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      Spatially coordinated dynamic gene transcription in living pituitary tissue

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          Abstract

          Transcription at individual genes in single cells is often pulsatile and stochastic. A key question emerges regarding how this behaviour contributes to tissue phenotype, but it has been a challenge to quantitatively analyse this in living cells over time, as opposed to studying snap-shots of gene expression state. We have used imaging of reporter gene expression to track transcription in living pituitary tissue. We integrated live-cell imaging data with statistical modelling for quantitative real-time estimation of the timing of switching between transcriptional states across a whole tissue. Multiple levels of transcription rate were identified, indicating that gene expression is not a simple binary ‘on-off’ process. Immature tissue displayed shorter durations of high-expressing states than the adult. In adult pituitary tissue, direct cell contacts involving gap junctions allowed local spatial coordination of prolactin gene expression. Our findings identify how heterogeneous transcriptional dynamics of single cells may contribute to overall tissue behaviour.

          DOI: http://dx.doi.org/10.7554/eLife.08494.001

          eLife digest

          Although humans have thousands of genes, only a fraction of these are expressed in any given cell. Each cell type expresses only the genes that are relevant to its particular job or that are necessary for general cell maintenance. Even these genes are not expressed all the time: most cells express genes in bursts, and the cells that make up a tissue produce these bursts at different times. This makes it easier for the tissue to respond to new conditions.

          The pituitary gland, found at the base of the brain, is often studied to investigate changes in gene expression. The pituitary gland is found in all animals that have a backbone, and it makes and releases many different hormones. For example, one type of pituitary cell expresses the gene that encodes a hormone called prolactin. This hormone has a range of roles, including stimulating milk production and regulating fertility in mammals. The coordinated production of prolactin by pituitary cells is important for reproduction, but it is not clear how (or whether) individual prolactin-producing cells in the gland communicate to coordinate bursting patterns of expression of the prolactin gene.

          Featherstone et al. marked the prolactin-encoding gene in the pituitary cells of rats with a gene that encodes a fluorescent protein; this enabled the gene’s activity to be observed in thin slices of living tissue using a microscope. Mathematical models were then used to analyse the recorded expression patterns.

          The results showed that in a single cell, the bursts of expression of the prolactin gene are randomly timed. This means that although the expression activity of an individual cell is unpredictable, the overall activity of a group of cells can be precisely determined. The model also showed that cells coordinate when they express the prolactin gene to a greater extent with their near neighbours than with cells that are further away in the tissue. Featherstone et al. found that this coordination depends on structures (called gap junctions) that connect the cells and allow signalling between them, and this tissue organisation is established during early development.

          The mechanisms underlying the timing of the bursts remain to be discovered. The timing for the prolactin gene seems to be dominated by a minimum delay that must occur before the next burst can be reactivated. Future challenges also include determining whether coordinated gene expression occurs in other tissues and whether this coordination is disrupted in disease.

          DOI: http://dx.doi.org/10.7554/eLife.08494.002

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          Most cited references42

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          Transcriptome-wide noise controls lineage choice in mammalian progenitor cells.

