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Systematic evaluation of chromosome conformation capture assays


Chromosome conformation capture (3C) assays are used to map chromatin interactions genome-wide. Chromatin interaction maps provide insights into the spatial organization of chromosomes and the mechanisms by which they fold. Hi-C and Micro-C are widely used 3C protocols that differ in key experimental parameters including cross-linking chemistry and chromatin fragmentation strategy. To understand how the choice of experimental protocol determines the ability to detect and quantify aspects of chromosome folding we have performed a systematic evaluation of 3C experimental parameters. We identified optimal protocol variants for either loop or compartment detection, optimizing fragment size and cross-linking chemistry. We used this knowledge to develop a greatly improved Hi-C protocol (Hi-C 3.0) that can detect both loops and compartments relatively effectively. In addition to providing benchmarked protocols, this work produced ultra-deep chromatin interaction maps using Micro-C, conventional Hi-C and Hi-C 3.0 for key cell lines used by the 4D Nucleome project.

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Fig. 1: Outline of the experimental design.
Fig. 2: Extra cross-linking yields more intra-chromosomal contacts.
Fig. 3: Fragment size and cross-linking affect compartment strength.
Fig. 4: Fragment size and cross-linking determine loop detection.
Fig. 5: Characterization of interactions and chromatin features of loop anchors.
Fig. 6: Hi-C 3.0 detects both compartments and loops.

Data availability

Data are available at GEO under accession number GSE163666. Supplementary Table 1 lists datasets accessible through the 4DN data portal including 4DN accession numbers. Source data are provided with this paper.

Code availability

Scripts and notebooks used in this paper can be accessed at


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This work was supported by a grant from the National Institutes of Health Common Fund 4D Nucleome Program to J.D. and L.A.M. (U54-DK107980, UM1-HG011536), and a grant from the National Human Genome Research Institute (NHGRI) to J.D. (HG003143). J.D. is an investigator of the Howard Hughes Medical Institute. N.K. was supported by Human Frontiers Science Program (HFSP) grant LT000631/2017-L. The authors thank S. Henikoff and D. Janssens for sharing CUT&RUN data.

Author information




J.D. and J.H.G. conceived the study. L.Y. and N.K. performed 3C-based assays. J.H.G. coordinated data collection. B.A.O. led all data analysis and performed data analysis. S.A. performed data analysis. S.V.V. contributed analysis tools and performed analysis. H.O. pre-processed the sequencing data. A.N., H.O. and J.H.G. designed the cLIMS system to store metadata, K.M.P. and R.M. differentiated H1-hESC to DE and cultured the cells, and M.E.O. performed mitotic data synchronization and HFFc6 ATAC-Seq experiments. H.M. and R.M.J.G. generated H1-hESC ATAC-Seq and HFFc6 CUT&Tag datasets. O.J.R. and L.A.M. contributed data analysis. All authors contributed to the writing of the paper.

Corresponding authors

Correspondence to Johan H. Gibcus or Job Dekker.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Methods thanks Giacomo Cavalli, James Davies and Fulai Jin for their contribution to the peer review of this work. Lei Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 DNA fragmentation and clustering of correlation (HiCRep).

a,b. Cumulative distribution of the lengths of fragmented DNA obtained from fragment analyzer data in HFF cells stratified for different cross-linkers (a) and restriction enzymes (b). Gray lines indicate all datasets, colored lines indicate data obtained with the indicated nuclease/cross-linkers. c-g. Hierarchical clustering of HiCRep correlations for: all protocols comparing cell states (c), synchronized HeLa-S3 G1 cells (dark green) and non-synchronized HeLa-S3 cells (light green) (d), synchronized HeLa-S3 mitotic cells (e), H1-hESC and H1-hESC derived DE cells (f), 12 protocols applied to HFF cells (g). One color key is indicated for all of the heatmaps. h. Genome coverage of data generated using MNase, DdeI, DpnII and HindIII. The read density was normalized to reads per million, separated by the coverage in A and B compartments (Methods).

Source data

Extended Data Fig. 2 Cis and trans contact frequency differ between protocols.

a. The number of valid pairs in each of the 12 protocols applied to H1-hESC, DE, HeLa-S3-NS, HeLa-S3-G1 and HeLa-S3-M cells partitioned by genomic distances. b. Distance dependent contact probability of 12 protocols ordered as in (a), partitioned by fragmenting nucleases used (gray lines indicate all datasets, colored lines indicate datasets generated with the nucleases indicated for each plot). c. The relationship between the trans percent and the average slope of the distance dependent contact probability for the 12 protocols ordered as in Extended Data Fig. 2a. d. Quantification of protocol introduced noise as defined by inter-mitochondrial interactions (chrM with chr1-22), normalized by intra-mitochondrial (chrM with chrM) interactions.

