DNA methylation is a highly studied epigenetic modification that is involved in regulating genome function and plays fundamental roles in development and disease.¹ It is linked to a broad range of conditions, including inflammation, neurological disorders, and cancer. Some patterns of methylation are shared across cancer types, while others can differ between subtypes—highlighting the value of studying DNA methylation to uncover novel biomarkers and gain insight into disease mechanisms.

Due to its chemical stability, DNA methylation can be analyzed across a range of sample types, including fresh, frozen, and formalin-fixed paraffin-embedded (FFPE) tissues. Further, noninvasive approaches such as liquid biopsy are being increasingly used to measure methylation patterns, enabling new methods for diagnosis and patient monitoring.

DNA methylation is also emerging as a promising therapeutic target, with several DNA methyltransferase inhibitors already approved for clinical use. Although several assays exist for mapping DNA methylation, better tools are needed to make these analyses more accessible and cost-effective for biomedical researchers.

Tradeoffs in DNA methylation mapping

Choosing the right method for DNA methylation profiling requires balancing several competing factors—cost, genome coverage, and resolution (Table 1). Each assay comes with its own tradeoffs, which not only impact data quality but also the feasibility of certain research applications.

Whole-genome bisulfite sequencing (WGBS) remains the gold standard for comprehensive DNA methylation analysis. It provides base-pair resolution of the methylome, making it ideal for in-depth studies of 5-methylcytosine (5mC) patterns across the genome. However, harsh bisulfite treatment damages DNA and skews GC coverage, introducing biases that can overestimate DNA methylation levels.

As a result, WGBS is expensive due to its requirement for high cell numbers and deep sequencing—often more than 800 million reads per sample. These demands can make WGBS impractical for large-scale studies or experiments using limited or precious samples.

Enzymatic approaches offer an alternative to bisulfite conversion, facilitating non-destructive mapping of DNA methylation. While enzymatic methods provide better genome coverage, reduced GC bias, and lower input requirements compared to WGBS, they still require high sequencing depths (>600 million reads per sample) and resource-intensive computational processing and bioinformatics expertise.²

Long-read sequencing technologies provide another approach that directly detects DNA methylation on native, unconverted DNA. Because long reads span kilobase-length fragments, they enable analysis of methylation in repetitive or structurally complex regions that are inaccessible to short-read platforms. Despite these advantages, long-read methods typically require the input of large amounts of intact, high-molecular-weight DNA and produce increased per-base error rates compared to short-read approaches.³

To reduce cost and complexity, researchers often turn to targeted approaches such as reduced representation bisulfite sequencing (RRBS), methylation arrays, or hybridization-based panels. These methods are more affordable but typically examine only a small subset of the methylome (typically 3–15% of CpG sites) and tend to be biased toward CpG islands.^4,5 This limited scope may not provide the genome-wide coverage required for certain applications and can constrain the discovery of novel DNA methylation mechanisms.

Affinity-based techniques like methylated DNA immunoprecipitation sequencing (MeDIP-seq) are yet another strategy. This method uses antibodies to enrich for methylated DNA, reducing sequencing requirements compared to whole-genome DNA methylation profiling approaches. However, MeDIP-seq is technically challenging and has several limitations, including the requirement for high cell numbers and a preference towards hypermethylated and low-GC content regions.^4,6 Because this approach uses immunoprecipitation and often relies on poor quality 5-methylcytosine antibodies, MeDIP-seq suffers from poor reliability, resolution, and accuracy.

Together, these tradeoffs highlight the need for a more flexible methylation mapping approach that combines broad coverage and high sensitivity with lower sequencing requirements.

Cost-effective approach to mapping DNA methylation

To overcome the limitations of existing DNA methylation assays, researchers are turning to new methods that offer high-quality data at lower costs. One of these is EpiCypher’s CUTANA™ meCUT&RUN, a novel assay that enables sensitive and cost-effective profiling of methylated DNA across the genome (Figure 1). This platform provides a flexible solution that balances coverage, resolution, and scalability.

To develop the meCUT&RUN workflow, the CUT&RUN protocol was modified to enrich for methylated regions. Instead of using antibodies, the assay uses a GST-tagged methylation-binding domain derived from human MeCP2 to selectively capture methylated DNA fragments. This strategy avoids harsh chemical treatments like bisulfite conversion, which can degrade DNA and introduce bias. As a result, meCUT&RUN is well-suited for limited or difficult-to-obtain samples, including clinical specimens and primary cells.

A key feature of this platform is its compatibility with different library preparation options, allowing researchers to tailor the assay to their specific needs (Figure 1). For those looking for the most efficient approach, enriched methylated DNA fragments can be directly sequenced to provide a comprehensive genome-wide map of DNA methylation.

Alternatively, researchers seeking base-pair resolution may add an enzymatic conversion step (NEBNext^® EM-seq™ by New England Biolabs^®) after methylated DNA enrichment, though this approach requires additional sample processing steps and computational analysis.

Compared to targeted approaches like RRBS, microarrays, and hybridization panels, meCUT&RUN delivers broader and more uniform methylome coverage with significantly lower sequencing requirements—achieving performance similar to EM-seq with only 20–50 million reads (Figure 2A). Side-by-side comparisons show that meCUT&RUN identifies 80% of methylated CpGs (5mCs) captured by whole-genome EM-seq (Figure 2B). It also provides a more consistent detection of DNA methylation compared to RRBS across key genomic features, including enhancers, gene bodies, transcription start sites, and repetitive elements (Figure 2C).

Downsampling the number of cells used in the assay showed that the workflow performs reliably with as few as 10,000 cells per reaction (Figure 3), making it a more practical choice for larger studies or budget-conscious projects.

As interest in DNA methylation continues to grow across both basic and translational research, there is a pressing need for tools that offer flexibility, sensitivity, and affordability. meCUT&RUN provides an alternative to traditional assays by reducing sequencing burden while maintaining robust coverage and compatibility with low-input samples. By addressing common technical and cost-related challenges, this approach offers a practical solution for DNA methylation profiling that is accessible to a wide range of researchers.

Aaron Alcala, PhD, is a scientific grant writer for EpiCypher.

NEB^®, New England Biolabs^® and NEBNext^® are registered trademarks of New England Biolabs, Inc. EM-Seq™ is a trademark of New England Biolabs, Inc.

References

1. Mattei, A. L., Bailly, N. & Meissner, A. DNA methylation: a historical perspective. Trends in Genetics 38 (2022/07/01).

2. Wang, T., Loo, C. E., & Kohli, R. M. Enzymatic approaches for profiling cytosine methylation and hydroxymethylation. Molecular metabolism, 57, 101314 (2022).

3. Amarasinghe, S. L. et al. Opportunities and challenges in long-read sequencing data analysis. Genome Biology 2020 21:1 21 (2020-02-07).

4. Beck, D., Ben Maamar, M. & Skinner, M. K. Genome-wide CpG density and DNA methylation analysis method (MeDIP, RRBS, and WGBS) comparisons. Epigenetics 17, 518-530 (2022).

5. Sun, Z., Cunningham, J., Slager, S. & Kocher, J. P. Base resolution methylome profiling: considerations in platform selection, data preprocessing and analysis. Epigenomics 7, 813-828 (2015).

6. Lentini, A. et al. A reassessment of DNA-immunoprecipitation-based genomic profiling. Nat Methods 15, 499-504 (2018).