Work

Integrative single‑cell multi‑omics network analysis to elucidate epigenetic regulation in neurodevelopmental disorders

Year: 2026

Type: article

Abstract: Neurodevelopmental disorders (NDDs) arise from disruptions in molecular programs that guide early brain formation, yet the specific epigenetic and transcriptional mechanisms underlying these conditions remain poorly defined. Recent advances in single-cell technologies allow parallel profiling of gene expression, chromatin accessibility, and DNA methylation within individual cells; however, most existing studies remain unimodal and therefore unable to resolve convergent dysregulation across molecular layers. This study presents an integrative single-cell multi-omics framework that combines scRNA-seq, scATAC-seq, and single-cell DNA methylation data from developing human and mouse brain tissue, as well as patient-derived neural progenitor models. By applying canonical correlation analysis, manifold alignment, latent-variable modeling, and network inference, we construct cell-type-specific epigenetic regulatory networks and quantify how chromatin accessibility, methylation state, and transcriptional output collectively deviate in NDD-relevant cell populations. Our analyses reveal that neural progenitors and excitatory neurons exhibit the strongest multimodal alterations, characterized by promoter hypermethylation, loss of enhancer accessibility, and downregulation of neurogenic and synaptic pathways. Integrative network modeling identifies SOX11 and CHD8 as central, multi-layer master regulators whose disrupted activity contributes to aberrant lineage specification. Quantitative evaluation of the Single-Cell Multi-Omics Network demonstrates high enhancer-gene linkage accuracy, consistent cross-species regulatory conservation, and efficient regulatory module reconstruction. Collectively, this integrative approach provides a unified view of epigenetic and transcriptional dysregulation in NDDs, generating mechanistic hypotheses with potential implications for biomarker discovery and targeted therapeutic intervention.

Source: SLAS TECHNOLOGY

DOI: 10.1016/j.slast.2026.100395