Nothing Special   »   [go: up one dir, main page]
Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Analysis
  • Published:

Quantitative comparison of genome-wide DNA methylation mapping technologies

Nature Biotechnology volume 28, pages 1106–1114 (2010)Cite this article

Subjects

Abstract

DNA methylation plays a key role in regulating eukaryotic gene expression. Although mitotically heritable and stable over time, patterns of DNA methylation frequently change in response to cell differentiation, disease and environmental influences. Several methods have been developed to map DNA methylation on a genomic scale. Here, we benchmark four of these approaches by analyzing two human embryonic stem cell lines derived from genetically unrelated embryos and a matched pair of colon tumor and adjacent normal colon tissue obtained from the same donor. Our analysis reveals that methylated DNA immunoprecipitation sequencing (MeDIP-seq), methylated DNA capture by affinity purification (MethylCap-seq), reduced representation bisulfite sequencing (RRBS) and the Infinium HumanMethylation27 assay all produce accurate DNA methylation data. However, these methods differ in their ability to detect differentially methylated regions between pairs of samples. We highlight strengths and weaknesses of the four methods and give practical recommendations for the design of epigenomic case-control studies.

Access through your institution
Buy or subscribe

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

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Outline of the DNA methylation technology comparison.
Figure 2: Comparison of DNA methylation maps obtained with four different methods.
Figure 3: Quantification of DNA methylation with MeDIP-seq, MethylCap-seq and RRBS.
Figure 4: Genomic coverage of MeDIP-seq, MethylCap-seq, RRBS and Infinium.
Figure 5: Detection of DMRs with MeDIP-seq, MethylCap-seq and RRBS.

Similar content being viewed by others

References

  1. Bird, A. DNA methylation patterns and epigenetic memory. Genes Dev. 16, 6 (2002).

    CAS  PubMed  Google Scholar 

  2. Baylin, S.B. & Ohm, J.E. Epigenetic gene silencing in cancer—a mechanism for early oncogenic pathway addiction? Nat. Rev. Cancer 6, 107–116 (2006).

    Article  CAS  Google Scholar 

  3. Esteller, M. Epigenetics in cancer. N. Engl. J. Med. 358, 1148–1159 (2008).

    Article  CAS  Google Scholar 

  4. Feinberg, A.P. & Tycko, B. The history of cancer epigenetics. Nat. Rev. Cancer 4, 143–153 (2004).

    Article  CAS  Google Scholar 

  5. Issa, J.P. CpG island methylator phenotype in cancer. Nat. Rev. Cancer 4, 988–993 (2004).

    Article  CAS  Google Scholar 

  6. Jones, P.A. & Laird, P.W. Cancer epigenetics comes of age. Nat. Genet. 21, 163–167 (1999).

    Article  CAS  Google Scholar 

  7. Richardson, B. Primer: epigenetics of autoimmunity. Nat. Clin. Pract. Rheumatol. 3, 521–527 (2007).

    Article  CAS  Google Scholar 

  8. Tobi, E.W. et al. DNA methylation differences after exposure to prenatal famine are common and timing- and sex-specific. Hum. Mol. Genet. 18, 4046–4053 (2009).

    Article  CAS  Google Scholar 

  9. Urdinguio, R.G., Sanchez-Mut, J.V. & Esteller, M. Epigenetic mechanisms in neurological diseases: genes, syndromes, and therapies. Lancet Neurol. 8, 1056–1072 (2009).

    Article  CAS  Google Scholar 

  10. Bock, C. Epigenetic biomarker development. Epigenomics 1, 99–110 (2009).

    Article  CAS  Google Scholar 

  11. Meissner, A. et al. Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature 454, 766–770 (2008).

    Article  CAS  Google Scholar 

  12. Down, T.A. et al. A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis. Nat. Biotechnol. 26, 779–785 (2008).

    Article  CAS  Google Scholar 

  13. Weber, M. et al. Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nat. Genet. 37, 853–862 (2005).

    Article  CAS  Google Scholar 

  14. Brinkman, A.B. et al. Whole-genome DNA methylation profiling using MethylCap-seq-seq. Methods published online, doi:10.1016/j.ymeth.2010.06.012 (11 June 2010).

  15. Rauch, T. & Pfeifer, G.P. Methylated-CpG island recovery assay: a new technique for the rapid detection of methylated-CpG islands in cancer. Lab. Invest. 85, 1172–1180 (2005).

