11
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Cohesin and CTCF control the dynamics of chromosome folding

      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

          In mammals, interactions between sequences within topologically associating domains enable control of gene expression across large genomic distances. Yet it is unknown how frequently such contacts occur, how long they last and how they depend on the dynamics of chromosome folding and loop extrusion activity of cohesin. By imaging chromosomal locations at high spatial and temporal resolution in living cells, we show that interactions within topologically associating domains are transient and occur frequently during the course of a cell cycle. Interactions become more frequent and longer in the presence of convergent CTCF sites, resulting in suppression of variability in chromosome folding across time. Supported by physical models of chromosome dynamics, our data suggest that CTCF-anchored loops last around 10 min. Our results show that long-range transcriptional regulation might rely on transient physical proximity, and that cohesin and CTCF stabilize highly dynamic chromosome structures, facilitating selected subsets of chromosomal interactions.

          Abstract

          Live-cell imaging shows that interactions within topologically associating domains are transient and frequent throughout the cell cycle. Convergent CTCF sites regulate the frequency and duration of interactions, which last a few minutes on average.

          Related collections

          Most cited references57

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

          Topological Domains in Mammalian Genomes Identified by Analysis of Chromatin Interactions

          The spatial organization of the genome is intimately linked to its biological function, yet our understanding of higher order genomic structure is coarse, fragmented and incomplete. In the nucleus of eukaryotic cells, interphase chromosomes occupy distinct chromosome territories (CT), and numerous models have been proposed for how chromosomes fold within CTs 1 . These models, however, provide only few mechanistic details about the relationship between higher order chromatin structure and genome function. Recent advances in genomic technologies have led to rapid revolutions in the study of 3D genome organization. In particular, Hi-C has been introduced as a method for identifying higher order chromatin interactions genome wide 2 . In the present study, we investigated the 3D organization of the human and mouse genomes in embryonic stem cells and terminally differentiated cell types at unprecedented resolution. We identify large, megabase-sized local chromatin interaction domains, which we term “topological domains”, as a pervasive structural feature of the genome organization. These domains correlate with regions of the genome that constrain the spread of heterochromatin. The domains are stable across different cell types and highly conserved across species, suggesting that topological domains are an inherent property of mammalian genomes. Lastly, we find that the boundaries of topological domains are enriched for the insulator binding protein CTCF, housekeeping genes, tRNAs, and SINE retrotransposons, suggesting that these factors may play a role in establishing the topological domain structure of the genome.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Cellpose: a generalist algorithm for cellular segmentation

            Many biological applications require the segmentation of cell bodies, membranes and nuclei from microscopy images. Deep learning has enabled great progress on this problem, but current methods are specialized for images that have large training datasets. Here we introduce a generalist, deep learning-based segmentation method called Cellpose, which can precisely segment cells from a wide range of image types and does not require model retraining or parameter adjustments. Cellpose was trained on a new dataset of highly varied images of cells, containing over 70,000 segmented objects. We also demonstrate a three-dimensional (3D) extension of Cellpose that reuses the two-dimensional (2D) model and does not require 3D-labeled data. To support community contributions to the training data, we developed software for manual labeling and for curation of the automated results. Periodically retraining the model on the community-contributed data will ensure that Cellpose improves constantly.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found
              Is Open Access

              LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales

                Bookmark

                Author and article information

                Contributors
                luca.giorgetti@fmi.ch
                Journal
                Nat Genet
                Nat Genet
                Nature Genetics
                Nature Publishing Group US (New York )
                1061-4036
                1546-1718
                5 December 2022
                5 December 2022
                2022
                : 54
                : 12
                : 1907-1918
                Affiliations
                [1 ]GRID grid.482245.d, ISNI 0000 0001 2110 3787, Friedrich Miescher Institute for Biomedical Research, ; Basel, Switzerland
                [2 ]GRID grid.6612.3, ISNI 0000 0004 1937 0642, University of Basel, ; Basel, Switzerland
                [3 ]GRID grid.15140.31, ISNI 0000 0001 2175 9188, École Normale Supérieure de Lyon, ; Lyon, France
                [4 ]GRID grid.7849.2, ISNI 0000 0001 2150 7757, Université Claude Bernard Lyon I, ; Lyon, France
                [5 ]GRID grid.4708.b, ISNI 0000 0004 1757 2822, Università degli Studi di Milano, ; Milan, Italy
                [6 ]GRID grid.6045.7, ISNI 0000 0004 1757 5281, INFN, ; Milan, Italy
                [7 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Cardiovascular Research Institute, , University of California San Francisco, ; San Francisco, CA USA
                [8 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Department of Biochemistry and Biophysics, , University of California San Francisco, ; San Francisco, CA USA
                Author information
                http://orcid.org/0000-0003-1741-7104
                http://orcid.org/0000-0002-3625-0939
                http://orcid.org/0000-0001-7702-6207
                http://orcid.org/0000-0003-1041-8913
                http://orcid.org/0000-0001-7234-1435
                http://orcid.org/0000-0002-8347-4396
                http://orcid.org/0000-0001-9868-1809
                http://orcid.org/0000-0002-9664-9087
                Article
                1232
                10.1038/s41588-022-01232-7
                9729113
                36471076
                9257c345-26b3-4f5c-b47f-f2d7183a616e
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 4 April 2022
                : 19 October 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/100010663, EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council);
                Award ID: 759366
                Award ID: 813282
                Award ID: 813327
                Award ID: 759366
                Award ID: 748091
                Award ID: 759366
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001711, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation);
                Award ID: 310030_192642
                Award ID: 310030_192642
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s), under exclusive licence to Springer Nature America, Inc. 2022

                Genetics
                molecular biology,biophysics
                Genetics
                molecular biology, biophysics

                Comments

                Comment on this article