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Heterochromatin drives compartmentalization of inverted and conventional nuclei

Nature volume 570, pages 395–399 (2019)Cite this article

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A Publisher Correction to this article was published on 02 August 2019

This article has been updated

Abstract

The nucleus of mammalian cells displays a distinct spatial segregation of active euchromatic and inactive heterochromatic regions of the genome1,2. In conventional nuclei, microscopy shows that euchromatin is localized in the nuclear interior and heterochromatin at the nuclear periphery1,2. Genome-wide chromosome conformation capture (Hi-C) analyses show this segregation as a plaid pattern of contact enrichment within euchromatin and heterochromatin compartments3, and depletion between them. Many mechanisms for the formation of compartments have been proposed, such as attraction of heterochromatin to the nuclear lamina2,4, preferential attraction of similar chromatin to each other1,4,5,6,7,8,9,10,11,12, higher levels of chromatin mobility in active chromatin13,14,15 and transcription-related clustering of euchromatin16,17. However, these hypotheses have remained inconclusive, owing to the difficulty of disentangling intra-chromatin and chromatin–lamina interactions in conventional nuclei18. The marked reorganization of interphase chromosomes in the inverted nuclei of rods in nocturnal mammals19,20 provides an opportunity to elucidate the mechanisms that underlie spatial compartmentalization. Here we combine Hi-C analysis of inverted rod nuclei with microscopy and polymer simulations. We find that attractions between heterochromatic regions are crucial for establishing both compartmentalization and the concentric shells of pericentromeric heterochromatin, facultative heterochromatin and euchromatin in the inverted nucleus. When interactions between heterochromatin and the lamina are added, the same model recreates the conventional nuclear organization. In addition, our models allow us to rule out mechanisms of compartmentalization that involve strong euchromatin interactions. Together, our experiments and modelling suggest that attractions between heterochromatic regions are essential for the phase separation of the active and inactive genome in inverted and conventional nuclei, whereas interactions of the chromatin with the lamina are necessary to build the conventional architecture from these segregated phases.

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Fig. 1: Microscopy and Hi-C analysis of conventional and inverted nuclei.
Fig. 2: Morphology of the inverted nucleus restricts possible models of compartmentalization.
Fig. 3: Heterochromatin-based mechanisms quantitatively reproduce inverted and conventional nuclei.
Fig. 4: The time course and maintenance of compartment strength during nuclear inversion in the model and experiment.

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Data availability

Hi-C maps are available from the HiGlass browser (http://mirnylab.mit.edu/projects/invnuclei/) and from a public server (http://higlass.io/app/?config=JLOhiPILTmq6qDRicHMJqg). Hi-C maps are also available from the Gene Expression Omnibus (GEO) repository, accession number GSE111032.

Code availability

Software used to store and analyse Hi-C data can be accessed at https://bitbucket.org/mirnylab/hiclib and https://bitbucket.org/mirnylab/mirnylib. Data were also stored using the Cooler44 software (https://github.com/mirnylab/cooler).

Change history

  • 02 August 2019

    An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Acknowledgements

We thank S. Bultmann for help with rod cell sorting; A. S. Wang for help with sampling of Lbr−/− thymi; D. Devys for samples of retinas from R7E mice; all members of the Mirny laboratory for many discussions; N. Abdennur for help with CTCF motif analysis; and N. Abdennur and P. Kerpedjiev for help with the HiGlass Hi-C browser. This work has been supported by NSF 1504942, NIH GM114190, NIH HG003143, NIH HG007743 and by the Deutsche Forschungsgemeinschaft grants SO1054/3 (I.S.) and SFB1064 (I.S. and H.L.). M.F. was supported by the Department of Defense through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program. J.D. and L.A.M. acknowledge support from the National Institutes of Health Common Fund 4D Nucleome Program (DK107980). J.D. is an investigator of the Howard Hughes Medical Institute.

