Deciphering context-dependent epigenetic program by network-based prediction of clustered open regulatory elements from single-cell chromatin accessibility

Large-scale cis-regulatory domains, such as super-enhancers, are pivotal in orchestrating robust and cell-state-specific transcriptional programs that define cellular identity. However, current single-cell methods do not effectively identify these higher-order structures, obscuring the coordinated, domain-level epigenetic regulation essential for complex biological processes. Identifying such domain-scale representation at the single-cell level is critical for understanding the regulatory logic underlying development and disease. Here, we introduce enCORE, a computational framework that leverages enhancer-enhancer interaction networks to determine Clustered Open Regulatory Elements (COREs) from single-cell ATAC-sequencing. Our approach faithfully recapitulated established hematopoietic hierarchies and resolved lineage-specific regulatory programs by recovering canonical lineage-defining regulators, frequent chromatin interactions, and enrichment of fine-mapped autoimmune disease-associated genome-wide association study (GWAS) variants. In colorectal cancer, enCORE successfully captured tumor-associated H3K27ac programs and prioritized cancer-relevant regulators, pointing to USP7 as a potential therapeutic candidate supported by in silico perturbation. Our framework provides a fruitful approach for deciphering context-dependent epigenetic reprogramming.