Extended supplementary data for manuscript: Specialised super-enhancer networks in stem cells and neurons

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Item Type:	Dataset
Title:	Extended supplementary data for manuscript: Specialised super-enhancer networks in stem cells and neurons
Creators Name:	Harabulă, Izabela, Speakman, Liam, Musella, Francesco, Fiorillo, Luca, Zea-Redondo, Luna, Kukalev, Alexander, Beagrie, Robert A., Morris, Kelly J., Fernandes, Lucas, Irastorza-Azcarate, Ibai, Fernandes, Ana M., Carvalho, Silvia, Szabó, Dominik, Ferrai, Carmelo, Nicodemi, Mario, Welch, Lonnie and Pombo, Ana
Abstract:	Super-enhancers (SEs) are clusters of enhancers with high transcriptional activity that play essential roles in defining cell identity through regulation of nearby genes. SEs are also known to form extensive multiway chromatin interactions in embryonic stem cells, which span tens of megabases with other SEs and highly transcribed regions. However, the properties and contributions of SEs in shaping complex regulatory interactions, and how they differ between dividing and in terminally differentiated cells remain poorly understood. Here, we study the structural and functional properties of SEs in embryonic stem cells and dopaminergic neurons by combining Genome Architecture Mapping (GAM), chromatin accessibility, histone modification, and transcriptome data. We find that most SEs are cell-type specific and form extensive pairwise and multiway chromatin interactions with differentially expressed genes and other SEs. SE interactions frequently connect topologically associating domains across megabase genomic distances. Cell-type specific SEs establish complex chromatin interactions with other SEs and highly transcribed regions. SE network analysis identifies important SEs, with highest centrality. Central SEs contain binding motifs for cell-type specific transcription factors, and may act as regulatory hubs. The functional heterogeneity of SEs is also highlighted by their organization into modular sub-networks that differ in structure and spatial scale between ESCs and DNs, with more specific and strongly connected SE modules in post-mitotic neurons. Our results uncover the complexity and specificity of SE-based 3D regulatory networks and provide a resource for prioritizing SEs with potential roles in transcriptional regulation and disease.
Source:	Zenodo
Publisher:	CERN
Date:	2025
Additional Information:	The external datasets are currently private and scheduled to be released on Dec 01, 2026.
Official Publication:	https://doi.org/10.5281/zenodo.16779972
Related to:	URL URL Type https://edoc.mdc-berlin.de/25617/ Publication https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE305152 External Dataset https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE304720 External Dataset https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE304996 External Dataset https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE305012 External Dataset https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE305150 External Dataset https://github.com/pombo-lab/Harabula_Speakman_Superenhancer_networks_2025 Software

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