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Author notes

  1. These authors contributed equally: Martin Falk, Yana Feodorova, Natalia Naumova

Authors and Affiliations

  1. Institute for Medical Engineering and Science, and Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA

    Martin Falk, Maxim Imakaev, Geoffrey Fudenberg & Leonid A. Mirny

  2. Department of Medical Biology, Medical University-Plovdiv, Plovdiv, Bulgaria

    Yana Feodorova

  3. Biozentrum, Ludwig Maximilians University Munich, Planegg-Martinsried, Germany

    Yana Feodorova, Heinrich Leonhardt, Boris Joffe & Irina Solovei

  4. Howard Hughes Medical Institute, and Program in Systems Biology, Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA, USA

    Natalia Naumova, Bryan R. Lajoie & Job Dekker

  5. Epinomics Inc, Menlo Park, CA, USA

    Natalia Naumova

  6. Illumina Inc, San Diego, CA, USA

    Bryan R. Lajoie

  7. Gladstone Institutes of Data Science and Biotechnology, University of California, San Francisco, San Francisco, CA, USA

    Geoffrey Fudenberg

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Contributions

I.S., N.N., L.A.M. and J.D. conceived the project. Y.F. and I.S. obtained biological samples. N.N. performed Hi-C. M.F., M.I., G.F., B.R.L. and N.N. performed Hi-C analysis. M.F., with contributions from M.I., G.F. and L.A.M., performed simulations. Y.F., I.S. and B.J. conceived and performed microscopic experiments. M.F., Y.F. and I.S. prepared the figures. M.F., Y.F., G.F., I.S. and L.A.M. wrote the manuscript with contributions from N.N., M.I., H.L. and J.D.

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Correspondence to Geoffrey Fudenberg, Irina Solovei or Leonid A. Mirny.

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Extended data figures and tables

Extended Data Fig. 1 Hi-C replicates show reproducible features.

Hi-C maps are qualitatively similar between replicates. Hi-C maps (plotted as log10(contact frequencies)) for an 87-Mb region of chromosome 1. Compartment profiles indicating regions in the A (green) and B (red–brown) compartments are shown above the Hi-C maps. Full maps are available to browse on the HiGlass website (http://higlass.io/app/?config=JLOhiPILTmq6qDRicHMJqg). For quantitative comparisons, see Extended Data Figs. 3, 4, 5.

Extended Data Fig. 2 The majority of thymocytes are actively cycling cells in both wild-type and Lbr−/− mice.

Left, wild-type mice; right, Lbr−/− mice. Thymus cryosections were immunostained with antibodies for Ki-67, a marker of cycling cells, and phosphorylated histone H3 S10 (H3S10ph), a marker for G2 and mitotic cells. In agreement with the idea that Lbr−/− mice have a seemingly normal immune system45, the number of cycling thymocytes in thymi of Lbr−/− mice is comparable to that of wild-type mice. M, mitotic cells; G2, cells in mid/late G2. Ki-67 staining is shown as projections of 5-μm confocal stacks. Phosphorylated H3 S10 staining is shown as projections of 10-μm (for overviews) or 3-μm (for magnified areas) confocal stacks. Antibodies: mouse anti-phosphorylated H3 S10 (Abcam, ab14955) and rabbit anti-Ki-67 (Abcam, ab15580). Immunostaining and microscopy were performed as described in the Methods. Scale bars, 50 μm (top and middle) and 5 μm (bottom).

Extended Data Fig. 3 Quantitative analysis of TADs.

