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Published online before print
March 3, 2008, 10.1101/gr.7301508 Genome Res. 18:780-790, 2008 ©2008 by Cold Spring Harbor Laboratory Press; ISSN 1088-9051/08 $5.00 OPEN ACCESS ARTICLE
Methods Comprehensive high-throughput arrays for relative methylation (CHARM)1 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA; 2 Department of Medicine and Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA; 3 Orion Genomics, LLC, St. Louis, Missouri 63108, USA
This study was originally conceived to test in a rigorous way the specificity of three major approaches to high-throughput array-based DNA methylation analysis: (1) MeDIP, or methylated DNA immunoprecipitation, an example of antibody-mediated methyl-specific fractionation; (2) HELP, or HpaII tiny fragment enrichment by ligation-mediated PCR, an example of differential amplification of methylated DNA; and (3) fractionation by McrBC, an enzyme that cuts most methylated DNA. These results were validated using 1466 Illumina methylation probes on the GoldenGate methylation assay and further resolved discrepancies among the methods through quantitative methylation pyrosequencing analysis. While all three methods provide useful information, there were significant limitations to each, specifically bias toward CpG islands in MeDIP, relatively incomplete coverage in HELP, and location imprecision in McrBC. However, we found that with an original array design strategy using tiling arrays and statistical procedures that average information from neighboring genomic locations, much improved specificity and sensitivity could be achieved, e.g.,  100% sensitivity at 90% specificity with McrBC. We term this approach "comprehensive high-throughput arrays for relative methylation" (CHARM). While this approach was applied to McrBC analysis, the array design and computational algorithms are fractionation method-independent and make this a simple, general, relatively inexpensive tool suitable for genome-wide analysis, and in which individual samples can be assayed reliably at very high density, allowing locus-level genome-wide epigenetic discrimination of individuals, not just groups of samples. Furthermore, unlike the other approaches, CHARM is highly quantitative, a substantial advantage in application to the study of human disease.
The methylome is defined as the comprehensive picture of DNA methylation across the genome, and it is an important shift in focus from the individual gene level (Feinberg 2001  ). The rationale for this view is that our focus on methylation in the promoters of known genes is too constrained, that much of methylation is not where one looks. Despite introduction of the word "methylome" into the literature 6 yr ago, DNA methylation has made the least progress of any functional element in its understanding from a genomic perspective (Callinan and Feinberg 2006). This is ironic as DNA methylation is relatively well understood from a gene perspective, i.e., its method of propagation is well known, in comparison to chromatin modification, and DNA methylation has a strong link to the DNA sequence itself, i.e., encoding specifically at CpG dinucleotides, all of these much more so than other types of epigenetic information, such as chromatin modification.
Why has so little progress been made in understanding the methylome? Two major limitations may be responsible. First is a fundamental bias regarding the location of methylation modification in disease and even in studies of variation in tissues, i.e., largely restricted to "CpG islands," and limitations in the detection methods themselves. Bird introduced the concept of a CpG island in 1987 (Bird et al. 1987
The second reason for the slow pace of understanding the methylome is substantial limitations in current technology affecting sensitivity, specificity, throughput, quantitation, and cost among the currently used detection methods. The most commonly used methods can themselves be divided into three categories (Table 1): (1) Bisulfite DNA sequencing. This involves chemical conversion of cytosine to uracil by sodium bisulfite or metabisulfite, followed by PCR (which incorporates T for U), and then DNA sequencing. While providing single-base resolution, the cost is the highest of all the commonly used methods, tens of thousands of dollars for a megabase of sequence data, itself comprising 40,384 CpG dinucleotides assayed (Eckhardt et al. 2006
There are four major types of microarray-based methylation analysis. (1) Direct hybridization to CpG island arrays. This was one of the earliest methods; it was used to provide valuable data on tumor-type classification, for example (Gitan et al. 2002 ), and it still remains a useful discovery tool. However, its earliest developers have migrated away from this approach, since it requires presupposition about the potentially methylated sequences. (2) Methylated DNA immunoprecipitation (MeDIP), in which methylated DNA is fractionated using an antibody and then hybridized, with a differentially labeled total DNA control, to an oligonucleotide array (Weber et al. 2005). (3) Restriction enzyme digestion using methylcytosine-sensitive enzymes, followed by ligation-mediated PCR amplification of the targets. The paradigm of this method is the HELP (HpaII tiny fragment enrichment by ligation-mediated PCR) assay (Khulan et al. 