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MiOS, an integrated imaging and computational strategy to model gene folding with nucleosome resolution

Nature Structural & Molecular Biology volume 29, pages 1011–1023 (2022)Cite this article

Subjects

Abstract

The linear sequence of DNA provides invaluable information about genes and their regulatory elements along chromosomes. However, to fully understand gene function and regulation, we need to dissect how genes physically fold in the three-dimensional nuclear space. Here we describe immuno-OligoSTORM, an imaging strategy that reveals the distribution of nucleosomes within specific genes in super-resolution, through the simultaneous visualization of DNA and histones. We combine immuno-OligoSTORM with restraint-based and coarse-grained modeling approaches to integrate super-resolution imaging data with Hi-C contact frequencies and deconvoluted micrococcal nuclease-sequencing information. The resulting method, called Modeling immuno-OligoSTORM, allows quantitative modeling of genes with nucleosome resolution and provides information about chromatin accessibility for regulatory factors, such as RNA polymerase II. With Modeling immuno-OligoSTORM, we explore intercellular variability, transcriptional-dependent gene conformation, and folding of housekeeping and pluripotency-related genes in human pluripotent and differentiated cells, thereby obtaining the highest degree of data integration achieved so far to our knowledge.

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Fig. 1: iOS allows simultaneous labeling of genes and proteins in SR.
Fig. 2: Restraint-based model reproduces ensemble average data and cell-to-cell structural variability.
Fig. 3: Hi-C reveals changes in TAD organization between hFibs and hiPSCs.
Fig. 4: Hi-C derived models of a 2.3-Mb region of human chr12 segment reveal conformational changes between hFibs and hiPSCs.
Fig. 5: Combined iOS and Hi-C modeling in hFibs and hiPSCs.
Fig. 6: Single-cell-like deconvolution of capture MNase-seq-derived nucleosome coverage in the GAPDH-IFFO1 and NANOG regions labeled with OligoSTORM.
Fig. 7: Integrated MiOS models of GAPDH-IFFO1 and NANOG genes in hFibs and hiPSCs.

Data availability

Raw data for the capture MNase-seq (E-MTAB-10074) and Hi-C (E-MTAB-10073) sequencing experiments generated in this study were deposited at the European Nucleotide Archive under accession number PRJEB42293. Imaging and modeling datasets generated in this work are available upon request. We provide raw data related to plots and statistical source data in the Source data section provided with this paper.

Code availability

Stand-alone versions of the softwares used for chromatin coarse-grained simulations and for the fitting algorithms developed herein are available in the following repositories: Chromatin Dynamics (http://mmb.irbbarcelona.org/gitlab/juanpablo/chrom_dyn) and Chromatin Fitting (http://mmb.irbbarcelona.org/gitlab/juanpablo/fit_chrom).

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Acknowledgements

We acknowledge the support from the Barcelona Institute of Science and Technology (BIST) Ignite Grants (Seeding Stage 2017 and Second Phase 2018, to M.V.N. and P.D.D.); the European Union’s Horizon 2020 Research and Innovation Programme (CellViewer no. 686637 to M.L. and M.P.C.; ERC SimDNA no. 676556 to M.O.; and under the Marie Skłodowska-Curie grant agreement no. 754510 to J.P.A.); Ministerio de Ciencia e Innovación (grant no. 008506-PID2020-114080GB-I00 to M.P.C.), and an AGAUR grant from Secretaria d’Universitats i Recerca del Departament d’Empresa iConeixement de la Generalitat de Catalunya (grant no. 2017 SGR 1110 to M.O. and grant no. 006712 BFU2017-86760-P (AEI/FEDER, UE) to M.P.C.); Centro de Excelencia Severo Ochoa (grant nos. CEX2020-001049-S, MCIN/AEI/10.13039/501100011033 to CRG and CNAG authors and awarded to IRB Barcelona 2020-25); CERCA Programme/Generalitat de Catalunya (to CRG and CNAG authors); the People Program (Marie Curie Actions) FP7/2007–2013 under REA (grant no. 608959 to M.V.N.); Juan de la Cierva-Incorporación 2017 (to M.V.N.); PROBIST postdoctoral fellowship from Barcelona Institute of Science and Technology (to J.P.A.); INTREPiD Postdoctoral Programme cofunded by the European Commission (under grant agreement no. 754422 to X.G.); Grant for the recruitment of early-stage research staff FI-2020 (Operational Program of Catalonia 2014-2020 CCI grant no. 2014ES05SFOP007 of the European Social Fund to L.M.) and ‘La Caixa’ Foundation fellowship (ID 100010434 grant no. LCF/BQ/DR20/11790016 to L.M.); the Spanish Ministry of Science (for the EMBL partnership to CRG and CNAG authors and grant no. RTI2018-096704-B-100 to M.O.); Instituto de Salud Carlos Tercero (to CNAG authors; grant no. PT17/0009/0007 to M.O.); the Biomolecular and Bioinformatics Resources Platform (ISCIIIPT 13/000/0030 cofunded by the Fondo Europeo de Desarrollo Regional FEDER) (grant nos. Elixir-Excelerate: 676559; BioExcel2: 823830; and MuG: 676566 to M.O.); NIH grant nos. R01GM123289 and R01HD091797 (to J.A.A. and C.-t.W.); Bruker Inc. (to C.-t.W.); PEDECIBA (Programa de Desarrollo de las Ciencias Básicas) and SNI-ANII (Sistema Nacional de Investigadores, Agencia Nacional de Investigación e Innovación, Uruguay) (to P.D.D.); and ICREA (Institucio Catalana de Recerca i Estudis Avançats) (to M.O. and M.P.C.). We acknowledge the advanced light microscopy unit (ALMU) from CRG for their excellent technical support.

