A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping
Open accessAbstract
We use in situ Hi-C to probe the 3D architecture of genomes, constructing haploid and diploid maps of nine cell types. The densest, in human lymphoblastoid cells, contains 4.9 billion contacts, achieving 1 kb resolution. We find that genomes are partitioned into contact domains (median length, 185 kb), which are associated with distinct patterns of histone marks and segregate into six subcompartments. We identify ∼10,000 loops. These loops frequently link promoters and enhancers, correlate with gene activation, and show conservation across cell types and species. Loop anchors typically occur at domain boundaries and bind CTCF. CTCF sites at loop anchors occur predominantly (>90%) in a convergent orientation, with the asymmetric motifs "facing" one another. The inactive X chromosome splits into two massive domains and contains large loops anchored at CTCF-binding repeats.
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In the classic scenario, the boundary is presented by CTCF binding to its sequence motifs located in a divergent orientation at either end of the loop.
“In the classic scenario, this boundary is presented by CTCF binding to its sequence motifs located in a divergent orientation at either end of the loop [277, 438–440]”
From Transcriptional enhancers and their communication with gene promoters (2021) · cites this paper as [277]
The Results section reports that 92% of CTCF motif pairs at the examined loop loci are convergent, not divergent, and that convergent pairs are overwhelmingly more frequent than divergent pairs. The Discussion likewise says that motifs at loop anchors are convergent in more than 90% of cases. Page numbers were not supplied in our copy. (LLM, conf=0.95)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredStandard Hi-C cannot discriminate whether an interacting fragment originated from the same or the other allele, while allele-specific interaction maps can separate cis-allele from trans-allele interactions, improving analysis of chromatin dynamics and gene regulation.
“While standard Hi-C cannot discriminate whether an interacting fragment originated from the same or the other allele, allele-specific interaction maps can separate cis-allele from trans-allele interactions, thereby greatly improving the analysis of chromatin dynamics and gene regulation 20, 21.”
From SNPsplit: Allele-specific splitting of alignments between genomes with known SNP genotypes (2016) · cites this paper as [21]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured3D genomics methods such as Hi-C and Micro-C provide a genome-wide view of pairwise interactions but measure interaction probabilities only on a relative scale.
“However, these methods only measure 3D interaction probabilities on a relative scale.”
From Genome-wide absolute quantification of chromatin looping (2026) · cites this paper as [44]
The Results section ('In situ Hi-C methodology and maps'; page not provided) defines Hi-C output as counts of observed contacts between pairs of loci. The paper also describes normalizing contact matrices and comparing contact frequencies with expected values, consistent with relative measurements. It does not establish the stated limitation for Micro-C or explicitly say that interaction probabilities can only be measured on a relative scale. (LLM, conf=0.77)
Experimental — accuracy not yet measuredThe majority of specific enhancer-promoter loops are highly enriched within, compared with across, TAD boundaries.
“The majority of specific enhancer–promoter loops follow this pattern and are highly enriched within, compared with across, TAD boundaries [277, 401, 405].”
From Transcriptional enhancers and their communication with gene promoters (2021) · cites this paper as [277]
The Results section on promoter-anchored loops reports many enhancer–promoter loops, and the Results section on domain boundaries reports that loops often demarcate domains (page numbers unavailable in our copy). Neither passage establishes that a majority of enhancer–promoter loops lie within TAD boundaries or that they are enriched within versus across those boundaries. (LLM, conf=0.82)
Experimental — accuracy not yet measuredMany computational algorithms for Hi-C analysis explicitly correct for distance decay, identifying significant interactions based on how unusual their contact frequency is at a given distance.
“many computational algorithms for the analysis of data from Hi-C and related techniques explicitly correct for distance decay, identifying “significant interactions” based on how unusual their contact frequency is at a given distance [277, 468–471].”