2006). DNA is digested in parallel with MspI (resistant to DNA methylation), and then the HpaII and MspI products are amplified by ligation-mediated PCR and hybridized using separate fluorochromes to a customized array. As HpaII sites comprise 8% of CpG, that represents a fixed limit of sensitivity of the method. Alternatively, the restriction enzyme-digested DNA can be directly sequenced rather than hybridized to microarrays (Allinen et al. 2004), although one is still limited by the relatively small number of methylcytosine-sensitive restriction sites in the genome. (4) Restriction enzyme digestion of methylated DNA using McrBC, without PCR, and differential hybridization to an array. DNA is digested with McrBC, an enzyme with the unusual and desirable property of cutting methylated DNA promiscuously (recognition sequence RmC(N)55–103RmC), cleaving half of the methylated DNA in the genome and all methylated CpG islands (Sutherland et al. 1992). The enzyme is used on size-selected (1.5–4.0 kb) DNA to fractionate unmethylated (i.e., gel-purified high molecular weight) DNA after digestion, which is comparatively (two-color) hybridized with DNA similarly processed but not cut with the enzyme, on high density arrays. The original method was developed for Arabidopsis (Lippman et al. 2004), where it has value in eliminating the large fraction of methylated repetitive DNA in the plant genome. For mammalian genome application, a selection algorithm has been applied to obtain specific array probes thought to represent the state of a given methylation target (Ordway et al. 2006).
Although all of the microarray approaches are in common use, they have not been directly compared to each other, and our original goals were relatively modest: to directly compare methods using the same DNA samples and the same arrays. However, we found significant limitations generally to hybridization-based methylation analysis that could largely be overcome with novel statistical procedures and array design algorithms. As will be described in the second portion of the paper, a fractionation method-independent approach, termed CHARM (comprehensive high-throughout arrays for relative methylation), can detect DNA genome-wide methylation with
Overall design Here we have designed a study to compare three array-based methylation detection technologies, MeDIP as an example of immunoprecipitation-based methods, McrBC fractionation as an example of restriction enzyme fractionation, and HELP as an example of differential methylcytosine sensitive ligation-mediated PCR. As our test samples, two paired cell lines were used: HCT116, a highly methylated colorectal carcinoma line, and a DNA methyltransferase I and 3B double-knockout cell line (DKO), with comparatively low levels of methylation (Rhee et al. 2002 ). These data were also compared to direct bisulfite methylation analysis using the Illumina GoldenGate methylation assay (Bibikova et al. 2006) on 1466 CpG sites in 466 genes.
For all three assay types, design-specific arrays have already been designed, and we followed these designs, referred to here as canonical arrays. However, to enable direct comparison on the same arrays, samples were hybridized to NimbleGen’s Promoter 2 array and designed two tiling arrays (see Methods), which are referred to herein as common arrays. Note that in the case of MeDIP, one of the common arrays is the same as the canonical array, i.e., the Promoter 2 array. Because of the flexibility of design, the NimbleGen platform was used in all cases, which has also been used by the originators of these assay systems. In each case, a competitive hybridization approach was performed, in which samples were differentially labeled with Cy3 and Cy5 as described in the experimental protocols, specifically: (1) for MeDIP, methyl-enriched DNA with Cy5 and total DNA with Cy3; (2) for HELP, HpaII amplified with Cy5 and MspI with Cy3; and (3) for McrBC, methyl-depleted with Cy5, and total with Cy3. Note that McrBC dye-swaps were created as recommended by the original publication for mammalian DNA (Ordway et al. 2006
To decide among various strategies for measuring the same quantity, one looks to optimize sensitivity and specificity. Because specificity can be easily improved at the cost of sensitivity, and vice versa, one needs to assess both independently. We designed our experiments to assess sensitivity and specificity in the practical context of detecting methylated sites. To appropriately assess how experimental variability affects specificity, two technical replicates were performed for each method/sample-type pair (see Table 2). Measurements of methylation should be the same in both replicates, and deviation from equal values serves a measure of precision, which directly affects the specificity for measurements of methylation levels. The assessment of specificity was also facilitated by the use of the DKO samples. These provided many unmethylated sites useful for this assessment: Methods with low specificity will be more likely to call unmethylated sites as methylated. The HCT116 samples permitted a comprehensive assessment of sensitivity as many sites were methylated: Methods with high sensitivity will be more likely to call methylated sites as methylated (true positives). The Illumina GoldenGate assay was used as a reference against which all microarray methods were compared.
