Skip to main content
SpringerLink
Log in

Chromatin Immunoprecipitation Followed by Next-Generation Sequencing (ChIP-Seq) Analysis in Ewing Sarcoma

  • Protocol
  • First Online:
Ewing Sarcoma

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2226))

Abstract

ChIP-seq is the method of choice for profiling protein–DNA interactions, and notably for characterizing the landscape of transcription factor binding and histone modifications. This technique has been widely used to study numerous aspects of tumor biology and led to the development of several promising cancer therapies. In Ewing sarcoma research, ChIP-seq provided important insights into the mechanism of action of the major oncogenic fusion protein EWSR1-FLI1 and related epigenetic and transcriptional changes. In this chapter, we provide a detailed pipeline to analyze ChIP-seq experiments from the preprocessing of raw data to tertiary analysis of detected binding sites. We also advise on best practice to prepare tumor samples prior to sequencing.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Audia JE, Campbell RM (2016) Histone modifications and cancer. Cold Spring Harb Perspect Biol 8:a019521

    Article  PubMed  PubMed Central  Google Scholar 

  2. Cosgrove MS, Wolberger C (2005) How does the histone code work? Biochem Cell Biol 83:468–476

    Article  CAS  PubMed  Google Scholar 

  3. Suganuma T, Workman JL (2011) Signals and combinatorial functions of histone modifications. Annu Rev Biochem 80:473–499

    Article  CAS  PubMed  Google Scholar 

  4. Josling GA, Selvarajah SA, Petter M et al (2012) The role of bromodomain proteins in regulating gene expression. Genes (Basel) 3:320–343

    Article  CAS  Google Scholar 

  5. Connelly KE, Weaver TM, Alpsoy A et al (2019) Engagement of DNA and H3K27me3 by the CBX8 chromodomain drives chromatin association. Nucleic Acids Res 47:2289–2305

    Article  CAS  PubMed  Google Scholar 

  6. Reiter F, Wienerroither S, Stark A (2017) Combinatorial function of transcription factors and cofactors. Curr Opin Genet Dev 43:73–81

    Article  CAS  PubMed  Google Scholar 

  7. Groth A, Rocha W, Verreault A et al (2007) Chromatin challenges during DNA replication and repair. Cell 128:721–733

    Article  CAS  PubMed  Google Scholar 

  8. Gilmour DS, Lis JT (1984) Detecting protein-DNA interactions in vivo: distribution of RNA polymerase on specific bacterial genes. Proc Natl Acad Sci U S A 81:4275–4279

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Gilmour DS, Lis JT (1985) In vivo interactions of RNA polymerase II with genes of Drosophila melanogaster. Mol Cell Biol 5:2009–2018

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Solomon MJ, Larsen PL, Varshavsky A (1988) Mapping proteinDNA interactions in vivo with formaldehyde: evidence that histone H4 is retained on a highly transcribed gene. Cell 53:937–947

    Article  CAS  PubMed  Google Scholar 

  11. Beischlag TV, Prefontaine GG, Hankinson O (2018) ChIP-re-ChIP: co-occupancy analysis by sequential chromatin immunoprecipitation. In: Visa N, Jordán-Pla A (eds) Chromatin immunoprecipitation: methods and protocols. Springer, New York, NY, pp 103–112

    Chapter  Google Scholar 

  12. Zhang J, Dominguez-Sola D, Hussein S et al (2015) Disruption of KMT2D perturbs germinal center B cell development and promotes lymphomagenesis. Nat Med 21:1190–1198

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Alekseyenko AA, Walsh EM, Wang X et al (2015) The oncogenic BRD4-NUT chromatin regulator drives aberrant transcription within large topological domains. Genes Dev 29:1507–1523

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Rui L, Emre NCT, Kruhlak MJ et al (2010) Cooperative epigenetic modulation by cancer amplicon genes. Cancer Cell 18:590–605

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Boeva V, Louis-Brennetot C, Peltier A et al (2017) Heterogeneity of neuroblastoma cell identity defined by transcriptional circuitries. Nat Genet 49:1408–1413

    Article  CAS  PubMed  Google Scholar 

  16. Funato K, Major T, Lewis PW et al (2014) Use of human embryonic stem cells to model pediatric gliomas with H3.3K27M histone mutation. Science 346:1529–1533

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Drelon C, Berthon A, Mathieu M et al (2016) EZH2 is overexpressed in adrenocortical carcinoma and is associated with disease progression. Hum Mol Genet 25(13):2789–2800

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Kim KH, Roberts CWM (2016) Targeting EZH2 in cancer. Nat Med 22:128–134