From Transcriptional enhancers and their communication with gene promoters (2021) · cites this paper as [277]
The Experimental Procedures section (“Annotation of Peaks”; page unavailable) describes an algorithm that identifies statistically significant contact frequencies by comparing each Hi-C pixel with its surrounding neighborhoods. That supports the unusual-contact-frequency part of the claim. The paper does not establish that many algorithms explicitly correct for distance decay or test contacts against an expectation at a given genomic distance; its stated comparison is with local background. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Contacts between convergent CTCF binding sites are established by the extrusion of chromatin loops with cohesin.
“Contacts between convergent CTCF binding sites are established by the extrusion of chromatin loops with cohesin [42,46,48,93].”
From Enhancer Function in the 3D Genome (2023) · cites this paper as [46]
The Results section reports that most loop-anchor CTCF motif pairs are convergent, and the Discussion reports that cohesin subunits occur at over 86% of loop anchors. The paper does not establish loop extrusion as the mechanism; the Discussion instead says the mechanism remains to be clarified and suggests CTCF dimerization. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe CTCF insulator protein creates chromatin loops and boundaries that partition topological domains.
“Recent studies have solidified the role of the CTCF insulator protein in creating chromatin loops and boundaries that partition such domains15.”
From Insulator dysfunction and oncogene activation in IDH mutant gliomas (2016) · cites this paper as [15]
Many enhancers are spatially proximate to their target promoter in 3D, but exceptions have been described.
“Many enhancers are spatially proximate to their target promoter in 3D109,110, but exceptions have been described111-114.”
From Towards a comprehensive catalogue of validated and target-linked human enhancers (2020) · cites this paper as [109]
Convergent binding sites for CTCF located at TAD borders block the movement of loop extruders.
“Convergent binding sites for the versatile transcription factor CTCF (CCCTC-binding factor) located at TAD borders [46] block the movement of loop extruders [47,48,49].”
From Enhancer Function in the 3D Genome (2023) · cites this paper as [46]
Engagement of the CTCF N-terminus to stabilize cohesin could account for the preference for convergent CTCF sites at TAD boundaries.
“Stabilization of cohesin by engagement of the CTCF N-terminus may in fact explain why TAD boundaries arise preferentially when CTCF binding sites are convergently oriented9,13–16,38,39.”
From The structural basis for cohesin–CTCF-anchored loops (2020) · cites this paper as [15]
CTCF and cohesin colocalize on chromatin and are located at loop anchors and contact domain boundaries.
“Many studies have shown that the insulator protein CTCF and the ring-shaped cohesin complex colocalize on chromatin (Wendt et al., 2008) and lie at the anchors of loops (Rao et al., 2014; Splinter et al., 2006) and the boundaries of contact domains”
From Cohesin Loss Eliminates All Loop Domains (2017) · cites this paper as Rao et al., 2014
Similar results were found when examining boundaries between intervals enriched vs depleted for H3K27Ac or H3K27me3.
“The results were similar when we examined boundaries between intervals that were enriched vs. depleted for H3K27Ac (which marks intervals in the “A” compartment (Rao et al., 2014)) or intervals that were enriched vs. depleted for H3K27me3 (which marks intervals in the “B1” subcompartment (Rao et al., 2014)) (Fig. 3F, S3A–D, see Methods).”
From Cohesin Loss Eliminates All Loop Domains (2017) · cites this paper as Rao et al., 2014
Cohesin-independent loops resemble superloops on the inactive X chromosome in being very large, not forming contact domains, having anchors that form cliques, and being marked by H3K27 acetylation.
“In many respects, the cohesin-independent loops resemble the superloops we previously observed on the inactive X chromosome (Darrow et al., 2016; Rao et al., 2014): they are very large (up to 77Mb), the intervals they span do not form contact domains, and their anchors tend to form cliques and are marked by H3K27 acetylation (Fig. S4F).”
From Cohesin Loss Eliminates All Loop Domains (2017) · cites this paper as Rao et al., 2014
The second mechanism is cohesin-independent compartmentalization of chromatin intervals with similar histone marks.
“The second mechanism is the cohesin-independent compartmentalization of chromatin intervals with similar histone marks (Lieberman-Aiden et al., 2009; Rao et al., 2014).”
