Upgrade to Pro — share decks privately, control downloads, hide ads and more …

BMMB554: Genome Conformation

BMMB554: Genome Conformation

Slides on genome conformation analysis (3C, 5C, Hi-C)

2e0188eb9315b0ecb64537ccfe70eab8?s=128

Anton Nekrutenko

April 12, 2017
Tweet

More Decks by Anton Nekrutenko

Other Decks in Education

Transcript

  1. Chromatin interactions and genome architecture (courtesy of James Taylor)

  2. Adapted from Wasserman and Sandelin, Nature Review Genetics. 2004 Transcription

    Initiation Complex Gene Promoter cis-regulatory module How do cis-regulatory modules interact with their target promoters?
  3. Insulator proteins

  4. yellow wing blade body cuticle mouth parts denticle belts bristles,

    denticle belts, aristae tarsal claws (Geyer and Corces, Genes and Development, 1992, etc) The yellow locus in Drosophila
  5. yellow gypsy retrotransposon (700bp) wing blade body cuticle mouth parts

    denticle belts bristles, denticle belts, aristae tarsal claws (Geyer and Corces, Genes and Development, 1992, etc) The yellow locus in Drosophila
  6. yellow gypsy retrotransposon (700bp) su(Hw) binding site cluster wing blade

    body cuticle mouth parts denticle belts bristles, denticle belts, aristae tarsal claws (Geyer and Corces, Genes and Development, 1992, etc) The yellow locus in Drosophila
  7. yellow -1868 -+660 +1310 +2940 -700 su(Hw) binding site cluster

    wing blade body cuticle mouth parts denticle belts bristles, denticle belts, aristae tarsal claws (Geyer and Corces, Genes and Development, 1992, etc) The yellow locus in Drosophila
  8. Repression of transcription by su(Hw) - 1868 -800R -800 -700R

    +660R +660 +1310 +1310R +2490R body mouth wing cuticle hooks TATA ~ ' + + t + i " + i + i + + + + + + I ! = + + + i i + + + , t J + + + i bristles tarsal claws Figure 2. Summary of y phenotypes in trans- formed lines. (Top) The relative location with respect to the TATA box of different tissue- specific enhancers responsible for the expres- sion of the y gene in various tissues. Numbers at left indicate the location of the insertion site of the su(Hw)-binding region into the y gene in the various plasmids used for germ- line transformation. Each lane summarizes information on transformed lines obtained with each plasmid. The position of the in- serted sequences relative to various y enhanc- ers is indicated diagrammatically by a triangle that represents the su(Hw)-binding region; the solid circles represent the su(Hw) protein; the arrow indicates the orientation of the in- serted sequences relative to the y gene. The coloration of each tissue is indicated by + (wild type) or - (mutant) signs. Cold Spring Harbor Laboratory Press on November 1, 2015 - Published by genesdev.cshlp.org Downloaded from yellow -1868 -+660 +1310 +2940 -700 su(Hw) binding site cluster Blocks tissue specific enhancer activity regardless of location relative to TSS or insert orientation
  9. (West, Gaszner and Felsenfeld, Genes and Development, 2002) Insulators (operationally)

    block enhancer activity
  10. Insulator proteins (Yang and Corces, Current Opinion in Genetics &

    Development, 2012)
  11. (Muravyova et al, Science, 2001) con- t in- The ructs

    to yel- y en- d En- par- white ) in- as a yel- es as n ar- e di- tion. frag- n to rizes indicating that the yellow gene was activated by its enhancers in the majority of the E P O R T S
  12. (Muravyova et al, Science, 2001) con- t in- The ructs

    to yel- y en- d En- par- white ) in- as a yel- es as n ar- e di- tion. frag- n to rizes indicating that the yellow gene was activated by its enhancers in the majority of the E P O R T S
  13. (Muravyova et al, Science, 2001) Fig. 2. Transposon constructs to

    test white enhancer action. The white box (Eye) indicates the eye enhancer of the white gene, and the thick arrows marked FRT represent the target sites of the Flp recombinase. The other symbols are the same as in Fig. 1. The two columns on the right summarize the results, with ϩ indicating that the yellow or white genes were activated by their respective R E P O R T S
  14. (Muravyova et al, Science, 2001) Fig. 2. Transposon constructs to

    test white enhancer action. The white box (Eye) indicates the eye enhancer of the white gene, and the thick arrows marked FRT represent the target sites of the Flp recombinase. The other symbols are the same as in Fig. 1. The two columns on the right summarize the results, with ϩ indicating that the yellow or white genes were activated by their respective R E P O R T S
  15. (Muravyova et al, Science, 2001) pression was studied in a

    su(Hw)– back- ground. In five lines, the absence of Su(Hw) protein reduced white expression, implying blocked. Howe flanked by two ed at position transcription s the yellow gen and wings. In expression dec not change in activation of t yellow enhanc posed insulato yellow, the in tors between promoters may stead of block lator between removed, yield expression in pressed, showi enhancers in the majority of the lines. Fig. 3. Model of the double insulator bypass. (A) A single insulator blocks enhancer-promot- er interaction. (B) Two insulators may interact with one another through the protein complex- es bound to them, forming a loop and bringing the enhancers closer to the promoter.
  16. (Legend on facing page) 278 GENES & DEVELOPMENT (West, Gaszner

    and Felsenfeld, Genes and Development, 2002)
  17. West et al. (West, Gaszner and Felsenfeld, Genes and Development,

    2002)
  18. disk cells t and either left untrea green and and

    U are DNA visua staining is the panels. the green d G–I and pr and Y show of the resu Probes A (g treated wil (green), B ( NaCl-extra Probes A (g NaCl-extra (Byrd and Corces, Journal of Cell Biology, 2003) Wild type, gypsy insulator at base of loop ct6 mutant has another gypsy in the middle Evidence for insulator loop formation:
  19. Phillips and Corces, Cell, 2009

