dc.contributor.author
Tomás-Daza, Laureano
dc.contributor.author
Rovirosa, Llorenç
dc.contributor.author
López-Martí, Paula
dc.contributor.author
Nieto-Aliseda, Andrea
dc.contributor.author
Serra, François
dc.contributor.author
Planas-Riverola, Ainoa
dc.contributor.author
Molina, Òscar
dc.contributor.author
McDonald, Rebeca
dc.contributor.author
Ghevaert, Cedric
dc.contributor.author
Cuatrecasas, Esther
dc.contributor.author
Costa, Dolors
dc.contributor.author
Camós Guijosa, Mireia
dc.contributor.author
Bueno, Clara
dc.contributor.author
Menéndez, Pablo
dc.contributor.author
Valencia, Alfonso
dc.contributor.author
Javierre, Biola M.
dc.date.issued
2025-02-05T16:17:45Z
dc.date.issued
2025-02-05T16:17:45Z
dc.date.issued
2023-01-17
dc.date.issued
2025-02-05T16:17:45Z
dc.identifier
https://hdl.handle.net/2445/218536
dc.description.abstract
Long-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
application/pdf
dc.publisher
Nature Publishing Group
dc.relation
Reproducció del document publicat a: https://doi.org/10.1038/s41467-023-35911-8
dc.relation
Nature Communications, 2023, vol. 14
dc.relation
https://doi.org/10.1038/s41467-023-35911-8
dc.rights
cc-by (c) Tomás-Daza, L. et al., 2023
dc.rights
http://creativecommons.org/licenses/by/4.0/
dc.rights
info:eu-repo/semantics/openAccess
dc.source
Articles publicats en revistes (Ciències Fisiològiques)
dc.subject
Expressió gènica
dc.subject
Transcripció genètica
dc.subject
Gene expression
dc.subject
Genetic transcription
dc.title
Low input capture Hi-C (liCHi-C) identifies promoter-enhancer interactions at high-resolution
dc.type
info:eu-repo/semantics/article
dc.type
info:eu-repo/semantics/publishedVersion