From Cohesin Loss Eliminates All Loop Domains (2017) · cites this paper as Rao et al., 2014
Prior work with similar methods found a strong link between having a loop domain and large expression increases for genes at its anchor.
“earlier studies, using similar methods, that showed a strong correlation between the presence of a loop domain and many-fold increases in the expression of genes at the loop domain anchor (Kagey et al., 2010; Rao et al., 2014).”
From Cohesin Loss Eliminates All Loop Domains (2017) · cites this paper as Rao et al., 2014
The principal component approach is inaccurate for compartment structure at high resolution, but it remains useful for gross compartmentalization comparisons.
“We showed in (Rao et al., 2014) that this method does not capture compartment structure accurately at high resolutions; however it is useful for comparing gross compartmentalization patterns.”
From Cohesin Loss Eliminates All Loop Domains (2017) · cites this paper as Rao et al., 2014
Not all domains are loop domains.
“Since, as we noted in (Rao et al., 2014), not all domains are loop domains”
From Cohesin Loss Eliminates All Loop Domains (2017) · cites this paper as Rao et al., 2014
Compartment intervals typically generate non-loop domains.
“As we noted in (Rao et al., 2014), non-loop domains are usually created by compartment intervals.”
From Cohesin Loss Eliminates All Loop Domains (2017) · cites this paper as Rao et al., 2014
These boundary counts are likely conservative upper bounds on true median compartment size due to stringent peak calling and difficulty detecting compartment structure in non-extremely-high-resolution maps.
“These are very likely conservative upper bounds on the true median compartment size, since we utilized stringent peak calling and compartment structure can be difficult to detect in maps that are not extremely high resolution (Rao et al., 2014).”
From Cohesin Loss Eliminates All Loop Domains (2017) · cites this paper as Rao et al., 2014
Using modified HiCCUPS at 50 and 100kb with specified parameters, they annotated 88 loops.
“This annotation yielded 88 loops.”
From Cohesin Loss Eliminates All Loop Domains (2017) · cites this paper as Rao et al., 2014
Using default HiCCUPS parameters at 5,10,25kb with <4.5-fold enrichment filter, they annotated 81 loops in treated RAD21-mAC cells.
“Using this procedure, we annotated 81 loops in treated RAD21-mAC cells.”
From Cohesin Loss Eliminates All Loop Domains (2017) · cites this paper as Rao et al., 2014
518 loops called in one of the Rao et al 2014 cell types but not GM12878, or vice versa, were associated with 10-fold upregulation of a gene whose promoter lay at the loop anchor.
“In order to increase statistical power, we identified 518 loops that had been called in one of our cell types in Rao et al 2014 (HMEC, IMR90, K562, HUVEC, HeLa, NHEK) but not in GM12878, or vice versa and had been associated with 10-fold upregulation of a gene whose promoter lay at the loop anchor.”
From Cohesin Loss Eliminates All Loop Domains (2017) · cites this paper as Rao et al 2014
In HCT-116 and GM12878, only 68 cell-type specific loops were identified, compared to about 600 each per pair of cell types in Rao et al 2014.
“To our surprise, we only identified 68 cell type specific loops (compared to ~600 each per pair of cell types in Rao et al 2014).”
From Cohesin Loss Eliminates All Loop Domains (2017) · cites this paper as Rao et al 2014
Previous analyses suggested that a subset of cell-type specific loops is associated with very strong gene activation, more than 10-fold upregulation of gene expression in the cell type where the loop is present.
“Our previous analyses (Rao et al 2014) have suggested that a subset of cell-type specific loops is associated with very strong gene activation (>10-fold upregulation of gene expression in the cell type where the loop is present).”
From Cohesin Loss Eliminates All Loop Domains (2017) · cites this paper as Rao et al 2014
Chromatin loops are formed almost exclusively between convergent CTCF sites.
