5
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Multidimensional analysis of Gammaherpesvirus RNA expression reveals unexpected heterogeneity of gene expression

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Virus-host interactions are frequently studied in bulk cell populations, obscuring cell-to-cell variation. Here we investigate endogenous herpesvirus gene expression at the single-cell level, combining a sensitive and robust fluorescent in situ hybridization platform with multiparameter flow cytometry, to study the expression of gammaherpesvirus non-coding RNAs (ncRNAs) during lytic replication, latent infection and reactivation in vitro. This method allowed robust detection of viral ncRNAs of murine gammaherpesvirus 68 (γHV68), Kaposi’s sarcoma associated herpesvirus and Epstein-Barr virus, revealing variable expression at the single-cell level. By quantifying the inter-relationship of viral ncRNA, viral mRNA, viral protein and host mRNA regulation during γHV68 infection, we find heterogeneous and asynchronous gene expression during latency and reactivation, with reactivation from latency identified by a distinct gene expression profile within rare cells. Further, during lytic replication with γHV68, we find many cells have limited viral gene expression, with only a fraction of cells showing robust gene expression, dynamic RNA localization, and progressive infection. Lytic viral gene expression was enhanced in primary fibroblasts and by conditions associated with enhanced viral replication, with multiple subpopulations of cells present in even highly permissive infection conditions. These findings, powered by single-cell analysis integrated with automated clustering algorithms, suggest inefficient or abortive γHV infection in many cells, and identify substantial heterogeneity in viral gene expression at the single-cell level.

          Author summary

          The gammaherpesviruses are a group of DNA tumor viruses that establish lifelong infection. How these viruses infect and manipulate cells has frequently been studied in bulk populations of cells. While these studies have been incredibly insightful, there is limited understanding of how virus infection proceeds within a single cell. Here we present a new approach to quantify gammaherpesvirus gene expression at the single-cell level. This method allows us to detect cell-to-cell variation in the expression of virus non-coding RNAs, an important and understudied class of RNAs which do not encode for proteins. By examining multiple features of virus gene expression, this method further reveals significant variation in infection between cells across multiple stages of infection, even in conditions generally thought to be highly uniform. These studies emphasize that gammaherpesvirus infection can be surprisingly heterogeneous when viewed at the level of the individual cell. Because this approach can be broadly applied across diverse viruses, this study affords new opportunities to understand the complexity of virus infection within single cells.

          Related collections

          Most cited references32

          • Record: found
          • Abstract: found
          • Article: not found

          Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells

          Recent molecular studies have revealed that, even when derived from a seemingly homogenous population, individual cells can exhibit substantial differences in gene expression, protein levels, and phenotypic output 1–5 , with important functional consequences 4,5 . Existing studies of cellular heterogeneity, however, have typically measured only a few pre-selected RNAs 1,2 or proteins 5,6 simultaneously because genomic profiling methods 3 could not be applied to single cells until very recently 7–10 . Here, we use single-cell RNA-Seq to investigate heterogeneity in the response of bone marrow derived dendritic cells (BMDCs) to lipopolysaccharide (LPS). We find extensive, and previously unobserved, bimodal variation in mRNA abundance and splicing patterns, which we validate by RNA-fluorescence in situ hybridization (RNA-FISH) for select transcripts. In particular, hundreds of key immune genes are bimodally expressed across cells, surprisingly even for genes that are very highly expressed at the population average. Moreover, splicing patterns demonstrate previously unobserved levels of heterogeneity between cells. Some of the observed bimodality can be attributed to closely related, yet distinct, known maturity states of BMDCs; other portions reflect differences in the usage of key regulatory circuits. For example, we identify a module of 137 highly variable, yet co-regulated, antiviral response genes. Using cells from knockout mice, we show that variability in this module may be propagated through an interferon feedback circuit involving the transcriptional regulators Stat2 and Irf7. Our study demonstrates the power and promise of single-cell genomics in uncovering functional diversity between cells and in deciphering cell states and circuits.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Automated Mapping of Phenotype Space with Single-Cell Data

            Accurate and rapid identification of cell populations is key to discovering novelty in multidimensional single cell experiments. We present a population finding algorithm X-shift that can process large datasets using fast KNN estimation of cell event density and automatically arranges populations by a marker-based classification system. X-shift analysis of mouse bone marrow data resolved the majority of known and several previously undescribed cell populations. Interestingly, previously known cell populations, as well as intermediate cell populations in early hematopoietic development, were described via novel marker combinations that were defined via routes to their locations in expressed marker space. X-shift provides a rapid, reliable approach to managed cell subset analysis that maximizes automation that not only best mimics human intuition, but as we show provides access to novel insights that “prior knowledge” might prevent the researcher from visualizing.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Control of Transcript Variability in Single Mammalian Cells.

