Deconvolution model to resolve cytometric microbial community patterns in flowing waters

Amalfitano, Stefano; Fazi, Stefano; Ejarque, Elisabet; Freixa, Anna; Romaní, Anna M.; Butturini, Andrea

doi:10.1002/cyto.a.23304

Cited by 40 publications

(29 citation statements)

References 57 publications

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“…The TCC was determined by their signatures in a plot of the side scatter vs. the green fluorescence. The intensity of green fluorescence emitted by SYBR positive cells allowed for the discrimination among cell groups exhibiting two different nucleic acid content (cells with low NA content-LNA; cells with high NA content-HNA) [27]. This method was successfully applied for prokaryotic cell counting in different groundwater settings [15,21].…”

Section: Microbial Community Characterization By Flow Cytometrymentioning

confidence: 99%

Physiological Profiling and Functional Diversity of Groundwater Microbial Communities in a Municipal Solid Waste Landfill Area

Melita

Amalfitano

Preziosi

et al. 2019

Water

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The disposal of municipal solid wastes in landfills represents a major threat for aquifer environments at the global scale. The aim of this study was to explore how groundwater geochemical characteristics can influence the microbial community functioning and the potential degradation patterns of selected organic substrates in response to different levels of landfill-induced alterations. Groundwaters collected from a landfill area were monitored by assessing major physical-chemical parameters and the microbiological contamination levels (total coliforms and fecal indicators-Colilert-18). The aquatic microbial community was further characterized by flow cytometry and Biolog EcoPlates TM assay. Three groundwater conditions (i.e., pristine, mixed, and altered) were identified according to their distinct geochemical profiles. The altered groundwaters showed relatively higher values of organic matter concentration and total cell counts, along with the presence of fecal indicator bacteria, in comparison to samples from pristine and mixed conditions. The kinetic profiles of the Biolog substrate degradation showed that the microbial community thriving in altered conditions was relatively more efficient in metabolizing a larger number of organic substrates, including those with complex molecular structures. We concluded that the assessment of physiological profiling and functional diversity at the microbial community level could represent a supportive tool to understand the potential consequences of the organic contamination of impacted aquifers, thus complementing the current strategies for groundwater management.

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Section: Microbial Community Characterization By Flow Cytometrymentioning

confidence: 99%

Physiological Profiling and Functional Diversity of Groundwater Microbial Communities in a Municipal Solid Waste Landfill Area

Melita

Amalfitano

Preziosi

et al. 2019

Water

Self Cite

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“…Because it is in an adaptive strategy as well, by defining small clusters in regions of high density and vice versa, it reduces the number of sample-describing variables considerably compared to fixed binning approaches. Other adaptive binning strategies have been proposed for microbial FCM data as well, however these still only investigate bivariate interactions (Amalfitano et al, 2018;Huang et al, 2018).…”

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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“…In contrast to the highly resolving DAPI the resolution of microbial communities by SYBR Green is much lower and results mainly in only two subcommunities such as low nucleic acid (LNA) and high nucleic acid (HNA) bacteria. Recently, an attempt was made to resolve these two subcommunities even further by applying a deconvolution model [35].…”

Section: Previous Workmentioning

confidence: 99%

flowEMMi: An automated model-based clustering tool for microbial cytometric data

Ludwig

Siederdissen

Liu

et al. 2019

Preprint

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Background: Flow cytometry (FCM) is a powerful single-cell based measurement method to ascertain multidimensional optical properties of millions of cells. FCM is widely used in medical diagnostics and health research. There is also a broad range of applications in the analysis of complex microbial communities. The main concern in microbial community analyses is to track the dynamics of microbial subcommunities. So far, this can be achieved with the help of time-consuming manual clustering procedures that require extensive user-dependent input. In addition, several tools have recently been developed by using different approaches which, however, focus mainly on the clustering of medical FCM data or of microbial samples with a well-known background, while much less work has been done on high-throughput, online algorithms for two-channel FCM. Results:We bridge this gap with flowEMMi, a model-based clustering tool based on multivariate Gaussian mixture models with subsampling and foreground/background separation. These extensions provide a fast and accurate identification of cell clusters in FCM data, in particular for microbial community FCM data that are often affected by irrelevant information like technical noise, beads or cell debris. flowEMMi outperforms other available tools with regard to running time and information content of the clustering results and provides near-online results and optional heuristics to reduce the running-time further.Conclusions: flowEMMi is a useful tool for the automated cluster analysis of microbial FCM data. It overcomes the user-dependent and time-consuming manual clustering procedure and provides consistent results with ancillary information and statistical proof.

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Deconvolution model to resolve cytometric microbial community patterns in flowing waters

Cited by 40 publications

References 57 publications

Physiological Profiling and Functional Diversity of Groundwater Microbial Communities in a Municipal Solid Waste Landfill Area

Physiological Profiling and Functional Diversity of Groundwater Microbial Communities in a Municipal Solid Waste Landfill Area

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

flowEMMi: An automated model-based clustering tool for microbial cytometric data

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