Source data

Extended Data Fig. 3 Quantitative compartment detection differ between protocols.

a. Hierarchical clustering of Spearman correlations of Eigenvectors (PC1) for 63 protocols. Clustering shows strong correlations between compartments from data obtained with varying protocols applied to the same cell types and weaker correlations for data obtained with the same protocols applied to different cell types. b–e. A-A and B-B compartment strength of saddle plots for fixation versus enzyme stratified by cell state: DE (b), H1-hESC (c), HeLa-S3-NS (d), HFF (e). For each cell type, saddle plot quantification was done for cis and trans reads separately.

Source data

Extended Data Fig. 4 FA+DSG cross-linking produces reproducible chromatin loops.

a. Interaction heatmaps (log transformed) of experiments for H1-ESC cells obtained from the following cross-linker-enzyme combinations (from left to right): FA-DpnII, FA+DSG-DpnII and FA+DSG-MNase. b. Interaction heatmaps of protocols specified in Extended Data Fig. 4a for HFFc6 cells. c.Upset plots of loops detected with different replicates for H1-hESC show: 1) total number of loops detected in Replicate 1, Replicate 2 and merged replicates on the right side (gray bars), 2) number of loops detected in the one, two or three experiments shown in black bars. Loops found with only one or multiple experiments are highlighted and connected with black dots. Here Upset plots investigate the consistency of loops between each of the replicates and combined replicates for FA-DpnII, FA+DSG-DpnII and FA+DSG-MNase in H1-hESC. d. Upset plots (as explained in Extended Data Fig. 4c) of loops detected with different replicates for HFFc6 cells.

Extended Data Fig. 5 Fine fragmentation and DSG cross-linking improves loop detection.

a. Loops for HFFc6 shows the 1) total number of loops detected with FA-DpnII, FA+DSG-DpnII and FA+DSG-MNase(gray bars, right side), 2) number of overlapping loops detected (black bars). Overlapping loops are connected with black dots. b. Pileups of the loops from Fig. 4a. Numbers represent signal enrichment over local background (Methods). c. Individual loop strength (as in panel b) between protocol pairs in HFFc6. Protocols (left to right): FA-DpnII v/s FA+DSG-DpnII, FA+DSG-DpnII v/s FA+DSG-MNase and FA-DpnII v/s FA+DSG-MNase. Plots display two sets of looping interactions - Union (red squares) and Intersection (blue circles) from the three protocols. Color scale represents density of loop interactions. d,e.Aggregated loop strengths of intersection loop set from matrix of 12 protocols (described in Fig. 1a) for H1-hESC (d), and HFFc6 (e).

Source data

Extended Data Fig. 6 Finer fragments lead to detection of Promoter-Enhancer loops.

a. H1-hESC loops versus loop anchors. Expected for anchors in one loop: y=2x b,c. FA-DpnII loops subtracted from FA+DSG-DpnII (b) or FA+DSG-MNase (c) Union of loops detected at the same anchors. d,e. Valencies of loop anchors for H1-hESC (d), HFFc6 (e) from FA-DpnII, FA+DSG-DpnII, FA+DSG-MNase. FA-DpnII is used as a guiding example. Categories are: anchors from 1 protocol (FA-DpnII), anchors from 2 protocols (FA-DpnII and either FA+DSG-DpnII or FA+DSG-MNase) and anchors from all 3 protocols. f. CTCF, SMC1, H3K4me3 and H3K27ac enrichments at loop anchors for all protocols (intersection) or FA+DSG-MNase alone in H1-hESC.Average enrichments centered on open chromatin regions within anchor coordinates. g. Top: cCREs from common and FA+DSG-MNase specific loop anchors (Extended Data Fig. 5f). Bottom: stratified percentage of Promoter-Enhancer cCREs without CTCF enrichment. h. Enrichment of CTCF, SMC1, H3K4me3 and H3K27ac for left (Anchor1) and right (Anchor 2) anchor in H1-hESC using FA-DpnII, FA+DSG-DpnII or FA+DSG-MNase.

