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      A Simple FCM Method to Avoid Misinterpretation in Saccharomyces cerevisiae Cell Cycle Assessment between G0 and Sub-G1

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      1 , 2 , 3 , 1 , 2 , 3 , *
      PLoS ONE
      Public Library of Science

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          Abstract

          Extensively developed for medical and clinical applications, flow cytometry is now being used for diverse applications in food microbiology. Most uses of flow cytometry for yeast cells are derived from methods for mammalian cells, but yeast cells can present specificities that must be taken into account for rigorous analysis of the data output to avoid any misinterpretation. We report an analysis of Saccharomyces cerevisiae cell cycle progression that highlights possible errors. The cell cycle was analyzed using an intercalating fluorochrome to assess cell DNA content. In analyses of yeast cultures, the presence of a sub-G1 peak in the fluorescent signal is often interpreted as a loss of DNA due to its fragmentation associated with apoptosis. However, the cell wall and its stucture may interfere with the fluorescent signal recorded. These observations indicate that misinterpretation of yeast DNA profiles is possible in analyses based on some of the most common probes: cells in G0 appeared to have a lower DNA content and may have been mistaken as a sub-G1 population. However, careful selection of the fluorochrome for DNA quantification allowed a direct discrimination between G0 and G1 yeast cell cycle steps, without additional labeling. We present and discuss results obtained with five current fluorochromes. These observations led us to recommend to use SYTOX Green for cycle analysis of living cells and SYBR Green I for the identification of the apoptosis sub-G1 population identification or the DNA ploidy application.

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          flowCore: a Bioconductor package for high throughput flow cytometry

          Background Recent advances in automation technologies have enabled the use of flow cytometry for high throughput screening, generating large complex data sets often in clinical trials or drug discovery settings. However, data management and data analysis methods have not advanced sufficiently far from the initial small-scale studies to support modeling in the presence of multiple covariates. Results We developed a set of flexible open source computational tools in the R package flowCore to facilitate the analysis of these complex data. A key component of which is having suitable data structures that support the application of similar operations to a collection of samples or a clinical cohort. In addition, our software constitutes a shared and extensible research platform that enables collaboration between bioinformaticians, computer scientists, statisticians, biologists and clinicians. This platform will foster the development of novel analytic methods for flow cytometry. Conclusion The software has been applied in the analysis of various data sets and its data structures have proven to be highly efficient in capturing and organizing the analytic work flow. Finally, a number of additional Bioconductor packages successfully build on the infrastructure provided by flowCore, open new avenues for flow data analysis.
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            Isolation of quiescent and nonquiescent cells from yeast stationary-phase cultures

            Quiescence is the most common and, arguably, most poorly understood cell cycle state. This is in part because pure populations of quiescent cells are typically difficult to isolate. We report the isolation and characterization of quiescent and nonquiescent cells from stationary-phase (SP) yeast cultures by density-gradient centrifugation. Quiescent cells are dense, unbudded daughter cells formed after glucose exhaustion. They synchronously reenter the mitotic cell cycle, suggesting that they are in a G0 state. Nonquiescent cells are less dense, heterogeneous, and composed of replicatively older, asynchronous cells that rapidly lose the ability to reproduce. Microscopic and flow cytometric analysis revealed that nonquiescent cells accumulate more reactive oxygen species than quiescent cells, and over 21 d, about half exhibit signs of apoptosis and necrosis. The ability to isolate both quiescent and nonquiescent yeast cells from SP cultures provides a novel, tractable experimental system for studies of quiescence, chronological and replicative aging, apoptosis, and the cell cycle.
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              Improved flow cytometric analysis of the budding yeast cell cycle.

              The budding yeast, Saccharomyces cerevisiae has been a remarkably useful model system for the study of eukaryotic cell cycle regulation. Flow cytometric analysis of DNA content in budding yeast has become a standard tool for the analysis of cell cycle progression. However, popular protocols utilizing the DNA binding dye, propidium iodide, suffer from a number of drawbacks that confound accurate analysis by flow cytometry. Here we show the utility of the DNA binding dye, SYTOX Green, in the cell cycle analysis of yeast. Samples analyzed using SYTOX Green exhibited better coefficients of variation, improved linearity between DNA content and fluorescence, and decreased peak drift associated with changes in dye concentration, growth conditions or cell size.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                2 January 2014
                : 9
                : 1
                : e84645
                Affiliations
                [1 ]INRA, UMR1083, Montpellier, France
                [2 ]SupAgro Montpellier, UMR1083, Montpellier, France
                [3 ]Universit Montpellier 1, UMR1083, Montpellier, France
                Texas A&M University, United States of America
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: PD. Performed the experiments: PD. Analyzed the data: PD CT. Contributed reagents/materials/analysis tools: PD. Wrote the paper: PD CT.

                Article
                PONE-D-13-40052
                10.1371/journal.pone.0084645
                3879310
                24392149
                b7aeb515-875d-46bc-bf41-a62265baa3b8
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 30 September 2013
                : 25 November 2013
                Page count
                Pages: 8
                Funding
                The authors have no support or funding to report.
                Categories
                Research Article
                Biology
                Biochemistry
                Nucleic Acids
                DNA
                Microbiology
                Mycology
                Yeast
                Applied Microbiology
                Model Organisms
                Yeast and Fungal Models
                Saccharomyces Cerevisiae
                Molecular Cell Biology
                Cytometry
                Flow Cytometry

                Uncategorized
                Uncategorized

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