Quantification of methylation measurements
Note that M is a continuous variable, so that methylation could be assessed in a quantitative way, which has not been performed previously for array-based methylation analysis. This is critical for biological analysis, since epigenetic information is often chromosome-specific, e.g., imprinted genes. Furthermore, DNA methylation may have a threshold effect for regulating gene expression, e.g.,
MeDIP is comparatively imprecise The standard deviation (SD) of these differences, taken across probes, is a useful summary that relates directly to the range of M-values one should expect from samples with no difference in methylation status. For McrBC, the standard deviations (SDs) were 0.20 and 0.15 for the DKO and HCT116 samples, respectively (Table 3). For the HELP method, the SD was 0.27 for both samples. Finally, the MeDIP showed the worst precision with SDs of 0.55 and 0.60 for the DKO and HCT116 samples, respectively (Table 3). These results were for the M-values obtained from the canonical arrays. A graphical assessment of precision is shown in Supplemental Figure 2.
McrBC and HELP can discriminate DKO from HCT116 A global assessment of sensitivity was performed by comparing the distribution of the M-values from the HCT116 and DKO samples, i.e., a highly methylated and a highly unmethylated reference sample, respectively. Thus the expected M-values for DKO sample should mostly be centered at 0, and HCT116 should be shifted to a substantial number of positive values. Figure 1 demonstrates that the MeDIP method can barely distinguish between the two cell lines of differing methylation on a global scale, although at individual loci differences are clearly seen (discussed below). The McrBC and HELP arrays perform better at globally distinguishing the DKO from the HCT116 sample, with HELP to a somewhat greater degree.
Site-specific comparison of methods The ability to distinguish sample methylation globally is not nearly as important as the ability to detect methylation at high genomic resolution. We therefore compared the performance of each method at the individual CpG level using the Illumina platform as reference standard, based on studies from us and others (Bibikova et al. 2006 ; Ladd-Acosta 2007), as well as data from this study shown in Supplemental Figure 3. For each method/array combination, each CpG assayed by the Illumina platform was matched to one M-value obtained from the microarray data. We now describe how this mapping was obtained for each method. For the McrBC method, we predicted the start and end of the resulting genomic segments after cutting at every ACG or GCG. These are referred to as the McrBC segments. For each probe on the Illumina platform representing an ACG or GCG we assigned two segments: those ending and starting on that CG. Next, for each probe on the microarray, we determined which McrBC segment contained it. Finally, the median M-value for all the microarray probes mapped to each Illumina probe was assigned as the microarray M-value. The McrBC canonical arrays used three to four replicate probes for 21,143 locations, as recommended by those authors (Ordway et al. 2006). Thus, at least four probes were used with each Illumina probe. A similar approach was used for the HELP method except the cleavage occurs at CCGG sites (Khulan et al. 2006). The HELP canonical arrays used 14–15 tiled probes in each of the HELP segments. The canonical arrays for MeDIP were the Promoter 2 arrays, which represent 12,892 promoter regions. We matched every CpG inside these promoter regions to the closest probe also in that region. We were then able to map CpGs represented by an Illumina probe, and included in one of the promoter regions, to one microarray probe M-value. Figure 2 demonstrates this comparison, using 587, 57, 51, and 1188 Illumina CpGs corresponding to specific CpGs on the MeDIP, McrBC, HELP, and CHARM arrays, respectively. The set of CpG covered by all platforms was too small to provide meaningful results, thus we based our comparisons on the different sets mapped by the different array types.