              A central question in biology is whether variability between genetically identical cells exposed to the same culture conditions is largely stochastic or deterministic. Using image-based transcriptomics in millions of single human cells, we find that while variability of cytoplasmic transcript abundance is large, it is for most genes minimally stochastic and can be predicted with multivariate models of the phenotypic state and population context of single cells. Computational multiplexing of these predictive signatures across hundreds of genes revealed a complex regulatory system that controls the observed variability of transcript abundance between individual cells. Mathematical modeling and experimental validation show that nuclear retention and transport of transcripts between the nucleus and the cytoplasm is central to buffering stochastic transcriptional fluctuations in mammalian gene expression. Our work indicates that cellular compartmentalization confines transcriptional noise to the nucleus, thereby preventing it from interfering with the control of single-cell transcript abundance in the cytoplasm.
                Bookmark

                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: ValidationRole: VisualizationRole: Writing – review & editing
                Role: Formal analysisRole: SoftwareRole: Visualization
                Role: Formal analysisRole: Visualization
                Role: Investigation
                Role: InvestigationRole: Methodology
                Role: MethodologyRole: Resources
                Role: Formal analysisRole: MethodologyRole: ResourcesRole: Visualization
                Role: Formal analysisRole: MethodologyRole: ResourcesRole: Visualization
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Pathog
                PLoS Pathog
                plos
                plospath
                PLoS Pathogens
                Public Library of Science (San Francisco, CA USA )
                1553-7366
                1553-7374
                5 June 2019
                June 2019
                : 15
                : 6
                : e1007849
                Affiliations
                [1 ] Department of Immunology and Microbiology, University of Colorado Denver | Anschutz Medical Campus, Aurora, CO, United States of America
                [2 ] Department of Anesthesiology, University of Colorado Denver | Anschutz Medical Campus, Aurora, CO, United States of America
                [3 ] MilliporeSigma, a business of Merck KGaA, Darmstadt, Germany (Seattle, WA, United States of America)
                [4 ] Luminex Corporation, Austin, TX, United States of America
                University of Wisconsin, UNITED STATES
                Author notes

                The following authors, L.M.O., A.K.K., R.E.K., A.N.K., C.B.C, R.R., L.v.D. and E.T.C. have declared that no competing interests exist. I have read the journal's policy and the following authors of this manuscript have the following competing interests: T.C. and B.A. are employees of EMD Millipore and Luminex respectively and have a potential financial conflict of interest.

                Author information
                http://orcid.org/0000-0002-7125-7036
                http://orcid.org/0000-0002-9831-386X
                http://orcid.org/0000-0002-1181-7919
                http://orcid.org/0000-0001-6447-9311
                http://orcid.org/0000-0003-2662-5554
                http://orcid.org/0000-0002-7972-9544
                Article
                PPATHOGENS-D-18-02308
                10.1371/journal.ppat.1007849
                6576797
                31166996
                a0d20e80-8def-4a45-8e91-fec760c0bcce
                © 2019 Oko et al

                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
                : 5 December 2018
                : 17 May 2019
                Page count
                Figures: 9, Tables: 0, Pages: 29
                Funding
                This research was funded by National Institutes of Health grants R01CA103632 and R01CA168558 to L.F.V.D., R21AI134084 to E.T.C. and L.F.V.D., and by an American Heart Association National Scientist Development grant (#13SDG14510023), a Colorado Clinical and Translational Sciences Initiative Novel methods development grant, and funding from the University of Colorado Dept. of Anesthesiology to E.T.C.. The Colorado CTSI is supported by NIH/NCATS Colorado CTSA Grant Number UL1 TR002535. The Flow Cytometry Shared Resource of the University of Colorado Cancer Center receives direct funding support from the National Cancer Institute through Cancer Center Support Grant P30CA046934. Contents are the authors’ sole responsibility and do not necessarily represent official NIH views. 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
                Genetics
                Gene Expression
                Viral Gene Expression
                Biology and Life Sciences
                Genetics
                Microbial Genetics
                Viral Genetics
                Viral Gene Expression
                Biology and Life Sciences
                Microbiology
                Virology
                Viral Genetics
                Viral Gene Expression
                Biology and Life Sciences
                Genetics
                Gene Expression
                Biology and Life Sciences
                Biochemistry
                Proteins
                Contractile Proteins
                Actins
                Biology and Life Sciences
                Biochemistry
                Proteins
                Cytoskeletal Proteins
                Actins
                Research and Analysis Methods
                Spectrum Analysis Techniques
                Spectrophotometry
                Cytophotometry
                Flow Cytometry
                Biology and Life Sciences
                Microbiology
                Virology
                Viral Replication
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Immune Cells
                Antibody-Producing Cells
                B Cells
                Biology and Life Sciences
                Immunology
                Immune Cells
                Antibody-Producing Cells
                B Cells
                Medicine and Health Sciences
                Immunology
                Immune Cells
                Antibody-Producing Cells
                B Cells
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Blood Cells
                White Blood Cells
                B Cells
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Immune Cells
                White Blood Cells
                B Cells
                Biology and Life Sciences
                Immunology
                Immune Cells
                White Blood Cells
                B Cells
                Medicine and Health Sciences
                Immunology
                Immune Cells
                White Blood Cells
                B Cells
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Connective Tissue Cells
                Fibroblasts
                Biology and Life Sciences
                Anatomy
                Biological Tissue
                Connective Tissue
                Connective Tissue Cells
                Fibroblasts
                Medicine and Health Sciences
                Anatomy
                Biological Tissue
                Connective Tissue
                Connective Tissue Cells
                Fibroblasts
                Biology and life sciences
                Organisms
                Viruses
                RNA viruses
                Custom metadata
                vor-update-to-uncorrected-proof
                2019-06-17
                All relevant data are within the manuscript and its Supporting Information files.

                Infectious disease & Microbiology
                Infectious disease & Microbiology

                Comments

                Comment on this article