a, Average TADs, based on domain calls from embryonic stem cells39. Ticks indicate start and end of TADs. The visual suggestion is that TADs are weakest in rods and strongest in non-rod neurons, with wild-type and Lbr−/− thymocytes having intermediate strength. b, TAD strength is weakest in rods and strongest in non-rod neurons. TAD strength is the ratio of average contacts within the TAD (pink triangle) to average contacts between TADs (blue triangles). TAD strength is calculated separately for each autosome in two replicates. n = 38 chromosomes. Centre line is the median, the box ranges from the lower to upper quartiles and whiskers extend to 1.5× the interquartile range. c, Spearman correlation of insulation profiles across multiple mouse cell types, clustered hierarchically. Data were obtained from previous studies (GEO accession numbers GSE35156 and GSE63525)46,47, as indicated by the name of the first author in the row and column labels. ES, embryonic stem cells. d, Average insulation profile (Methods) around TAD boundaries called in embryonic stem cells39. The minimum insulation score of each profile is set to zero. We symmetrize noise by reflecting around the TAD boundary and averaging the reflected and original profiles. e, Decay of contact probability, P(s), as a function of genomic separation, s. Shaded areas are bounded by P(s) curves for biological replicas. All P(s) curves are normalized to their value at 10 kb. For rods, the steeper slope below 1 Mb and lack of a rollover in contrast to the other three cell types is indicative of weaker TADs, as previously described48. f, TAD strength as a function of cell type (columns) and cell type from which TADs are called (rows). Data were obtained from previous studies (GEO accession numbers GSE98671, GSE63525 and GSE93431)39,47,48, as indicated by the name of the first author in the row and column labels. Note that rods cluster with cell types with demonstrated weaker TADs. TAD strength is computed with the lavaburst approach (see Methods). g, Average insulation profile (Methods) oriented around the top 104 scoring CTCF motifs. For scoring, we used the FIMO algorithm49, with a position weight matrix for the M1 motif as previously described50. The minimum insulation score of each profile is set to zero, and the CTCF motif points to the left. This provides a TAD-call independent method of inferring TAD strength, given that CTCF is frequently present at the borders of TADs. h, Snapshot of HiGlass51 view of the four datasets, close to the diagonal (chromosome 12: 77,538,523–85,180,785 and chromosome 12: 79,240,367–82,837,977; 32-kb resolution). Rods almost completely lack TADs and non-rod neurons have very strong TADs, upon inspection. Datasets can be browsed in a more in-depth fashion on the HiGlass website (http://higlass.io/app/?config=JLOhiPILTmq6qDRicHMJqg).

Extended Data Fig. 4 Quantitative analysis of territories.

a, Hi-C contact maps for chromosomes 1, 2 and 3 show both a checkerboard pattern in cis (within a chromosome) and trans (between chromosomes), reflecting compartmentalization, and more frequent cis than trans contacts, reflecting chromosome territoriality. Views are shown for the second biological replicate, binned at 500 kb. b, Average number of contacts between pairs of chromosomes. Average cis contacts are much higher than trans contacts. Maps are normalized by their sums. c, Average contacts in trans. For every unique pair of chromosomes, we averaged the first 60 Mb, binned at 500-kb resolution. Maps are normalized to their means and plotted in log-space. There is evidence of weak enrichment among chromocentre-proximal regions in trans, independent of inversion status. d, Consistent with the low cis-contact fraction revealed by Hi-C, chromosome 11 visualized by FISH (green) has a more diffuse territory in post-mitotic rods and non-rod neurons in comparison to cycling thymocytes of both genotypes. Projections of 2-μm confocal stacks. Scale bars, 5 μm. The chromosome painting was performed in four independent experiments. e, Chromosome territoriality, measured as the ratio of cis contacts to cis and trans contacts, is weaker in rods and non-rod neurons in comparison to conventional and inverted thymocytes. The schematic illustrates the compared regions. f, Scatterplot of compartmentalization and territoriality. The two metrics are not necessarily related.

Extended Data Fig. 5 Quantitative analysis of compartments.

