Quantitation and Comparison of Phenotypic Heterogeneity Among Single Cells of Monoclonal Microbial Populations
Phenotypic heterogeneity within microbial populations arises even when the cells are exposed to putatively constant and homogeneous conditions. The outcome of this phenomenon can affect the whole function of the population, resulting in, for example, new "adapted" metabolic strategies and...
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| Vydáno v: | Frontiers in microbiology Ročník 10; s. 2814 |
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Switzerland
Frontiers Media S.A
20.12.2019
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| Abstract | Phenotypic heterogeneity within microbial populations arises even when the cells are exposed to putatively constant and homogeneous conditions. The outcome of this phenomenon can affect the whole function of the population, resulting in, for example, new "adapted" metabolic strategies and impacting its fitness at given environmental conditions. Accounting for phenotypic heterogeneity becomes thus necessary, due to its relevance in medical and applied microbiology as well as in environmental processes. Still, a comprehensive evaluation of this phenomenon requires a common and unique method of quantitation, which allows for the comparison between different studies carried out with different approaches. Consequently, in this study, two widely applicable indices for quantitation of heterogeneity were developed. The heterogeneity coefficient (HC) is valid when the population follows unimodal activity, while the differentiation tendency index (DTI) accounts for heterogeneity implying outbreak of subpopulations and multimodal activity. We demonstrated the applicability of HC and DTI for heterogeneity quantitation on stable isotope probing with nanoscale secondary ion mass spectrometry (SIP-nanoSIMS), flow cytometry, and optical microscopy datasets. The HC was found to provide a more accurate and precise measure of heterogeneity, being at the same time consistent with the coefficient of variation (CV) applied so far. The DTI is able to describe the differentiation in single-cell activity within monoclonal populations resolving subpopulations with low cell abundance, individual cells with similar phenotypic features (e.g., isotopic content close to natural abundance, as detected with nanoSIMS). The developed quantitation approach allows for a better understanding on the impact and the implications of phenotypic heterogeneity in environmental, medical and applied microbiology, microbial ecology, cell biology, and biotechnology. |
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| AbstractList | Phenotypic heterogeneity within microbial populations arises even when the cells are exposed to putatively constant and homogeneous conditions. The outcome of this phenomenon can affect the whole function of the population, resulting in, for example, new "adapted" metabolic strategies and impacting its fitness at given environmental conditions. Accounting for phenotypic heterogeneity becomes thus necessary, due to its relevance in medical and applied microbiology as well as in environmental processes. Still, a comprehensive evaluation of this phenomenon requires a common and unique method of quantitation, which allows for the comparison between different studies carried out with different approaches. Consequently, in this study, two widely applicable indices for quantitation of heterogeneity were developed. The heterogeneity coefficient (HC) is valid when the population follows unimodal activity, while the differentiation tendency index (DTI) accounts for heterogeneity implying outbreak of subpopulations and multimodal activity. We demonstrated the applicability of HC and DTI for heterogeneity quantitation on stable isotope probing with nanoscale secondary ion mass spectrometry (SIP-nanoSIMS), flow cytometry, and optical microscopy datasets. The HC was found to provide a more accurate and precise measure of heterogeneity, being at the same time consistent with the coefficient of variation (CV) applied so far. The DTI is able to describe the differentiation in single-cell activity within monoclonal populations resolving subpopulations with low cell abundance, individual cells with similar phenotypic features (e.g., isotopic content close to natural abundance, as detected with nanoSIMS). The developed quantitation approach allows for a better understanding on the impact and the implications of phenotypic heterogeneity in environmental, medical and applied microbiology, microbial ecology, cell biology, and biotechnology. Phenotypic heterogeneity within microbial populations arises even when the cells are exposed to putatively constant and homogeneous conditions. The outcome of this phenomenon can affect the whole function of the population, resulting in, for example, new "adapted" metabolic strategies and impacting its fitness at given environmental conditions. Accounting for phenotypic heterogeneity becomes thus necessary, due to its relevance in medical and applied microbiology as well as in environmental processes. Still, a comprehensive evaluation of this phenomenon requires a common and unique method of quantitation, which allows for the comparison between different studies carried out with different approaches. Consequently, in this study, two widely applicable indices for quantitation of heterogeneity were developed. The heterogeneity coefficient (HC) is valid when the population follows unimodal activity, while the differentiation tendency index (DTI) accounts for heterogeneity implying outbreak of subpopulations and multimodal