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High-Throughput Techniques for DNA Methylation Profiling

  • Sophie Petropoulos
  • David Cheishvili
  • Moshe SzyfEmail author
Protocol
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Part of the Methods in Pharmacology and Toxicology book series (MIPT)

Abstract

In this chapter, commonly used methods to assess the genome-wide DNA methylation status are reviewed and compared. The methods described in this chapter include enrichment-based method, Methylated DNA Immunoprecipitation (MeDIP), paired with microarray technology and next generation sequencing, and sodium bisulfate-based techniques including Infinium HumanMethylation450 BeadChip (Illumina 450 K) and Reduced Representation Bisulfite Sequencing (RRBS).

An overview of each protocol, including description as to why particular steps are required or critical, is outlined. Further, the protocols are compared and advantages and disadvantages of each are discussed.

Key words

DNA methylation Sodium bisulfite Methylated DNA immunoprecipitation (MeDIP) Infinium HumanMethylation450 BeadChip (Illumina 450 K) Reduced Representation Bisulfite Sequencing (RRBS) Microarray Next generation sequencing 

Notes

Acknowledgments

S.P. is supported by the Mats Sundin Fellowship in Developmental Health. D. C. is supported by fellowship from the Israel Cancer Research Foundation.

References

  1. 1.
    Bibikova M, Barnes B, Tsan C et al (2011) High density DNA methylation array with single CpG site resolution. Genomics 98:288–295CrossRefPubMedGoogle Scholar
  2. 2.
    Gold M, Gefter M, Hausmann R, Hurwitz J (1966) Methylation of DNA. J Gen Physiol 49:5–28CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Razin A, Riggs AD (1980) DNA methylation and gene function. Science 210:604–610CrossRefPubMedGoogle Scholar
  4. 4.
    Li E, Bestor TH, Jaenisch R (1992) Targeted mutation of the DNA methyltransferase gene results in embryonic lethality. Cell 69:915–926CrossRefPubMedGoogle Scholar
  5. 5.
    Okano M, Bell DW, Haber DA, Li E (1999) DNA methyltransferases Dnmt3a and Dnmt3b are essential for de novo methylation and mammalian development. Cell 99:247–257CrossRefPubMedGoogle Scholar
  6. 6.
    Comb M, Goodman HM (1990) CpG methylation inhibits proenkephalin gene expression and binding of the transcription factor AP-2. Nucleic Acids Res 18:3975–3982CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Lewis JD, Meehan RR, Henzel WJ et al (1992) Purification, sequence, and cellular localization of a novel chromosomal protein that binds to methylated DNA. Cell 69:905–914CrossRefPubMedGoogle Scholar
  8. 8.
    Jones PL, Veenstra GJ, Wade PA et al (1998) Methylated DNA and MeCP2 recruit histone deacetylase to repress transcription. Nat Genet 19:187–191CrossRefPubMedGoogle Scholar
  9. 9.
    Nan X, Ng HH, Johnson CA et al (1998) Transcriptional repression by the methyl-CpG-binding protein MeCP2 involves a histone deacetylase complex. Nature 393:386–389CrossRefPubMedGoogle Scholar
  10. 10.
    Yang X, Han H, De Carvalho DD, Lay FD, Jones PA, Liang G (2014) Gene body methylation can alter gene expression and is a therapeutic target in cancer. Cancer Cell 26:577–590CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Jjingo D, Conley AB, Yi SV, Lunyak VV, Jordan IK (2012) On the presence and role of human gene-body DNA methylation. Oncotarget 3:462–474CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Schultz MD, He Y, Whitaker JW et al (2015) Human body epigenome maps reveal noncanonical DNA methylation variation. Nature 523:212–216CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Zhao M, Liu S, Luo S et al (2014) DNA methylation and mRNA and microRNA expression of SLE CD4+ T cells correlate with disease phenotype. J Autoimmun 54:127–136CrossRefPubMedGoogle Scholar
  14. 14.
    Karpurapu M, Ranjan R, Deng J et al (2014) Krüppel like factor 4 promoter undergoes active demethylation during monocyte/macrophage differentiation. PLoS One 9, e93362CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Zhao M, Wang Z, Yung S, Lu Q (2015) Epigenetic dynamics in immunity and autoimmunity. Int J Biochem Cell Biol 67:65–74CrossRefPubMedGoogle Scholar
  16. 16.
    Lardenoije R, Iatrou A, Kenis G et al (2015) The epigenetics of aging and neurodegeneration. Prog Neurobiol 131:21–64CrossRefPubMedGoogle Scholar
  17. 17.
    Landgrave-Gómez J, Mercado-Gómez O, Guevara-Guzmán R (2015) Epigenetic mechanisms in neurological and neurodegenerative diseases. Front Cell Neurosci 9:58PubMedPubMedCentralGoogle Scholar
  18. 18.
    Paska AV, Hudler P (2015) Aberrant methylation patterns in cancer: a clinical view. Biochem Medica 25:161–176CrossRefGoogle Scholar
  19. 19.