a, Saddle plots23 (see Methods) of contact frequency enrichment show the extent of compartmentalization across cell types in cis. b, Spearman correlation of compartment profiles across multiple mouse cell types, clustered hierarchically. Data were obtained from previous studies (GEO accession numbers GSE35156, GSE35519 and GSE40173)46,52,53, as indicated by the name of the first author in the row and column labels. Spearman’s r(LBR1, WT1) = 0.95, P < 1 × 10−10, n = 4,780; r(LBR1, LBR2) = 0.98, P < 1 × 10−10, n = 4,780; r(WT1, WT2) = 0.99, P < 1 × 10−10, n = 4,780. P values are from two-sided tests. Positions of compartments are almost exactly the same between wild-type thymocytes and Lbr−/− thymocytes, approaching that of biological replicates, which indicates that inversion does not change compartment positions as such. c–e, Fractions of loci that remain the same when comparing two different cell types, as well as fractions of loci that switched from B to A and from A to B. The sequence of cell types is taken from the clustering of their compartment profiles. f, Compartment strength across multiple mouse cell types (calculated separately for each autosome, n = 19 for datasets not considered in main text; n = 38 for two replicates of main text datasets. Centre line is the median, the box ranges from the lower to upper quartiles and whiskers extend to 1.5× the interquartile range.

Extended Data Fig. 6 Exploring the space of model classes reveals that only a small fraction can reproduce the inverted nuclear geometry.

a, Even the second-best group of models do not display the ring-like structure that is characteristic of the inverted nucleus (the next-best eight models, indicated in pink, after the eight best models described in the main text, which are indicated in gold). Densities are computed from 50 simulated configurations. b, In agreement with the Flory–Huggins theory, we find that if the cross-type attraction (for example, A–B) is greater than both of the same-type attractions (A–A and B–B), the two monomer types will not segregate. For models 8, 11 and 15, this is true of both A–B and B–C terms, and as expected, there is mixing between A and B monomers, and B and C monomers in simulations. Similarly, models 9 and 10 have mixed A and C monomers and high A–C attraction; models 12 and 13 have mixed A and B monomers and higher A–B attraction; and model 14 has mixed B and C monomers, with high B–C attraction. c, Averaging the parameter orders of the second-best model classes reveals that they depart from the best-performing models, in aggregate. d, We illustrate particular models with strong euchromatic interactions to show that such models do not compare well with microscopy, even on a quantitative level. In particular, we show the four worst-performing models (pink dots, models 716–719), all of which are characterized by strong euchromatic interactions (b). We also show the best-performing model with A–A as its strongest interaction (gold dot, model 250) and the best-performing model with A–A as its second strongest interaction (gold dot, model 61). Neither of these models compare well with experimental microscopy results. Densities are computed from 50 simulated configurations. e, All of the poorly performing models discussed in d were characterized by strong A–A interactions. f, Averaging the worst four models shows that they are characterized by strong A–A interactions.

Extended Data Fig. 7 The heterochromatin-dominated model is robust to perturbations and outperforms a variety of alternative models.

a, Adding in a fraction of B monomers attracted to the lamina, in an analogy to trace amounts of peripheral heterochromatin in rods54, does not significantly change agreement with the microscopy results. Representative configurations as this fraction is increased are shown. Boxes indicate density peak distance with whiskers extending to 1.5× the interquartile range. n = 50, number of time points sampled across 3 simulation replicates. b, Adding in small fractions of A monomers attracted to the lamina (below 20%) does not significantly change the conventional morphology of simulated nuclei. Representative configurations as this fraction is increased are shown. Quantities plotted as in a. This simulation reflects a potential phenomenon of association between highly transcribed genes and nuclear pores. Of note, we have not observed this phenomenon in the nuclei of mouse cells, including rod cells, in which all euchromatin is adjacent to the nuclear lamina (Supplementary Fig. 2). n = 8 simulated chromosomes. c, Average compartment strength across simulated chromosomes (n = 8) as a function of B–B and B–Lam attractions. The zone of parameter space for which simulated Hi-C compartment strength agrees with experimental compartment strength is essentially unchanged for simulations with some interior chromocentres, compared to simulations with no interior chromocentres. Representative configurations of each of these models are displayed. Orange outline indicates regions in parameter space for which the simulated Hi-C has compartmentalization in agreement with experimental Hi-C data (median ± 1 s.d. for wild-type thymocytes). d, For B–B = 0.5 and all other parameters as in the main text, increasing the ratio of A–A to B–B results in worse agreement with microscopy. This is particularly visible above A–A/B–B = 0.5. Representative configurations as this fraction is increased are shown. Quantities plotted as in a. n = 8 simulated chromosomes. Additional models are considered in Supplementary Fig. 6.

