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      Real-time single-cell characterization of the eukaryotic transcription cycle reveals correlations between RNA initiation, elongation, and cleavage

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          Abstract

          The eukaryotic transcription cycle consists of three main steps: initiation, elongation, and cleavage of the nascent RNA transcript. Although each of these steps can be regulated as well as coupled with each other, their in vivo dissection has remained challenging because available experimental readouts lack sufficient spatiotemporal resolution to separate the contributions from each of these steps. Here, we describe a novel application of Bayesian inference techniques to simultaneously infer the effective parameters of the transcription cycle in real time and at the single-cell level using a two-color MS2/PP7 reporter gene and the developing fruit fly embryo as a case study. Our method enables detailed investigations into cell-to-cell variability in transcription-cycle parameters as well as single-cell correlations between these parameters. These measurements, combined with theoretical modeling, suggest a substantial variability in the elongation rate of individual RNA polymerase molecules. We further illustrate the power of this technique by uncovering a novel mechanistic connection between RNA polymerase density and nascent RNA cleavage efficiency. Thus, our approach makes it possible to shed light on the regulatory mechanisms in play during each step of the transcription cycle in individual, living cells at high spatiotemporal resolution.

          Author summary

          Live cell imaging using fluorescence microscopy provides an exciting way to visualize the transcription cycle in living organisms with great amounts of precision. However, the output of these technologies is often complex and can be hard to interpret. We have developed a computational framework for analyzing the transcription cycle that quantifies rates of RNA initiation, elongation, and cleavage, given input datasets from live cell imaging. Using the developing fruit fly embryo as a case study, we demonstrate that our methodology can quantitatively describe the whole transcription cycle at single-cell resolution. These results allow us to investigate a plethora of avenues, from couplings between different aspects of the transcription cycle at the single-cell level to comparisons with theoretical predictions of distributions of elongation rates across cells. We envision our methodology to provide a unified computational framework for the analysis of transcriptional data obtained from live cell imaging.

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          Most cited references148

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          Transcription factors: from enhancer binding to developmental control.

          Developmental progression is driven by specific spatiotemporal domains of gene expression, which give rise to stereotypically patterned embryos even in the presence of environmental and genetic variation. Views of how transcription factors regulate gene expression are changing owing to recent genome-wide studies of transcription factor binding and RNA expression. Such studies reveal patterns that, at first glance, seem to contrast with the robustness of the developmental processes they encode. Here, we review our current knowledge of transcription factor function from genomic and genetic studies and discuss how different strategies, including extensive cooperative regulation (both direct and indirect), progressive priming of regulatory elements, and the integration of activities from multiple enhancers, confer specificity and robustness to transcriptional regulation during development.
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            Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification.

            State-of-the-art light and electron microscopes are capable of acquiring large image datasets, but quantitatively evaluating the data often involves manually annotating structures of interest. This process is time-consuming and often a major bottleneck in the evaluation pipeline. To overcome this problem, we have introduced the Trainable Weka Segmentation (TWS), a machine learning tool that leverages a limited number of manual annotations in order to train a classifier and segment the remaining data automatically. In addition, TWS can provide unsupervised segmentation learning schemes (clustering) and can be customized to employ user-designed image features or classifiers.
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              Stochastic gene expression in a single cell.