  20. Insulator proteins Architectural Proteins

  21. How do we link CRMs to target genes?

  22. (HapMap SNPs, Expression data from Stranger / Dermitzakis) Genetic (eQTL)

    linking chr7: 3929 3478 115750000 115800000 distal-pTRRs Probes from GSE3612 (GPL3090) Your Sequence from Blat Search UCSC Known Genes (June, 05) Based on UniProt, RefSeq, and GenBank mRNA Gencode Reference Genes UC Davis ChIP/Chip NimbleGen (C-Myc ab, HeLa Cells) University of North Carolina FAIRE Signal Myc_stimulated CAV2/NM_001233 CAV1/NM_001753 AF172085/AF172085 AC002066.1 CAV2 CAV2 CAV2 CAV1 CAV1 CAV1 CAV1 AC006159.3 UCD C-Myc FAIRE Signal putative CRM in intron of CAV1 Expression probes for CAV2 AA AC 7.80 7.90 8.00 8.10 Expression of probe 3478 ~ Genotype at rs12668226 Genotype AA AC 7.90 7.95 8.00 8.05 8.10 Expression of probe 3929 ~ Genotype at rs12668226 Genotype AA AC 7.80 7.90 8.00 8.10 Expression of probe 3478 ~ Genotype at rs12668226 Genotype AA AC 7.90 7.95 8.00 8.05 8.10 Expression of probe 3929 ~ Genotype at rs12668226 Genotype Allele of SNP in CRM associated with expression
  23. Expression H3K4me1 H3K27Ac Colors indicate activity annotation across 12 cell

    types Correlation of expression/marks across cell types implies link Linking through correlated activity
  24. How do we link CRMs to target genes? Turn it

    into a sequencing problem
  25. antibody to chicken C␮ (M1) (Southern Biotechnology Associates, Birmingham, AL)

    and then with polyclonal fluorescein isothiocyanate–conjugated goat antibodies to mouse IgG (Fab) 2 (Sigma). Predominantly sIgM(ϩ) subclones were excluded from the analysis, because they most likely originated from cells that were already sIgM(ϩ) at the time of subcloning. 23. For Ig light chain sequencing, PCR amplification and sequencing of the rearranged light chain V segments were performed as previously described (19), except that high-fidelity PfuTurbo polymer- ase (Stratagene) was used with primer pair V␭1/ V␭2 for PCR, and primer V␭3 was used for se- quencing (17). Only one nucleotide change, which most likely reflects a PCR-introduced artifact, was noticed in the V-J-3Ј intron region in a total of 80 0.5-kb-long sequences from AIDϪ/ϪE cells. 24. We thank M. Reth and T. Brummer for kindly provid- ing the MerCreMer plasmid vector; P. Carninci and Y. Hayashizaki for construction of the riken1 bursal cDNA library; A. Peters and K. Jablonski for excellent technical help; and C. Stocking and J. Lo ¨hrer for carefully reading the manuscript. Supported by grant Bu 631/2-1 from the Deutsche Forschungsgemein- shaft, by the European Union Framework V programs “Chicken Image” and “Genetics in a Cell Line,” and by Japan Society for the Promotion of Science Postdoc- toral Fellowships for Research Abroad. 22 October 2001; accepted 18 December 2001 Capturing Chromosome Conformation Job Dekker,1* Karsten Rippe,2 Martijn Dekker,3 Nancy Kleckner1 We describe an approach to detect the frequency of interaction between any two genomic loci. Generation of a matrix of interaction frequencies between sites on the same or different chromosomes reveals their relative spatial disposition and provides information about the physical properties of the chromatin fiber. This methodology can be applied to the spatial organization of entire genomes in organisms from bacteria to human. Using the yeast Saccharomyces cerevisiae, we could confirm known qualitative features of chromosome organization within the nucleus and dynamic changes in that organization during meiosis. We also analyzed yeast chromosome III at the G 1 stage of the cell cycle. We found that chromatin is highly flexible throughout. Furthermore, functionally distinct AT- and GC-rich domains were found to exhibit different conformations, and a population-average 3D model of chro- mosome III could be determined. Chromosome III emerges as a contorted ring. Important chromosomal activities have been linked with both structural properties and spatial conformations of chromosomes. Local properties of the chromatin fiber influence gene expression, origin firing, and DNA re- pair [e.g., (1, 2)]. Higher order structural features—such as formation of the 30-nm fiber, chromatin loops and axes, and inter- chromosomal connections—are important for chromosome morphogenesis and also have roles in gene expression and recombination. Activities such as transcription and timing of replication have been related to overall spa- affords a resolution of 100 to 200 nm at best, which is insufficient to define chromosome conformation. DNA binding proteins fused to green fluorescent protein permit visualization of individual loci, but only a few positions can be examined simultaneously. Multiple loci can be visualized with fluorescence in situ hybridization (FISH), but this requires severe treatment that may affect chromosome organization. We developed a high-throughput method- ology, Chromosome Conformation Capture (3C), which can be used to analyze the over- of purified nuclei is largely intact, as shown below. For quantification of cross-linking fre- quencies, cross-linked DNA is digested with a restriction enzyme and then subjected to ligation at very low DNA concentration. Un- der such conditions, ligation of cross-linked fragments, which is intramolecular, is strong- ly favored over ligation of random fragments, which is intermolecular. Cross-linking is then reversed and individual ligation products are detected and quantified by the polymerase chain reaction (PCR) using locus-specific primers. Control template is generated in which all possible ligation products are present in equal abundance (7). The cross- linking frequency (X) of two specific loci is determined by quantitative PCR reactions us- ing control and cross-linked templates, and X is expressed as the ratio of the amount of product obtained using the cross-linked tem- plate to the amount of product obtained with the control template (Fig. 1B). X should be directly proportional to the frequency with which the two corresponding genomic sites interact (10). Control experiments show that formation of ligation products is strictly dependent on both ligation and cross-linking (Fig. 1C). In general, X decreases with increasing separa- tion distance in kb along chromosome III (“genomic site separation”). Cross-linking frequencies for both the left telomere and the centromere of chromosome III with each of R E P O R T S on April 19, 2012 www.sciencemag.org Downloaded from sites on the same or different chromosomes reveals their relative spatial disposition and provides information about the physical properties of the chromatin fiber. This methodology can be applied to the spatial organization of entire genomes in organisms from bacteria to human. Using the yeast Saccharomyces cerevisiae, we could confirm known qualitative features of chromosome organization within the nucleus and dynamic changes in that organization during meiosis. We also analyzed yeast chromosome III at the G 1 stage of the cell cycle. We found that chromatin is highly flexible throughout. Furthermore, functionally distinct AT- and GC-rich domains were found to exhibit different conformations, and a population-average 3D model of chro- mosome III could be determined. Chromosome III emerges as a contorted ring. Important chromosomal activities have been linked with both structural properties and spatial conformations of chromosomes. Local properties of the chromatin fiber influence gene expression, origin firing, and DNA re- pair [e.g., (1, 2)]. Higher order structural features—such as formation of the 30-nm fiber, chromatin loops and axes, and inter- chromosomal connections—are important for chromosome morphogenesis and also have roles in gene expression and recombination. Activities such as transcription and timing of replication have been related to overall spa- tial nuclear disposition of different regions and their relationships to the nuclear enve- lope [e.g., (3–6)]. At each of these levels, chromosome organization is highly dynamic, varying both during the cell cycle and among different cell types. Analysis of chromosome conformation is complicated by technical limitations. Elec- tron microscopy, while affording high reso- lution, is laborious and not easily applicable to studies of specific loci. Light microscopy affords a resolution of 100 to 200 nm at best, which is insufficient to define chromosome conformation. DNA binding proteins fused to green fluorescent protein permit visualization of individual loci, but only a few positions can be examined simultaneously. Multiple loci can be visualized with fluorescence in situ hybridization (FISH), but this requires severe treatment that may affect chromosome organization. We developed a high-throughput method- ology, Chromosome Conformation Capture (3C), which can be used to analyze the over- all spatial organization of chromosomes and to investigate their physical properties at high resolution. The principle of our approach is outlined in Fig. 1A (7). Intact nuclei are isolated (8) and subjected to formaldehyde fixation, which cross-links proteins to other proteins and to DNA. The overall result is cross-linking of physically touching seg- ments throughout the genome via contacts between their DNA-bound proteins. The rel- ative frequencies with which different sites have become cross-linked are then deter- mined. Analysis of genome-wide interaction frequencies provides information about gen- eral nuclear organization as well as physical properties and conformations of chromo- somes. We have used intact yeast nuclei for all experiments. Although the method can be performed using intact cells, the signals are considerably lower, making quantification difficult (9). The general nuclear organization which is intermolecular. Cross-linking is then reversed and individual ligation products are detected and quantified by the polymerase chain reaction (PCR) using locus-specific primers. Control template is generated in which all possible ligation products are present in equal abundance (7). The cross- linking frequency (X) of two specific loci is determined by quantitative PCR reactions us- ing control and cross-linked templates, and X is expressed as the ratio of the amount of product obtained using the cross-linked tem- plate to the amount of product obtained with the control template (Fig. 1B). X should be directly proportional to the frequency with which the two corresponding genomic sites interact (10). Control experiments show that formation of ligation products is strictly dependent on both ligation and cross-linking (Fig. 1C). In general, X decreases with increasing separa- tion distance in kb along chromosome III (“genomic site separation”). Cross-linking frequencies for both the left telomere and the centromere of chromosome III with each of 12 other positions along that same chromo- some (Fig. 1, C and D) were determined using nuclei isolated from exponentially growing haploid cells. Interestingly, the two telomeres of chromosome III interact more frequently than predicted from their genomic site separation, which suggests that the chro- mosome ends are in close spatial proximity. This is expected because yeast telomeres are known to occur in clusters (11, 12). We next applied our method to an analysis of centromeres and of homologous chromo- somes (“homologs”) during meiosis in yeast (7). In mitotic and premeiotic cells, centro- meres are clustered near the spindle pole body (13, 14) and homologous chromosomes are loosely associated (15–17). These fea- tures change markedly when cells enter mei- osis (13). The centromere cluster is rapidly lost and is not restored until just before the first meiotic division. Loose interactions be- 1Department of Molecular and Cellular Biology, Har- vard University, Cambridge, MA 02138, USA. 2Mole- kulare Genetik (H0700), Deutsches Krebsforschungs- zentrum, Im Neuenheimer Feld 280, and Kirchhoff- Institut fu ¨r Physik, Physik Molekularbiologischer Pro- zesse, Universita ¨t Heidelberg, Schro ¨derstrasse 90, D-69120 Heidelberg, Germany. 32e Oosterparklaan 272, 3544 AX Utrecht, Netherlands. *To whom correspondence should be addressed. E- mail: jdekker@fas.harvard.edu 15 FEBRUARY 2002 VOL 295 SCIENCE www.sciencemag.org 1306 on April 19, 201 www.sciencemag.org Downloaded from
  26. Chromatin Conformation Capture = 3C Dekker et al. 2002

