Assessing the efficiency and significance of Methylated DNA Immunoprecipitation (MeDIP) assays in using in vitro methylated genomic DNA
- Jinsong Jia†1, 2, 3,
- Aleksandra Pekowska†1, 2, 3,
- Sebastien Jaeger1, 2, 3,
- Touati Benoukraf1, 2, 3,
- Pierre Ferrier1, 2, 3Email author and
- Salvatore Spicuglia1, 2, 3Email author
© Ferrier et al; licensee BioMed Central Ltd. 2010
Received: 3 May 2010
Accepted: 16 September 2010
Published: 16 September 2010
DNA methylation contributes to the regulation of gene expression during development and cellular differentiation. The recently developed Methylated DNA ImmunoPrecipitation (MeDIP) assay allows a comprehensive analysis of this epigenetic mark at the genomic level in normal and disease-derived cells. However, estimating the efficiency of the MeDIP technique is difficult without previous knowledge of the methylation status of a given cell population. Attempts to circumvent this problem have involved the use of in vitro methylated DNA in parallel to the investigated samples. Taking advantage of this stratagem, we sought to improve the sensitivity of the approach and to assess potential biases resulting from DNA amplification and hybridization procedures using MeDIP samples.
We performed MeDIP assays using in vitro methylated DNA, with or without previous DNA amplification, and hybridization to a human promoter array. We observed that CpG content at gene promoters indeed correlates strongly with the MeDIP signal obtained using in vitro methylated DNA, even when lowering significantly the amount of starting material. In analyzing MeDIP products that were subjected to whole genome amplification (WGA), we also revealed a strong bias against CpG-rich promoters during this amplification procedure, which may potentially affect the significance of the resulting data.
We illustrate the use of in vitro methylated DNA to assess the efficiency and accuracy of MeDIP procedures. We report that efficient and reproducible genome-wide data can be obtained via MeDIP experiments using relatively low amount of starting genomic DNA; and emphasize for the precaution that must be taken in data analysis when an additional DNA amplification step is required.
DNA methylation at CpG dinucleotides is a major epigenetic modification with direct implications in many aspects of mammalian biology, including development and disease . In normal tissues, most promoter-associated CpGs remain unmethylated, although DNA methylation does occur at promoters of a small set of genes where it generally leads to transcriptional silencing. On the other hand, cancer cells undergo dramatic changes in the level and distribution of DNA methylation . Indeed, the DNA methylation-dependent silencing of many tumor suppressor genes is now recognized as a major mechanism of gene inactivation that complements genetic lesions. Recent technological advances have allowed the comprehensive analysis of DNA methylation profiles in normal and disease-associated cells [3–6]. In particular, the Methylated DNA ImmunoPrecipitation (MeDIP) assay appears to be an efficient, reproducible and cost-effective approach to characterize the methylome of large collections of DNA samples [7–10]. The overall experimental strategy is based on immunoprecipitation of methylated CpGs using a specific anti-5-methylcytidine antibody (MeDIP), as a rule followed by DNA amplification and hybridization to, typically, either CpG islands or promoter arrays. However, because the efficiency of the MeDIP assay relates to the methylated (m)CpG content and distribution within each particular genomic region , the quantification of DNA methylation remains approximate [11, 12]. To accurately quantify CpG methylation levels, others have used in vitro methylated DNA in parallel to the investigated, untreated samples [e.g., [12, 13]]. Here, we took advantage of this stratagem to further evaluate potential bias resulting from using MeDIP samples for DNA amplification, labeling and hybridization procedures; and also to better access the sensitivity of the overall approach.
Results and Discussion
We have used in vitro methylated DNA samples in an unbiased approach to assess the efficiency of the MeDIP procedure. Verifying genome-wide correlations between MeDIP signals from in vitro methylated DNA samples and actual CpG contents at gene promoters was important since in vitro methylated DNA was proposed to be used routinely to assess absolute methylation levels in MeDIP assays [e.g. [12, 13]]. In this regard, a recently developed algorithm aimed at the estimation of methylation levels at individual promoters, has been implemented to also take into account the signals from in vitro methylated DNA samples . Recent works using single-base resolution maps of methylated cytosines in human embryonic stem cells have identified cytosine methylation in a non-CpG context [6, 18]. Because MeDIP apparently enables the capture of non-CpG methylation , experimental and in silico attempts to quantify absolute levels of DNA methylation should also take this particular feature into account. While our results reveal the loss of signal for CpG-rich regions following WGA, quantitative information can still be retrieved for low and intermediate CpG promoters (Figure 3). The latter observation is particularly relevant as it is currently thought that these regions precisely undergo de novo methylation in transformed cells [e.g., ]. These points need to be considered when only a limited amount of DNA is available (e.g., in analysis of tumor tissues or rare cell subpopulations) and amplification-based methods such as WGA are unavoidable. Notice that the bias observed after WGA amplification in this study of DNA methylation may be of a broader interest, as this amplification procedure is commonly used in several other types of quantitative assays, including ChIP-on-chip, comparative genomic hybridization (CGH) and single nucleotide polymorphism (SNP) [21–24]. Finally, we also demonstrate here that consistent and accurate genome-wide methylation data can be reproducibly attained in array hybridization using MeDIP materials obtained from as little as 2 μg of starting genomic DNA (an amount that is regularly available from cancer tissue biopsies), without the need for additional amplification steps.