    Chiang N-J, Shan Y-S, Hung W-C, Chen L-T (2015) Epigenetic regulation in the carcinogenesis of cholangiocarcinoma. Int J Biochem Cell Biol 67:110–114CrossRefPubMedGoogle Scholar
  20. 20.
    Sui X, Zhu J, Zhou J et al (2015) Epigenetic modifications as regulatory elements of autophagy in cancer. Cancer Lett 360:106–113CrossRefPubMedGoogle Scholar
  21. 21.
    Bock C, Tomazou EM, Brinkman AB et al (2010) Quantitative comparison of genome-wide DNA methylation mapping technologies. Nat Biotechnol 28:1106–1114CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Laird PW (2010) Principles and challenges of genomewide DNA methylation analysis. Nat Rev Genet 11:191–203CrossRefPubMedGoogle Scholar
  23. 23.
    Beck S, Rakyan VK (2008) The methylome: approaches for global DNA methylation profiling. Trends Genet 24:231–237CrossRefPubMedGoogle Scholar
  24. 24.
    Weber M, Davies JJ, Wittig D et al (2005) Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nat Genet 37:853–862CrossRefPubMedGoogle Scholar
  25. 25.
    Lisanti S, von Zglinicki T, Mathers JC (2012) Standardization and quality controls for the methylated DNA immunoprecipitation technique. Epigenetics 7:615–625CrossRefPubMedGoogle Scholar
  26. 26.
    Borgel J, Guibert S, Weber M (2012) Methylated DNA immunoprecipitation (MeDIP) from low amounts of cells. Methods Mol Biol 925:149–158CrossRefPubMedGoogle Scholar
  27. 27.
    Zhao M-T, Whyte JJ, Hopkins GM, Kirk MD, Prather RS (2014) Methylated DNA immunoprecipitation and high-throughput sequencing (MeDIP-seq) using low amounts of genomic DNA. Cell Reprogram 16:175–184CrossRefPubMedGoogle Scholar
  28. 28.
    Harris RA, Wang T, Coarfa C et al (2010) Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications. Nat Biotechnol 28:1097–1105CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Down TA, Rakyan VK, Turner DJ et al (2008) A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis. Nat Biotechnol 26:779–785CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Pelizzola M, Koga Y, Urban AE et al (2008) MEDME: an experimental and analytical methodology for the estimation of DNA methylation levels based on microarray derived MeDIP-enrichment. Genome Res 18:1652–1659CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Stevens M, Cheng JB, Li D et al (2013) Estimating absolute methylation levels at single-CpG resolution from methylation enrichment and restriction enzyme sequencing methods. Genome Res 23:1541–1553CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Otto C, Reiche K, Hackermuller J (2012) Detection of differentially expressed segments in tiling array data. Bioinformatics 28:1471–1479CrossRefPubMedGoogle Scholar
  33. 33.
    Sorek R, Cossart P (2010) Prokaryotic transcriptomics: a new view on regulation, physiology and pathogenicity. Nat Rev Genet 11:9–16CrossRefPubMedGoogle Scholar
  34. 34.
    Jia J, Pekowska A, Jaeger S, Benoukraf T, Ferrier P, Spicuglia S (2010) Assessing the efficiency and significance of Methylated DNA Immunoprecipitation (MeDIP) assays in using in vitro methylated genomic DNA. BMC Res Notes 3:240CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Clark C, Palta P, Joyce CJ et al (2012) A comparison of the whole genome approach of MeDIP-seq to the targeted approach of the Infinium HumanMethylation450 BeadChip(®) for methylome profiling. PLoS One 7:e50233CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Meissner A (2005) Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Res 33:5868–5877CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Frommer M, McDonald LE, Millar DS et al (1992) A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands. Proc Natl Acad Sci U S A 89:1827–1831CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Grunau C, Clark SJ, Rosenthal A (2001) Bisulfite genomic sequencing: systematic investigation of critical experimental parameters. Nucleic Acids Res 29:E65–E65CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Guo F, Yan L, Guo H et al (2015) The transcriptome and DNA methylome landscapes of human primordial germ cells. Cell 161:1437–1452CrossRefPubMedGoogle Scholar
  40. 40.