    Article  CAS  Google Scholar 

  16. Serre, D., Lee, B.H. & Ting, A.H. MBD-isolated Genome Sequencing provides a high-throughput and comprehensive survey of DNA methylation in the human genome. Nucleic Acids Res. 38, 391–399 (2010).

    Article  CAS  Google Scholar 

  17. Bibikova, M. et al. Genome-wide DNA methylation profiling using Infinium assay. Epigenomics 1, 177–200 (2009).

    Article  CAS  Google Scholar 

  18. Eckhardt, F. et al. DNA methylation profiling of human chromosomes 6, 20 and 22. Nat. Genet. 38, 1378–1385 (2006).

    Article  CAS  Google Scholar 

  19. Brunner, A.L. et al. Distinct DNA methylation patterns characterize differentiated human embryonic stem cells and developing human fetal liver. Genome Res. 19, 1044–1056 (2009).

    Article  CAS  Google Scholar 

  20. Irizarry, R.A. et al. Comprehensive high-throughput arrays for relative methylation (CHARM). Genome Res. 18, 780–790 (2008).

    Article  CAS  Google Scholar 

  21. Oda, M. et al. High-resolution genome-wide cytosine methylation profiling with simultaneous copy number analysis and optimization for limited cell numbers. Nucleic Acids Res. 37, 3829–3839 (2009).

    Article  CAS  Google Scholar 

  22. Gu, H. et al. Genome-scale DNA methylation mapping of clinical samples at single-nucleotide resolution. Nat. Methods 7, 133–136 (2010).

    Article  CAS  Google Scholar 

  23. Cowan, C.A. et al. Derivation of embryonic stem-cell lines from human blastocysts. N. Engl. J. Med. 350, 1353–1356 (2004).

    Article  CAS  Google Scholar 

  24. Weisenberger, D.J. et al. Comprehensive DNA methylation analysis on the Illumina Infinium assay platform (Illumina, San Diego, California, USA, 2008). 〈http://www.illumina.com/Documents/products/appnotes/appnote_infinium_methylation.pdf〉. (2008).

  25. Bock, C. et al. Inter-individual variation of DNA methylation and its implications for large-scale epigenome mapping. Nucleic Acids Res. 36, e55 (2008).

    Article  Google Scholar 

  26. Pelizzola, M. et al. MEDME: an experimental and analytical methodology for the estimation of DNA methylation levels based on microarray derived MeDIP-enrichment. Genome Res. 18, 1652–1659 (2008).

    Article  CAS  Google Scholar 

  27. Robinson, M.D., Statham, A.L., Speed, T.P. & Clark, S.J. Protocol matters: which methylome are you actually studying? Epigenomics 2, 587 (2010).

    Article  CAS  Google Scholar 

  28. Faul, F. et al. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 39, 175–191 (2007).

    Article  Google Scholar 

  29. Beroukhim, R. et al. The landscape of somatic copy-number alteration across human cancers. Nature 463, 899–905 (2010).

    Article  CAS  Google Scholar 

  30. Redon, R. et al. Global variation in copy number in the human genome. Nature 444, 444–454 (2006).

    Article  CAS  Google Scholar 

  31. Irizarry, R.A. et al. The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores. Nat. Genet. 41, 178–186 (2009).

    Article  CAS  Google Scholar 

  32. Hellebrekers, D.M. et al. GATA4 and GATA5 are potential tumor suppressors and biomarkers in colorectal cancer. Clin. Cancer Res. 15, 3990–3997 (2009).

    Article  CAS  Google Scholar 

  33. Zhang, W. et al. Epigenetic inactivation of the canonical Wnt antagonist SRY-box containing gene 17 in colorectal cancer. Cancer Res. 68, 2764–2772 (2008).

    Article  CAS  Google Scholar 

  34. Tenesa, A. et al. Genome-wide association scan identifies a colorectal cancer susceptibility locus on 11q23 and replicates risk loci at 8q24 and 18q21. Nat. Genet. 40, 631–637 (2008).

    Article  CAS  Google Scholar 

  35. Lofton-Day, C. et al. DNA methylation biomarkers for blood-based colorectal cancer screening. Clin. Chem. 54, 414–423 (2008).