“Recent work has shown that chromatin loops are formed almost exclusively between “convergent” CTCF sites (i.e., sites with consensus CTCF motifs pointing toward each other) (Rao et al., 2014, Vietri Rudan et al., 2015)”
From The cohesin release factor WAPL restricts chromatin loop extension (2017) · cites this paper as Rao et al., 2014
The median loop length increased by more than 200 kb in ΔWAPL cells compared to wild-type (575 kb versus 370 kb).
“When we performed systematic loop calling on the full-genome contact maps (Rao et al., 2014), we found that the median loop length increased by more than 200 kb in the ΔWAPL cells compared to the wild-type (575 kb versus 370 kb).”
From The cohesin release factor WAPL restricts chromatin loop extension (2017) · cites this paper as Rao et al., 2014
No statistical methods were used to pre-determine sample sizes, but the sample sizes are similar to those reported in previous publications.
“No statistical methods were used to pre-determine sample sizes but our sample sizes are similar to those reported in previous publications17,27,28.”
From Three-dimensional genome restructuring across timescales of activity-induced neuronal gene expression (2020) · cites this paper as [17]
Sub-TADs often appear in a cell specific manner and may be identified via an increased density of chromatin interactions and specific histone modifications in a specific cell type.
“sub-TADs (40kb to 3Mb, median size of 185kb19) often appear in a cell specific manner and, as in the case of the 80kb sub-TAD corresponding to the H3K4me1 and histone acetylation-marked α-globin domain, may be identified via an increased density of chromatin interactions (as seen by Capture–C, Fig. 2A) and specific histone modifications in a specific cell type.”
From Tissue-specific CTCF-cohesin-mediated chromatin architecture delimits enhancer interactions and function in vivo (2017) · cites this paper as [19]
CTCF/Cohesin sites dispersed within TADs are thought to contribute to the formation of smaller self-interacting domains termed sub-TADs, contact domains, and insulated neighborhoods.
“These CTCF/Cohesin sites are thought to contribute to the formation of smaller self-interacting domains that have been termed sub-TADs18, contact domains19, and insulated neighborhoods11,20.”
From Tissue-specific CTCF-cohesin-mediated chromatin architecture delimits enhancer interactions and function in vivo (2017) · cites this paper as [19]
Mammalian genomes are organized into a hierarchy of Megabase-scale topologically associating domains (TADs) and smaller, nested subTADs.
“Mammalian genomes are organized into a hierarchy of Megabase (Mb)-scale topologically associating domains (TADs) (Dixon et al., 2012; Nora et al., 2012) and smaller, nested subTADs (Dixon et al., 2012; Nora et al., 2012; Phillips-Cremins et al., 2013; Rao et al., 2014).”
From Disease-Associated Short Tandem Repeats Co-localize with Chromatin Domain Boundaries (2018) · cites this paper as Rao et al., 2014
The doublet CTCF sites contained CTCF motifs in the reverse orientation and looped to distal CTCF occupied motifs oriented in the forward direction.
“Consistent with the established principles of CTCF motif convergency (Rao et al., 2014), the doublet CTCF sites contained CTCF motifs in the reverse orientation and looped to distal CTCF occupied motifs oriented in the forward direction (Figure 6C-D).”
From Disease-Associated Short Tandem Repeats Co-localize with Chromatin Domain Boundaries (2018) · cites this paper as Rao et al., 2014
Very few studies using mostly in vitro cultured cells have achieved high resolution in Hi-C.
“very few studies using mostly in vitro cultured cells have achieved high resolution (Rao et al., 2014).”
From Multiscale 3D Genome Rewiring during Mouse Neural Development (2017) · cites this paper as Rao et al., 2014
Chromatin insulation occurs at multiple levels and leads to a hierarchical 3D nuclear organization, whereby smaller, less insulated regions (sub-TADs/contact domains) are nested inside TADs.
“Chromatin insulation occurs at multiple levels and leads to a hierarchical 3D nuclear organization, whereby smaller, less insulated regions (called also sub-TADs [Flavahan et al., 2016, Phillips-Cremins et al., 2013] or contact domains [Rao et al., 2014]) are nested inside TADs, which are usually bigger and more strongly insulated (Dixon et al., 2012, Nora et al., 2012, Sexton et al., 2012).”
