Heterogeneity in Pure Microbial Systems: Experimental Measurements and Modeling

González-Cabaleiro, Rebeca; Mitchell, Anca M.; Smith, William Thomas Lingwood; Wipat, Anil; Ofiţeru, Irina Dana

doi:10.3389/fmicb.2017.01813

Cited by 51 publications

(33 citation statements)

References 99 publications

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“…We demonstrated that interactions between bacterial populations lead to an adjustment of the individual phenotypic diversities of the interacting populations. Since phenotypic heterogeneity is playing an important role in pathogenicity and virulence (14), antibiotic resistance (12,54), biotechnological applications (20,23,55,56), and ecosystem properties (57), it is crucial to understand its influencing factors. The experimental design presented here provides a framework within which further ecological hypotheses regarding phenotypic heterogeneity and microbial interactions can be tested.…”

Section: Discussionmentioning

confidence: 99%

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Coculturing Bacteria Leads to Reduced Phenotypic Heterogeneities

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Appl Environ Microbiol

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Isogenic bacterial populations are known to exhibit phenotypic heterogeneity at the single-cell level. Because of difficulties in assessing the phenotypic heterogeneity of a single taxon in a mixed community, the importance of this deeper level of organization remains relatively unknown for natural communities. In this study, we have used membrane-based microcosms that allow the probing of the phenotypic heterogeneity of a single taxon while interacting with a synthetic or natural community. Individual taxa were studied under axenic conditions, as members of a coculture with physical separation, and as a mixed culture. Phenotypic heterogeneity was assessed through both flow cytometry and Raman spectroscopy. Using this setup, we investigated the effect of microbial interactions on the individual phenotypic heterogeneities of two interacting drinking water isolates. Through flow cytometry we have demonstrated that interactions between these bacteria lead to a reduction of their individual phenotypic diversities and that this adjustment is conditional on the bacterial taxon. Single-cell Raman spectroscopy confirmed a taxondependent phenotypic shift due to the interaction. In conclusion, our data suggest that bacterial interactions may be a general driver of phenotypic heterogeneity in mixed microbial populations. IMPORTANCE Laboratory studies have shown the impact of phenotypic heterogeneity on the survival and functionality of isogenic populations. Because phenotypic heterogeneity plays an important role in pathogenicity and virulence, antibiotic resistance, biotechnological applications, and ecosystem properties, it is crucial to understand its influencing factors. An unanswered question is whether bacteria in mixed communities influence the phenotypic heterogeneity of their community partners. We found that coculturing bacteria leads to a reduction in their individual phenotypic heterogeneities, which led us to the hypothesis that the individual phenotypic diversity of a taxon is dependent on the community composition.

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Section: Discussionmentioning

confidence: 99%

“…Two laser-based methods that are suitable for assessing phenotypes are flow cytometry and Raman spectroscopy (21)(22)(23). Two types of light can be detected by the flow cytometer: scattered light and fluorescence.…”

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confidence: 99%

Coculturing Bacteria Leads to Reduced Phenotypic Heterogeneities

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et al. 2019

Appl Environ Microbiol

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“…We demonstrated that interactions between bacterial populations lead to an adjustment of the individual phenotypic diversities of the interacting populations. As phenotypic heterogeneity is playing an important role in pathogenicity and virulence (14), antibiotics resistance (12, 60), biotechnological applications (20, 23, 61, 62), ecosystem properties (63), it is crucial to understand its influencing factors. The experimental design presented in this study provides a framework within which further ecological hypotheses regarding phenotypic heterogeneity and microbial interactions can be tested.…”

Section: Resultsmentioning

confidence: 99%

“…Two laser-based methods that are suitable for assessing phenotypes are flow cytometry and Raman spectroscopy (21–23). Two types of light can be detected by the flow cytometer, that is scattered light and fluorescence.…”

