Skip to main content
SpringerLink
Log in
Book cover

Disease Gene Identification pp 233–254Cite as

Methods for CpG Methylation Array Profiling Via Bisulfite Conversion

  • Protocol
  • First Online:

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1706))

Abstract

DNA methylation is a key factor in epigenetic regulation, and contributes to the pathogenesis of many diseases, including various forms of cancers, and epigenetic events such X inactivation, cellular differentiation and proliferation, and embryonic development. The most conserved epigenetic modification in plants, animals, and fungi is 5-methylcytosine (5mC), which has been well characterized across a diverse range of species. Many technologies have been developed to measure modifications in methylation with respect to biological processes, and the most common method, long considered a gold standard for identifying regions of methylation, is bisulfite conversion. In this technique, DNA is treated with bisulfite, which converts cytosine residues to uracil, but does not affect cytosine residues that have been methylated, such as 5-methylcytosines. Following bisulfite conversion, the only cytosine residues remaining in the DNA, therefore, are those that have been methylated. Subsequent sequencing can then distinguish between unmethylated cytosines, which are displayed as thymines in the resulting amplified sequence of the sense strand, and 5-methylcytosines, which are displayed as cytosines in the resulting amplified sequence of the sense strand, at the single nucleotide level. In this chapter, we describe an array-based protocol for identifying methylated DNA regions. We discuss protocols for DNA quantification, bisulfite conversion, library preparation, and chip assembly, and present an overview of current methods for the analysis of methylation data.

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

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

References

  1. Bird A, Taggart M, Frommer M, Miller OJ, Macleod D (1985) A fraction of the mouse genome that is derived from islands of nonmethylated, CpG-rich DNA. Cell 40(1):91–99

    Article  CAS  PubMed  Google Scholar 

  2. Holliday R, Pugh JE (1975) DNA modification mechanisms and gene activity during development. Science 187(4173):226–232

    Article  CAS  PubMed  Google Scholar 

  3. Bird A (2007) Perceptions of epigenetics. Nature 447(7143):396–398. https://doi.org/10.1038/nature05913

    Article  CAS  PubMed  Google Scholar 

  4. Dayeh T, Volkov P, Salo S, Hall E, Nilsson E, Olsson AH et al (2014) Genome-wide DNA methylation analysis of human pancreatic islets from type 2 diabetic and non-diabetic donors identifies candidate genes that influence insulin secretion. PLoS Genet 10(3):e1004160. https://doi.org/10.1371/journal.pgen.1004160. PubMed PMID: 24603685; PubMed Central PMCID: PMCPMC3945174.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Kulis M, Esteller M (2010) DNA methylation and cancer. Adv Genet 70:27–56. https://doi.org/10.1016/B978-0-12-380866-0.60002-2

    PubMed  Google Scholar 

  6. Li E (2002) Chromatin modification and epigenetic reprogramming in mammalian development. Nat Rev Genet 3(9):662–673. https://doi.org/10.1038/nrg887

    Article  CAS  PubMed  Google Scholar 

  7. Lu H, Liu X, Deng Y, Qing H (2013) DNA methylation, a hand behind neurodegenerative diseases. Front Aging Neurosci 5:85. https://doi.org/10.3389/fnagi.2013.00085. PubMed PMID: 24367332; PubMed Central PMCID: PMCPMC3851782.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Zhong J, Agha G, Baccarelli AA (2016) The role of DNA methylation in cardiovascular risk and disease: methodological aspects, study design, and data analysis for epidemiological studies. Circ Res 118(1):119–131. https://doi.org/10.1161/CIRCRESAHA.115.305206. PubMed PMID: 26837743; PubMed Central PMCID: PMCPMC4743554

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Goll MG, Bestor TH (2005) Eukaryotic cytosine methyltransferases. Annu Rev Biochem 74:481–514. https://doi.org/10.1146/annurev.biochem.74.010904.153721

    Article  CAS  PubMed  Google Scholar 

  10. Feng S, Jacobsen SE, Reik W (2010) Epigenetic reprogramming in plant and animal development. Science 330(6004):622–627. https://doi.org/10.1126/science.1190614. PubMed PMID: 21030646; PubMed Central PMCID: PMCPMC2989926

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Deaton AM, Bird A (2011) CpG islands and the regulation of transcription. Genes Dev 25(10):1010–1022. https://doi.org/10.1101/gad.2037511. Epub 2011/05/18. doi. PubMed PMID: 21576262; PubMed Central PMCID: PMC3093116.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Harris RA, Wang T, Coarfa C, Nagarajan RP, Hong C, Downey SL et al (2010) Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications. Nat Biotechnol 28(10):1097–1105. https://doi.org/10.1038/nbt.1682. PubMed PMID: 20852635; PubMed Central PMCID: PMCPMC2955169