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kelly AD, Issa J-PJ (2017) The promise of epigenetic therapy: reprogramming the cancer epigenome. Curr Opin Genet Dev 42:68–77

    Article  CAS  PubMed  Google Scholar 

  20. Yashiro-Ohtani Y, Wang H, Zang C et al (2014) Long-range enhancer activity determines Myc sensitivity to notch inhibitors in T cell leukemia. Proc Natl Acad Sci U S A 111:E4946–E4953

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Lupiáñez DG, Kraft K, Heinrich V et al (2015) Disruptions of topological chromatin domains cause pathogenic rewiring of gene-enhancer interactions. Cell 161:1012–1025

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Braun BS, Frieden R, Lessnick SL et al (1995) Identification of target genes for the Ewing’s sarcoma EWS/FLI fusion protein by representational difference analysis. Mol Cell Biol 15:4623–4630

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Sand LGL, Szuhai K, Hogendoorn PCW (2015) Sequencing overview of Ewing sarcoma: a journey across genomic, Epigenomic and transcriptomic landscapes. Int J Mol Sci 16:16176–16215

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Kovar H (2010) Downstream EWS/FLI1—upstream Ewing’s sarcoma. Genome Med 2:8

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Guillon N, Tirode F, Boeva V et al (2009) The oncogenic EWS-FLI1 protein binds in vivo GGAA microsatellite sequences with potential transcriptional activation function. PLoS One 4:e4932

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Johnson KM, Taslim C, Saund RS et al (2017) Identification of two types of GGAA-microsatellites and their roles in EWS/FLI binding and gene regulation in Ewing sarcoma. PLoS One 12:e0186275

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Dallmayer M, Li J, Ohmura S et al (2019) Targeting the CALCB/RAMP1 axis inhibits growth of Ewing sarcoma. Cell Death Dis 10:1–13

    Article  CAS  Google Scholar 

  28. Grünewald TGP, Bernard V, Gilardi-Hebenstreit P et al (2015) Chimeric EWSR1-FLI1 regulates the Ewing sarcoma susceptibility gene EGR2 via a GGAA microsatellite. Nat Genet 47:1073–1078

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Riggi N, Knoechel B, Gillespie SM et al (2014) EWS-FLI1 utilizes divergent chromatin remodeling mechanisms to directly activate or repress enhancer elements in Ewing sarcoma. Cancer Cell 26:668–681

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Lin L, Huang M, Shi X et al (2019) Super-enhancer-associated MEIS1 promotes transcriptional dysregulation in Ewing sarcoma in co-operation with EWS-FLI1. Nucleic Acids Res 47:1255–1267

    Article  CAS  PubMed  Google Scholar 

  31. Chaturvedi A, Hoffman LM, Jensen CC et al (2014) Molecular dissection of the mechanism by which EWS/FLI expression compromises actin cytoskeletal integrity and cell adhesion in Ewing sarcoma. Mol Biol Cell 25:2695–2709

    Article  PubMed  PubMed Central  Google Scholar 

  32. Katschnig AM, Kauer MO, Schwentner R et al (2017) EWS-FLI1 perturbs MRTFB/YAP-1/TEAD target gene regulation inhibiting cytoskeletal autoregulatory feedback in Ewing sarcoma. Oncogene 36:5995–6005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Johnson KM, Mahler NR, Saund RS et al (2017) Role for the EWS domain of EWS/FLI in binding GGAA-microsatellites required for Ewing sarcoma anchorage independent growth. Proc Natl Acad Sci U S A 114:9870–9875

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Bilke S, Schwentner R, Yang F et al (2013) Oncogenic ETS fusions deregulate E2F3 target genes in Ewing sarcoma and prostate cancer. Genome Res 23:1797–1809

    Article  PubMed  PubMed Central  Google Scholar 

  35. Harlow ML, Chasse MH, Boguslawski EA et al (2019) Trabectedin inhibits EWS-FLI1 and evicts SWI/SNF from chromatin in a schedule-dependent manner. Clin Cancer Res 25:3417–3429

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. R Development Core Team (2010) a language and environment for statistical computing: reference index, R Foundation for Statistical Computing, Vienna

    Google Scholar 

  37. Ashoor H, Hérault A, Kamoun A et al (2013) HMCan: a method for detecting chromatin modifications in cancer samples using ChIP-seq data. Bioinformatics 29:2979–2986

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Li H, Durbin R (2009) Fast and accurate short read alignment with burrows–wheeler transform. Bioinformatics 25:1754–1760

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Li H, Handsaker B, Wysoker A et al (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Robinson JT, Thorvaldsdóttir H, Winckler W et al (2011) Integrative genomics viewer. Nat Biotechnol 29:24–26