Source data

Extended Data Fig. 7 Insulation quantification is robust to experimental variations.

a-d. Rows: HFFc6 deep data from FA-DpnII (top), FA+DSG-DpnII (middle) and FA+DSG-MNase (bottom). Columns: boundary strength distributions with strength threshold (a) (Methods), pileups in FA-DpnII, FA+DSG-DpnII and FA+DSG-MNase for aggregate insulation scores at loop anchors (left), strong insulation boundaries (middle) and loop anchors colocalizing with strong insulation boundaries (right) (b). Left panel: boundary strength distribution of matrix data (Fig. 1a) for FA-DpnII, FA+DSG-DpnII and FA+DSG-MNase. Right panel:correlation of strong boundaries between deep and matrix data for FA-DpnII, FA+DSG-DpnII and FA+DSG-MNase (c). Aggregate insulation profile of weak and strong boundaries from matrix data for HFFc6 for cross-linkers and nucleases (d). e–h. H1-hESC data displayed like Extended Data Fig. 7a-d. i. Number of boundaries (y-axis) stratified by number of protocols (1 to 12; see Fig. 1a) wherein a given boundary was detected (x-axis). j. Insulation strength of boundaries stratified as in (i). k. Mean insulation strength for boundaries detected in at least half of protocols for various cross-linkers and enzyme combinations of H1-hESC, DE, HFF and HeLa-S3-NS (Methods). l. Mean insulation strength of loop anchors detected in all three deep protocols for both HFFc6 and H1-hESC, averaged for 12 protocols of H1-hESC and HFF.

Source data

Extended Data Fig. 8 Hi-C 3.0 performs similar to Micro-C.

a. Fragment size distributions from Fragment Analyzer for specified protocols. b. Cumulative distributions of fragmented DNA in HFF cells stratified for cross-linking agents (top row) or restriction enzymes (bottom row). Dashed lines in each of the panels represent expected fragment size distribution from in silico digestion of hg38 for enzymes indicated. Gray lines represent all data from all other enzymes (columns). c. Comparison of CTCF, SMC1, H3K4me3 and H3K27ac enrichments at loop anchors centered at open chromatin regions. Open chromatin regions (ATAC Seq) located within the anchor coordinates were used to center the average enrichments. Anchors were separated into sets detected by FA+DSG-DdeI+DpnII, FA+DSG-MNase or both. d. Percentage of cCREs and promoter-enhancer elements located at loop anchors specific to FA+DSG-DdeI+DpnII, FA+DSG-MNase or shared between them.

Source data

Extended Data Fig. 9 Sequencing depth impact loop detection but not compartmentalization.

a. Spearman correlation of the eigenvectors for different sequencing depths in H1-hESC. Each point represents one sampled experiment. X-axis shows the sequencing depth (200 M reads-2B reads) and the y- axis shows the correlation of the eigenvectors for each depth with the eigenvector of the experiment with 2 Billion reads. The bottom plot shows the zoomed correlations. b. Compartment strength of A compartment for experiments with different read depths quantified in cis and trans for H1-hESC. c. Compartment strength of B compartment for experiments with different read depths quantified in cis and trans for H1-hESC. d. # of loops detected in experiments with different read depths in H1-hESC. e-f. Analysis that is shown in a-d repeated for experiments performed in HFFc6 cells.

Source data

Supplementary information

Supplementary Fig. 1

Supplementary Fig. 1

Reporting Summary

Supplementary Table 1

Mapping statistics of all experiments used in this study

Source data

Source Data Fig. 2

Data points of cis/trans ratio, scaling plots and P(s) average slope for HFF.

Source Data Fig. 3

Compartment strength of all protocols.

Source Data Fig. 4

Loop strength of intersection and union loop lists.

Source Data Fig. 5

Loop and anchor relationship, protocol-specific and shared loop anchor lists.

Source Data Fig. 6

Data points of compartment strength and loop strength including Hi-C 3.0.

Source Data Extended Data Fig. 1

Fragment size distributions for cumulative plot and HiCRep correlations.

Source Data Extended Data Fig. 2

Data points of cis/trans ratio, scaling plots, P(s) average slope and mitochondrial interactions for all cell types.

Source Data Extended Data Fig. 3

Compartment strength of all protocols, first eigenvector correlations and coverages data points.

Source Data Extended Data Fig. 5

Loop strength of intersection and union loop lists.

Source Data Extended Data Fig. 6

Loop and anchor relationship, protocol-specific and shared loop anchor lists.

Source Data Extended Data Fig. 7

Number of commonly detected insulation boundaries and the strength of these boundaries.

Source Data Extended Data Fig. 8

Fragment size distributions for cumulative plot including Hi-C 3.0.

Source Data Extended Data Fig. 9

Spearman correlation of first eigenvectors, compartment strengths and number of detected loops for sampled experiments.

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Akgol Oksuz, B., Yang, L., Abraham, S. et al. Systematic evaluation of chromosome conformation capture assays. Nat Methods 18, 1046–1055 (2021).

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