Dataset Information

Flow Cytometric Single-Cell Identification of Populations in Synthetic Bacterial Communities.

ABSTRACT: Bacterial cells can be characterized in terms of their cell properties using flow cytometry. Flow cytometry is able to deliver multiparametric measurements of up to 50,000 cells per second. However, there has not yet been a thorough survey concerning the identification of the population to which bacterial single cells belong based on flow cytometry data. This paper not only aims to assess the quality of flow cytometry data when measuring bacterial populations, but also suggests an alternative approach for analyzing synthetic microbial communities. We created so-called in silico communities, which allow us to explore the possibilities of bacterial flow cytometry data using supervised machine learning techniques. We can identify single cells with an accuracy >90% for more than half of the communities consisting out of two bacterial populations. In order to assess to what extent an in silico community is representative for its synthetic counterpart, we created so-called abundance gradients, a combination of synthetic (i.e., in vitro) communities containing two bacterial populations in varying abundances. By showing that we are able to retrieve an abundance gradient using a combination of in silico communities and supervised machine learning techniques, we argue that in silico communities form a viable representation for synthetic bacterial communities, opening up new opportunities for the analysis of synthetic communities and bacterial flow cytometry data in general.

SUBMITTER: Rubbens P

PROVIDER: S-EPMC5266259 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Flow Cytometric Single-Cell Identification of Populations in Synthetic Bacterial Communities.

Rubbens Peter P Props Ruben R Boon Nico N Waegeman Willem W

PloS one 20170125 1

Bacterial cells can be characterized in terms of their cell properties using flow cytometry. Flow cytometry is able to deliver multiparametric measurements of up to 50,000 cells per second. However, there has not yet been a thorough survey concerning the identification of the population to which bacterial single cells belong based on flow cytometry data. This paper not only aims to assess the quality of flow cytometry data when measuring bacterial populations, but also suggests an alternative ap ...[more]

PMID: 28122063

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Project description:The accuracy of flow cytometric (FCM) quantifications of microbial populations in sediments varies with FCM settings, cell extraction and staining protocols, as well as sample types. In the present study, we improve the accuracy of FCM for enumerating microorganisms inhabiting diverse lake and marine sediment types based on extensive tests with FCM settings, extraction buffer chemical compositions, cell separation methods, and staining procedures. Tests on the FCM settings, (e.g., acquisition time, rates of events) and salinity of extraction solutions show minor impacts on FCM enumerations and yields of cell extraction, respectively. Existing methods involving hydrofluoric acid (HF) treatment to release sediment-attached cells into solution prove effective on both marine and freshwater samples. Yet, different staining techniques (direct staining of cell extracts, staining of membrane-filtered cell extracts) produce clear differences in cell number estimates. We demonstrate that, while labor-intensive membrane-staining generates high cell staining efficiency and accurate cell counts that are consistent across FCM and epifluorescence microscopy-based (EFM) quantification methods, accurate cell counts determined by more time- and labor-efficient direct staining require consideration of dye concentration, sample dilution, and lithology. Yet, good agreement between the two staining methods can be achieved through sample-specific adjustments of dye concentrations and sample dilutions during direct staining. We thus present a complete protocol for FCM-based cell quantification, that includes all steps from the initial sample fixation to the final enumeration, with recommendations for buffer compositions, direct and membrane-based staining procedures, and the final FCM assay. This protocol is versatile, accurate, and reliable, as is evident from good agreement with cell quantifications by EFM and quantitative polymerase chain reaction (qPCR) of 16S rRNA genes across a wide range of sedimentary sample types.

Dataset Information

Flow Cytometric Single-Cell Identification of Populations in Synthetic Bacterial Communities.

Publications

Flow Cytometric Single-Cell Identification of Populations in Synthetic Bacterial Communities.

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