DNA preparation and in vitro methylation
Genomic DNA from the human T-acute lymphoblastic leukemia cell line SilALL  was sonicated to a range of 300-500 bp, using a Bioruptor (Diagenode, Liège, Belgium). In vitro methylation was achieved by incubating the sonicated DNA with 20 units of the CpG-methyltransferase M.SssI (New England Biolabs, Frankfurt, Germany) for 4 h at 37°C , followed by DNA purification with the Qiaquick PCR kit (Qiagen, Hilden, Germany).
Methylated DNA Immunoprecipitation (MeDIP) assays
Methylated DNA was immunoprecipitated as described previously  with a few modifications. Briefly, 2 μg of denatured DNA was incubated with 2 μg of anti-5-methylcytidine antibody (Eurogentec, Seraing, Belgium) in IP buffer (10 mM Na-Phosphate pH 7.0, 0.14 M NaCl, 0.05% Triton X-100) for 2 h at 4°C. Antibody-bound DNA was collected with 40 μl of Dynabeads M-280 sheep anti-mouse IgG (Invitrogen Dynal, Oslo, Norway) for 1 h at 4°C on a rotating wheel and successively washed with buffer I (0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Tris-HCl pH 8.1, 150 mM NaCl), buffer I complemented with 500 mM NaCl, LiCl buffer (250 mM LiCl, 1% IGEPAL-CA630, 1% deoxycholic acid, 1 mM EDTA, 10 mM Tris-HCl pH 8.1) and twice with TE (10 mM Tris·Cl, 1 mM EDTA pH 8.0). The beads were resuspended in 125 μl PK buffer (50 mM Tris pH 8.0, 10 mM EDTA, 0.5% SDS, 35 μg proteinase K) and incubated for 3 h at 50°C. DNA was extracted by standard phenol/chloroform procedure and purified as above. The DNA from one MeDIP experiment was subjected to amplification using the WGA-2 kit (Sigma-Aldrich, Taufkirchen, Germany). Subsequently, 2 μg of DNA from either 10 pooled samples or a WGA amplified sample (MeDIPWGA), along with their corresponding whole genomic DNA (input), were labeled using the BioPrime Array CGH Genomic Labeling System (Invitrogen) and hybridized to a custom human promoter array (Agilent, Santa Clara, USA) containing 236,992 probes, following the microarray manufacturer's instructions. In experiments shown in Figure 3, the DNA obtained from a single MeDIP experiment (usually ~250 ng) was labeled using a modified protocol in which Cy3-dUTP and Cy5-dUTP were replaced by Cy3-dCTP and Cy5-dCTP during the labeling procedure. In these conditions, we generally obtained more than 4 μg of efficiently labeled DNA (Note that, when using the classical labeling conditions and 2 μg of starting DNA material, we did not obtain labeled samples of high-enough quality to be hybridized onto Agilent arrays; Additional file 2). Finally, median-normalized log2 enrichment ratios (Medip/Input) were calculated using CoCAS software . Experiments were performed in duplicate and showed very high correlation in all cases (R2 > 0.93).
To integrate the contributions of CpG dinucleotides around each probe to the MeDIP signal, we calculated a local CpG density. To compute CpG densities, we weighted the CpGs found in the 800 bp genomic region surrounding each probe by their distance to the probe using a Gaussian distribution.
List of abbreviations used
Methylated DNA Immunoprecipitation
Whole Genome Amplification
comparative genomic hybridization
We thank members of the PF lab for helpful discussions and Dr. J-C Andrau for critical reading of the manuscript. We thank the TAGC platform (Marseille, France) for providing access to the Agilent scanner. Work in the PF laboratory is supported by institutional grants from Inserm and the CNRS, and by specific grants from the "Fondation Princesse Grace de Monaco", the "Association pour la Recherche sur le Cancer" (ARC), the "Agence Nationale de la Recherche" (ANR), the "Institut National du Cancer" (INCa) and the Commission of the European Communities. JJ is supported by the "Fondation Franco-Chinoise pour la Science et ses Applications", the China Scholarship Council and Marseille-Nice Genopole. AP and TB were supported by a Marie Curie research training fellowship and the ANR, respectively; both are now supported by the "Fondation pour la Recherche Medicale" (FRM).