  27. Dekker et al, Science, 2002 Chromosome conformation capture (3C) genomic

    DNA interaction complex
  28. cross linked interaction

  29. restriction enzyme cut sites

  30. None
  31. Ligate cut ends

  32. 3C library contains many such ligation products A given ligation

    product can be quantified using specific PCR primers
  33. Dekker et al. 2002

  34. 3C allows us to target one or a few specific

    interactions Can this be applied genome wide?
  35. (D.R.B.). The contents are the responsibility of the authors and

    do not necessarily reflect the views of USAID or the U.S. government. The authors declare competing financial interests. Protocol G Principal Investigators: G. Miiro, J. Serwanga, A. Pozniak, D. McPhee, Supporting Online Material www.sciencemag.org/cgi/content/full/1178746/DC1 Materials and Methods SOM Text 7 July 2009; accepted 26 August 2009 Published online 3 September 2009; 10.1126/science.1178746 Include this information when citing this paper. Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome Erez Lieberman-Aiden,1,2,3,4* Nynke L. van Berkum,5* Louise Williams,1 Maxim Imakaev,2 Tobias Ragoczy,6,7 Agnes Telling,6,7 Ido Amit,1 Bryan R. Lajoie,5 Peter J. Sabo,8 Michael O. Dorschner,8 Richard Sandstrom,8 Bradley Bernstein,1,9 M. A. Bender,10 Mark Groudine,6,7 Andreas Gnirke,1 John Stamatoyannopoulos,8 Leonid A. Mirny,2,11 Eric S. Lander,1,12,13† Job Dekker5† We describe Hi-C, a method that probes the three-dimensional architecture of whole genomes by coupling proximity-based ligation with massively parallel sequencing. We constructed spatial proximity maps of the human genome with Hi-C at a resolution of 1 megabase. These maps confirm the presence of chromosome territories and the spatial proximity of small, gene-rich chromosomes. We identified an additional level of genome organization that is characterized by the spatial segregation of open and closed chromatin to form two genome-wide compartments. At the megabase scale, the chromatin conformation is consistent with a fractal globule, a knot-free, polymer conformation that enables maximally dense packing while preserving the ability to easily fold and unfold any genomic locus. The fractal globule is distinct from the more commonly used globular equilibrium model. Our results demonstrate the power of Hi-C to map the dynamic conformations of whole genomes. The three-dimensional (3D) conformation of chromosomes is involved in compartmen- talizing the nucleus and bringing widely separated functional elements into close spatial proximity (1–5). Understanding how chromosomes fold can provide insight into the complex relation- ships between chromatin structure, gene activity, and the functional state of the cell. Yet beyond the scale of nucleosomes, little is known about chro- matin organization. Long-range interactions between specific pairs of loci can be evaluated with chromosome con- formation capture (3C), using spatially constrained ligation followed by locus-specific polymerase chain reaction (PCR) (6). Adaptations of 3C have extended the process with the use of inverse PCR (4C) (7, 8) or multiplexed ligation-mediated am- plification (5C) (9). Still, these techniques require choosing a set of target loci and do not allow unbiased genomewide analysis. Here, we report a method called Hi-C that adapts the above approach to enable purification of ligation products followed by massively par- allel sequencing. Hi-C allows unbiased identifi- cation of chromatin interactions across an entire genome.We briefly summarize the process: cells are crosslinked with formaldehyde; DNA is di- gested with a restriction enzyme that leaves a 5′ overhang; the 5′ overhang is filled, including a biotinylated residue; and the resulting blunt-end fragments are ligated under dilute conditions that We created a Hi-C library from a karyotyp- ically normal human lymphoblastoid cell line (GM06990) and sequenced it on two lanes of an Illumina Genome Analyzer (Illumina, San Diego, CA), generating 8.4 million read pairs that could be uniquely aligned to the human genome reference sequence; of these, 6.7 million corre- sponded to long-range contacts between seg- ments >20 kb apart. We constructed a genome-wide contact matrix M by dividing the genome into 1-Mb regions (“loci”) and defining the matrix entry mij to be the number of ligation products between locus i and locus j (10). This matrix reflects an ensemble average of the interactions present in the original sample of cells; it can be visually represented as a heatmap, with intensity indicating contact fre- quency (Fig. 1B). We tested whether Hi-C results were repro- ducible by repeating the experiment with the same restriction enzyme (HindIII) and with a different one (NcoI). We observed that contact matrices for these new libraries (Fig. 1, C and D) were extremely similar to the original contact matrix [Pearson’s r = 0.990 (HindIII) and r = 0.814 (NcoI); P was negligible (<10–300) in both cases]. We therefore combined the three data sets in subsequent analyses. We first tested whether our data are consistent with known features of genome organization (1): specifically, chromosome territories (the tendency of distant loci on the same chromosome to be near one another in space) and patterns in subnuclear positioning (the tendency of certain chromosome pairs to be near one another). We calculated the average intrachromosomal contact probability, In (s), for pairs of loci sepa- rated by a genomic distance s (distance in base pairs along the nucleotide sequence) on chromo- some n. In (s) decreases monotonically on every chromosome, suggesting polymer-like behavior in which the 3D distance between loci increases with increasing genomic distance; these findings are in agreement with 3C and fluorescence in situ hybridization (FISH) (6, 11). Even at distances 1Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), MA 02139, USA. 2Division of Health Sciences and Technology, MIT, Cambridge, MA 02139, USA. 3Program for Evolutionary Dynamics, Department of Organismic and Evolutionary Biology, Department of Math- ematics, Harvard University, Cambridge, MA 02138, USA. 4Department of Applied Mathematics, Harvard University, Cambridge, MA 02138, USA. 5Program in Gene Function and Expression and Department of Biochemistry and Mo- lecular Pharmacology, University