    Boyle P, Clement K, Gu H et al (2012) Gel-free multiplexed reduced representation bisulfite sequencing for large-scale DNA methylation profiling. Genome Biol 13:R92CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Gu H, Bock C, Mikkelsen TS et al (2010) Genome-scale DNA methylation mapping of clinical samples at single-nucleotide resolution. Nat Methods 7:133–136CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Smith ZD, Gu H, Bock C, Gnirke A, Meissner A (2009) High-throughput bisulfite sequencing in mammalian genomes. Methods 48:226–232CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Liang F, Tang B, Wang Y et al (2014) WBSA: web service for bisulfite sequencing data analysis. PLoS One 9:e86707CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Irizarry RA, Ladd-Acosta C, Wen B et al (2009) The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores. Nat Genet 41:178–186CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Maunakea AK, Nagarajan RP, Bilenky M et al (2010) Conserved role of intragenic DNA methylation in regulating alternative promoters. Nature 466:253–257CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Roessler J, Ammerpohl O, Gutwein J et al (2012) Quantitative cross-validation and content analysis of the 450k DNA methylation array from Illumina Inc. BMC Res Notes 5:210CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Sandoval J, Heyn H, Moran S et al (2011) Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome. Epigenetics 6:692–702CrossRefPubMedGoogle Scholar
  48. 48.
    Harper KN, Peters BA, Gamble MV (2013) Batch effects and pathway analysis: two potential perils in cancer studies involving DNA methylation array analysis. Cancer Epidemiol Biomarkers Prev 22:1052–1060CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Dedeurwaerder S, Defrance M, Calonne E, Denis H, Sotiriou C, Fuks F (2011) Evaluation of the infinium methylation 450K technology. Epigenomics 3:771–784CrossRefPubMedGoogle Scholar
  50. 50.
    Touleimat N, Tost J (2012) Complete pipeline for Infinium(®) Human Methylation 450K BeadChip data processing using subset quantile normalization for accurate DNA methylation estimation. Epigenomics 4:325–341CrossRefPubMedGoogle Scholar
  51. 51.
    Aryee MJ, Jaffe AE, Corrada-Bravo H et al (2014) Minfi: a flexible and comprehensive bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 30:1363–1369CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Maksimovic J, Gordon L, Oshlack A (2012) SWAN: Subset-quantile within array normalization for illumina infinium HumanMethylation450 BeadChips. Genome Biol 13:R44CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Teschendorff AE, Marabita F, Lechner M et al (2013) A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics 29:189–196CrossRefPubMedGoogle Scholar
  54. 54.
    Morris TJ, Butcher LM, Feber A et al (2014) ChAMP: 450k chip analysis methylation pipeline. Bioinformatics 30:428–430CrossRefPubMedGoogle Scholar
  55. 55.
    Pidsley R, Y Wong CC, Volta M, Lunnon K, Mill J, Schalkwyk LC (2013) A data-driven approach to preprocessing Illumina 450K methylation array data. BMC Genomics 14:293CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Triche TJ, Weisenberger DJ, Van Den Berg D, Laird PW, Siegmund KD (2013) Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Res 41:e90CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Du P, Zhang X, Huang C-C et al (2010) Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics 11:587CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Bibikova M, Fan J-B (2009) GoldenGate assay for DNA methylation profiling. Methods Mol Biol 507:149–163CrossRefPubMedGoogle Scholar
  59. 59.
    Bibikova M, Lin Z, Zhou L et al (2006) High-throughput DNA methylation profiling using universal bead arrays. Genome Res 16:383–393CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Sean Davis, Pan Du, Sven Bilke, Tim Triche, Jr. MB. methylumi: Handle Illumina methylation data. 2015: R package version 2.14.0Google Scholar
  61. 61.
    Zhuang J, Widschwendter M, Teschendorff AE (2012) A comparison of feature selection and classification methods in DNA methylation studies using the Illumina Infinium platform. BMC Bioinformatics 13:59CrossRefPubMedPubMedCentralGoogle Scholar
  62. 62.
    Du P, Kibbe WA, Lin SM (2008) lumi: a pipeline for processing Illumina microarray. Bioinformatics 24:1547–1548CrossRefPubMedGoogle Scholar
  63. 63.
    Wang D, Yan L, Hu Q et al (2012) IMA: an R package for high-throughput analysis of Illumina’s 450K Infinium methylation data. Bioinformatics 28:729–730CrossRefPubMedPubMedCentralGoogle Scholar
  64. 64.
    Aryee KDHMJ. minfi: Analyze Illumina’s 450 K methylation arrays. 2013: R package version 1.2.0. 2012Google Scholar
  65. 65.
    Butcher LM, Beck S (2015) Probe Lasso: a novel method to rope in differentially methylated regions with 450K DNA methylation data. Methods 72:21–28CrossRefPubMedPubMedCentralGoogle Scholar
  66. 66.
    Chen Y, Lemire M, Choufani S et al (2013) Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics8:203–209CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Sophie Petropoulos
    • 1
  • David Cheishvili
    • 2
  • Moshe Szyf
    • 2
    Email author
  1. 1.Department of Clinical Science, Intervention and Technology (CLINTEC)Karolinska InstitutetStockholmSweden
  2. 2.Department of Pharmacology and TherapeuticsMcGill University Medical SchoolMontrealCanada

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