              Clonal populations of cells exhibit substantial phenotypic variation. Such heterogeneity can be essential for many biological processes and is conjectured to arise from stochasticity, or noise, in gene expression. We constructed strains of Escherichia coli that enable detection of noise and discrimination between the two mechanisms by which it is generated. Both stochasticity inherent in the biochemical process of gene expression (intrinsic noise) and fluctuations in other cellular components (extrinsic noise) contribute substantially to overall variation. Transcription rate, regulatory dynamics, and genetic factors control the amplitude of noise. These results establish a quantitative foundation for modeling noise in genetic networks and reveal how low intracellular copy numbers of molecules can fundamentally limit the precision of gene regulation.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: Methodology
                Role: Resources
                Role: Resources
                Role: Resources
                Role: Resources
                Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                May 2021
                18 May 2021
                : 17
                : 5
                : e1008999
                Affiliations
                [1 ] Department of Physics, University of California at Berkeley, Berkeley, California, United States of America
                [2 ] Institute of Pharmacy and Molecular Biotechnology, University of Heidelberg, Heidelberg, Germany
                [3 ] Biophysics Graduate Group, University of California at Berkeley, Berkeley, California, United States of America
                [4 ] Department of Plant and Microbial Biology, University of California at Berkeley, Berkeley, California, United States of America
                [5 ] Department of Molecular and Cell Biology, University of California at Berkeley, Berkeley, California, United States of America
                [6 ] Institute for Quantitative Biosciences-QB3, University of California at Berkeley, Berkeley, California, United States of America
                University of Pittsburgh, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0003-0204-0105
                https://orcid.org/0000-0002-0918-9992
                https://orcid.org/0000-0003-0139-3865
                https://orcid.org/0000-0003-2451-5036
                https://orcid.org/0000-0003-2867-3840
                https://orcid.org/0000-0002-5212-3649
                Article
                PCOMPBIOL-D-20-01950
                10.1371/journal.pcbi.1008999
                8162642
                34003867
                1bd92dee-aa9b-491d-98c6-123c7c9996d1
                © 2021 Liu 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
                : 28 October 2020
                : 23 April 2021
                Page count
                Figures: 5, Tables: 0, Pages: 26
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000861, Burroughs Wellcome Fund;
                Funded by: funder-id http://dx.doi.org/10.13039/100000879, Alfred P. Sloan Foundation;
                Funded by: Human Frontiers Science Program
                Funded by: funder-id http://dx.doi.org/10.13039/100014185, Searle Scholars Program;
                Funded by: funder-id http://dx.doi.org/10.13039/100010319, Shurl and Kay Curci Foundation;
                Funded by: funder-id http://dx.doi.org/10.13039/100010336, Hellman Foundation;
                Funded by: funder-id http://dx.doi.org/10.13039/100000052, NIH Office of the Director;
                Award ID: DP2 OD024541-01
                Funded by: funder-id http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: 1652236
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: DGE 1752814
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: DGE 1752814
                Award Recipient :
                Funded by: UC Berkeley Chancellor’s Fellowship
                Award Recipient :
                Funded by: Korea Foundation for Advanced Studies
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100014037, National Defense Science and Engineering Graduate;
                Award ID: 430
                Award Recipient :
                This work was supported by the Burroughs Wellcome Fund Career Award at the Scientific Interface ( https://www.bwfund.org/grant-programs/interfaces-science/career-awards-scientific-interface), the Sloan Research Foundation ( https://sloan.org/), the Human Frontiers Science Program ( https://www.hfsp.org/), the Searle Scholars Program ( https://www.searlescholars.net/), the Shurl and Kay Curci Foundation ( http://thecurcifoundation.org/), the Hellman Foundation ( https://www.hellmanfoundation.org/), the NIH Director’s New Innovator Award (DP2 OD024541-01, https://commonfund.nih.gov/newinnovator), and an NSF CAREER Award (1652236, https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=503214) (HGG), an NSF GRFP (DGE 1752814, https://www.nsfgrfp.org/) (EE, MT), a UC Berkeley Chancellor’s Fellowship (EE, https://grad.berkeley.edu/admissions/apply/fellowships-entering/), a KFAS scholarship (YJK, https://www.kfas.or.kr/ScholarShip/ScholarShip0201.aspx?pCulture=en), and an 430 DoD NDSEG graduate fellowship (JL, https://ndseg.sysplus.com/). 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
                Gene Regulation
                Transcriptional Control
                Biology and Life Sciences
                Developmental Biology
                Embryology
                Embryos
                Biology and Life Sciences
                Cell Biology
                Signal Transduction
                Mechanisms of Signal Transduction
                Signal Initiation
                Biology and life sciences
                Biochemistry
                Nucleic acids
                RNA
                Messenger RNA
                Research and analysis methods
                Mathematical and statistical techniques
                Statistical methods
                Monte Carlo method
                Physical sciences
                Mathematics
                Statistics
                Statistical methods
                Monte Carlo method
                Biology and life sciences
                Genetics
                Gene expression
                DNA transcription
                Biology and Life Sciences
                Genetics
                Gene Types
                Reporter Genes
                Research and Analysis Methods
                Animal Studies
                Experimental Organism Systems
                Model Organisms
                Drosophila Melanogaster
                Research and Analysis Methods
                Model Organisms
                Drosophila Melanogaster
                Research and Analysis Methods
                Animal Studies
                Experimental Organism Systems
                Animal Models
                Drosophila Melanogaster
                Biology and Life Sciences
                Zoology
                Entomology
                Insects
                Drosophila
                Drosophila Melanogaster
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Invertebrates
                Arthropoda
                Insects
                Drosophila
                Drosophila Melanogaster
                Biology and Life Sciences
                Zoology
                Animals
                Invertebrates
                Arthropoda
                Insects
                Drosophila
                Drosophila Melanogaster
                Custom metadata
                vor-update-to-uncorrected-proof
                2021-05-28
                All software is available on GitHub at https://github.com/GarciaLab/TranscriptionCycleInference Data is attached as a supplementary zip file S1 Data.

                Quantitative & Systems biology
                Quantitative & Systems biology

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