- Klose RJ, Bird AP: Genomic DNA methylation: the mark and its mediators. Trends Biochem Sci. 2006, 31 (2): 89-97. 10.1016/j.tibs.2005.12.008.PubMedView ArticleGoogle Scholar
- Esteller M: Epigenetic gene silencing in cancer: the DNA hypermethylome. Human Molecular Genetics. 2007, 16 (Spec No 1): R50-R59. 10.1093/hmg/ddm018.PubMedView ArticleGoogle Scholar
- Zilberman D, Henikoff S: Genome-wide analysis of DNA methylation patterns. Development. 2007, 134 (22): 3959-3965. 10.1242/dev.001131.PubMedView ArticleGoogle Scholar
- Jacinto FV, Ballestar E, Esteller M: Methyl-DNA immunoprecipitation (MeDIP): hunting down the DNA methylome. Biotechniques. 2008, 44 (1): 35-10.2144/000112708. 37, 39PubMedView ArticleGoogle Scholar
- Lister R, O'Malley RC, Tonti-Filippini J, Gregory BD, Berry CC, Millar AH, Ecker JR: Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell. 2008, 133 (3): 523-536. 10.1016/j.cell.2008.03.029.PubMed CentralPubMedView ArticleGoogle Scholar
- Lister R, Pelizzola M, Dowen RH, Hawkins RD, Hon G, Tonti-Filippini J, Nery JR, Lee L, Ye Z, Ngo QM: Human DNA methylomes at base resolution show widespread epigenomic differences. Nature. 2009, 462 (7271): 315-322. 10.1038/nature08514.PubMed CentralPubMedView ArticleGoogle Scholar
- Keshet I, Schlesinger Y, Farkash S, Rand E, Hecht M, Segal E, Pikarski E, Young RA, Niveleau A, Cedar H: Evidence for an instructive mechanism of de novo methylation in cancer cells. Nat Genet. 2006, 38 (2): 149-153. 10.1038/ng1719.PubMedView ArticleGoogle Scholar
- Weber M, Davies JJ, Wittig D, Oakeley EJ, Haase M, Lam WL, Schubeler D: Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nat Genet. 2005, 37 (8): 853-862. 10.1038/ng1598.PubMedView ArticleGoogle Scholar
- Weber M, Hellmann I, Stadler MB, Ramos L, Paabo S, Rebhan M, Schubeler D: Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome. Nat Genet. 2007, 39 (4): 457-466. 10.1038/ng1990.PubMedView ArticleGoogle Scholar
- Koga Y, Pelizzola M, Cheng E, Krauthammer M, Sznol M, Ariyan S, Narayan D, Molinaro AM, Halaban R, Weissman SM: Genome-wide screen of promoter methylation identifies novel markers in melanoma. Genome Res. 2009, 19 (8): 1462-1470. 10.1101/gr.091447.109.PubMed CentralPubMedView ArticleGoogle Scholar
- Down TA, Rakyan VK, Turner DJ, Flicek P, Li H, Kulesha E, Graf S, Johnson N, Herrero J, Tomazou EM: A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis. Nat Biotechnol. 2008, 26 (7): 779-785. 10.1038/nbt1414.PubMed CentralPubMedView ArticleGoogle Scholar
- Pelizzola M, Koga Y, Urban AE, Krauthammer M, Weissman S, Halaban R, Molinaro AM: MEDME: an experimental and analytical methodology for the estimation of DNA methylation levels based on microarray derived MeDIP-enrichment. Genome Res. 2008, 18 (10): 1652-1659. 10.1101/gr.080721.108.PubMed CentralPubMedView ArticleGoogle Scholar
- Gal-Yam EN, Egger G, Iniguez L, Holster H, Einarsson S, Zhang X, Lin JC, Liang G, Jones PA, Tanay A: Frequent switching of Polycomb repressive marks and DNA hypermethylation in the PC3 prostate cancer cell line. Proc Nat Acad Sci USA. 2008, 105 (35): 12979-12984. 10.1073/pnas.0806437105.PubMed CentralPubMedView ArticleGoogle Scholar
- Minowada J: Leukemia cell lines. Cancer Res. 1988, 10 (10): 1-18.Google Scholar
- Zhang X, Yazaki J, Sundaresan A, Cokus S, Chan SW, Chen H, Henderson IR, Shinn P, Pellegrini M, Jacobsen SE: Genome-wide high-resolution mapping and functional analysis of DNA methylation in arabidopsis. Cell. 2006, 126 (6): 1189-1201. 10.1016/j.cell.2006.08.003.PubMedView ArticleGoogle Scholar