    Article  CAS  Google Scholar 

  36. Caldwell, G.M. et al. The Wnt antagonist sFRP1 in colorectal tumorigenesis. Cancer Res. 64, 883–888 (2004).

    Article  CAS  Google Scholar 

  37. Hirata, H. et al. Wnt antagonist gene DKK2 is epigenetically silenced and inhibits renal cancer progression through apoptotic and cell cycle pathways. Clin. Cancer Res. 15, 5678–5687 (2009).

    Article  CAS  Google Scholar 

  38. Ehrlich, M. DNA hypomethylation in cancer cells. Epigenomics 1, 239–259 (2009).

    Article  CAS  Google Scholar 

  39. Jurka, J. Repbase update: a database and an electronic journal of repetitive elements. Trends Genet. 16, 418–420 (2000).

    Article  CAS  Google Scholar 

  40. Bestor, T.H. & Tycko, B. Creation of genomic methylation patterns. Nat. Genet. 12, 363–367 (1996).

    Article  CAS  Google Scholar 

  41. Esteller, M. Cancer epigenomics: DNA methylomes and histone-modification maps. Nat. Rev. Genet. 8, 286–298 (2007).

    Article  CAS  Google Scholar 

  42. Jones, P.A. & Baylin, S.B. The epigenomics of cancer. Cell 128, 683–692 (2007).

    Article  CAS  Google Scholar 

  43. Feinberg, A.P. Phenotypic plasticity and the epigenetics of human disease. Nature 447, 433–440 (2007).

    Article  CAS  Google Scholar 

  44. Manolio, T.A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).

    Article  CAS  Google Scholar 

  45. Foley, D.L. et al. Prospects for epigenetic epidemiology. Am. J. Epidemiol. 169, 389–400 (2009).

    Article  Google Scholar 

  46. Heijmans, B.T. et al. The epigenome: archive of the prenatal environment. Epigenetics 4, 526–531 (2009).

    Article  CAS  Google Scholar 

  47. Doi, A. et al. Differential methylation of tissue- and cancer-specific CpG island shores distinguishes human induced pluripotent stem cells, embryonic stem cells and fibroblasts. Nat. Genet. 41, 1350–1353 (2009).

    Article  CAS  Google Scholar 

  48. Smiraglia, D.J. et al. Excessive CpG island hypermethylation in cancer cell lines versus primary human malignancies. Hum. Mol. Genet. 10, 1413–1419 (2001).

    Article  CAS  Google Scholar 

  49. Lister, R. et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462, 315–322 (2009).

    Article  CAS  Google Scholar 

  50. Popp, C. et al. Genome-wide erasure of DNA methylation in mouse primordial germ cells is affected by AID deficiency. Nature 463, 1101–1105 (2010).

    Article  CAS  Google Scholar 

  51. Horard, B. et al. Global analysis of DNA methylation and transcription of human repetitive sequences. Epigenetics 4, 339–350 (2009).

    Article  CAS  Google Scholar 

  52. Rodriguez, J. et al. Genome-wide tracking of unmethylated DNA Alu repeats in normal and cancer cells. Nucleic Acids Res. 36, 770–784 (2008).

    Article  CAS  Google Scholar 

  53. Weisenberger, D.J. et al. Analysis of repetitive element DNA methylation by MethyLight. Nucleic Acids Res. 33, 6823–6836 (2005).

    Article  CAS  Google Scholar 

  54. Yoder, J.A., Walsh, C.P. & Bestor, T.H. Cytosine methylation and the ecology of intragenomic parasites. Trends Genet. 13, 335–340 (1997).

    Article  CAS  Google Scholar 

  55. Bock, C. et al. CpG island mapping by epigenome prediction. PLoS Comput. Biol. 3, e110 (2007).

    Article  Google Scholar 

  56. Pruitt, K.D., Tatusova, T. & Maglott, D.R. NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 35 (Database issue), D61–D65 (2007).

    Article  CAS  Google Scholar 

  57. Smith, Z.D. et al. High-throughput bisulfite sequencing in mammalian genomes. Methods 48, 226–232 (2009).

    Article  CAS  Google Scholar 

  58. Rakyan, V.K. et al. An integrated resource for genome-wide identification and analysis of human tissue-specific differentially methylated regions (tDMRs). Genome Res. 18, 1518–1529 (2008).

