surface water, and the human body, it will contribute to the development of control schemes to manipulate microbial community structures and performances. |
| first_indexed | 2024-12-19T03:08:16Z |
| format | Article |
| id | doaj.art-2beaa2d1778843ca9001548d4ef255d5 |
| institution | Directory Open Access Journal |
| issn | 2379-5042 |
| language | English |
| last_indexed | 2024-12-19T03:08:16Z |
| publishDate | 2018-02-01 |
| publisher | American Society for Microbiology |
| record_format | Article |
| series | mSphere |
| spelling | doaj.art-2beaa2d1778843ca9001548d4ef255d52022-12-21T20:38:04ZengAmerican Society for MicrobiologymSphere2379-50422018-02-013110.1128/mSphere.00564-17Ecological Stability Properties of Microbial Communities Assessed by Flow CytometryZishu Liu0Nicolas Cichocki1Fabian Bonk2Susanne Günther3Florian Schattenberg4Hauke Harms5Florian Centler6Susann Müller7Department of Environmental Microbiology, UFZ-Helmholtz Centre for Environmental Research, Leipzig, GermanyDepartment of Environmental Microbiology, UFZ-Helmholtz Centre for Environmental Research, Leipzig, GermanyDepartment of Environmental Microbiology, UFZ-Helmholtz Centre for Environmental Research, Leipzig, GermanyDepartment of Environmental Microbiology, UFZ-Helmholtz Centre for Environmental Research, Leipzig, GermanyDepartment of Environmental Microbiology, UFZ-Helmholtz Centre for Environmental Research, Leipzig, GermanyDepartment of Environmental Microbiology, UFZ-Helmholtz Centre for Environmental Research, Leipzig, GermanyDepartment of Environmental Microbiology, UFZ-Helmholtz Centre for Environmental Research, Leipzig, GermanyDepartment of Environmental Microbiology, UFZ-Helmholtz Centre for Environmental Research, Leipzig, GermanyABSTRACT Natural microbial communities affect human life in countless ways, ranging from global biogeochemical cycles to the treatment of wastewater and health via the human microbiome. In order to probe, monitor, and eventually control these communities, fast detection and evaluation methods are required. In order to facilitate rapid community analysis and monitor a community’s dynamic behavior with high resolution, we here apply community flow cytometry, which provides single-cell-based high-dimensional data characterizing communities with high acuity over time. To interpret time series data, we draw inspiration from macroecology, in which a rich set of concepts has been developed for describing population dynamics. We focus on the stability paradigm as a promising candidate to interpret such data in an intuitive and actionable way and present a rapid workflow to monitor stability properties of complex microbial ecosystems. Based on single-cell data, we compute the stability properties resistance, resilience, displacement speed, and elasticity. For resilience, we also introduce a method which can be implemented for continuous online community monitoring. The proposed workflow was tested in a long-term continuous reactor experiment employing both an artificial and a complex microbial community, which were exposed to identical short-term disturbances. The computed stability properties uncovered the superior stability of the complex community and demonstrated the global applicability of the protocol to any microbiome. The workflow is able to support high temporal sample densities below bacterial generation times. This may provide new opportunities to unravel unknown ecological paradigms of natural microbial communities, with applications to environmental, biotechnological, and health-related microbiomes. IMPORTANCE Microbial communities drive many processes which affect human well-being directly, as in the human microbiome, or indirectly, as in natural environments or in biotechnological applications. Due to their complexity, their dynamics over time is difficult to monitor, and current sequence-based approaches are limited with respect to the temporal resolution. However, in order to eventually control microbial community dynamics, monitoring schemes of high temporal resolution are required. Flow cytometry provides single-cell-based data in the required temporal resolution, and we here use such data to compute stability properties as easy to interpret univariate indicators of microbial community dynamics. Such monitoring tools will allow for a fast, continuous, and cost-effective screening of stability states of microbiomes. Applicable to various environments, including bioreactors, surface water, and the human body, it will contribute to the development of control schemes to manipulate microbial community structures and performances.https://journals.asm.org/doi/10.1128/mSphere.00564-17constancymicrobial communitiesmicrobial ecologymicrobial flow cytometryresilienceresistance |
| spellingShingle | Zishu Liu Nicolas Cichocki Fabian Bonk Susanne Günther Florian Schattenberg Hauke Harms Florian Centler Susann Müller Ecological Stability Properties of Microbial Communities Assessed by Flow Cytometry mSphere constancy microbial communities microbial ecology microbial flow cytometry resilience resistance |
| title | Ecological Stability Properties of Microbial Communities Assessed by Flow Cytometry |
| title_full | Ecological Stability Properties of Microbial Communities Assessed by Flow Cytometry |
| title_fullStr | Ecological Stability Properties of Microbial Communities Assessed by Flow Cytometry |
| title_full_unstemmed | Ecological Stability Properties of Microbial Communities Assessed by Flow Cytometry |
| title_short | Ecological Stability Properties of Microbial Communities Assessed by Flow Cytometry |
| title_sort | ecological stability properties of microbial communities assessed by flow cytometry |
| topic | constancy microbial communities microbial ecology microbial flow cytometry resilience resistance |
| url | https://journals.asm.org/doi/10.1128/mSphere.00564-17 |
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