- Hiura H, Sugawara A, Ogawa H, John RM, Miyauchi N, Miyanari Y, Horiike T, Li Y, Yaegashi N, Sasaki H: A tripartite paternally methylated region within the Gpr1-Zdbf2 imprinted domain on mouse chromosome 1 identified by meDIP-on-chip. Nucleic Acids Res. 2010Google Scholar
- Saxonov S, Berg P, Brutlag DL: A genome-wide analysis of CpG dinucleotides in the human genome distinguishes two distinct classes of promoters. Proc Natl Acad Sci USA. 2006, 103 (5): 1412-1417. 10.1073/pnas.0510310103.PubMed CentralPubMedView ArticleGoogle Scholar
- Ramsahoye BH, Biniszkiewicz D, Lyko F, Clark V, Bird AP, Jaenisch R: Non-CpG methylation is prevalent in embryonic stem cells and may be mediated by DNA methyltransferase 3a. Proc Natl Acad Sci USA. 2000, 97 (10): 5237-5242. 10.1073/pnas.97.10.5237.PubMed CentralPubMedView ArticleGoogle Scholar
- Barres R, Osler ME, Yan J, Rune A, Fritz T, Caidahl K, Krook A, Zierath JR: Non-CpG methylation of the PGC-1alpha promoter through DNMT3B controls mitochondrial density. Cell Metab. 2009, 10 (3): 189-198. 10.1016/j.cmet.2009.07.011.PubMedView ArticleGoogle Scholar
- Irizarry RA, Ladd-Acosta C, Wen B, Wu Z, Montano C, Onyango P, Cui H, Gabo K, Rongione M, Webster M: The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores. Nat Genet. 2009, 41 (2): 178-186. 10.1038/ng.298.PubMed CentralPubMedView ArticleGoogle Scholar
- Little SE, Vuononvirta R, Reis-Filho JS, Natrajan R, Iravani M, Fenwick K, Mackay A, Ashworth A, Pritchard-Jones K, Jones C: Array CGH using whole genome amplification of fresh-frozen and formalin-fixed, paraffin-embedded tumor DNA. Genomics. 2006, 87 (2): 298-306. 10.1016/j.ygeno.2005.09.019.PubMedView ArticleGoogle Scholar
- Hughes S, Arneson N, Done S, Squire J: The use of whole genome amplification in the study of human disease. Prog Biophys Mol Biol. 2005, 88 (1): 173-189. 10.1016/j.pbiomolbio.2004.01.007.PubMedView ArticleGoogle Scholar
- Bergen AW, Haque KA, Qi Y, Beerman MB, Garcia-Closas M, Rothman N, Chanock SJ: Comparison of yield and genotyping performance of multiple displacement amplification and OmniPlex whole genome amplified DNA generated from multiple DNA sources. Hum Mutat. 2005, 26 (3): 262-270. 10.1002/humu.20213.PubMedView ArticleGoogle Scholar
- Barker DL, Hansen MS, Faruqi AF, Giannola D, Irsula OR, Lasken RS, Latterich M, Makarov V, Oliphant A, Pinter JH: Two methods of whole-genome amplification enable accurate genotyping across a 2320-SNP linkage panel. Genome Res. 2004, 14 (5): 901-907. 10.1101/gr.1949704.PubMed CentralPubMedView ArticleGoogle Scholar
- Fatemi M, Pao MM, Jeong S, Gal-Yam EN, Egger G, Weisenberger DJ, Jones PA: Footprinting of mammalian promoters: use of a CpG DNA methyltransferase revealing nucleosome positions at a single molecule level. Nucleic Acids Res. 2005, 33 (20): e176-10.1093/nar/gni180.PubMed CentralPubMedView ArticleGoogle Scholar
- Benoukraf T, Cauchy P, Fenouil R, Jeanniard A, Koch F, Jaeger S, Thieffry D, Imbert J, Andrau JC, Spicuglia S: CoCAS: a ChIP-on-chip analysis suite. Bioinformatics (Oxford, England). 2009, 25 (7): 954-955. 10.1093/bioinformatics/btp075.View ArticleGoogle Scholar
- Taylor KH, Pena-Hernandez KE, Davis JW, Arthur GL, Duff DJ, Shi H, Rahmatpanah FB, Sjahputera O, Caldwell CW: Large-scale CpG methylation analysis identifies novel candidate genes and reveals methylation hotspots in acute lymphoblastic leukemia. Cancer Res. 2007, 67 (6): 2617-2625. 10.1158/0008-5472.CAN-06-3993.PubMedView ArticleGoogle Scholar