Novel enzyme-based reduced representation method for DNA methylation profiling with low inputs.

Commonly used bisulfite-based procedures for DNA methylation sequencing can degrade DNA, worsening signal-to-noise ratios in samples with low DNA input. Enzymatic methylation sequencing (EM-seq) has been proposed as a less biased alternative for methylation profiling with greater genome coverage. Reduced representation approaches enrich samples for CpG-rich genomic regions, thereby enhancing throughput and cost effectiveness. We hypothesized that enzyme-based technology could be adapted for reduced representation methylation sequencing to enable DNA methylation profiling of low-input samples. We leveraged the well-established differences in methylation profile between mouse CD4+ T cell populations to compare the performance of our reduced representation EM-seq (RREM-seq) procedure against an established reduced representation bisulfite sequencing (RRBS) protocol. While the RRBS method failed to generate reliable DNA libraries when using <2 ng of DNA, the RREM-seq method successfully generated reliable DNA libraries from 1-25 ng of mouse and human DNA. Low-input (≤2-ng) RREM-seq libraries demonstrated superior regulatory genomic element coverage compared with RRBS libraries with >10-fold higher DNA input. RREM-seq also successfully detected lineage-defining methylation differences between alveolar conventional T and regulatory T cells obtained from patients with severe SARS-CoV-2 pneumonia. Our RREM-seq method enables single-nucleotide resolution methylation profiling using low-input samples, including from clinical sources.