Skip to main content
Book cover

Enhancers and Promoters pp 307–320Cite as

Transcriptional Activation of Heterochromatin by Recruitment of dCas9 Activators

Part of the Methods in Molecular Biology book series (MIMB,volume 2351)

Abstract

The transition from silenced heterochromatin to a biologically active state and vice versa is a fundamental part of the implementation of cell type-specific gene expression programs. To reveal structure–function relationships and dissect the underlying mechanisms, experiments that ectopically induce transcription are highly informative. In particular, the approach to perturb chromatin states by recruiting fusions of the catalytically inactive dCas9 protein in a sequence-specific manner to a locus of interest has been used in numerous applications. Here, we describe how this approach can be applied to activate pericentric heterochromatin (PCH) in mouse cells as a prototypic silenced state by providing protocols for the following workflow: (a) Recruitment of dCas9 fusion constructs with the strong transcriptional activator VPR to PCH. (b) Analysis of the resulting changes in chromatin compaction, epigenetic marks, and active transcription by fluorescence microscopy-based readouts. (c) Automated analysis of the resulting images with a set of scripts in the R programming language. Furthermore, we discuss how parameters for chromatin decondensation and active transcription are extracted from these experiments and can be combined with other readouts to gain insights into PCH activation.

Keywords

  • Pericentric heterochromatin
  • Decondensation
  • Chromocenter
  • CRISPR
  • dCas9
  • Fluorescence microscopy
  • Mouse embryonic fibroblasts
  • Image quantification

This is a preview of subscription content, access via your institution.

Protocol
USD   49.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-1-0716-1597-3_17
  • Chapter length: 14 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   119.00
Price excludes VAT (USA)
  • ISBN: 978-1-0716-1597-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   159.99
Price excludes VAT (USA)
Hardcover Book
USD   219.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1
Fig. 2

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Probst AV, Almouzni G (2008) Pericentric heterochromatin: dynamic organization during early development in mammals. Differentiation 76(1):15–23. https://doi.org/10.1111/j.1432-0436.2007.00220.x

    CrossRef  CAS  PubMed  Google Scholar 

  2. Fodor BD, Shukeir N, Reuter G, Jenuwein T (2010) Mammalian Su(var) genes in chromatin control. Annu Rev Cell Dev Biol 26:471–501. https://doi.org/10.1146/annurev.cellbio.042308.113225

    CrossRef  CAS  PubMed  Google Scholar 

  3. Probst AV, Okamoto I, Casanova M, El Marjou F, Le Baccon P, Almouzni G (2010) A strand-specific burst in transcription of pericentric satellites is required for chromocenter formation and early mouse development. Dev Cell 19(4):625–638. https://doi.org/10.1016/j.devcel.2010.09.002

    CrossRef  CAS  PubMed  Google Scholar 

  4. Muller-Ott K, Erdel F, Matveeva A, Mallm JP, Rademacher A, Hahn M, Bauer C, Zhang Q, Kaltofen S, Schotta G, Hofer T, Rippe K (2014) Specificity, propagation, and memory of pericentric heterochromatin. Mol Syst Biol 10(8):746. https://doi.org/10.15252/msb.20145377

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  5. Tosolini M, Brochard V, Adenot P, Chebrout M, Grillo G, Navia V, Beaujean N, Francastel C, Bonnet-Garnier A, Jouneau A (2018) Contrasting epigenetic states of heterochromatin in the different types of mouse pluripotent stem cells. Sci Rep 8(1):5776. https://doi.org/10.1038/s41598-018-23822-4

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  6. Chavez A, Scheiman J, Vora S, Pruitt BW, Tuttle M, PRI E, Lin S, Kiani S, Guzman CD, Wiegand DJ, Ter-Ovanesyan D, Braff JL, Davidsohn N, Housden BE, Perrimon N, Weiss R, Aach J, Collins JJ, Church GM (2015) Highly efficient Cas9-mediated transcriptional programming. Nat Methods 12(4):326–328. https://doi.org/10.1038/nmeth.3312

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  7. Erdel F, Rademacher A, Vlijm R, Tunnermann J, Frank L, Weinmann R, Schweigert E, Yserentant K, Hummert J, Bauer C, Schumacher S, Al Alwash A, Normand C, Herten DP, Engelhardt J, Rippe K (2020) Mouse heterochromatin adopts digital compaction states without showing hallmarks of HP1-driven liquid-liquid phase separation. Mol Cell 78(2):236–249.e237. https://doi.org/10.1016/j.molcel.2020.02.005

    CrossRef  CAS  Google Scholar 

  8. Le Berre G, Hossard V, Riou JF, Guieysse-Peugeot AL (2019) Repression of TERRA expression by subtelomeric DNA methylation is dependent on NRF1 binding. Int J Mol Sci 20(11):2791. https://doi.org/10.3390/ijms20112791

    CrossRef  CAS  PubMed Central  Google Scholar 

  9. Brane AC, Tollefsbol TO (2019) Targeting telomeres and telomerase: studies in aging and disease utilizing CRISPR/Cas9 technology. Cell 8(2):186. https://doi.org/10.3390/cells8020186