Section: Introductionmentioning

confidence: 99%

Coculturing bacteria leads to reduced phenotypic heterogeneities

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et al. 2018

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19Isogenic bacterial populations are known to exhibit phenotypic heterogeneity at the 20 single cell level. Because of difficulties in assessing the phenotypic heterogeneity of a 21 65 5 difficult to assess the heterogeneity of a single taxon within a mixed community. 66 Recently, several experimental approaches that assess the metabolic diversity of a 67 single taxon in natural communities have been developed (19, 20). However, these 68 approaches rely on FISH-probes that bind to 16S rRNA gene sequences for 69 identification of the taxon of interest. Hence, they do not allow to exclude the possibility 70 that some of the observed phenotypic differences are caused by minor genetic 71 differences between bacteria with very similar 16S rRNA genes. 72Two laser-based methods that are suitable for assessing phenotypes are flow cytometry 73 and Raman spectroscopy (21)(22)(23). Two types of light can be detected by the flow 74 cytometer, that is scattered light and fluorescence. The scattered light provides 75 information about the basic characteristics of the cells (e.g. size, shape and surface 76 properties), while the fluorescence data provides additional information about the cell 77 properties for which it has been stained (e.g. nucleic acid content, metabolic activity, 78 etc.) (24). Flow cytometry thus gives information regarding morphological as well as 79 specific physiological properties of single-cells. The Raman spectrum of a single cell 80 consists of a combination of the individual spectra of all the compounds that make up 81 this cell (e.g. proteins, nucleic acids, fatty acids, etc.). This results in a complex spectrum, 82 which can be interpreted as a chemical fingerprint of the cell (25, 26). Hence, single-cell 83 Raman spectra offer an in depth view on the biochemical composition of each 84 phenotype. 85A tool that can help to answer questions that are difficult to study directly in natural 86 communities is a synthetic ecosystem. A synthetic ecosystem consists of a selected set 87 of species under specific conditions. They are controllable and have a reduced 88 complexity in comparison to natural communities (27). Hence, they provide a way to 89 6 test ecological theories in order to better understand the rules of nature (28). A specific 90 setup for these synthetic ecosystems are co-cultures. The principle of such a system is 91 that two or more bacterial populations are cultivated together with some degree of 92 contact between them, which allows to study their interactions (29). 93An unanswered question, and the focus of this study, is whether bacteria in mixed 94 communities influence the phenotypic heterogeneity of their community partners. Here, 95 we used a synthetic community setup where two isolates were used as model 96 organisms. Four synthetic communities were created. The isolates were grown in 97 axenic cultures as a reference for non-interacting genotypes. To be able to study the 98 individual community members separately after they have been interacting via their 99 joint medi...

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“…Since microbial populations are often complex and consist of a community of multiple species, single‐cell analysis may be vital in dissecting molecular heterogeneity between cells . The applications are wide‐ranging from deciphering phylogenetic trees and evolutionary mechanisms, to discovering novel metabolic features within a microbiome, monitoring and optimizing the productivity in industrial bioprocesses, showing the diversity of symbiotic interactions as well as viral integrations, to elucidating “rare biospheres.” Currently, microbiomes are predominantly categorized by sequencing of the small ribosomal 16S subunit using targeted primers. This targeted technique only amplifies a small proportion of the entire genomic content within a microbial cell, often misses less efficiently amplified rare cells within a population and cannot amplify certain members of the Actinobacteria and Crenarchaeota .…”

Section: Applications Of Single‐cell Technology In Biomedical Researcmentioning

confidence: 99%

Defining Cell Identity with Single‐Cell Omics

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Cells are a fundamental unit of life, and the ability to study the phenotypes and behaviors of individual cells is crucial to understanding the workings of complex biological systems. Cell phenotypes (epigenomic, transcriptomic, proteomic, and metabolomic) exhibit dramatic heterogeneity between and within the different cell types and states underlying cellular functional diversity. Cell genotypes can also display heterogeneity throughout an organism, in the form of somatic genetic variation—most notably in the emergence and evolution of tumors. Recent technical advances in single‐cell isolation and the development of omics approaches sensitive enough to reveal these aspects of cell identity have enabled a revolution in the study of multicellular systems. In this review, we discuss the technologies available to resolve the genomes, epigenomes, transcriptomes, proteomes, and metabolomes of single cells from a wide variety of living systems.

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Heterogeneity in Pure Microbial Systems: Experimental Measurements and Modeling

Cited by 51 publications

References 99 publications

Coculturing Bacteria Leads to Reduced Phenotypic Heterogeneities

Coculturing Bacteria Leads to Reduced Phenotypic Heterogeneities

Coculturing bacteria leads to reduced phenotypic heterogeneities

Defining Cell Identity with Single‐Cell Omics

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