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Investigate Global Chromosomal Interaction by Hi-C in Human Naive CD4 T Cells

  • Xiangzhi MengEmail author
  • Nicole Riley
  • Ryan Thompson
  • Siddhartha Sharma
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1712)

Abstract

Hi-C is a methodology developed to reveal chromosomal interactions from a genome-wide perspective. Here, we described a protocol for generating Hi-C sequencing libraries in resting and activated human naive CD4 T cells to investigate activation-induced chromatin structure re-arrangement in T cell activation followed by a section reviewing the general concepts of Hi-C data analysis.

Key words

Chromosomal interaction Cell fixation Endo-restricted enzyme digestion De novo ligation Biotin-streptavidin High-throughput sequencing 

Notes

Acknowledgment

This is dedicated to the memory of Dr. Daniel R. Salomon. This work was supported by NIH grant 5U19 AI063603 and Mendez National Institute of Transplantation Foundation.

References

  1. 1.
    Tessarz P, Kouzarides T (2014) Histone core modifications regulating nucleosome structure and dynamics. Nat Rev Mol Cell Biol 15(11):703–708CrossRefPubMedGoogle Scholar
  2. 2.
    Negre N et al (2011) A cis-regulatory map of the drosophila genome. Nature 471(7339):527–531CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Li G et al (2012) Extensive promoter-centered chromatin interactions provide a topological basis for transcription regulation. Cell 148(1–2):84–98CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Apostolou E et al (2013) Genome-wide chromatin interactions of the Nanog locus in pluripotency, differentiation, and reprogramming. Cell Stem Cell 12(6):699–712CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Dekker J, Rippe K, Dekker M, Kleckner N (2002) Capturing chromosome conformation. Science 295(5558):1306–1311CrossRefPubMedGoogle Scholar
  6. 6.
    Weiner BM, Kleckner N (1994) Chromosome pairing via multiple interstitial interactions before and during meiosis in yeast. Cell 77(7):977–991CrossRefPubMedGoogle Scholar
  7. 7.
    Lieberman-Aiden E et al (2009) Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326(5950):289–293CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Dixon JR et al (2015) Chromatin architecture reorganization during stem cell differentiation. Nature 518(7539):331–336CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Dixon JR et al (2012) Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485(7398):376–380CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Rao SS et al (2014) A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159(7):1665–1680CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Jin F et al (2013) A high-resolution map of the three-dimensional chromatin interactome in human cells. Nature 503(7475):290–294PubMedPubMedCentralGoogle Scholar
  12. 12.
    Duan Z et al (2012) A genome-wide 3C-method for characterizing the three-dimensional architectures of genomes. Methods 58(3):277–288CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Li W, Gong K, Li Q, Alber F, Zhou XJ (2015) Hi-corrector: a fast, scalable and memory-efficient package for normalizing large-scale Hi-C data. Bioinformatics 31(6):960–962CrossRefPubMedGoogle Scholar
  14. 14.
    Hu M et al (2012) HiCNorm: removing biases in Hi-C data via Poisson regression. Bioinformatics 28(23):3131–3133CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Servant N et al (2015) HiC-pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol 16:259CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Lun AT, Smyth GK (2015) diffHic: a bioconductor package to detect differential genomic interactions in Hi-C data. BMC Bioinformatics 16:258CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Sauria ME, Phillips-Cremins JE, Corces VG, Taylor J (2015) HiFive: a tool suite for easy and efficient HiC and 5C data analysis. Genome Biol 16:237CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Heinz S et al (2010) Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell 38(4):576–589CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Hu M et al (2013) Bayesian inference of spatial organizations of chromosomes. PLoS Comput Biol 9(1):e1002893CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Lajoie BR, Dekker J, Kaplan N (2015) The Hitchhiker's guide to Hi-C analysis: practical guidelines. Methods 72:65–75CrossRefPubMedGoogle Scholar
  21. 21.
    Dekker J, Marti-Renom MA, Mirny LA (2013) Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data. Nat Rev Genet 14(6):390–403CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10(3):R25CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Li R, Li Y, Kristiansen K, Wang J (2008) SOAP: short oligonucleotide alignment program. Bioinformatics 24(5):713–714CrossRefPubMedGoogle Scholar
  24. 24.
    Imakaev M et al (2012) Iterative correction of Hi-C data reveals hallmarks of chromosome organization. Nat Methods 9(10):999–1003CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2018

Authors and Affiliations

  • Xiangzhi Meng
    • 1
    Email author
  • Nicole Riley
    • 2
  • Ryan Thompson
    • 3
  • Siddhartha Sharma
    • 4
  1. 1.Department of Molecular MedicineThe Scripps Research InstituteLa JollaUSA
  2. 2.Department of Inflammation BiologyLa Jolla Institute of Allergy and ImmunologyLa JollaUSA
  3. 3.Department of Integrative Structural and Computational BiologyThe Scripps Research InstituteLa JollaUSA
  4. 4.Department of Immunology and MicrobiologyThe Scripps Research InstituteLa JollaUSA

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