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

Laureano Tomás-Daza, Llorenç Rovirosa, Paula López-Martí, Andrea Nieto-Aliseda, François Serra, Ainoa Planas-Riverola, Oscar Molina, Rebecca McDonald, Cedric Ghevaert, Esther Cuatrecasas, Dolors Costa, Mireia Camós, Clara Bueno, Pablo Menéndez, Alfonso Valencia and Biola M. Javierre ()
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Laureano Tomás-Daza: Josep Carreras Leukaemia Research Institute
Llorenç Rovirosa: Josep Carreras Leukaemia Research Institute
Paula López-Martí: Josep Carreras Leukaemia Research Institute
Andrea Nieto-Aliseda: Josep Carreras Leukaemia Research Institute
François Serra: Josep Carreras Leukaemia Research Institute
Ainoa Planas-Riverola: Josep Carreras Leukaemia Research Institute
Oscar Molina: Josep Carreras Leukaemia Research Institute
Rebecca McDonald: Wellcome-MRC Cambridge Stem Cell Institute
Cedric Ghevaert: Wellcome-MRC Cambridge Stem Cell Institute
Esther Cuatrecasas: Sant Joan de Déu Hospital, Esplugues de Llobregat
Dolors Costa: Hospital Clinic
Mireia Camós: Sant Joan de Déu Research Institute, Esplugues de Llobregat
Clara Bueno: Josep Carreras Leukaemia Research Institute
Pablo Menéndez: Josep Carreras Leukaemia Research Institute
Alfonso Valencia: Barcelona Supercomputing Center
Biola M. Javierre: Josep Carreras Leukaemia Research Institute

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-35911-8

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