Coculturing Bacteria Leads to Reduced Phenotypic Heterogeneities

Heyse, Jasmine; Buysschaert, Benjamin; Props, Ruben; Rubbens, Peter; Skirtach, André G.; Waegeman, Willem

doi:10.1128/aem.02814-18

Cited by 37 publications

(29 citation statements)

References 65 publications

(69 reference statements)

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“…Indeed, microbial identification and microbial interactions can be performed with a label-free Raman spectroscopy (Lorenz et al, 2017). In this area, a very refined investigation is dedicated to detect phenotypic heterogeneity of bacteria, where Raman spectroscopy offers advantages over flow cytometry (Heyse et al, 2019). Coating bacteria with inorganic noble metal nanoparticles facilitates the enhancement of the Raman scattering signal (Zhou et al, 2014).…”

Section: Hybrid and Composite Materialsmentioning

confidence: 99%

Hierarchy of Hybrid Materials—The Place of Inorganics-in-Organics in it, Their Composition and Applications

et al. 2019

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Hybrid materials, or hybrids incorporating both organic and inorganic constituents, are emerging as a very potent and promising class of materials due to the diverse, but complementary nature of the properties inherent of these different classes of materials. The complementarity leads to a perfect synergy of properties of desired material and eventually an end-product. The diversity of resultant properties and materials used in the construction of hybrids, leads to a very broad range of application areas generated by engaging very different research communities. We provide here a general classification of hybrid materials, wherein organics– in -inorganics (inorganic materials modified by organic moieties) are distinguished from inorganics– in –organics (organic materials or matrices modified by inorganic constituents). In the former area, the surface functionalization of colloids is distinguished as a stand-alone sub-area. The latter area—functionalization of organic materials by inorganic additives—is the focus of the current review. Inorganic constituents, often in the form of small particles or structures, are made of minerals, clays, semiconductors, metals, carbons, and ceramics. They are shown to be incorporated into organic matrices, which can be distinguished as two classes: chemical and biological. Chemical organic matrices include coatings, vehicles and capsules assembled into: hydrogels, layer-by-layer assembly, polymer brushes, block co-polymers and other assemblies. Biological organic matrices encompass bio-molecules (lipids, polysaccharides, proteins and enzymes, and nucleic acids) as well as higher level organisms: cells, bacteria, and microorganisms. In addition to providing details of the above classification and analysis of the composition of hybrids, we also highlight some antagonistic yin-&-yang properties of organic and inorganic materials, review applications and provide an outlook to emerging trends.

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Section: Hybrid and Composite Materialsmentioning

confidence: 99%

Hierarchy of Hybrid Materials—The Place of Inorganics-in-Organics in it, Their Composition and Applications

et al. 2019

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“…These populations are known to exhibit considerable heterogeneity due to cell size diversity and cell cycle variations (Vives-Rego et al, 2003). Our research group has recently shown that the cytometric diversity of an individual population reduces when that population is part of a co-culture (Heyse et al, 2019). Therefore, data used for the in silico community creation setup cannot be used to study environmental samples, as we hypothesize that members of natural communities will have a different cytometric fingerprint as opposed to populations that were grown and measured individually.…”

Section: Discussionmentioning

confidence: 99%

PhenoGMM: Gaussian mixture modelling of microbial cytometry data enables efficient predictions of biodiversity

Rubbens

Props

Kerckhof

et al. 2019

Preprint

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Motivation:Microbial flow cytometry allows to rapidly characterize microbial community diversity and dynamics. Recent research has demonstrated a strong connection between the cytometric diversity and taxonomic diversity based on 16S rRNA gene amplicon sequencing data. This creates the opportunity to integrate both types of data to study and predict the microbial community diversity in an automated and efficient way. However, microbial flow cytometry data results in a number of unique challenges that need to be addressed. Results: The results of our work are threefold: i) We expand current microbial cytometry fingerprinting approaches by using a model-based fingerprinting approach based upon Gaussian Mixture Models, which we called PhenoGMM. ii) We show that microbial diversity can be rapidly estimated by PhenoGMM. In combination with a supervised machine learning model, diversity estimations based on 16S rRNA gene amplicon sequencing data can be predicted. iii) We evaluate our method extensively by using multiple datasets from different ecosystems and compare its predictive power with a generic binning fingerprinting approach that is commonly used in microbial flow cytometry. These results confirm the strong connection between the genetic make-up of a microbial community and its phenotypic properties as measured by flow cytometry.

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“…The HC calculation developed in the present study for nanoSIMS-derived data, including CSE correction, were incorporated in the up-to-date version of the supplementary Excel template ( Supplementary Table S1). The developed HC expression can be applied as a measure of heterogeneity not only of cellular anabolic activity, but also of any parameter measured at the single-cell level within a population: for example, length, volume, fluorescence yield, and gene expression (Nikolic et al, 2013;Gangwe Nana et al, 2018;Simşek and Kim, 2018;Heyse et al, 2019). The supplementary Excel template was therefore extended for HC calculation on the data acquired with other single-cell-resolved techniques.…”

Section: Hc Applicability To Nanosims Datamentioning

confidence: 99%

Quantitation and Comparison of Phenotypic Heterogeneity Among Single Cells of Monoclonal Microbial Populations

Calabrese

Voloshynovska²,

Musat

et al. 2019

Front. Microbiol.

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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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Coculturing Bacteria Leads to Reduced Phenotypic Heterogeneities

Cited by 37 publications

References 65 publications

Hierarchy of Hybrid Materials—The Place of Inorganics-in-Organics in it, Their Composition and Applications

Hierarchy of Hybrid Materials—The Place of Inorganics-in-Organics in it, Their Composition and Applications

PhenoGMM: Gaussian mixture modelling of microbial cytometry data enables efficient predictions of biodiversity

Quantitation and Comparison of Phenotypic Heterogeneity Among Single Cells of Monoclonal Microbial Populations

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