of Massachusetts Medical School, Worcester, MA 01605, USA. 6Fred Hutchinson Can- cer Research Center, Seattle, WA 98109, USA. 7Department on April 19, 2012 www.sciencemag.org Downloaded from coupling proximity-based ligation with massively parallel sequencing. We constructed spatial proximity maps of the human genome with Hi-C at a resolution of 1 megabase. These maps confirm the presence of chromosome territories and the spatial proximity of small, gene-rich chromosomes. We identified an additional level of genome organization that is characterized by the spatial segregation of open and closed chromatin to form two genome-wide compartments. At the megabase scale, the chromatin conformation is consistent with a fractal globule, a knot-free, polymer conformation that enables maximally dense packing while preserving the ability to easily fold and unfold any genomic locus. The fractal globule is distinct from the more commonly used globular equilibrium model. Our results demonstrate the power of Hi-C to map the dynamic conformations of whole genomes. The three-dimensional (3D) conformation of chromosomes is involved in compartmen- talizing the nucleus and bringing widely separated functional elements into close spatial proximity (1–5). Understanding how chromosomes fold can provide insight into the complex relation- ships between chromatin structure, gene activity, and the functional state of the cell. Yet beyond the scale of nucleosomes, little is known about chro- matin organization. Long-range interactions between specific pairs of loci can be evaluated with chromosome con- formation capture (3C), using spatially constrained ligation followed by locus-specific polymerase chain reaction (PCR) (6). Adaptations of 3C have extended the process with the use of inverse PCR (4C) (7, 8) or multiplexed ligation-mediated am- plification (5C) (9). Still, these techniques require choosing a set of target loci and do not allow unbiased genomewide analysis. Here, we report a method called Hi-C that adapts the above approach to enable purification of ligation products followed by massively par- allel sequencing. Hi-C allows unbiased identifi- cation of chromatin interactions across an entire genome.We briefly summarize the process: cells are crosslinked with formaldehyde; DNA is di- gested with a restriction enzyme that leaves a 5′ overhang; the 5′ overhang is filled, including a biotinylated residue; and the resulting blunt-end fragments are ligated under dilute conditions that favor ligation events between the cross-linked DNA fragments. The resulting DNA sample con- tains ligation products consisting of fragments that were originally in close spatial proximity in the nucleus, marked with biotin at the junction. A Hi-C library is created by shearing the DNA and selecting the biotin-containing fragments with streptavidin beads. The library is then ana- lyzed by using massively parallel DNA sequenc- ing, producing a catalog of interacting fragments (Fig. 1A) (10). average of the interactions present in the original sample of cells; it can be visually represented as a heatmap, with intensity indicating contact fre- quency (Fig. 1B). We tested whether Hi-C results were repro- ducible by repeating the experiment with the same restriction enzyme (HindIII) and with a different one (NcoI). We observed that contact matrices for these new libraries (Fig. 1, C and D) were extremely similar to the original contact matrix [Pearson’s r = 0.990 (HindIII) and r = 0.814 (NcoI); P was negligible (<10–300) in both cases]. We therefore combined the three data sets in subsequent analyses. We first tested whether our data are consistent with known features of genome organization (1): specifically, chromosome territories (the tendency of distant loci on the same chromosome to be near one another in space) and patterns in subnuclear positioning (the tendency of certain chromosome pairs to be near one another). We calculated the average intrachromosomal contact probability, In (s), for pairs of loci sepa- rated by a genomic distance s (distance in base pairs along the nucleotide sequence) on chromo- some n. In (s) decreases monotonically on every chromosome, suggesting polymer-like behavior in which the 3D distance between loci increases with increasing genomic distance; these findings are in agreement with 3C and fluorescence in situ hybridization (FISH) (6, 11). Even at distances greater than 200 Mb, In (s) is always much greater than the average contact probability between dif- ferent chromosomes (Fig. 2A). This implies the existence of chromosome territories. Interchromosomal contact probabilities be- tween pairs of chromosomes (Fig. 2B) show that small, gene-rich chromosomes (chromosomes 16, 17, 19, 20, 21, and 22) preferentially interact with each other. This is consistent with FISH studies showing that these chromosomes fre- quently colocalize in the center of the nucleus 1Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), MA 02139, USA. 2Division of Health Sciences and Technology, MIT, Cambridge, MA 02139, USA. 3Program for Evolutionary Dynamics, Department of Organismic and Evolutionary Biology, Department of Math- ematics, Harvard University, Cambridge, MA 02138, USA. 4Department of Applied Mathematics, Harvard University, Cambridge, MA 02138, USA. 5Program in Gene Function and Expression and Department of Biochemistry and Mo- lecular Pharmacology, University of Massachusetts Medical School, Worcester, MA 01605, USA. 6Fred Hutchinson Can- cer Research Center, Seattle, WA 98109, USA. 7Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA 98195, USA. 8Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. 9Department of Pathology, Harvard Medical School, Boston, MA 02115, USA. 10Department of Pediatrics, University of Wash- ington, Seattle, WA 98195, USA. 11Department of Physics, MIT, Cambridge, MA 02139, USA. 12Department of Biology, MIT, Cambridge, MA 02139, USA. 13Department of Systems Biol- ogy, Harvard Medical School, Boston, MA 02115, USA. *These authors contributed equally to this work. †To whom correspondence should be addressed. E-mail: lander@broadinstitute.org (E.S.L.); job.dekker@umassmed. edu (J.D.) www.sciencemag.org SCIENCE VOL 326 9 OCTOBER 2009 289 on A www.sciencemag.org Downloaded from
  36. None
  37. Fill ends with biotinylated nucleotides