From Multiscale 3D Genome Rewiring during Mouse Neural Development (2017) · cites this paper as Rao et al., 2014
Strong enrichment occurs between convergent CTCF sites, and loops are constrained primarily inside TADs and rarely cross TAD boundaries.
“We observed strong enrichment as expected (Guo et al., 2015, Rao et al., 2014), and the loops were constrained primarily inside TADs and rarely crossed TAD boundaries (Figures 3B and 3D).”
From Multiscale 3D Genome Rewiring during Mouse Neural Development (2017) · cites this paper as Rao et al., 2014
CTCF sites interact predominantly in the convergent orientation.
“We show that, consistent with previous findings (Rao et al., 2014), CTCF sites interact predominantly in the convergent orientation; however, the strength of this type of contact increases during differentiation.”
From Multiscale 3D Genome Rewiring during Mouse Neural Development (2017) · cites this paper as Rao et al., 2014
There was a high overlap (85% for K562 and 83.4% for MCF7) between the common boundaries and TAD boundaries found in cancer cell lines.
“We observed a high overlap with boundaries from a leukemia cell line K562 (ref. 17) and a breast cancer cell line MCF7 (ref. 20) (85% and 83.4%, respectively; Extended Data Fig. 1f,g).”
From Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer (2020) · cites this paper as [17]
Five distinct groups of TADs were identified by k-means clustering of chromatin state coverage: heterochromatin (61), low/quiescent (705), repressed (481), low-active (764) and active (365) domains.
“The obtained matrix was grouped using the k-means clustering approach and five distinct groups of TADs were identified similar to a previous classification of chromatin domains17,19,33. These groups comprised heterochromatin (61), low/quiescent (705), repressed (481), low-active (764) and active (365) domains (Fig. 4a, Supplementary Table 4).”
From Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer (2020) · cites this paper as [17]
TADs often have a nested, hierarchical configuration, where smaller TADs can make up larger TADs, and range in size from ∼40 kb to ∼3 MB with a median size of ∼185 kb.
“Moreover, TADs often have a nested, hierarchical configuration, where smaller TADs can make up larger TADs, and range in size from ∼40 kb to ∼3 MB with a median size of ∼185 kb16, though these numbers depend somewhat on the resolution of the Hi-C data used to identify them”
From Recent evidence that TADs and chromatin loops are dynamic structures (2018) · cites this paper as [16]
Epigenomic compartments range in size from 6 kb to more than 5 Mb.
“These epigenomic compartments range in size from 6 kb to more than 5 Mb.16”
From Recent evidence that TADs and chromatin loops are dynamic structures (2018) · cites this paper as [16]
Loop-anchored TADs almost always form between CTCF binding sites in a convergent orientation.
“A key piece of the puzzle came from the “convergent rule”: loop-anchored TADs almost always form between CTCF binding sites in a convergent orientation.16,20”
From Recent evidence that TADs and chromatin loops are dynamic structures (2018) · cites this paper as [16]
CTCF and cohesin play an important functional role in TAD formation and maintenance.
“suggesting an important functional role of CTCF and cohesin in TAD formation and maintenance (Fig. 2A).”
From Recent evidence that TADs and chromatin loops are dynamic structures (2018) · cites this paper as [16]
Almost all TADs are demarcated by CTCF ChIP-Seq peaks, but only around one-third of CTCF ChIP-Seq peaks flank TADs.
“Since almost all TADs are demarcated by CTCF ChIP-Seq peaks,16 but only around one-third of CTCF ChIP-Seq peaks flank TADs,1,7”
From Recent evidence that TADs and chromatin loops are dynamic structures (2018) · cites this paper as [16]
TAD boundaries are near universally enriched in CTCF and cohesin ChIP-Seq peaks.
