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Flow Cytometric Assessment of Bacterial Abundance in Soils, Sediments and Sludge.

ABSTRACT: Bacterial abundance is a fundamental measure in microbiology, but its assessment is often tedious, especially for soil, and sediment samples. To overcome this limitation, we adopted a time-efficient flow-cytometric (FCM) counting method involving cell detachment and separation from matrix particles by centrifugation in tubes receiving sample suspensions and Histodenz(®) solution. We used this approach to assess bacterial abundances in diverse soils (natural and agricultural), sediments (streams and lakes) and sludge from sand-filters in a drinking water treatment plant and compared the results to bacterial abundances determined by two established methods, epifluorescence microscopy (EM) and adenosine triphosphate (ATP) quantification. Cell abundances determined by FCM and EM correlated fairly well, although absolute cell abundances were generally lower when determined by FCM. FCM also showed significant relations with cell counts converted from ATP concentrations, although estimates derived from ATP determinations were typically higher, indicating the presence of ATP sources other than bacteria. Soil and sediment organic matter (OM) content influenced the goodness of fit between counts obtained with EM and FCM. In particular, bacterial abundance determined by FCM in samples containing less than 10% OM, such as stream sediment, was particularly well correlated with the cell counts assessed by EM. Overall, these results suggest that FCM following cell detachment and purification is a useful approach to increase sample throughput for determining bacterial abundances in soils, sediments and sludge. However, notable scatter and only partial concordance among the FCM and reference methods suggests that protocols require further improvement for assessments requiring high precision, especially when OM contents in samples are high.

SUBMITTER: Frossard A

PROVIDER: S-EPMC4905975 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Microbiome management research and applications rely on temporally resolved measurements of community composition. Current technologies to assess community composition make use of either cultivation or sequencing of genomic material, which can become time-consuming and/or laborious in case high-throughput measurements are required. Here, using data from a shrimp hatchery as an economically relevant case study, we combined 16S rRNA gene amplicon sequencing and flow cytometry data to develop a computational workflow that allows the prediction of taxon abundances based on flow cytometry measurements. The first stage of our pipeline consists of a classifier to predict the presence or absence of the taxon of interest, with yielded an average accuracy of 88.13% ± 4.78% across the top 50 operational taxonomic units (OTUs) of our data set. In the second stage, this classifier was combined with a regression model to predict the relative abundances of the taxon of interest, which yielded an average R2 of 0.35 ± 0.24 across the top 50 OTUs of our data set. Application of the models to flow cytometry time series data showed that the generated models can predict the temporal dynamics of a large fraction of the investigated taxa. Using cell sorting, we validated that the model correctly associates taxa to regions in the cytometric fingerprint, where they are detected using 16S rRNA gene amplicon sequencing. Finally, we applied the approach of our pipeline to two other data sets of microbial ecosystems. This pipeline represents an addition to the expanding toolbox for flow cytometry-based monitoring of bacterial communities and complements the current plating- and marker gene-based methods. IMPORTANCE Monitoring of microbial community composition is crucial for both microbiome management research and applications. Existing technologies, such as plating and amplicon sequencing, can become laborious and expensive when high-throughput measurements are required. In recent years, flow cytometry-based measurements of community diversity have been shown to correlate well with those derived from 16S rRNA gene amplicon sequencing in several aquatic ecosystems, suggesting that there is a link between the taxonomic community composition and phenotypic properties as derived through flow cytometry. Here, we further integrated 16S rRNA gene amplicon sequencing and flow cytometry survey data in order to construct models that enable the prediction of both the presence and the abundances of individual bacterial taxa in mixed communities using flow cytometric fingerprinting. The developed pipeline holds great potential to be integrated into routine monitoring schemes and early warning systems for biotechnological applications.

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.

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Flow Cytometric Assessment of Bacterial Abundance in Soils, Sediments and Sludge.

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