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

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 leads to DNA degradation, especially among unmethylated cytosines. This disproportionate damage to unmethylated cytosines contributes to inaccuracies in GC content representation. EM-seq, an enzyme-based cytosine conversion method, has been proposed as a less biased alternative to 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 inputs samples. We leveraged the well-established differences in methylation profile between mouse CD4+ T cell populations to compare reduced representation EM-seq (RREM-seq) performance against our previously published modified reduced representation bisulfite sequencing (mRRBS). While the mRRBS method failed to generate reliable DNA libraries when using <2-ng inputs (equivalent to DNA from around 350 cells), the RREM-seq method successfully generated DNA libraries from 1–25 ng of mouse and human genomic DNA. These libraries fell within the expected size range, and primer contamination was not observed. Low-input (<2-ng) RREM-seq libraries' final concentration, regulatory genomic element coverage, and methylation status within the lineage-defining Treg cell-specific super-enhancers were comparable to mRRBS libraries with more than 10-fold higher DNA input. RREM-seq libraries also successfully detected the methylation differences between alveolar Tconv and Treg cells in mechanically ventilated patients with severe SARS-CoV-2 pneumonia. Our results suggest that the RREM-seq method can generate reliable libraries for single-nucleotide resolution methylation profiling using low input clinical samples. Overall design: DNA libraries for methylation sequencing were generated using flow cytometry-sorted splenic CD4+ conventional T cells (Tconv cells) and CD4+CD25+FOXP3+ regulatory T cells (Treg cells) from healthy mice and paired alveolar CD4+ Tconv and Treg cells from mechanically ventilated patients with severe SARS-CoV-2 pneumonia. *************************************************************** Submitter states that missing raw data are being made available for controlled access in dbGaP. ***************************************************************