Sensitivity of HELP and MeDIP depends greatly on the CpG content Figure 2 plots M-values from each of the microarray platforms against the corresponding M-values obtained from the Illumina platform. Values from the HCT116 and DKO samples were combined. For clarity, in Figure 2, data are shown from one HCT116 and one DKO array for each method. Results for all other arrays, i.e., the replicates, are similar and are shown in Supplemental Figure 4. Figure 2 stratifies points by CpG density. The observed-to-expected ratio for 500-bp regions was computed around each microarray probe shown in Figure 2 (ratios are denoted with color and with a small number inside each point). In this window we defined the expected number of CpGs as the proportion of Cs multiplied by the proportion of Gs. The observed-to-expected ratio is simply the proportion of CpGs divided by the expected proportion of CpGs. Notice that the traditional definition of a CpG island requires this ratio to be >0.6. The probes were stratified into two groups: low CpG density (ratio  0.6) and high CpG density (ratio > 0.6). A regression line was fitted to each group (shown as red and blue lines for the low- and high-density groups, respectively). The correlation between Illumina M-values and microarray M-values is shown in Table 4. While McrBC showed similar sensitivity for both high- and low-density groups, HELP showed better sensitivity for the lower CpG density group than for the higher CpG density group.
Severe bias in current methods related to segment characteristics For HCT116 samples, we stratified the M-values obtained from the McrBC and HELP canonical arrays by segment size to produce Figure 3A. Because in this sample one expects many methylated CpGs, many large M-values are expected independent of the segment size. However, the strata related to large and small fragments had substantially fewer large M-values than the middle-sized segments. Notice in particular that the HELP method had no sensitivity for CpGs associated with segments smaller than 300 bp. The McrBC method had no sensitivity for CpGs associated with segments larger than 1500 bp. Best results were observed for segments of sizes 200–600 and 700–1200 bp for McrBC and HELP, respectively. The segment sizes for MeDIP are unpredictable, thus, this method was not included in this figure.
We also assessed the effect of CpG density with this stratification approach. As in Figure 2, we formed a 500-bp segment around the location of each probe and calculated the observed-to-expected ratio. These were then stratified by their observed-to-expected ratio (Fig. 3B). As first noticed in Figure 2, the HELP method has low sensitivity for high CpG density and the MeDIP method had low sensitivity for low CpG densities.
General limitations in single-CpG accuracy substantially improved by genome-weighted smoothing
The fact that the methylation status of neighboring CpGs tends to be highly correlated (Eckhardt et al. 2006
Figure 4 demonstrates the advantage of genome-weighted smoothing. In this figure, M-values are plotted against location on the genome. The points are the M-values observed for each probe. The averaged M-values for probes in the same McrBC and HELP segments are shown with orange and green lines for McrBC and HELP, respectively. The results obtained using genome-weighted smoothing (described above) are shown with black curves. Note that for the McrBC and MeDIP methods, the range of the probe-level and segment M-values associated with unmethylated (Fig. 4A) and methylated (Fig. 4B) regions overlap; the results from smoothing do not. For example, for McrBC the segment M-values range from –0.75 to 0.5 and from –0.75 to 3 for the unmethylated and methylated regions, respectively. The values obtained from smoothing range from –0.2 to 0.25 and from 0.6 to 2.5 for the unmethylated and methylated regions, respectively. The averaging performed in the smoothing procedure greatly reduces noise, and the fact that the averaging is local, i.e., performed in small regions, permits us to preserve the ability to discriminate. Supplemental Figure 5 shows examples of various other regions illustrating the value of this approach. The HELP method sometimes produced contradictory results at the same loci that were not apparent in the canonical design but were easy to see in the common array design (Fig. 4; Supplemental Fig. 5). This likely explained the lack of agreement with the reference method (Fig. 2). Because the HELP segments are small for the region shown in Figure 4, this result was expected, as Figure 3 demonstrates that the HELP method is not sensitive for small fragments. Supplemental Figure 5 shows several other examples.