  38. Blunt end ligation

  39. Purify DNA and shear randomly

  40. Select out biotinylated fragments and sequence from ends

  41. Select out biotinylated fragments and sequence from ends (for millions

    of ligations simultaneously)
  42. is n- on ee ky e- o- on ly hi-

    III is ed c- p- by -C n- wn o- o- is is ents all interactions between a 1-Mb locus and another 1-Mb locus; intensity corresponds to the total number of reads (0 to 50). Tick C and D) We compared the original experiment with results from a biological repeat using the same restriction enzyme [(C), range results using a different restriction enzyme [(D), NcoI, range from 0 to 100 reads]. B C D a- o- act e- 1, at A B Lieberman Aiden et al, Science, 2009 8.4 million read pairs, counts binned at 1Mb Map ends of sequenced fragments back to genome independently, each end-pair represents an interaction
  43. A B Heatmap 8 million read pairs 1 megabase bins

    Intensity represents number of reads linking each pair of bins (Lieberman Aiden et al, Science, 2009)
  44. C D (Lieberman Aiden et al, Science, 2009) Correlation map

    Intensity represents correlation between interaction profiles Suggests two broad groups?
  45. Fig. 3. The nucleus is segregated into tw E (Lieberman

    Aiden et al, Science, 2009) First principal component (eigenvector) from correlation matrix
  46. Lieberman Aiden et al, Science, 2009 n pairs of loci

