Dataset Information

High-Throughput Isolation of Giant Viruses in Liquid Medium Using Automated Flow Cytometry and Fluorescence Staining.

ABSTRACT: The isolation of giant viruses using amoeba co-culture is tedious and fastidious. Recently, the procedure was successfully associated with a method that detects amoebal lysis on agar plates. However, the procedure remains time-consuming and is limited to protozoa growing on agar. We present here advances for the isolation of giant viruses. A high-throughput automated method based on flow cytometry and fluorescent staining was used to detect the presence of giant viruses in liquid medium. Development was carried out with the Acanthamoeba polyphaga strain widely used in past and current co-culture experiments. The proof of concept was validated with virus suspensions: artificially contaminated samples but also environmental samples from which viruses were previously isolated. After validating the technique, and fortuitously isolating a new Mimivirus, we automated the technique on 96-well plates and tested it on clinical and environmental samples using other protozoa. This allowed us to detect more than 10 strains of previously known species of giant viruses and seven new strains of a new virus lineage. This automated high-throughput method demonstrated significant time saving, and higher sensitivity than older techniques. It thus creates the means to isolate giant viruses at high speed.

SUBMITTER: Khalil JY

PROVIDER: S-EPMC4731542 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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High-Throughput Isolation of Giant Viruses in Liquid Medium Using Automated Flow Cytometry and Fluorescence Staining.

Khalil Jacques Y B JY Robert Stephane S Reteno Dorine G DG Andreani Julien J Raoult Didier D La Scola Bernard B

Frontiers in microbiology 20160129

The isolation of giant viruses using amoeba co-culture is tedious and fastidious. Recently, the procedure was successfully associated with a method that detects amoebal lysis on agar plates. However, the procedure remains time-consuming and is limited to protozoa growing on agar. We present here advances for the isolation of giant viruses. A high-throughput automated method based on flow cytometry and fluorescent staining was used to detect the presence of giant viruses in liquid medium. Develop ...[more]

PMID: 26858703

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Project description:BackgroundIn a high throughput setting, effective flow cytometry data analysis depends heavily on proper data preprocessing. While usual preprocessing steps of quality assessment, outlier removal, normalization, and gating have received considerable scrutiny from the community, the influence of data transformation on the output of high throughput analysis has been largely overlooked. Flow cytometry measurements can vary over several orders of magnitude, cell populations can have variances that depend on their mean fluorescence intensities, and may exhibit heavily-skewed distributions. Consequently, the choice of data transformation can influence the output of automated gating. An appropriate data transformation aids in data visualization and gating of cell populations across the range of data. Experience shows that the choice of transformation is data specific. Our goal here is to compare the performance of different transformations applied to flow cytometry data in the context of automated gating in a high throughput, fully automated setting. We examine the most common transformations used in flow cytometry, including the generalized hyperbolic arcsine, biexponential, linlog, and generalized Box-Cox, all within the BioConductor flowCore framework that is widely used in high throughput, automated flow cytometry data analysis. All of these transformations have adjustable parameters whose effects upon the data are non-intuitive for most users. By making some modelling assumptions about the transformed data, we develop maximum likelihood criteria to optimize parameter choice for these different transformations.ResultsWe compare the performance of parameter-optimized and default-parameter (in flowCore) data transformations on real and simulated data by measuring the variation in the locations of cell populations across samples, discovered via automated gating in both the scatter and fluorescence channels. We find that parameter-optimized transformations improve visualization, reduce variability in the location of discovered cell populations across samples, and decrease the misclassification (mis-gating) of individual events when compared to default-parameter counterparts.ConclusionsOur results indicate that the preferred transformation for fluorescence channels is a parameter- optimized biexponential or generalized Box-Cox, in accordance with current best practices. Interestingly, for populations in the scatter channels, we find that the optimized hyperbolic arcsine may be a better choice in a high-throughput setting than current standard practice of no transformation. However, generally speaking, the choice of transformation remains data-dependent. We have implemented our algorithm in the BioConductor package, flowTrans, which is publicly available.

Project description:"Candidatus Accumulibacter" is the dominant polyphosphate-accumulating organism (PAO) in denitrifying phosphorus removal (DPR) systems. In order to investigate the community structure and clade morphotypes of "Candidatus Accumulibacter" in DPR systems through flow cytometry (FCM), denitrifying phosphorus removal of almost 100% using nitrite and nitrate as the electron acceptor was achieved in sequencing batch reactors (SBRs). An optimal method of flow cytometry combined with fluorescence in situ hybridization and SYBR green I staining (FISH-staining-flow cytometry) was developed to quantify PAOs in DPR systems. By setting the width value of FCM, bacterial cells in a sludge sample were divided into three groups in different morphotypes, namely, coccus, coccobacillus, and bacillus. Average percentages that the three different PAO populations accounted for among total bacteria from SBR1 (SBR2) were 42% (45%), 14% (13%), and 4% (2%). FCM showed that the ratios of PAOs to total bacteria in the two reactors were 61% and 59%, and the quantitative PCR (qPCR) results indicated that IIC was the dominant "Candidatus Accumulibacter" clade in both denitrifying phosphorus removal systems, reaching 50% of the total "Candidatus Accumulibacter" bacteria. The subdominant clade in the reactor with nitrite as the electron acceptor was IID, accounting for 31% of the total "Candidatus Accumulibacter" bacteria. The FCM and qPCR results suggested that clades IIC and IID were both coccus, clade IIF was coccobacillus, and clade IA was bacillus. FISH analysis also indicated that PAOs were major cocci in the systems. An equivalence test of FCM-based quantification confirmed the accuracy of FISH-staining-flow cytometry, which can meet the quantitative requirements for PAOs in complex activated sludge samples.IMPORTANCE As one group of the most important functional phosphorus removal organisms, "Candidatus Accumulibacter," affiliated with the Rhodocyclus group of the Betaproteobacteria, is a widely recognized and studied PAO in the field of biological wastewater treatment. The morphotypes and population structure of clade-level "Candidatus Accumulibacter" were studied through novel FISH-staining-flow cytometry, which involved denitrifying phosphorus removal (DPR) achieving carbon and energy savings and simultaneous removal of N and P, thus inferring the different denitrifying phosphorus removal abilities of these clades. Additionally, based on this method, in situ quantification for specific polyphosphate-accumulating organisms (PAOs) enables a more efficient process and more accurate result. The establishment of FISH-staining-flow cytometry makes cell sorting of clade-level noncultivated organisms available.

Dataset Information

High-Throughput Isolation of Giant Viruses in Liquid Medium Using Automated Flow Cytometry and Fluorescence Staining.

Publications

High-Throughput Isolation of Giant Viruses in Liquid Medium Using Automated Flow Cytometry and Fluorescence Staining.

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