Work

DeepCNet: A multimodal deep learning model for predicting cell type-specific gene expression and promoter-enhancer interactions from single-cell multiome data

Year: 2026

Type: preprint

Abstract: Understanding promoter-enhancer (PE) interactions is essential for elucidating gene regulation mechanisms. Although existing computational methods leverage DNA sequence to predict gene expression, many were developed primarily for bulk-level genomic data and emphasize predictive accuracy rather than regulatory inference. Here, we propose DeepCNet, a multimodal deep learning framework that predicts cell type-specific gene expression and infers cell type-specific PE interactions, enabling the construction of transcription factor gene regulatory networks (TF-GRNs) from single-cell multiome data. Evaluated on two single-cell multiome datasets from blood and brain tissues, DeepCNet consistently outperforms state-of-the-art methods in predicting cell type-specific gene expression. Importantly, PE interactions inferred by DeepCNet showed greater concordance with orthogonal chromatin interaction and CRISPR perturbation data than competing approaches. Analysis of cell type–specific GRNs identified TF hubs and highlighted both cell type–specific and shared regulatory programs. DeepCNet further extends to two-condition single-cell multiome studies, enabling the identification of differential PE interactions and characterization of TF-GRN alterations across conditions. As an illustrative application, we applied DeepCNet to single-cell multiome data from one Alzheimer's disease (AD) and one healthy control sample, identifying candidate differential PE interactions at AD risk loci and candidate TF hubs associated with AD-related pathways. Together, these results demonstrate the potential of DeepCNet to characterize transcriptional regulatory programs and cooperative TF interactions underlying cell type- and condition-specific gene expression.

Source: Research Square

DOI: 10.21203/rs.3.rs-8716015/v1