    in com- uggests that compart- cked (15). The FISH s observation; loci in stronger tendency for (DNAseI) sensitivity, Spearman’s r = 0.651, P negligible] (16, 17). Compartment A also shows enrichment for both activating (H3K36 trimethyl- ation, Spearman’s r = 0.601, P < 10–296) and repressive (H3K27 trimethylation, Spearman’s r = 0.282, P < 10–56) chromatin marks (18). transcribed chromatin. We repeated our experiment with K562 cells, an erythroleukemia cell line with an aberrant kar- yotype (19). We again observed two compart- ments; these were similar in composition to those observed in GM06990 cells [Pearson’s r = 0.732, of the (A) tion ged ows een re- (fit tion y as no- 200 ium les. e is rm- ope 3/2, pec- ctal ope (C) ain, ong. ance rom or- qui- e is are im- y in ule. our ding both sec- ots. hree nts, osed the yan, ries. mes A C D B on April 19, 2012 www.sciencemag.org Downloaded from a function of distance (1 mono- mer ~ 6 nucleosomes ~ 1200 base pairs) (10) for equilibrium (red) and fractal (blue) globules. The slope for a fractal globule is very nearly –1 (cyan), confirm- ing our prediction (10). The slope for an equilibrium globule is –3/2, matching prior theoretical expec- tations. The slope for the fractal globule closely resembles the slope we observed in the genome. (C) (Top) An unfolded polymer chain, 4000 monomers (4.8 Mb) long. Coloration corresponds to distance from one endpoint, ranging from blue to cyan, green, yellow, or- ange, and red. (Middle) An equi- librium globule. The structure is highly entangled; loci that are nearby along the contour (sim- ilar color) need not be nearby in 3D. (Bottom) A fractal globule. Nearby loci along the contour tend to be nearby in 3D, leading to monochromatic blocks both on the surface and in cross sec- tion. The structure lacks knots. (D) Genome architecture at three scales. (Top) Two compartments, corresponding to open and closed chromatin, spatially partition the genome. Chromosomes (blue, cyan, green) occupy distinct territories. (Middle) Individual chromosomes weave back and forth between the open and closed chromatin compartments. (Bottom) At the scale of single megabases, the chromosome consists of a series of fractal globules. C D 9 OCTOBER 2009 VOL 326 SCIENCE www.sciencemag.org 292 www.sciencema Downloaded from al compart- s of the ge- ts identified own genetic A correlates Spearman’s ression [via Spearman’s d accessible onuclease I = 0.651, P also shows 6 trimethyl- 10–296) and Spearman’s marks (18). We repeated the above analysis at a resolution of 100 kb (Fig. 3G) and saw that, although the correlation of compartment A with all other ge- nomic and epigenetic features remained strong (Spearman’s r > 0.4, P negligible), the correla- tion with the sole repressive mark, H3K27 trimeth- ylation, was dramatically attenuated (Spearman’s r = 0.046, P < 10–15). On the basis of these re- sults we concluded that compartment A is more closely associated with open, accessible, actively transcribed chromatin. We repeated our experiment with K562 cells, an erythroleukemia cell line with an aberrant kar- yotype (19). We again observed two compart- ments; these were similar in composition to those observed in GM06990 cells [Pearson’s r = 0.732, D B on April 19, 2012 www.sciencemag.org Downloaded from Observed Simulated Distance distribution consistent with “fractal globule”
  47. o- o- is is ents all interactions between a 1-Mb

    locus and another 1-Mb locus; intensity corresponds to the total number of reads (0 to 50). Tick C and D) We compared the original experiment with results from a biological repeat using the same restriction enzyme [(C), range results using a different restriction enzyme [(D), NcoI, range from 0 to 100 reads]. a- o- ct e- 1, at n- ck rs o- r- 10 ly o- o- ed n- ed n- mosomes. Red indicates enrichment, and blue indicates depletion (range from 0.5 to 2). Small, gene-rich chromosomes tend to interact sting that they cluster together in the nucleus. A B 9 OCTOBER 2009 VOL 326 SCIENCE www.sciencemag.org Lieberman Aiden et al, Science, 2009
  48. Conformation at the super-megabase scale

