ArticleNature communications2023
Low input capture Hi-C (liCHi-C) identifies promoter-enhancer interactions at high-resolution.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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Who cites it
11 citing papers in PubMed, 17 citations in OpenAlex.
- High-resolution promoter interaction analysis implicates genes involved in the activation of Type 3 Innate Lymphoid Cells in autoimmune disease risk.bioRxiv : the preprint server for biology · 2026Article
- Analysis of Chromatin Structure Using Network Approaches: A Step-by-Step Guide.Methods in molecular biology (Clifton, N.J.) · 2026Article
- A combined genomic arrhythmia propensity score delineates cumulative risk.Cell reports. Medicine · 2025Article
- Enhancer regulation in cancer: from epigenetics to mArchives of pharmacal research · 2025Review
- Techniques to detect physical interactions between genomic regions.Epigenomics · 2025Review
- Genome Mountaineering: Expanding Horizons of the 3D Genome for the Intrepid Evolutionary Adventurer.Genome biology and evolution · 2025Review
- Leveraging Hi-C Data to Detect Chromosomal Reorganizations.Methods in molecular biology (Clifton, N.J.) · 2025Article
- The genome in space and time comes of age.Nucleus (Austin, Tex.) · 2024Article
- Reconstructing human-specific regulatory functions in model systems.Current opinion in genetics & development · 2024Review
- RNA sequestration in P-bodies sustains myeloid leukaemia.Nature cell biology · 2024Article
- Neutrophil nucleus: shaping the past and the future.Journal of leukocyte biology · 2023Article
Corrections and comments
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Authors and funding
16 authors at 7 institutions in 2 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.