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

Abstract

DNA methylation at cytosine-phospho-guanine (CpG) residues is a vital biological process that regulates cell identity and function. Although widely used, bisulfite-based cytosine conversion procedures for DNA methylation sequencing require high temperature and extreme pH, which lead to DNA degradation, especially among unmethylated cytosines. Enzymatic methylation sequencing (EM-seq), an enzyme-based cytosine conversion method, has been proposed as a less biased alternative for methylation profiling. Compared to bisulfite-based methods, EM-seq boasts greater genome coverage with less GC bias and has the potential to cover more CpGs with the same number of reads (i.e., higher signal-to-noise ratio). 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 high-resolution DNA methylation profiling on low-input samples, including those obtained from clinical specimens. We leveraged the well-established differences in methylation profile between mouse CD4+ T cell populations to compare the performance of a novel 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 (equivalent to DNA from around 350 cells), the RREM-seq method successfully generated reliable DNA libraries from 1-25 ng of mouse and human DNA. Ultra-low-input (<2-ng) RREM-seq libraries' final concentration, regulatory genomic element coverage, and methylation status within lineage-defining Treg cell-specific super-enhancers were comparable to RRBS libraries with more than 10-fold higher DNA input. RREM-seq also successfully detected lineage-defining methylation differences between alveolar Tconv and Treg cells obtained from mechanically ventilated 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.