  49. LETTER doi:10.1038/nature11082 Topological domains in mammalian genomes identified by analysis

    of chromatin interactions Jesse R. Dixon1,2,3, Siddarth Selvaraj1,4, Feng Yue1, Audrey Kim1, Yan Li1, Yin Shen1, Ming Hu5, Jun S. Liu5 & Bing Ren1,6 The spatial organization of the genome is intimately linked to its biological function, yet our understanding of higher order genomic structure is coarse, fragmented and incomplete. In the nucleus of eukaryotic cells, interphase chromosomes occupy distinct chro- mosome territories, and numerous models have been proposed for how chromosomes fold within chromosome territories1. These models, however, provide only few mechanistic details about the relationship between higher order chromatin structure and genome function.Recentadvancesin genomic technologieshaveledtorapid advances in the study of three-dimensional genome organiza- tion. In particular, Hi-C has been introduced as a method for iden- tifying higher order chromatin interactions genome wide2. Here we investigate the three-dimensional organization of the human and mouse genomes in embryonic stem cells and terminally differen- tiated cell types at unprecedented resolution. We identify large, megabase-sized local chromatin interaction domains, which we term ‘topological domains’, as a pervasive structural feature of the genome organization. These domains correlate with regions of the genome that constrain the spread of heterochromatin. The domains are stable across different cell types and highly conserved across species, indicating that topological domains are an inherent property of mammalian genomes. Finally, we find that the boundaries of topological domains are enriched for the insulator binding protein CTCF, housekeeping genes, transfer RNAs and high quality and accurately capture the higher order chromatin struc- tures in mammalian cells. We next visualized two-dimensional interaction matrices using a variety of binsizes to identify interactionpatterns revealed as a resultof our high sequencing depth (Supplementary Fig. 7). We noticed that at bin sizes less than 100 kilobases (kb), highly self-interacting regions begin to emerge (Fig. 1a and Supplementary Fig. 7, seen as ‘triangles’ on the heat map). These regions, which we term topological domains, are bounded by narrow segments where the chromatin interactions appear to end abruptly. We hypothesized that these abrupt transitions may represent boundary regions in the genome that separate topo- logical domains. To identify systematically all such topological domains in the genome, we devised a simple statistic termed the directionality index to quantify the degree of upstream or downstream interaction bias for a genomic region, which varies considerably at the periphery of the topological domains (Fig. 1b; see Supplementary Methods for details). The directionality index was reproducible (Supplementary Table 2) and pervasive, with 52% of the genome having a directionality index that was not expected by random chance (Fig. 1c, false discovery rate 5 1%). We then used a Hidden Markov model (HMM) based on the directionality index to identify biased ‘states’ and therefore infer the locations of topological domains in the genome (Fig. 1a; see Supplementary Methods for details). The domains defined by HMM investigate the three-dimensional organization of the human and mouse genomes in embryonic stem cells and terminally differen- tiated cell types at unprecedented resolution. We identify large, megabase-sized local chromatin interaction domains, which we term ‘topological domains’, as a pervasive structural feature of the genome organization. These domains correlate with regions of the genome that constrain the spread of heterochromatin. The domains are stable across different cell types and highly conserved across species, indicating that topological domains are an inherent property of mammalian genomes. Finally, we find that the boundaries of topological domains are enriched for the insulator binding protein CTCF, housekeeping genes, transfer RNAs and short interspersed element (SINE) retrotransposons, indicating that these factors may have a role in establishing the topological domain structure of the genome. To study chromatin structure in mammalian cells, we determined genome-wide chromatin interaction frequencies by performing the Hi-C experiment2 in mouse embryonic stem (ES) cells,human ES cells, and human IMR90 fibroblasts. Together with Hi-C data for the mouse cortex generated in a separate study (Y. Shen et al., manuscript in preparation), we analysed over 1.7-billion read pairs of Hi-C data corresponding to pluripotent and differentiated cells (Supplemen- tary Table 1). We normalized the Hi-C interactions for biases in the data (Supplementary Figs 1 and 2)3. To validate the quality of our Hi-C data, we compared the data with previous chromosome conformation capture (3C), chromosome conformation capture carbon copy (5C), and fluorescence in situ hybridization (FISH) results4–6. Our IMR90 Hi-C data show a high degree of similarity when compared to a previ- ously generated5Cdatasetfromlungfibroblasts(SupplementaryFig.4). In addition, our mouse ES cell Hi-C data correctly recovered a previ- ously described cell-type-specific interaction at the Phc1 gene5 (Supplementary Fig. 5). Furthermore, the Hi-C interaction frequencies in mouse ES cells are well-correlated with the mean spatial distance separating six loci as measured by two-dimensional FISH6 (Supplementary Fig. 6), demonstrating that the normalized Hi-C data can accurately reproduce the expected nuclear distance using an inde- pendent method. These results demonstrate that our Hi-C data are of To identify systematically all such topological domains in the genome, we devised a simple statistic termed the directionality index to quantify the degree of upstream or downstream interaction bias for a genomic region, which varies considerably at the periphery of the topological domains (Fig. 1b; see Supplementary Methods for details). The directionality index was reproducible (Supplementary Table 2) and pervasive, with 52% of the genome having a directionality index that was not expected by random chance (Fig. 1c, false discovery rate 5 1%). We then used a Hidden Markov model (HMM) based on the directionality index to identify biased ‘states’ and therefore infer the locations of topological domains in the genome (Fig. 1a; see Supplementary Methods for details). The domains defined by HMM were reproducible between replicates (Supplementary Fig. 8). Therefore, we combined the data from the HindIII replicates and identified 2,200 topological domains in mouse ES cells with a median size of 880 kb that occupy ,91% of the genome (Supplementary Fig. 9). As expected, the frequency of intra-domain interactions is higher than inter-domain interactions (Fig. 1d, e). Similarly, FISH probes6 in the same topological domain (Fig. 1f) are closer in nuclear space than probes in different topological domains (Fig. 1g), despite similar genomic distances between probe pairs (Fig. 1h, i). These find- ings are best explained bya model of the organizationof genomic DNA into spatial modules linked by short chromatin segments. We define the genomic regions between topological domains as either ‘topo- logical boundary regions’ or ‘unorganized chromatin’, depending on their sizes (Supplementary Fig. 9). We next investigated the relationship between the topological domains and the transcriptional control process. The Hoxa locus is separated into two compartments by an experimentally validated insu- lator4,7,8, which we observed corresponds to a topological domain boundary in both mouse (Fig. 1a) and human (Fig. 2a). Therefore, we hypothesized that the boundaries of the topological domains might correspond to insulator or barrier elements. Many known insulator or barrier elements are bound by the zinc- finger-containing protein CTCF (refs 9–11). We see a strong enrich- ment of CTCF at the topological boundary regions (Fig. 2b and Supplementary Fig. 10), indicating that topological boundary regions 1Ludwig Institute for Cancer Research, 9500 Gilman Drive, La Jolla, California 92093, USA. 2Medical Scientist Training Program, University of California, San Diego, La Jolla, California 92093, USA. 3Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, California 92093, USA. 4Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, California 92093, USA. 5Department of Statistics, Harvard University, 1 Oxford Street, Cambridge, Massachusetts 02138, USA. 6University of California, San Diego School of Medicine, Department of Cellular and Molecular Medicine, Institute of Genomic Medicine, UCSD Moores Cancer Center, 9500 Gilman Drive, La Jolla, California 92093, USA. 0 0 M O N T H 2 0 1 2 | V O L 0 0 0 | N A T U R E | 1 Macmillan Publishers Limited. All rights reserved ©2012 (11 April 2012)
  50. •Human and Mouse Embryonic stem cells, human fibroblasts and mouse

    cortex •~1.7 Billion read pairs sequenced •Read pair counts binned to 40kb and 20kb bins
  51. CTCF H3K4me3 RNA PolII p300 H3K4me1 HMM state DI Domains

    1.0 0.8 0.6 0.4 mpirical tive density False positive rate 1% DI (actual) DI (random) 00 700 nts B Interactions downstream e 0 100 Normalized interacting counts Chr6: 50000000 51000000 52000000 53000000 54000000 2410003K15Rik Igf2bp3 Tra2a Ccdc126 D330028D13Rik Stk31 Npy Mpp6 Dfna5 Osbpl3 Cycs 5430402O13Rik Npvf C530044C16Rik Mir148a Nfe2l3 Hnrnpa2b1 Cbx3 Snx10 Skap2 Hoxa1 Hoxa2 Hoxa3 Hoxa4 Hoxa5 Hoxa6 Mira Hoxa7 Hoxa9 Mir196b Hoxa10 Hoxa11 Hoxa13 5730457N03Rik Evx1 Hibadh Tax1bp1 Jazf1 9430076C15Rik Creb5 Tril Cpvl Chn2 50 - –50 _ 5 - 0.2 _ 5 - 0.3 _ 5 - 0.5_ 3 - 0.2 _ 3 - 0.2 _ b a c a b RESEARCH LETTER
  52. d , m hESC DI IMR90 DI IMR90 H3K9me3 hESC

    H3K9me3 hESC domain IMR90 domain Boundary ± 500 kb Boundary ± 500 kb 0 3.0 log 2 (H3K9me3/input) 0 3.0 log 2 (H3K9me3/input) 3.0 –3.0 log 2 (Dam–laminB1/Dam) Chr2: 2 Mb hg18 138000000 139000000 140000000 THSD7B HNMT SPOPL NXPH2 LOC647012 30 _ –30 _ 30 _ –30 _ 16 _ 0 _ 16 _ 0 _ 50 0 Normalized interacting counts e Boundary ± 500 kb Boundary ± 500 kb Boundary ± 500 kb Figure 2 | Topological boundaries demonstrate classical insulator or barrier element features. a, Two-dimensional heatmap surroundingthe Hoxa Chromatin organized into “topological domains”, mean size ~800kb
  53. 1.0 0 0.8 0.6 0.4 0.2 0 10 20 30