          Phenotypic cell-to-cell variability within clonal populations may be a manifestation of 'gene expression noise', or it may reflect stable phenotypic variants. Such 'non-genetic cell individuality' can arise from the slow fluctuations of protein levels in mammalian cells. These fluctuations produce persistent cell individuality, thereby rendering a clonal population heterogeneous. However, it remains unknown whether this heterogeneity may account for the stochasticity of cell fate decisions in stem cells. Here we show that in clonal populations of mouse haematopoietic progenitor cells, spontaneous 'outlier' cells with either extremely high or low expression levels of the stem cell marker Sca-1 (also known as Ly6a; ref. 9) reconstitute the parental distribution of Sca-1 but do so only after more than one week. This slow relaxation is described by a gaussian mixture model that incorporates noise-driven transitions between discrete subpopulations, suggesting hidden multi-stability within one cell type. Despite clonality, the Sca-1 outliers had distinct transcriptomes. Although their unique gene expression profiles eventually reverted to that of the median cells, revealing an attractor state, they lasted long enough to confer a greatly different proclivity for choosing either the erythroid or the myeloid lineage. Preference in lineage choice was associated with increased expression of lineage-specific transcription factors, such as a >200-fold increase in Gata1 (ref. 10) among the erythroid-prone cells, or a >15-fold increased PU.1 (Sfpi1) (ref. 11) expression among myeloid-prone cells. Thus, clonal heterogeneity of gene expression level is not due to independent noise in the expression of individual genes, but reflects metastable states of a slowly fluctuating transcriptome that is distinct in individual cells and may govern the reversible, stochastic priming of multipotent progenitor cells in cell fate decision.
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            The Second-Order Analysis of Stationary Point Processes

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              Intercellular coupling confers robustness against mutations in the SCN circadian clock network.

              Molecular mechanisms of the mammalian circadian clock have been studied primarily by genetic perturbation and behavioral analysis. Here, we used bioluminescence imaging to monitor Per2 gene expression in tissues and cells from clock mutant mice. We discovered that Per1 and Cry1 are required for sustained rhythms in peripheral tissues and cells, and in neurons dissociated from the suprachiasmatic nuclei (SCN). Per2 is also required for sustained rhythms, whereas Cry2 and Per3 deficiencies cause only period length defects. However, oscillator network interactions in the SCN can compensate for Per1 or Cry1 deficiency, preserving sustained rhythmicity in mutant SCN slices and behavior. Thus, behavior does not necessarily reflect cell-autonomous clock phenotypes. Our studies reveal previously unappreciated requirements for Per1, Per2, and Cry1 in sustaining cellular circadian rhythmicity and demonstrate that SCN intercellular coupling is essential not only to synchronize component cellular oscillators but also for robustness against genetic perturbations.
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                Author and article information

                Contributors
                Role: Reviewing editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                01 February 2016
                2016
                : 5
                : e08494
                Affiliations
                [1 ]deptCentre for Endocrinology and Diabetes , University of Manchester , Manchester, United Kingdom
                [2 ]deptDepartment of Statistics , University of Warwick , Coventry, United Kingdom
                [3 ]deptWarwick Systems Biology , University of Warwick , Coventry, United Kingdom
                [4 ]deptSystems Biology Centre , University of Manchester , Manchester, United Kingdom
                [5 ]deptDepartment of Physiology, Anatomy and Genetics , University of Oxford , Oxford, United Kingdom
                [6 ]deptMRC Centre for Reproductive Health, Queen's Medical Research Institute , University of Edinburgh , Edinburgh, United Kingdom
                [7 ]deptThe Molecular Physiology Group, Centre for Cardiovascular Science, Queen's Medical Research Institute , University of Edinburgh , Edinburgh, United Kingdom
                [8]Albert Einstein College of Medicine , United States
                [9]Albert Einstein College of Medicine , United States
                Author notes
                Article
                08494
                10.7554/eLife.08494
                4749562
                26828110
                acc9f015-4960-4128-bd10-042764cb64c6
                © 2016, Featherstone et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 06 May 2015
                : 13 December 2015
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: Programme grant: WT091688
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000266, Engineering and Physical Sciences Research Council;
                Award ID: PhD Studentship grant: ASTAA1112.KXH
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MR/K015885/1
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000268, Biotechnology and Biological Sciences Research Council;
                Award ID: BB/K003097/1
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Cell Biology
                Computational and Systems Biology
                Custom metadata
                2.5
                Quantitative statistical modelling reveals local coordination of stochastic gene transcription dynamics in pituitary tissue, which is relevant for integrated tissue responses to physiological stimuli.

                Life sciences
                transcription dynamics,spatial organisation,stochastic modelling,live-cell microscopy,pituitary,rat

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