CHARM, comprehensive high-throughput arrays for relative methylation The first component of our method is a new tiling array specifically designed to maximize the number of assayed CpGs. For the reasons stated above, we did not want to restrict our attention to CpG islands. Instead, the number of CpGs assayed, for which we could reliably detect methylation status, were maximized. For example, because we rely on smoothing, isolated CpGs were not assayed. A careful analysis of different numbers of probes included in the smoothing demonstrated that at least 15 probe intensities were needed to obtain useful results (data not shown). The procedure for creating the array was as follows:
This array design would improve the detection strategy for any of the methods because it facilitates the smoothing strategy and assays many more CpGs. Probes associated with problematic segments (e.g., very small segments in the HELP assay) could be removed in the analysis stage. However, we selected McrBC for the application of this approach because of its superior sensitivity and specificity described earlier. Going forward, samples were also hybridized using the CHARM design as well as the MeDIP assay as well. We did not continue to use the HELP assay mainly because of its limited number of detectable sites (HpaII dependence). To detect methylated regions in the CHARM method, the M-values were normalized, as described in the Methods section, and processed using genome-weighted smoothing, as described above. Figure 2D shows the smoothed M-values obtained from CHARM plotted against the reference M-values. Comparing Figure 2D with Figure 2, A–C, demonstrates how CHARM greatly improved the results obtained with the other methods.
Although it is potentially useful to treat methylation state as a continuous variable (Rakyan et al. 2004
Finally, we note that the CHARM method, unlike MeDIP, HELP, or nonsmoothed McrBC, is highly quantitative, meaning that there was a linear relationship between methylation measured on the array and the reference methylation platform (Illumina), as shown clearly in Figure 2. The correlation coefficient comparing these two values was substantially better for CHARM compared to the other methods (Table 4), as was the ROC curve (Fig. 5).
In summary, there are two major results of this work. First, we have shown that there are substantial limitations to all three commonly used approaches for array-based DNA methylation analysis. In the case of MeDIP, the assay is of relatively worse specificity, and the method is not sensitive, particularly outside of CpG islands. HELP, while accurately distinguishing markedly different cell types globally, does not cover many CpG dinucleotides because of the dependence on HpaII restriction sites and often shows lack of agreement with the reference method. Of the three approaches, McrBC performed the best, but as seen in the ROC curves, the sensitivity was only 60% at 90% specificity as previously practiced. Second, since neighboring CpGs have been shown to be closely correlated, we developed a novel genome-weighted smoothing algorithm to measure methylation from raw microarray data. Combining this novel approach with the most robust method for fractionating methylated DNA (McrBC), we designed custom arrays ideally suited for methylation detection, as defined in the Results section. This approach is termed "comprehensive high-throughput arrays for relative methylation" (CHARM). CHARM offers the possibility of relatively inexpensive genome-wide analysis with high precision and accuracy. On the NimbleGen HD2 arrays, 2.1 million features can be studied in this way. The approach was data-driven, in that it used an independent assessment of 1466 CpG sites. Furthermore, the genome coverage on the array is genome sequence-driven, rather than based on arbitrary assumptions about the likely location of methylated sites (e.g., promoters) that might miss substantial numbers of regulatory sequences. Even with this unbiased, non-promoter-driven selection strategy, 87% of the Illumina-selected methylation cancer panel 1 genes are present on the HD2 array.