    40 50 1 – Empirical cumulative density DI (absolute value) False positive rate 1% DI (actual) DI (random) 0 10 20 30 40 0 0.5 1.0 1.5 2.0 Median normalized interaction counts Genomic distance (Mb) 0 100 200 300 400 500 600 700 Normalized interacting counts Distance of 80-kb P-value = 1.65 × 10–126 A B Interactions downstream Interactions upstream A B Biased upstream Biased downstream Degree of bias FISH probes: mESC DI HMM state FISH probes: mESC DI HMM state ‘Intra-domain’ ‘Inter-domain’ Domain 1 Domain 2 Domain d e Putative boundary Chr2: 2410003K15Rik Igf2bp3 Tra2a Ccdc126 D330028D13Rik Stk31 Npy Mpp6 Dfna5 Osbpl3 Cycs 5430402O13Rik Npvf C530044C16Rik Mir148a Nfe2l3 Hnrnpa2b1 Cbx3 Snx10 Skap2 Hoxa1 Hoxa2 Hoxa3 Hoxa4 Hoxa5 Hoxa6 Mira Hoxa7 Hoxa9 Mir196b Hoxa10 Hoxa11 Hoxa13 5730457N03Rik Evx1 Hibadh Tax1bp1 Jazf1 9430076C15Rik Creb5 Tril Cpvl Chn2 74500000 74600000 50 - Chr11: 96200000 96300000 50 - Intra Inter b Inter-domain Intra-domain f g c k between the topological domains and transcriptional he mammalian genome. ared the topological domains with previously described organizations of the genome, specifically with the A and B nts described by ref. 2, with lamina-associated domains replication time zones15,16, and large organized chromatin tion (LOCK) domains17. In all cases, we can see that topo- ains are related to, but independent from, each of these escribed domain-like structures (Supplementary Figs 12– , a subset of the domain boundaries we identify appear to nsition between either LAD and non-LAD regions of the g. 2f and Supplementary Fig. 12), the A and B compart- lementary Fig. 13, 14), and early and late replicating chro- plementary Fig. 14). Lastly, we can also confirm the eported similarities between the A and B compartments d late replication time zone (Supplementary Fig. 16)16. compared the locations of topological boundaries iden- h replicates of mouse ES cells and cortex, or between both human ES cells and IMR90 cells. In both human and t of the boundary regions are shared between cell types d Supplementary Fig. 17a), suggesting that the overall ucture between cell types is largely unchanged. At the mESC only 776 Cortex only 169 Overlap 893 hE Phc1 Nanog Grik2 (glutamate receptor) Snca Genes at mESC-specific interactions Genes at cortex-specific interactions - _ - _ - _ - _ - _ - _ - _ - _ Foxg1 3 0.2 0.2 5 0.5 5 0.3 5 Foxg 3 0.2 0.2 5 0.5 5 0.3 5 40 400 kb 51000000 H3K4me3 RNA Pol II CTCF H3K4me1 Cortex-enriched dynamic interacting reg g1 RNA-seq (r.p.k.m.) mESC-e interac Chr12 Chr12 0 40 Normalized interaction counts 0 40 Normalized interaction counts b a c d e 3 6 9 12 15 1,272 (9 ins and transcriptional th previously described ifically with the A and B ina-associated domains ge organized chromatin es, we can see that topo- ent from, each of these Supplementary Figs 12– es we identify appear to non-LAD regions of the the A and B compart- nd late replicating chro- can also confirm the mESC only 776 Cortex only 169 Overlap 893 Overlap 1,289 hESC only 678 IMR90 only 504 - _ - _ - _ - - _ - _ - _ - 3 0.2 5 0.5 5 0.3 5 0.2 5 0.5 5 0.3 5 400 kb 400 kb 51000000 51000000 H3K4me3 RNA Pol II CTCF Cortex-enriched dynamic interacting region Chr12 Chr12 0 40 Normalized interaction counts 0 40 Normalized interaction counts b a LETTER RESEARCH Chromatin packing model for domains Domains largely constant between cell types
  54. Can we resolve sub-megabase scale structure?

  55. Chromosome conformation capture carbon-copy

  56. Chromosome Conformation Capture Carbon Copy / 5C (Dostie et al.

    2006) Sequence specific probe Universal primer
  57. Chromosome Conformation Capture Carbon Copy / 5C (Dostie et al.

    2006) For junctions present in the library, probes anneal and are ligated (Forward and reverse probes are separate sets, ligations are always forward to reverse)
  58. Chromosome Conformation Capture Carbon Copy / 5C (Dostie et al.

    2006) Ligated probe sequences amplified and sequenced
  59. For many primers simultaneously 3C 5C Probe Ligation Amplification

  60. Probe design strategy for unbiased and high resolution mapping •Focus

    on regions on ~1MB in length •Alternating primers tiled across entire region •Probes represent fragments on ~1 to 10kb in length
  61. Universal primer Universal primer Alternating forward (blue) and reverse (red)

    primers across an ~1MB region Interrogate ~50% of the interactions in a region at single fragment resolution
  62. What does the data look like?

  63. Reverse Probes Forward Probes Single region Log counts (white is

    high, blue is missing)
  64. Chromosome Conformation Capture Carbon Copy = 5C Dostie et al.

    2006
  65. Hi-C Lieberman-AIden et al. 2009

  66. Hi-C Lieberman-AIden et al. 2009

  67. Hi-C in Genome Assembly Burton et al. 2013

  68. Hi-C in Genome Assembly

  69. Burton et al. 2013 The principle

  70. Kaplan & Dekker 2013 cis versus trans

  71. Burton et al. 2013

  72. Kaplan & Dekker 2013 Scaffold augmentation

  73. Hi-C in Haplotyping Burton et al. 2013

  74. Salvaraj et al. 2013

  75. cis versus trans Salvaraj et al. 2013

  76. Salvaraj et al. 2013

  77. Salvaraj et al. 2013