The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Functional single-cell analyses: flow cytometry and cell sorting of microbial populations and communities
Abstract
The still poorly explored world of microbial functioning is about to be uncovered by a combined application of old and new technologies. Bacteria, especially, are still in the dark with respect to their phylogenetic affiliations as well as their metabolic capabilities and functions. However, with the advent of sophisticated flow cytometric and cell sorting technologies in microbiological labs, there is now the possibility to gain this knowledge at the single-cell level without cumbersome cultivation approaches. Cytometry also facilitates the understanding of physiological diversity in seemingly likewise acting populations. Both individuality and diversity lead to the complex and concerted actions of microbial consortia. This review provides an overview of the state of the art in the field. It deals with the handling of microorganisms from the very beginning (i.e. sampling, and detachment and fixation procedures) and goes on to discuss the pitfalls and problems in analysing cells without any further treatment. If information cannot be gained by specific staining procedures, phylogenetic technologies, transcriptomic and proteomic approaches may be options for achieving advanced insights. All in all, flow cytometry will be a mediator technology to gain a deeper insight into the heterogeneity of populations and the functioning of microbial communities.
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Cites
Lateral gene transfer and the nature of bacterial innovation
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“Presently, 4 20 such guidelines are either under development or have already been published (Taylor et al., 2008).”
BackgroundMIFlowCyt: The minimum information about a flow cytometry experiment
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Applications of flow cytometry to clinical microbiology
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The scale−up of microbial batch and fed−batch fermentation processes
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Huys, G., Botteldoorn, N., Delvigne, F., De Vuyst, L., Heyndrickx, M., Pot, B., Dubois, J., & Daube, G. (2013) · Molecular Nutrition & Food Research · doi:10.1002/mnfr.201300065 · Cited by 132
The active human gut microbiota differs from the total microbiota
Peris-Bondia, F., Latorre, A., Artacho, A., Moya, A., & D'Auria, G. (2011) · PLoS ONE · doi:10.1371/journal.pone.0022448 · Cited by 113
Profiling and quantifying endogenous molecules in single cells using nano-DESI MS
Bergman, H.-M., & Lanekoff, I. (2017) · The Analyst · doi:10.1039/c7an00885f · Cited by 109
Pereira, A. C., Ramos, B., Reis, A. C., & Cunha, M. V. (2020) · Microorganisms · doi:10.3390/microorganisms8091380 · Cited by 101
Metadata
- Authors
- Müller, S., & Nebe-von-Caron, G.
- Venue
- FEMS Microbiology Reviews
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-07
- Volume
- 34
- Issue
- 4
- Pages
- 554-587
- ISSN
- 0168-6445 (ISSN-L)
- 1574-6976
- OpenAlex
- W2074692830
- Cited by
- 356
- References
- 7
- FWCI
- 20.16
- Type
- journal article
- Language
- en
- Open access
- closed
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- Müller, S., & Nebe-von-Caron, G. (2010). Functional single-cell analyses: flow cytometry and cell sorting of microbial populations and communities. FEMS Microbiology Reviews. https://doi.org/10.1111/j.1574-6976.2010.00214.x
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