What were the likely inherent limitations of MeDIP and HELP shown by these experiments? The results obtained with the MeDIP method barely distinguished the HCT116 and DKO samples. A likely reason is that the immunoprecipitation (IP) step is not specific, i.e., unmethylated CpGs pass the filter of IP. This is consistent with the observation that detection was biased toward very high CpG content. Furthermore, note that the IP sample will be enriched with CpGs regardless of the number of segments that pass the filter. This is likely to result in cross-hybridization problems, e.g., probes with more CpGs might result in higher intensities only because of cross-hybridization with the high CpG content sample. In expression arrays it has been shown that background noise, such as cross-hybridization, greatly reduces sensitivity in cases were nominal amounts of target are low (Irizarry et al. 2006
While HELP outperformed other methods in distinguishing the highly methylated HCT116 from the relatively unmethylated DKO globally, at the single-CpG level, the HELP method performed barely better than MeDIP. A possible explanation for this apparent contradiction is that the HELP method depends upon differences in ability of a fragment to be amplified, but the PCR step does not always amplify as expected. For example, in dense CpG regions, the smaller pieces, which are expected to amplify, might be too small for the PCR to work properly. Evidence that this phenomenon is occurring is the fact that the microarray data for HELP sometimes appears flipped in plots, such as in Figure 4: fragments were methylation-amplified opposite from the expected. It is important to note that the canonical design for HELP carefully selects regions where this phenomenon is unlikely to occur. But as mentioned, this greatly limits the coverage of the method. More sophisticated post-processing algorithms have been and likely will be further developed to correct for measurement discrepancies (Khulan et al. 2006
McrBC fractionation was originally applied to analysis of the plant genome (Martienssen et al. 2005
Future work on CHARM includes the development of preprocessing algorithms that correct for sequence and segment effects. The resulting methods should improve the performance of CHARM within CpG islands. Finally, we note that while CHARM offers state-of-the-art, cost-effective methylation analysis,
Cell culture and genomic DNA isolation HCT116 cells (American Type Culture Collection) and DNMT1/DNMT3B (DKO) cells (Rhee et al. 2002 ) were cultured in McCoy’s 5A modified medium containing 10% fetal bovine serum and 1% penicillin/streptomycin. Genomic DNA was isolated from HCT116 and DKO cell lines and was prepared using the MasterPure DNA purification kit (EpiCentre) as specified by the manufacturer.
McrBC assay sample preparation
HELP assay sample preparation
MeDIP assay sample preparation
Illumina GoldenGate assay sample preparation
Quantitative methylation analysis using pyrosequencing CpG-unbiased primers were designed to PCR amplify 38, 16, and 14 CpG sites, respectively, in three genes, HLA-F, KCNK4, and HLTF (previously known as SMARCA3), showing conflicting methylation across MeDIP, McrBC, and Illumina assays (Supplemental Table 1). Nested PCR was performed under standard conditions. Amplicons were analyzed on a PSQ HS 96 pyrosequencer as specified by the manufacturer (Biotage) and CpG sites quantified, from 0% to 100% methylation, using Pyro Q-CpG software.
Microarray design
Data analysis
We therefore developed a method that does not require the assumption that M = 0 for most probes. This method used genome sequence information and our knowledge of the fragment selection method to select what we call pseudo-housekeeping probes for which one can in fact assume M = 0. We then apply the Loess normalization procedure developed for expression arrays to the pseudo-housekeeping genes, obtain the correction curve, and use this curve to correct M-values for all probes. An additional advantage of this approach is that it provides a flexible way to adapt existing techniques, developed for expression arrays, to methylation data. Details of this normalization procedure are described in Bolstad et al. (2003)
Genome-weighted smoothing
This work was supported by NIH grant HG003233. We thank John Greally and Masako Suzuki for performing the HELP ligation and amplification steps to ensure that we did not perform this procedure incorrectly.
4 Present address: NimbeleGen Systems, Inc., Madison WI 53711.  E-MAIL afeinberg{at}jhu.edu; (410) 614-9819.
E-mail rafa{at}jhu.edu; fax (410) 955-0958. [Supplemental material is available online at www.genome.org.] Article published online before print. Article publication date are at http://www.genome.org/cgi/doi/10.1101/gr.7301508.
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Received October 12, 2007; accepted in revised format December 24, 2007.  CiteULike  Connotea  Del.icio.us  Digg  Reddit  Technorati What's this?
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Abstract
Results
300 bp between consecutive probes was divided into two new regions. 
