What question did this study set out to answer?

The aim is to decode the hierarchical regulatory network guiding cell fate using single-cell multi-omics data.

February 8, 2026Briefings in Bioinformatics1 citationsOpen Access

Deciphering hierarchical regulatory network of cell fate via an epigenetics-informed heterogeneous graph transformer on single-cell multi-omics data

NFNan FengAnhui Medical University CLChao LiuDalian Medical University ZYZ. P. YaoShandong University

Key Points

The aim is to decode the hierarchical regulatory network guiding cell fate using single-cell multi-omics data.
Proposed SMOGT model captures hierarchical and causal properties through a graph representation.
Incorporated epigenetic mechanisms into the heterogeneous graph transformer.
Employed a semi-supervised strategy for improved network accuracy.
Utilized multi-layer random walk for identifying driver regulators.
Applied validation against benchmarked ChIP-seq and HiC-seq datasets.
SMOGT offered significantly higher accuracy in predicting transcriptional regulation compared to existing methods.
Identified crucial driver transcription factors that influence hematopoietic stem cell differentiation.
Revealed a therapeutic window for reversing epithelial-to-mesenchymal transition in melanoma.
Uncovered hub-cis regulatory elements with notable prognostic value in Acute Myeloid Leukemia.

Abstract

Abstract The precise control of cell fate is driven by a hierarchical regulatory network (HRNet) where transcription factors (TFs) and cis-regulatory elements (CREs) orchestrate the expression of target genes (TGs) through complex causal actions. While single-cell multi-omics technologies provide multi-dimensional data to resolve regulatory networks, existing methods often fail to capture their hierarchical and causal properties. We propose SMOGT (Single-cell Multi-Omics Graph Transformer), a graph representation learning method to decipher HRNet. SMOGT embeds epigenetic mechanism into Heterogeneous Graph Transformer (HGT) by structuring information flow along a hierarchical-guided meta-path (TF-TF → TF-CRE → CRE-CRE → CRE-TG), and employs a semi-supervised strategy to ensure network accuracy. Validated against ChIP-seq and HiC-seq benchmarked datasets, SMOGT showed significantly higher accuracy in predicting transcriptional regulation (TF-CRE) and long-range chromatin conformation (CRE-CRE). The HRNet scaffolds downstream modules that mechanistically link network architecture to cell fate. The multi-layer random walk (MRWR) module identifies driver regulators and their TGs. The BioStreamNet module predicts shifts in cell fate trajectories following in silico perturbations within gene-specific HRNet formed by extracting regulatory weights during TG expression prediction. In hematopoietic stem cell differentiation, SMOGT elucidated the hierarchical causal cascade from driver TFs that governs lineage commitment. In melanoma epithelial-to-mesenchymal transition (EMT), it revealed a critical therapeutic window for reversing the process, and in Acute Myeloid Leukemia (AML), it uncovered hub-CREs with significant prognostic value. By accurately modeling hierarchical causality, SMOGT provides a robust tool to dissect and predict cell fate dynamics in both development and disease.

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