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Kurdyukov S, Bullock M (2016) DNA methylation analysis: choosing the right method. Biology (Basel) 5(1). https://doi.org/10.3390/biology5010003. PubMed PMID: 26751487; PubMed Central PMCID: PMCPMC4810160

  14. Frommer M, McDonald LE, Millar DS, Collis CM, Watt F, Grigg GW 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(5):1827–1831. PubMed PMID: 1542678; PubMed Central PMCID: PMCPMC48546

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Weber M, Davies JJ, Wittig D, Oakeley EJ, Haase M, Lam WL et al (2005) Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nat Genet 37(8):853–862. https://doi.org/10.1038/ng1598

    Article  CAS  PubMed  Google Scholar 

  16. Cokus SJ, Feng S, Zhang X, Chen Z, Merriman B, Haudenschild CD et al (2008) Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning. Nature 452(7184):215–219. https://doi.org/10.1038/nature06745. PubMed PMID: 18278030; PubMed Central PMCID: PMCPMC2377394

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Bibikova M, Barnes B, Tsan C, Ho V, Klotzle B, Le JM et al (2011) High density DNA methylation array with single CpG site resolution. Genomics 98(4):288–295. https://doi.org/10.1016/j.ygeno.2011.07.007

    Article  CAS  PubMed  Google Scholar 

  18. Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD et al (2014) Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 30(10):1363–1369. https://doi.org/10.1093/bioinformatics/btu049. PubMed PMID: 24478339; PubMed Central PMCID: PMCPMC4016708

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Price ME, Cotton AM, Lam LL, Farre P, Emberly E, Brown CJ et al (2013) Additional annotation enhances potential for biologically-relevant analysis of the Illumina Infinium HumanMethylation450 BeadChip array. Epigenetics Chromatin 6(1):4. https://doi.org/10.1186/1756-8935-6-4. PubMed PMID: 23452981; PubMed Central PMCID: PMCPMC3740789

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Zhang X, Mu W, Zhang W (2012) On the analysis of the illumina 450k array data: probes ambiguously mapped to the human genome. Front Genet 3:73. https://doi.org/10.3389/fgene.2012.00073. PubMed PMID: 22586432; PubMed Central PMCID: PMCPMC3343275

    PubMed  PubMed Central  Google Scholar 

  21. Chen YA, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW et al (2013) Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics 8(2):203–209. https://doi.org/10.4161/epi.23470. PubMed PMID: 23314698; PubMed Central PMCID: PMCPMC3592906

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Teschendorff AE, Marabita F, Lechner M, Bartlett T, Tegner J, Gomez-Cabrero D et al (2013) A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data. Bioinformatics 29(2):189–196. https://doi.org/10.1093/bioinformatics/bts680. PubMed PMID: 23175756; PubMed Central PMCID: PMCPMC3546795

    Article  CAS  PubMed  Google Scholar 

  23. Pidsley R, CC YW, Volta M, Lunnon K, Mill J, Schalkwyk LC (2013) A data-driven approach to preprocessing Illumina 450K methylation array data. BMC Genomics 14:293. https://doi.org/10.1186/1471-2164-14-293. PubMed PMID: 23631413; PubMed Central PMCID: PMCPMC3769145

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Dedeurwaerder S, Defrance M, Bizet M, Calonne E, Bontempi G, Fuks F (2014) A comprehensive overview of Infinium HumanMethylation450 data processing. Brief Bioinform 15(6):929–941. https://doi.org/10.1093/bib/bbt054. PubMed PMID: 23990268; PubMed Central PMCID: PMCPMC4239800

    Article  PubMed  Google Scholar 

  25. Maksimovic J, Gordon L, Oshlack A (2012) SWAN: subset-quantile within array normalization for illumina infinium HumanMethylation450 BeadChips. Genome Biol 13(6):R44. https://doi.org/10.1186/gb-2012-13-6-r44. PubMed PMID: 22703947; PubMed Central PMCID: PMCPMC3446316

    Article  PubMed  PubMed Central  Google Scholar 

  26. Touleimat N, Tost J (2012) Complete pipeline for Infinium((R)) human methylation 450K BeadChip data processing using subset quantile normalization for accurate DNA methylation estimation. Epigenomics 4(3):325–341. https://doi.org/10.2217/epi.12.21

    Article  CAS  PubMed  Google Scholar 

  27. Du P, Kibbe WA, Lin SM (2008) lumi: a pipeline for processing Illumina microarray. Bioinformatics 24(13):1547–1548. https://doi.org/10.1093/bioinformatics/btn224

