Liu Qianli, Helmin Kathryn A, Dortzbach Zachary D, Reyes Flores Carla P, Torres Acosta Manuel A, Gurkan Jonathan K, Joudi Anthony M, Mambetsariev Nurbek, Morales-Nebreda Luisa, Kang Mengjia, Rasmussen Luke, Pérez-Leonor Xóchitl G, Abdala-Valencia Hiam, Singer Benjamin D
Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States.
Driskill Graduate Program, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States.
Nucleic Acids Res. 2025 Jun 20;53(12). doi: 10.1093/nar/gkaf558.
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.
常用的基于亚硫酸氢盐的DNA甲基化测序方法会降解DNA，从而恶化低DNA输入量样本中的信噪比。酶促甲基化测序（EM-seq）已被提出作为一种偏差较小的替代方法，用于甲基化分析，具有更大的基因组覆盖范围。简化代表性方法可富集富含CpG的基因组区域的样本，从而提高通量和成本效益。我们假设基于酶的技术可适用于简化代表性甲基化测序，以实现低输入量样本的DNA甲基化分析。我们利用小鼠CD4+T细胞群体之间已确立的甲基化谱差异，将我们的简化代表性EM-seq（RREM-seq）方法与既定的简化代表性亚硫酸氢盐测序（RRBS）方案的性能进行比较。当使用<2 ng DNA时，RRBS方法无法生成可靠的DNA文库，而RREM-seq方法成功地从1-25 ng的小鼠和人类DNA中生成了可靠的DNA文库。与DNA输入量高出10倍以上的RRBS文库相比，低输入量（≤2 ng）的RREM-seq文库显示出更高的调控基因组元件覆盖率。RREM-seq还成功检测到了从重症SARS-CoV-2肺炎患者中获得的肺泡常规T细胞和调节性T细胞之间定义谱系的甲基化差异。我们的RREM-seq方法能够使用低输入量样本，包括临床来源的样本，进行单核苷酸分辨率的甲基化分析。