activity. We demonstrated the applicability of HC and DTI for heterogeneity quantitation on stable isotope probing with nanoscale secondary ion mass spectrometry (SIP-nanoSIMS), flow cytometry, and optical microscopy datasets. The HC was found to provide a more accurate and precise measure of heterogeneity, being at the same time consistent with the coefficient of variation (CV) applied so far. The DTI is able to describe the differentiation in single-cell activity within monoclonal populations resolving subpopulations with low cell abundance, individual cells with similar phenotypic features (e.g., isotopic content close to natural abundance, as detected with nanoSIMS). The developed quantitation approach allows for a better understanding on the impact and the implications of phenotypic heterogeneity in environmental, medical and applied microbiology, microbial ecology, cell biology, and biotechnology.Phenotypic heterogeneity within microbial populations arises even when the cells are exposed to putatively constant and homogeneous conditions. The outcome of this phenomenon can affect the whole function of the population, resulting in, for example, new "adapted" metabolic strategies and impacting its fitness at given environmental conditions. Accounting for phenotypic heterogeneity becomes thus necessary, due to its relevance in medical and applied microbiology as well as in environmental processes. Still, a comprehensive evaluation of this phenomenon requires a common and unique method of quantitation, which allows for the comparison between different studies carried out with different approaches. Consequently, in this study, two widely applicable indices for quantitation of heterogeneity were developed. The heterogeneity coefficient (HC) is valid when the population follows unimodal activity, while the differentiation tendency index (DTI) accounts for heterogeneity implying outbreak of subpopulations and multimodal activity. We demonstrated the applicability of HC and DTI for heterogeneity quantitation on stable isotope probing with nanoscale secondary ion mass spectrometry (SIP-nanoSIMS), flow cytometry, and optical microscopy datasets. The HC was found to provide a more accurate and precise measure of heterogeneity, being at the same time consistent with the coefficient of variation (CV) applied so far. The DTI is able to describe the differentiation in single-cell activity within monoclonal populations resolving subpopulations with low cell abundance, individual cells with similar phenotypic features (e.g., isotopic content close to natural abundance, as detected with nanoSIMS). The developed quantitation approach allows for a better understanding on the impact and the implications of phenotypic heterogeneity in environmental, medical and applied microbiology, microbial ecology, cell biology, and biotechnology. |
| Author | Musat, Florin Richnow, Hans H. Thullner, Martin Müller, Susann Wick, Lukas Y. Stryhanyuk, Hryhoriy Schlömann, Michael Calabrese, Federica Lambrecht, Johannes Musat, Niculina Voloshynovska, Iryna |
| AuthorAffiliation | 2 le-tex Publishing Services GmbH , Leipzig , Germany 3 Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ , Leipzig , Germany 4 Institute of Biosciences, TU-Bergakademie Freiberg , Freiberg , Germany 1 Department of Isotope Biogeochemistry, Helmholtz Centre for Environmental Research-UFZ , Leipzig , Germany |
| AuthorAffiliation_xml | – name: 2 le-tex Publishing Services GmbH , Leipzig , Germany – name: 4 Institute of Biosciences, TU-Bergakademie Freiberg , Freiberg , Germany – name: 1 Department of Isotope Biogeochemistry, Helmholtz Centre for Environmental Research-UFZ , Leipzig , Germany – name: 3 Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ , Leipzig , Germany |
| Author_xml | – sequence: 1 givenname: Federica surname: Calabrese fullname: Calabrese, Federica – sequence: 2 givenname: Iryna surname: Voloshynovska fullname: Voloshynovska, Iryna – sequence: 3 givenname: Florin surname: Musat fullname: Musat, Florin – sequence: 4 givenname: Martin surname: Thullner fullname: Thullner, Martin – sequence: 5 givenname: Michael surname: Schlömann fullname: Schlömann, Michael – sequence: 6 givenname: Hans H. surname: Richnow fullname: Richnow, Hans H. – sequence: 7 givenname: Johannes surname: Lambrecht fullname: Lambrecht, Johannes – sequence: 8 givenname: Susann surname: Müller fullname: Müller, Susann – sequence: 9 givenname: Lukas Y. surname: Wick fullname: Wick, Lukas Y. – sequence: 10 givenname: Niculina surname: Musat fullname: Musat, Niculina – sequence: 11 givenname: Hryhoriy surname: Stryhanyuk fullname: Stryhanyuk, Hryhoriy |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31921014$$D View this record in MEDLINE/PubMed |
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| Copyright | Copyright © 2019 Calabrese, Voloshynovska, Musat, Thullner, Schlömann, Richnow, Lambrecht, Müller, Wick, Musat and Stryhanyuk. Copyright © 2019 Calabrese, Voloshynovska, Musat, Thullner, Schlömann, Richnow, Lambrecht, Müller, Wick, Musat and Stryhanyuk. 2019 Calabrese, Voloshynovska, Musat, Thullner, Schlömann, Richnow, Lambrecht, Müller, Wick, Musat and Stryhanyuk |
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| Keywords | single-cell resolution SIP–nanoSIMS Zipf's law heterogeneity quantitation phenotypic heterogeneity flow cytometry multimodality anabolic activity |
| Language | English |
| License | Copyright © 2019 Calabrese, Voloshynovska, Musat, Thullner, Schlömann, Richnow, Lambrecht, Müller, Wick, Musat and Stryhanyuk. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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