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.
Subscribe to Journal
Get full journal access for 1 year
only $9.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Scripts and notebooks used in this paper can be accessed at https://github.com/dekkerlab/matrix_paper
Dekker, J., Rippe, K., Dekker, M. & Kleckner, N. Capturing chromosome conformation. Science 295, 1306–1311 (2002).
Denker, A. & de Laat, W. The second decade of 3C technologies: detailed insights into nuclear organization. Genes Dev. 30, 1357–1382 (2016).
Rao, S. S. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).
Kagey, M. H. et al. Mediator and cohesin connect gene expression and chromatin architecture. Nature 467, 430–435 (2010).
Fudenberg, G. et al. Formation of chromosomal domains by loop extrusion. Cell Rep. 15, 2038–2049 (2016).
Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).
Hsieh, T. S., Fudenberg, G., Goloborodko, A. & Rando, O. J. Micro-C XL: assaying chromosome conformation from the nucleosome to the entire genome. Nat. Methods 13, 1009–1011 (2016).
Krietenstein, N. et al. Ultrastructural details of mammalian chromosome architecture. Mol. Cell 78, 554–565 (2020).
Hsieh, T. S. et al. Resolving the 3D landscape of transcription-linked mammalian chromatin folding. Mol. Cell 78, 539–553 (2020).
Abdennur, N. & Mirny, L. A. Cooler: scalable storage for Hi-C data and other genomically labeled arrays. Bioinformatics 36, 311–316 (2020).
Imakaev, M. et al. Iterative correction of Hi-C data reveals hallmarks of chromosome organization. Nat. Methods 9, 999–1003 (2012).
Yang, T. et al. HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient. Genome Res. 27, 1939–1949 (2017).
Yardimci, G. G. et al. Measuring the reproducibility and quality of Hi-C data. Genome Biol. 20, 57 (2019).
Lajoie, B. R., Dekker, J. & Kaplan, N. The hitchhiker’s guide to Hi-C analysis: practical guidelines. Methods 72, 65–75 (2015).
Schmitt, A. D., Hu, M. & Ren, B. Genome-wide mapping and analysis of chromosome architecture. Nat. Rev. Mol. Cell Biol. 17, 743–755 (2016).
Naumova, N. et al. Organization of the mitotic chromosome. Science 342, 948–953 (2013).
Nora, E. P. et al. Targeted degradation of CTCF decouples local insulation of chromosome domains from genomic compartmentalization. Cell 169, 930–944 (2017).
Schwarzer, W. et al. Two independent modes of chromatin organization revealed by cohesin removal. Nature 551, 51–56 (2017).
Dekker, J. et al. The 4D nucleome project. Nature 549, 219–226 (2017).
Janssens, D. H. et al. Automated in situ chromatin profiling efficiently resolves cell types and gene regulatory programs. Epigenetics Chromatin 11, 74 (2018).
Zhang, J. et al. An integrative ENCODE resource for cancer genomics. Nat. Commun. 11, 3696 (2020).
Skene, P. J. & Henikoff, S. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. Elife 6, e21856 (2017).
Kaya-Okur, H. S. et al. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat. Commun. 10, 1930 (2019).
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).
ENCODE Project Consortium Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 583, 699–710 (2020).
Li, Y. et al. The structural basis for cohesin-CTCF-anchored loops. Nature 578, 472–476 (2020).
Hou, C., Li, L., Qin, Z. S. & Corces, V. G. Gene density, transcription, and insulators contribute to the partition of the drosophila genome into physical domains. Mol. Cell 48, 471–484 (2012).
Dixon, J. R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012).
Nora, E. P. et al. Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature 485, 381–385 (2012).
Crane, E. et al. Condensin-driven remodelling of X chromosome topology during dosage compensation. Nature 523, 240–244 (2015).
Tavares-Cadete, F., Norouzi, D., Dekker, B., Liu, Y. & Dekker, J. Multi-contact 3C reveals that the human genome during interphase is largely not entangled. Nat. Struct. Mol. Biol. 27, 1105–1114 (2020).
Kerpedjiev, P. et al. HiGlass: web-based visual exploration and analysis of genome interaction maps. Genome Biol. 19, 125 (2018).
Belaghzal, H., Dekker, J. & Gibcus, J. H. Hi-C 2.0: an optimized Hi-C procedure for high-resolution genome-wide mapping of chromosome conformation. Methods 123, 56–65 (2017).
Genga, R. M. J. et al. Single-cell RNA-sequencing-based CRISPRi screening resolves molecular drivers of early human endoderm development. Cell Rep. 27, 708–718 (2019).
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Ewels, P. A. et al. The nf-core framework for community-curated bioinformatics pipelines. Nat. Biotechnol. 38, 276–278 (2020).
Ramírez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).
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.
The authors declare no competing interests.
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.
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).
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.
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.
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.
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).
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.
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.
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.
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.
Data points of cis/trans ratio, scaling plots and P(s) average slope for HFF.
Compartment strength of all protocols.
Loop strength of intersection and union loop lists.
Loop and anchor relationship, protocol-specific and shared loop anchor lists.
Data points of compartment strength and loop strength including Hi-C 3.0.
Fragment size distributions for cumulative plot and HiCRep correlations.
Data points of cis/trans ratio, scaling plots, P(s) average slope and mitochondrial interactions for all cell types.
Compartment strength of all protocols, first eigenvector correlations and coverages data points.
Loop strength of intersection and union loop lists.
Loop and anchor relationship, protocol-specific and shared loop anchor lists.
Number of commonly detected insulation boundaries and the strength of these boundaries.
Fragment size distributions for cumulative plot including Hi-C 3.0.
Spearman correlation of first eigenvectors, compartment strengths and number of detected loops for sampled experiments.
About this article
Cite this article
Akgol Oksuz, B., Yang, L., Abraham, S. et al. Systematic evaluation of chromosome conformation capture assays. Nat Methods 18, 1046–1055 (2021). https://doi.org/10.1038/s41592-021-01248-7