    CrossRef  CAS  Google Scholar 

  10. Qin P, Parlak M, Kuscu C, Bandaria J, Mir M, Szlachta K, Singh R, Darzacq X, Yildiz A, Adli M (2017) Live cell imaging of low- and non-repetitive chromosome loci using CRISPR-Cas9. Nat Commun 8:14725. https://doi.org/10.1038/ncomms14725

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  11. Chen B, Gilbert LA, Cimini BA, Schnitzbauer J, Zhang W, Li GW, Park J, Blackburn EH, Weissman JS, Qi LS, Huang B (2013) Dynamic imaging of genomic loci in living human cells by an optimized CRISPR/Cas system. Cell 155(7):1479–1491. https://doi.org/10.1016/j.cell.2013.12.001

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  12. Janicki SM, Tsukamoto T, Salghetti SE, Tansey WP, Sachidanandam R, Prasanth KV, Ried T, Shav-Tal Y, Bertrand E, Singer RH, Spector DL (2004) From silencing to gene expression: real-time analysis in single cells. Cell 116(5):683–698. https://doi.org/10.1016/s0092-8674(04)00171-0

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  13. Rademacher A, Erdel F, Trojanowski J, Schumacher S, Rippe K (2017) Real-time observation of light-controlled transcription in living cells. J Cell Sci 130(24):4213–4224. https://doi.org/10.1242/jcs.205534

    CrossRef  CAS  PubMed  Google Scholar 

  14. R Core Team (2013) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria

    Google Scholar 

  15. RStudio Team (2015) RStudio: Integrated Development for R. RStudio, Inc., Boston, MA

    Google Scholar 

  16. Pau G, Fuchs F, Sklyar O, Boutros M, Huber W (2010) EBImage—an R package for image processing with applications to cellular phenotypes. Bioinformatics 26(7):979–981. https://doi.org/10.1093/bioinformatics/btq046

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  17. Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B, Tinevez JY, White DJ, Hartenstein V, Eliceiri K, Tomancak P, Cardona A (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9(7):676–682. https://doi.org/10.1038/nmeth.2019

    CrossRef  CAS  PubMed  Google Scholar 

  18. Schneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9(7):671–675

    CrossRef  CAS  Google Scholar 

  19. Gunkel M, Chung I, Worz S, Deeg KI, Simon R, Sauter G, Jones DTW, Korshunov A, Rohr K, Erfle H, Rippe K (2017) Quantification of telomere features in tumor tissue sections by an automated 3D imaging-based workflow. Methods 114:60–73. https://doi.org/10.1016/j.ymeth.2016.09.014

    CrossRef  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This work was funded by the Deutsche Forschungsgemeinschaft (DFG) Priority Program 2191 “Molecular Mechanisms of Functional Phase Separation” via grant RI1283/16-1 and the START-HD project of the HMLS program of the University of Heidelberg. Data storage at SDS@hd was supported by the Ministry of Science, Research and the Arts Baden-Württemberg (MWK) and the DFG through grants INST 35/1314-1 FUGG and INST 35/1503-1 FUGG.

Author information

Authors and Affiliations

  1. Division of Chromatin Networks, German Cancer Research Center (DKFZ) and Bioquant, Heidelberg, Germany

    Lukas Frank, Robin Weinmann, Jorge Trojanowski & Karsten Rippe

  2. LBME, Centre de Biologie Intégrative (CBI), CNRS, UPS, Toulouse, France

    Fabian Erdel

Authors
  1. Lukas Frank
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Robin Weinmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fabian Erdel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jorge Trojanowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Karsten Rippe
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Karsten Rippe .

Editor information

Editors and Affiliations

  1. Institute of Biochemistry, University of Giessen, Gießen, Hessen, Germany

    Prof. Dr. Tilman Borggrefe

  2. Institute of Biochemistry, University of Giessen, Gießen, Hessen, Germany

    Dr. Benedetto Daniele Giaimo

1 Electronic Supplementary Material

494735_1_En_17_MOESM1_ESM.docx

R image segmentation and quantification scripts and functions with associated sample data: The script SegmentCC.R with associated external functions makeNucMask() and makeChromocenterMask() (as .R files) is used for image segmentation with subsequent quantification. The scripts curateCC.R and plotCC.R enable semi-automated curation and plotting of results obtained by SegmentCC.R (see Fig. 1b–d for workflow). Furthermore, sample .tif images are provided, intended for use with the SegmentCC.R script. Images are z-maximum intensity projections of image stacks acquired for iMEFs that have been transfected with MSR-targeting sgRNA and dCas9-GFP-VPR constructs. All scripts and sample data are also available at https://github.com/RippeLab/Chromocenters.

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Verify currency and authenticity via CrossMark

Cite this protocol

Frank, L., Weinmann, R., Erdel, F., Trojanowski, J., Rippe, K. (2021). Transcriptional Activation of Heterochromatin by Recruitment of dCas9 Activators. In: Borggrefe, T., Giaimo, B.D. (eds) Enhancers and Promoters. Methods in Molecular Biology, vol 2351. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1597-3_17

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1597-3_17

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1596-6

  • Online ISBN: 978-1-0716-1597-3

  • eBook Packages: Springer Protocols