Extended Data Fig. 8 Chromocentres merge during nuclear inversion and pass through a partially inverted morphology.

a, Distance between chromocentres decreases once interactions with the lamina have been removed, quantitatively showing the fusion of C monomer droplets. To see this, we find the centre of mass of the C monomer blocks on each of the eight chromosomes in our simulation. We then compute the average distance between all possible pairs of the eight centres of mass, and normalize by the maximum possible total separation in the nucleus—that is, the diameter of the nucleus times the number of chromosome pairs. Light-blue lines show individual trajectories, the dark-blue line shows the average over trajectories. Following release from the lamina (vertical black line), this metric decreases, quantitatively confirming what we see visually in the associated configurations (numerals). b, Following three representative simulations starting from an initial condition in which chromosomes are in mitotic-like condensed cylindrical conformations, we find that our inverted nucleus model reaches its equilibrium configuration through a pathway that passes through a state highly reminiscent of the partial inversion seen in Lbr−/− thymocytes. As a proxy for detailed mechanistic modelling of the complexities of mitotic exit, we begin from cylinders that are randomly oriented, as opposed to aligned. Scale bar, 2 μm. c, Distance between chromocentres decreases once interactions with the lamina have been removed, while the overall volume of the nucleus shrinks at the same time. Quantities plotted as in a, with an additional black line for volume decrease relative to initial volume. We see that the qualitative trends in morphology remain the same as in the case of constant volume (Fig. 4a).

Extended Data Fig. 9 Small chromosome segments faithfully localize to and move together with chromatin of their own compartment during nuclear inversion.

The nuclear positions of short chromosome segments of different gene densities belonging to either the A or B compartment were studied using FISH with a cocktail of BAC probes on retinal cryosections at six developmental stages: P0, P6, P13, P21, P28 and adult (AD; 3.5 months). For the analysis of BAC signal distribution, three stages were considered: P0, with conventional nuclei of rod progenitors; P13, with rod nuclei in a transient state of inversion; and adult, with fully inverted rod nuclei. Cells with conventional nuclear organization in the inner nuclear layer (INL) of adult retina were used as a control. Between 100 and 120 alleles per chromosomal region were analysed. a, Immuno-FISH experiment showing how FISH signals were classified according to their localization in the three major nuclear zones. EC, euchromatin; HC, heterochromatin; cHC, constitutive heterochromatin. Definitions of these three types of chromatin have been previously published1. BAC 12 maps to the most peripheral euchromatic shell of the rod nucleus stained with anti-H3K4me3 antibody. This nuclear zone is adjacent to the nuclear periphery and contains the genic part of the mouse genome (see Supplementary Fig. 2). BACs 2 and 11 are located in the heterochromatic zone of the nucleus encircling the chromocentre and stained with anti-H4K20me3 antibody. Thus, classification of BAC signals based on DAPI staining is justified by immunostaining of histone modifications and enables the signal distribution analysis described in b–d. Top, localization of BAC signals (blue, white arrows) and histone modifications (green) in DAPI-counterstained nuclei (red). Numbers in the lower left corners indicate the BAC numbers (for their coordinates, see Methods). Bottom, greyscale images of DAPI and positions of the BAC signals (red arrows) represented by false-coloured mask. b–d, Analysis of BAC signal positions after FISH with BAC cocktail probes mapping to selected chromosome regions. Top, schematics of the chromosome regions on MMU1 (b), MMU2 (c) and MMU6 (d). The coloured segments differ in their gene content and assignment to either the A or B compartment. The striped boxes with numbers below indicate the BACs used for FISH. Bottom left, graphs showing the distribution of the segments within rod nuclei at the three developmental stages and adult cells of the inner nuclear layer. The bars represent the proportion of signals in each nuclear zone: adjacent to constitutive heterochromatin (dark grey), within heterochromatin (light grey) and within euchromatin (white). Bottom middle, schematics showing typical segment distribution of the studied regions. Bottom right, representative nuclei after three-colour (b) or four-colour (c, d) FISH. The images are maximum-intensity projections of short (1.4–2-μm) stacks. False colours assigned to segments correspond to the colour code used in each panel. The experiment was repeated twice. For an example of the localization of a single gene and its movement together with chromatin of the A compartment during nuclear inversion, see Supplementary Fig. 3.