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Ernst J, Kellis M (2012) ChromHMM: automating chromatin-state discovery and characterization. Nat Methods 9:215–216

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Amemiya HM, Kundaje A, Boyle AP (2019) The ENCODE Blacklist: Identification of Problematic Regions of the Genome. Sci Rep 9:1–5

    Article  CAS  Google Scholar 

  43. Rodríguez-Ubreva J, Ballestar E (2014) Chromatin immunoprecipitation. In: Stockert JC, Espada J, Blázquez-Castro A (eds) Functional analysis of DNA and chromatin. Humana Press, Totowa, NJ, pp 309–318

    Chapter  Google Scholar 

  44. Wiehle L, Breiling A (2016) Chromatin immunoprecipitation. In: Lanzuolo C, Bodega B (eds) Polycomb group proteins: methods and protocols. Springer, New York, NY, pp 7–21

    Chapter  Google Scholar 

  45. Wardle FC, Tan H (2015) A ChIP on the shoulder? Chromatin immunoprecipitation and validation strategies for ChIP antibodies. F1000Res 4:235

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Egelhofer TA, Minoda A, Klugman S et al (2011) An assessment of histone-modification antibody quality. Nat Struct Mol Biol 18:91–93

    Article  CAS  PubMed  Google Scholar 

  47. Goens G, Rusu D, Bultot L et al (2009) Characterization and quality control of antibodies used in ChIP assays. Methods Mol Biol 567:27–43

    Article  CAS  PubMed  Google Scholar 

  48. Landt SG, Marinov GK, Kundaje A et al (2012) ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res 22:1813–1831

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Kinsey M, Smith R, Iyer AK et al (2009) EWS/FLI and its downstream target NR0B1 interact directly to modulate transcription and oncogenesis in Ewing’s sarcoma. Cancer Res 69:9047–9055

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Shimizu R, Tanaka M, Tsutsumi S et al (2018) EWS-FLI1 regulates a transcriptional program in cooperation with Foxq1 in mouse Ewing sarcoma. Cancer Sci 109:2907–2918

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Zhang Y, Liu T, Meyer CA et al (2008) Model-based analysis of ChIP-Seq (MACS). Genome Biol 9:R137

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Chen Y, Negre N, Li Q et al (2012) Systematic evaluation of factors influencing ChIP-seq fidelity. Nat Methods 9:609–614

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Buisine N, Kerdivel G, Sachs LM (2018) De novo transcriptomic approach to study thyroid hormone receptor action in non-mammalian models. In: Plateroti M, Samarut J (eds) Thyroid hormone nuclear receptor: methods and protocols. Springer, New York, NY, pp 265–285

    Chapter  Google Scholar 

  54. Davis-Turak J, Courtney SM, Hazard ES et al (2017) Genomics pipelines and data integration: challenges and opportunities in the research setting. Expert Rev Mol Diagn 17:225–237

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Liang K, Keleş S (2012) Normalization of ChIP-seq data with control. BMC Bioinformatics 13:199

    Article  PubMed  PubMed Central  Google Scholar 

  56. Nair NU, Sahu AD, Bucher P et al (2012) ChIPnorm: a statistical method for normalizing and identifying differential regions in histone modification ChIP-seq libraries. PLoS One 7:e39573

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Taslim C, Wu J, Yan P et al (2009) Comparative study on ChIP-seq data: normalization and binding pattern characterization. Bioinformatics 25:2334–2340

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Shao Z, Zhang Y, Yuan G-C et al (2012) MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets. Genome Biol 13:R16

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Boeva V (2016) Analysis of genomic sequence motifs for deciphering transcription factor binding and transcriptional regulation in eukaryotic cells. Front Genet 7:24

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Kulakovskiy IV, Boeva VA, Favorov AV et al (2010) Deep and wide digging for binding motifs in ChIP-Seq data. Bioinformatics 26:2622–2623

    Article  CAS  PubMed  Google Scholar 

  61. McLeay RC, Bailey TL (2010) Motif enrichment analysis: a unified framework and an evaluation on ChIP data. BMC Bioinformatics 11:165

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Herrmann C, Van de Sande B, Potier D et al (2012) I-cisTarget: an integrative genomics method for the prediction of regulatory features and cis-regulatory modules. Nucleic Acids Res 40:e114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Heinz S, Benner C, Spann N et al (2010) Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell 38:576–589

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Thomas-Chollier M, Sand O, Turatsinze J-V et al (2008) RSAT: regulatory sequence analysis tools. Nucleic Acids Res 36:W119–W127