“After TADs were discovered through Hi-C studies, it quickly became clear that TAD boundaries are near universally enriched in CTCF and cohesin ChIP-Seq peaks,3,4,16”
From Recent evidence that TADs and chromatin loops are dynamic structures (2018) · cites this paper as [16]
The best agreement with experimental Hi-C data was obtained when cohesin extrudes for ∼120-240 kb before falling off and a mean separation of ∼120 kb between extrusion complexes.
“The best agreement with experimental Hi-C data16 was obtained when cohesin extrudes for ∼120-240 kb before falling off and a mean separation of ∼120 kb between extrusion complexes (note that these are average numbers).8”
From Recent evidence that TADs and chromatin loops are dynamic structures (2018) · cites this paper as [16]
~5,000 chromatin loops whose boundaries are occupied by CTCF and cohesin in multiple human cell types were identified and termed loop domains.
“Another study used high-resolution hi-C technology to identify ~5,000 chromatin loops whose boundaries are occupied by CTCF and cohesin in multiple human cell types; these were termed “loop domains” (Rao et al., 2014).”
From Insulated Neighborhoods: Structural and Functional Units of Mammalian Gene Control (2016) · cites this paper as Rao et al., 2014
Sub-TADs, loop domains, and CTCF-contact domains generally represent the same structural unit as insulated neighborhoods.
“An analysis of the structures described in these studies suggests that they generally represent the same structural unit as the insulated neighborhoods described here (Figure 5).”
From Insulated Neighborhoods: Structural and Functional Units of Mammalian Gene Control (2016) · cites this paper as Rao et al., 2014
70% of loop domains, and 54% CTCF contact domains have the same boundaries as an insulated neighborhood in human lymphoblastoid cells.
“For example, 70% of loop domains, and 54% CTCF contact domains have the same boundaries as an insulated neighborhood in human lymphoblastoid cells (Rao et al., 2014; Tang et al., 2015).”
From Insulated Neighborhoods: Structural and Functional Units of Mammalian Gene Control (2016) · cites this paper as Rao et al., 2014
Genome-wide, high-resolution understanding of chromosome cell-cycle dynamics is still limited by current technologies.
“genome-wide, high-resolution understanding of chromosome cell-cycle dynamics11 is still limited by current technologies”
From Cell-cycle dynamics of chromosomal organization at single-cell resolution (2017) · cites this paper as [7]
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- Authors
- Rao, S. S. P., Huntley, M. H., Durand, N. C., Stamenova, E. K., Bochkov, I. D., Robinson, J. T., Sanborn, A. L., Machol, I., Omer, A. D., Lander, E. S., & Aiden, E. L.
- Affiliations
- Broad Institute (US)
- Baylor College of Medicine (US)
- Corresponding author
- Lander, Eric S.
- Aiden, Erez Lieberman
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2014-12
- Volume
- 159
- Issue
- 7
- Pages
- 1665-1680
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC5635824
- OpenAlex
- W1973062929
- Cited by
- 9,212
- References
- 82
- Citations by year
- 2026: 997 · 2025: 1122 · 2024: 1245 · 2023: 1152 · 2022: 775 · 2021: 900 · 2020: 751 · 2019: 685 · 2018: 557 · 2017: 457 · 2016: 348 · 2015: 207 · 2014: 4 · 2012: 2
- FWCI
- 139.72
- Type
- journal article
- Language
- en
- Update notices
- erratum (2015-07-30)
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- UWA Profiles and Research Repository (UWA) (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (DP2OD008540)
- National Heart, Lung, and Blood Institute (U01HL130010)
- National Human Genome Research Institute (U54HG003067, HG003067, P50HG006193, HG006193)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Chromosomal and Genetic Variations
- Keywords
- 3D genome organization, chromatin looping, Hi-C, CTCF binding sites, loop anchors, enhancer-promoter interactions, histone modifications, X chromosome inactivation, lymphoblastoid cell lines
- MeSH terms
- CCCTC-Binding Factor, Animals, Cell Line, Cell Nucleus, Chromatin, Gene Expression Regulation, Humans, Molecular Conformation, Regulatory Sequences, Nucleic Acid, Repressor Proteins, Genome, Human, Histone Code, Mice
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