    Article  CAS  PubMed  Google Scholar 

  28. Xu Z, Niu L, Li L, Taylor JA (2016) ENmix: a novel background correction method for Illumina HumanMethylation450 BeadChip. Nucleic Acids Res 44(3):e20. https://doi.org/10.1093/nar/gkv907. PubMed PMID: 26384415; PubMed Central PMCID: PMCPMC4756845

    Article  PubMed  Google Scholar 

  29. Heiss JA, Brenner H (2015) Between-array normalization for 450K data. Front Genet 6:92. https://doi.org/10.3389/fgene.2015.00092. PubMed PMID: 25806048; PubMed Central PMCID: PMCPMC4354407

    Article  PubMed  PubMed Central  Google Scholar 

  30. Fortin JP, Labbe A, Lemire M, Zanke BW, Hudson TJ, Fertig EJ et al (2014) Functional normalization of 450k methylation array data improves replication in large cancer studies. Genome Biol 15(11):503. https://doi.org/10.1186/s13059-014-0503-2. PubMed PMID: 25599564; PubMed Central PMCID: PMCPMC4283580

    Article  PubMed  PubMed Central  Google Scholar 

  31. Johnson WE, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8(1):118–127. https://doi.org/10.1093/biostatistics/kxj037. PubMed PMID: 16632515

    Article  PubMed  Google Scholar 

  32. Teschendorff AE, Zhuang J, Widschwendter M (2011) Independent surrogate variable analysis to deconvolve confounding factors in large-scale microarray profiling studies. Bioinformatics 27(11):1496–1505. https://doi.org/10.1093/bioinformatics/btr171

    Article  CAS  PubMed  Google Scholar 

  33. Leek JT, Storey JD (2007) Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet 3(9):e161–e135. https://doi.org/10.1371/journal.pgen.0030161. Epub 2007/10/03. PubMed PMID: 17907809; PubMed Central PMCID: PMC1994707

    Article  PubMed Central  Google Scholar 

  34. Jaffe AE, Irizarry RA (2014) Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol 15(2):R31. https://doi.org/10.1186/gb-2014-15-2-r31. PubMed PMID: 24495553; PubMed Central PMCID: PMCPMC4053810

    Article  PubMed  PubMed Central  Google Scholar 

  35. Morris TJ, Butcher LM, Feber A, Teschendorff AE, Chakravarthy AR, Wojdacz TK et al (2014) ChAMP: 450k Chip analysis methylation pipeline. Bioinformatics 30(3):428–430. https://doi.org/10.1093/bioinformatics/btt684. PubMed PMID: 24336642; PubMed Central PMCID: PMCPMC3904520

    Article  CAS  PubMed  Google Scholar 

  36. Warden CD, Lee H, Tompkins JD, Li X, Wang C, Riggs AD et al (2013) COHCAP: an integrative genomic pipeline for single-nucleotide resolution DNA methylation analysis. Nucleic Acids Res 41(11):e117. https://doi.org/10.1093/nar/gkt242. PubMed PMID: 23598999; PubMed Central PMCID: PMCPMC3675470

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

We thank Diego Portillo Santos for editing and generating the figures.

Author information

Authors and Affiliations

  1. Center for Genes, Environment and Health, Department of Biomedical Research, National Jewish Health, 1400 Jackson Street, Denver, CO, 80206, USA

    Fatjon Leti

  2. Neurogenomics Division, Translational Genomics Research Institute, 445 N 5th St, Phoenix, AZ, 85004, USA

    Lorida Llaci

  3. Division of Biostatistics, University of California, Berkeley, 101 Haviland Hall, Berkeley, CA, 94720, USA

    Ivana Malenica

  4. Translational Genomics Research Institute, 445 N 5th Street, Phoenix, AZ, 85004, USA

    Johanna K. DiStefano

Authors
  1. Fatjon Leti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lorida Llaci
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ivana Malenica
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Johanna K. DiStefano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fatjon Leti .

Editor information

Editors and Affiliations

  1. Translational Genomics Research Institute, Phoenix, Arizona, USA

    Johanna K. DiStefano

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Leti, F., Llaci, L., Malenica, I., DiStefano, J.K. (2018). Methods for CpG Methylation Array Profiling Via Bisulfite Conversion. In: DiStefano, J. (eds) Disease Gene Identification. Methods in Molecular Biology, vol 1706. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7471-9_13

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7471-9_13

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7470-2

  • Online ISBN: 978-1-4939-7471-9

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation