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      Single-cell RNA sequencing reveals intrinsic and extrinsic regulatory heterogeneity in yeast responding to stress

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          Abstract

          From bacteria to humans, individual cells within isogenic populations can show significant variation in stress tolerance, but the nature of this heterogeneity is not clear. To investigate this, we used single-cell RNA sequencing to quantify transcript heterogeneity in single Saccharomyces cerevisiae cells treated with and without salt stress to explore population variation and identify cellular covariates that influence the stress-responsive transcriptome. Leveraging the extensive knowledge of yeast transcriptional regulation, we uncovered significant regulatory variation in individual yeast cells, both before and after stress. We also discovered that a subset of cells appears to decouple expression of ribosomal protein genes from the environmental stress response in a manner partly correlated with the cell cycle but unrelated to the yeast ultradian metabolic cycle. Live-cell imaging of cells expressing pairs of fluorescent regulators, including the transcription factor Msn2 with Dot6, Sfp1, or MAP kinase Hog1, revealed both coordinated and decoupled nucleocytoplasmic shuttling. Together with transcriptomic analysis, our results suggest that cells maintain a cellular filter against decoupled bursts of transcription factor activation but mount a stress response upon coordinated regulation, even in a subset of unstressed cells.

          Author summary

          Genetically identical cells growing in the same environment can vary in their cellular state and behavior. Such heterogeneity may explain why some cells in an isogenic population can survive sudden severe environmental stress whereas other cells succumb. Cell-to-cell variation in gene expression has been linked to variable stress survival, but how and why transcript levels vary across the transcriptome in single cells is only beginning to emerge. Here, we used single-cell RNA sequencing (scRNA-seq) to measure cell-to-cell heterogeneity in the transcriptome of budding yeast ( Saccharomyces cerevisiae). We find surprising patterns of variation across known sets of transcription factor targets, indicating that cells vary in their transcriptome profile both before and after stress exposure. scRNA-seq analysis combined with live-cell imaging of transcription factor activation dynamics revealed some cells in which the stress response was coordinately activated and other cells in which the traditional response was decoupled, suggesting unrecognized regulatory nuances that expand our understanding of stress response and survival.

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          Most cited references92

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          Cluster analysis and display of genome-wide expression patterns.

          A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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            Bacterial persistence as a phenotypic switch.

            A fraction of a genetically homogeneous microbial population may survive exposure to stress such as antibiotic treatment. Unlike resistant mutants, cells regrown from such persistent bacteria remain sensitive to the antibiotic. We investigated the persistence of single cells of Escherichia coli with the use of microfluidic devices. Persistence was linked to preexisting heterogeneity in bacterial populations because phenotypic switching occurred between normally growing cells and persister cells having reduced growth rates. Quantitative measurements led to a simple mathematical description of the persistence switch. Inherent heterogeneity of bacterial populations may be important in adaptation to fluctuating environments and in the persistence of bacterial infections.
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              Genomic expression programs in the response of yeast cells to environmental changes.

              We explored genomic expression patterns in the yeast Saccharomyces cerevisiae responding to diverse environmental transitions. DNA microarrays were used to measure changes in transcript levels over time for almost every yeast gene, as cells responded to temperature shocks, hydrogen peroxide, the superoxide-generating drug menadione, the sulfhydryl-oxidizing agent diamide, the disulfide-reducing agent dithiothreitol, hyper- and hypo-osmotic shock, amino acid starvation, nitrogen source depletion, and progression into stationary phase. A large set of genes (approximately 900) showed a similar drastic response to almost all of these environmental changes. Additional features of the genomic responses were specialized for specific conditions. Promoter analysis and subsequent characterization of the responses of mutant strains implicated the transcription factors Yap1p, as well as Msn2p and Msn4p, in mediating specific features of the transcriptional response, while the identification of novel sequence elements provided clues to novel regulators. Physiological themes in the genomic responses to specific environmental stresses provided insights into the effects of those stresses on the cell.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: SupervisionRole: Writing – original draft
                Role: Methodology
                Role: InvestigationRole: MethodologyRole: Validation
                Role: Formal analysisRole: InvestigationRole: ValidationRole: Writing – review & editing
                Role: MethodologyRole: Visualization
                Role: Formal analysisRole: Investigation
                Role: Methodology
                Role: Methodology
                Role: Methodology
                Role: Supervision
                Role: Supervision
                Role: Supervision
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                14 December 2017
                December 2017
                14 December 2017
                : 15
                : 12
                : e2004050
                Affiliations
                [1 ] Laboratory of Genetics, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
                [2 ] Great Lakes Bioenergy Research Center, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
                [3 ] Department of Bioengineering, Stanford University, Stanford, California, United States of America
                [4 ] Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
                [5 ] Department of Energy Joint Genome Institute, Walnut Creek, California, United States of America
                [6 ] Chan Zuckerberg Biohub, San Francisco, California, United States of America
                [7 ] Department of Biomedical Engineering, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
                The Hebrew University of Jerusalem, Israel
                Author notes

                I have read the journal's policy and the authors of this manuscript have the following competing interests: SRQ is a co-founder of Fluidigm, whose technology was used as part of this project.

                [¤]

                Current address: Department of Medicine, University of California San Diego, La Jolla, California, United States of America

                ‡ These authors share first authorship on this work.

                Author information
                http://orcid.org/0000-0002-8182-257X
                Article
                pbio.2004050
                10.1371/journal.pbio.2004050
                5746276
                29240790
                68fd79dd-29a8-45dd-9c89-54498ef919cc

                This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                : 24 August 2017
                : 17 November 2017
                Page count
                Figures: 7, Tables: 0, Pages: 28
                Funding
                Burroughs Wellcome (grant number 1011875.01). Career Award at the Scientific Interface. Department of Energy BER (grant number DE-FC02-07ER64494). NIH (grant number R01GM083989). Department of Energy (grant number DE-AC02-05CH11231). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Cell Biology
                Cell Processes
                Cellular Stress Responses
                Biology and life sciences
                Biochemistry
                Nucleic acids
                RNA
                Messenger RNA
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Transcriptome Analysis
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Transcriptome Analysis
                Biology and life sciences
                Biochemistry
                Proteins
                DNA-binding proteins
                Transcription Factors
                Biology and Life Sciences
                Genetics
                Gene Expression
                Gene Regulation
                Transcription Factors
                Biology and Life Sciences
                Biochemistry
                Proteins
                Regulatory Proteins
                Transcription Factors
                Biology and Life Sciences
                Organisms
                Eukaryota
                Fungi
                Yeast
                Biology and Life Sciences
                Genetics
                Gene Types
                Regulator Genes
                Research and Analysis Methods
                Experimental Organism Systems
                Model Organisms
                Saccharomyces Cerevisiae
                Research and Analysis Methods
                Model Organisms
                Saccharomyces Cerevisiae
                Biology and Life Sciences
                Organisms
                Eukaryota
                Fungi
                Yeast
                Saccharomyces
                Saccharomyces Cerevisiae
                Research and Analysis Methods
                Experimental Organism Systems
                Yeast and Fungal Models
                Saccharomyces Cerevisiae
                Biology and Life Sciences
                Genetics
                Gene Expression
                Gene Regulation
                Transcriptional Control
                Custom metadata
                vor-update-to-uncorrected-proof
                2017-12-28
                All sequencing data are available in the NIH GEO databse ( https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE102475.

                Life sciences
                Life sciences

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