Extended Data Fig. 10 Coalescence of individual chromocentres into a large central chromocentre is irreversible.

a, Top, our model predicts that once nuclei invert and all individual chromocentres merge into a single central chromocentre, the reverse process—that is, resplitting into smaller chromocentres—will not take place after the reintroduction of lamina attractions. Although we expect B monomers to redistribute to the nuclear lamina, we do not expect C monomers of a single globule to reorganize into smaller globules. In this sense, our model predicts that the inversion and formation of the central chromocentre is irreversible. Bottom, simulations of de-inversion of inverted nuclei through the introduction of B–Lam and C–Lam attractions with strengths equal to the optimal B–Lam value from Fig. 3c, d. Note that according to our prediction, de-inverted nuclei only partially return to the conventional geometry. Slices with a thickness of 5% of the nuclear diameter are shown. b, In agreement with the model prediction, de-inverted nuclei do not return to a typical conventional architecture, as can be seen in de-differentiated rods of R7E mice expressing poly(Q)-expanded ataxin-7 (see Supplementary Fig. 5a, b for  a description of the phenotype). FISH with probes for major satellite repeats (blue), LINE-rich heterochromatin (red) and SINE-rich euchromatin (green) demonstrates that although euchromatin returns to the nuclear interior (filled arrowheads) and heterochromatin repositions to the lamina (empty arrowheads), a single large chromocentre remains and is typically positioned at the nuclear periphery (top, arrows). Notably, in approximately 30% of the nuclei, the large chromocentre does not relocate to the nuclear periphery but the nuclear lamina (green) makes deep narrow invaginations, contacting the chromocentre (bottom, arrows; see also Supplementary Fig. 5c). The remaining bulky chromocentre is surrounded by LINE-rich chromatin (bottom; empty arrowheads) and is often (71% of nuclei) in contact with the nuclear periphery as a result of deformation of nuclear shape (for more examples, see Supplementary Fig. 5c). For comparison, the two left columns show conventional nuclei of ganglion cells and inverted rod nuclei from a wild-type mouse. Images are single optical sections. Scale bars, 2 μm; scale bars apply to all images. Probes, FISH and microscopy are described in the Methods. Each experiment was repeated three times.

Supplementary information

Supplementary Information

This file contains Supplementary Figures 1-6, Supplementary Tables 1-2, Energies for Polymer Simulations and Supplementary References.

Reporting Summary

Video 1: Simulations show that distinct chromocenters exist for a finite amount of time in models with only lamina attraction but no chromocenter pinning.

Beginning simulations from initial configurations generated at optimal point (vi) in Fig 3d, we released chromocenter pinning from the lamina, while maintaining all monomer-monomer interactions. While we removed C-lamina pinning, we kept C-lamina and B-lamina attractions, which had equal magnitude. We then visualized 10 simulations replicates (columns), showing three simultaneous great circle sections from each (rows). Even without chromocenter pinning, distinct chromocenters persisted for a finite amount of time before eventually merging.

Video 2: Chromocenters fuse over time in simulations of nuclear inversion.

Beginning simulations from initial configurations generated at optimal point (vi) in Fig 3d, we removed interactions of monomers with the lamina, while maintaining all monomer-monomer interactions. We then visualized 10 simulations replicates (columns), showing three simultaneous great circle sections from each (rows). Chromocenters moved away from the lamina and merged as spherical droplets until they finally coalesced into a large central chromocenter.

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Falk, M., Feodorova, Y., Naumova, N. et al. Heterochromatin drives compartmentalization of inverted and conventional nuclei. Nature 570, 395–399 (2019). https://doi.org/10.1038/s41586-019-1275-3

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