    Article  CAS  Google Scholar 

  59. Li, H., Ruan, J. & Durbin, R. Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res. 18, 1851–1858 (2008).

    Article  CAS  Google Scholar 

  60. Bock, C. & Lengauer, T. Computational epigenetics. Bioinformatics 24, 1–10 (2008).

    Article  CAS  Google Scholar 

  61. Park, P.J. ChIP-seq: advantages and challenges of a maturing technology. Nat. Rev. Genet. 10, 669–680 (2009).

    Article  CAS  Google Scholar 

  62. Storey, J.D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 100, 9440–9445 (2003).

    Article  CAS  Google Scholar 

  63. Heintzman, N.D. et al. Histone modifications at human enhancers reflect global cell-type-specific gene expression. Nature 459, 108–112 (2009).

    Article  CAS  Google Scholar 

  64. Hajkova, P. et al. DNA-methylation analysis by the bisulfite-assisted genomic sequencing method. Methods Mol. Biol. 200, 143–154 (2002).

    CAS  PubMed  Google Scholar 

  65. Li, L.C. & Dahiya, R. MethPrimer: designing primers for methylation PCRs. Bioinformatics 18, 1427–1431 (2002).

    Article  CAS  Google Scholar 

  66. Bock, C. et al. BiQ Analyzer: visualization and quality control for DNA methylation data from bisulfite sequencing. Bioinformatics 21, 4067–4068 (2005).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank A. Crenshaw and M. Parkin (Broad Institute) for assistance with the Infinium assay and K. Halachev (Max Planck Institute for Informatics) for the provision of genome annotation files. C.B. is supported by a Feodor Lynen Fellowship from the Alexander von Humboldt Foundation. A.B.B. is supported by the Dutch Cancer Foundation (KWF, grant KUN 2008-4130). A.M. is supported by the Massachusetts Life Science Center and the Pew Charitable Trusts. The described work was in part funded by the Pew Charitable Trusts, the US National Institutes of Health Roadmap Initiative on Epigenomics (U01ES017155) and the European Union's CANCERDIP project (HEALTH-F2-2007-200620).

Author information

Author notes

  1. Christoph Bock and Eleni M Tomazou: These authors contributed equally to this work.

Authors and Affiliations

  1. Broad Institute, Cambridge, Massachusetts, USA

    Christoph Bock, Eleni M Tomazou, Fabian Müller, Hongcang Gu, Natalie Jäger, Andreas Gnirke & Alexander Meissner

  2. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA

    Christoph Bock, Eleni M Tomazou, Fabian Müller, Natalie Jäger & Alexander Meissner

  3. Harvard Stem Cell Institute, Cambridge, Massachusetts, USA

    Christoph Bock, Eleni M Tomazou, Fabian Müller, Natalie Jäger & Alexander Meissner

  4. Max Planck Institute for Informatics, Saarbrücken, Germany

    Christoph Bock & Fabian Müller

  5. Radboud University Department of Molecular Biology, Nijmegen Center for Molecular Life Sciences, Nijmegen, The Netherlands

    Arie B Brinkman, Femke Simmer & Hendrik G Stunnenberg

Authors
  1. Christoph Bock
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eleni M Tomazou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arie B Brinkman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fabian Müller
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Femke Simmer
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hongcang Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Natalie Jäger
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Andreas Gnirke
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Hendrik G Stunnenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Alexander Meissner
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.B., E.M.T. and A.M. conceived and designed the study; E.M.T., A.B.B., F.S. and H.G. performed the experiments; C.B., F.M. and N.J. analyzed the data; C.B., A.G., H.G.S. and A.M. interpreted the results; and C.B. and A.M. wrote the paper.

Corresponding authors

Correspondence to Christoph Bock or Alexander Meissner.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figs. 1–12 (PDF 2789 kb)

Supplementary Data 1

Validation of method-specific DMRs by clonal bisulfite sequencing (PDF 804 kb)

Supplementary Data 2

DNA methylation map of prototypic repeat sequences (PDF 4723 kb)

Supplementary Data 3

Differential DNA methylation of prototypic repeat sequences (PDF 3239 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bock, C., Tomazou, E., Brinkman, A. et al. Quantitative comparison of genome-wide DNA methylation mapping technologies. Nat Biotechnol 28, 1106–1114 (2010). https://doi.org/10.1038/nbt.1681

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nbt.1681

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research