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Yu G, Wang L-G, He Q-Y (2015) ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31:2382–2383

    Article  CAS  PubMed  Google Scholar 

  66. Chen X, Xu H, Yuan P et al (2008) Integration of external signaling pathways with the core transcriptional network in embryonic stem cells. Cell 133:1106–1117

    Article  CAS  PubMed  Google Scholar 

  67. Zhu LJ, Gazin C, Lawson ND et al (2010) ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC Bioinformatics 11:237

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Cheng C, Min R, Gerstein M (2011) TIP: a probabilistic method for identifying transcription factor target genes from ChIP-seq binding profiles. Bioinformatics 27:3221–3227

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. McLean CY, Bristor D, Hiller M et al (2010) GREAT improves functional interpretation of cis-regulatory regions. Nat Biotechnol 28:495–501

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Hnisz D, Abraham BJ, Lee TI et al (2013) Super-enhancers in the control of cell identity and disease. Cell 155:934–947

    Article  CAS  PubMed  Google Scholar 

  71. Whyte WA, Orlando DA, Hnisz D et al (2013) Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 153:307–319

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Lovén J, Hoke HA, Lin CY et al (2013) Selective inhibition of tumor oncogenes by disruption of super-enhancers. Cell 153:320–334

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  73. Chapuy B, McKeown MR, Lin CY et al (2013) Discovery and characterization of super-enhancer-associated dependencies in diffuse large B cell lymphoma. Cancer Cell 24:777–790

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Lin CY, Erkek S, Tong Y et al (2016) Active medulloblastoma enhancers reveal subgroup-specific cellular origins. Nature 530:57–62

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Saint-André V, Federation AJ, Lin CY et al (2016) Models of human core transcriptional regulatory circuitries. Genome Res 26:385–396

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  76. Kong Y (2011) Btrim: a fast, lightweight adapter and quality trimming program for next-generation sequencing technologies. Genomics 98:152–153

    Article  CAS  PubMed  Google Scholar 

  77. Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Criscuolo A, Brisse S (2013) AlienTrimmer: a tool to quickly and accurately trim off multiple short contaminant sequences from high-throughput sequencing reads. Genomics 102:500–506

    Article  CAS  PubMed  Google Scholar 

  79. Schubert M, Lindgreen S, Orlando L (2016) AdapterRemoval v2: rapid adapter trimming, identification, and read merging. BMC Res Notes 9:88

    Article  PubMed  PubMed Central  Google Scholar 

  80. Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357–359

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Dobin A, Davis CA, Schlesinger F et al (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29:15–21

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This work was funded through institutional support from Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale, ATIP-Avenir, the ARC Foundation (ARC-RAC16002KSA-R15093KS), the SIRIC CARPEM (No. INCA-DGOS-INSERM_12561), and the “Who Am I?” Laboratory of Excellence ANR-11-LABX-0071, funded by the French Government through its Investissement d′Avenir program, operated by the French National Research Agency (ANR-11-IDEX-0005-02).

Author information

Authors and Affiliations

  1. INSERM, U1016, Cochin Institute, CNRS UMR8104, Paris Descartes University, Paris, France

    Valentina Boeva

  2. Department of Computer Science, ETH Zurich, Institute for Machine Learning, Zurich, Switzerland

    Valentina Boeva

  3. Swiss Institute of Bioinformatics (SIB), Zürich, Switzerland

    Valentina Boeva

  4. Cochin Institute, INSERM U1016, CNRS UMR8104, University of Paris, Paris, France

    Gwenneg Kerdivel

Authors
  1. Gwenneg Kerdivel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Valentina Boeva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Gwenneg Kerdivel or Valentina Boeva .

Editor information

Editors and Affiliations

  1. Division of Translational Pediatric Sarcoma Research, German Cancer Research Center (DKFZ) Hopp-Children’s Cancer Center (KiTZ), Heidelberg, Germany

    Florencia Cidre-Aranaz

  2. Division of Translational Pediatric Sarcoma Research, German Cancer Research Center (DKFZ), Hopp-Children’s Cancer Center (KiTZ), Heidelberg, Germany

    Thomas G. P. Grünewald

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Kerdivel, G., Boeva, V. (2021). Chromatin Immunoprecipitation Followed by Next-Generation Sequencing (ChIP-Seq) Analysis in Ewing Sarcoma. In: Cidre-Aranaz, F., G. P. Grünewald, T. (eds) Ewing Sarcoma . Methods in Molecular Biology, vol 2226. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1020-6_21

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1020-6_21

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1019-0

  • Online ISBN: 978-1-0716-1020-6

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation