Low input capture Hi-C (liCHi-C) identifies promoter-enhancer interactions at high-resolution

dc.contributor.authorTomás-Daza, Laureano
dc.contributor.authorRovirosa, Llorenç
dc.contributor.authorLópez-Martí, Paula
dc.contributor.authorNieto-Aliseda, Andrea
dc.contributor.authorSerra, François
dc.contributor.authorPlanas-Riverola, Ainoa
dc.contributor.authorMolina, Òscar
dc.contributor.authorMcDonald, Rebeca
dc.contributor.authorGhevaert, Cedric
dc.contributor.authorCuatrecasas, Esther
dc.contributor.authorCosta, Dolors
dc.contributor.authorCamós Guijosa, Mireia
dc.contributor.authorBueno, Clara
dc.contributor.authorMenéndez, Pablo
dc.contributor.authorValencia, Alfonso
dc.contributor.authorJavierre, Biola M.
dc.date.accessioned2025-02-05T16:17:45Z
dc.date.available2025-02-05T16:17:45Z
dc.date.issued2023-01-17
dc.date.updated2025-02-05T16:17:45Z
dc.description.abstractLong-range interactions between regulatory elements and promoters are key in gene transcriptional control; however, their study requires large amounts of starting material, which is not compatible with clinical scenarios nor the study of rare cell populations. Here we introduce low input capture Hi-C (liCHi-C) as a cost-effective, flexible method to map and robustly compare promoter interactomes at high resolution. As proof of its broad applicability, we implement liCHi-C to study normal and malignant human hematopoietic hierarchy in clinical samples. We demonstrate that the dynamic promoter architecture identifies developmental trajectories and orchestrates transcriptional transitions during cell-state commitment. Moreover, liCHi-C enables the identification of disease-relevant cell types, genes and pathways potentially deregulated by non-coding alterations at distal regulatory elements. Finally, we show that liCHi-C can be harnessed to uncover genome-wide structural variants, resolve their breakpoints and infer their pathogenic effects. Collectively, our optimized liCHi-C method expands the study of 3D chromatin organization to unique, low-abundance cell populations, and offers an opportunity to uncover factors and regulatory networks involved in disease pathogenesis.
dc.format.extent16 p.
dc.format.mimetypeapplication/pdf
dc.identifier.idgrec752727
dc.identifier.issn2041-1723
dc.identifier.pmid36650138
dc.identifier.urihttps://hdl.handle.net/2445/218536
dc.language.isoeng
dc.publisherNature Publishing Group
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.1038/s41467-023-35911-8
dc.relation.ispartofNature Communications, 2023, vol. 14
dc.relation.urihttps://doi.org/10.1038/s41467-023-35911-8
dc.rightscc-by (c) Tomás-Daza, L. et al., 2023
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceArticles publicats en revistes (Ciències Fisiològiques)
dc.subject.classificationExpressió gènica
dc.subject.classificationGenoma humà
dc.subject.classificationCromatina
dc.subject.classificationTranscripció genètica
dc.subject.otherGene expression
dc.subject.otherHuman genome
dc.subject.otherChromatin
dc.subject.otherGenetic transcription
dc.titleLow input capture Hi-C (liCHi-C) identifies promoter-enhancer interactions at high-resolution
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion

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