Author information

Author notes

  1. These authors contributed equally: Maria Victoria Neguembor, Juan Pablo Arcon, Diana Buitrago.

  2. These authors jointly supervised this work: Pablo D. Dans, Maria Pia Cosma.

Authors and Affiliations

  1. Center for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona, Spain

    Maria Victoria Neguembor, Ximena Garate, Laura Martin & Maria Pia Cosma

  2. Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology, Barcelona, Spain

    Juan Pablo Arcon, Diana Buitrago, Rafael Lema, Jürgen Walther, Pablo Romero, Isabelle Brun Heath, Modesto Orozco & Pablo D. Dans

  3. Departamento de Física y Matemáticas, Universidad Autónoma de Manizales, Manizales, Colombia

    Diana Buitrago

  4. Department of Genetics, Harvard Medical School, Boston, MA, USA

    Jumana AlHaj Abed & Chao-ting Wu

  5. CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain

    Marta Gut & Julie Blanc

  6. Universitat Pompeu Fabra (UPF), Barcelona, Spain

    Marta Gut & Maria Pia Cosma

  7. Department of Physiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Melike Lakadamyali

  8. Faculty of Biology, University of Barcelona, Barcelona, Spain

    Modesto Orozco

  9. ICREA, Barcelona, Spain

    Modesto Orozco & Maria Pia Cosma

  10. Department of Biological Sciences, CENUR Litoral Norte, Universidad de la República (UdelaR), Salto, Uruguay

    Pablo D. Dans

  11. Bioinformatics Unit, Institut Pasteur de Montevideo, Montevideo, Uruguay

    Pablo D. Dans

  12. CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China

    Maria Pia Cosma

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  1. Maria Victoria Neguembor
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Contributions

The original idea and conceptualization were by M.V.N., P.D.D., I.B.H., M.P.C. and M.O. M.P.C., M.V.N., P.D.D., J.P.A. and M.O. wrote the article with contributions from all the authors. M.V.N. produced all imaging results (iOS and MiOS) together with X.G. and L.M. R.L. produced all the capture Hi-C/MNase-seq results which were postprocessed and analyzed by D.B., under the supervision of I.B.H. J.P.A. developed and validated the restraint-based model. J.P.A. and D.B. performed Hi-C-based simulations of chromosome segments. J.W. generated the coarse-grained chromatin structures at nucleosome level, and the deconvolution of MNase-seq signals. Fitting algorithms and fitting results were generated by P.R. together with J.W. All modeling, simulations and fitting results were supervised and analyzed by P.D.D. and M.O. M.P.C. supervised the generation and analyses of all the imaging results with contribution from M.L. Design of Oligopaint probes was performed by J.A.A. and C.-t.W. M.G. and J.B. performed the sequencing of capture Hi-C/MNase-seq experiments. P.D.D. and M.V.N. integrated all the results and were the scientific coordinators of the project.

Corresponding authors

Correspondence to Maria Victoria Neguembor, Modesto Orozco, Pablo D. Dans or Maria Pia Cosma.

Ethics declarations

Competing interests

C.-t.W. holds or has patent filings pertaining to imaging, and her laboratory holds a sponsored research agreement with Bruker Inc. Although non-equity holding, C.-t.W. is a cofounder of Acuity Spatial Genomics; through personal connections to George Church, she has equity in companies associated with him, including 10x Genomics and Twist. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Structural and Molecular Biology thanks Mattia Conte and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor::Sara Osman, in collaboration with the Nature Structural and Molecular Biology team.

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Extended data

Extended Data Fig. 1 Schematic overview of human chromosome 12 region analyzed by MiOS.

a Schematic representation of chr12:6,140,000:8,460,000 region, showing the position of genes (grey arrows), Oligopaint probes (in green for NANOG, STELLA and magenta for GAPDH-IFFO1), and capture probes (orange). The A/B compartment track shows active A (red) and repressed B (blue) compartments for hFibs (from Hi-C; taken from9. Epigenetic marks for hFibs IMR90 (blue) and hiPSCs 20-b (red) are displayed (ChIP-seq tracks taken from30,31. The positions of the regions analyzed for the target genes (from left to right: GAPDH-IFFO1, STELLA, and NANOG) are highlighted in grey. b Genomic coordinates of Oligopaint probes. c-f qRT-PCR analysis in hFibs and hiPSCs for expression of (c) GAPDH, (d) IFFO1, (e) NANOG, and (f) STELLA. Mean and standard deviation (SD) of 2^-dCt values to B-ACTIN are shown; n = 6, and n = 4, independent replicates for hFibs and hiPSCs, respectively; two-tailed unpaired t-test; p = 0.4692 (c), p = 0.0672 (d), p = 1.4e-13 (e), p = 1.02e-7 (f). g Quantification of localization precision of super-resolution images. Boxplots (median with interquartile range) and whisker plots (10–90 percentile) are shown for oligoSTORM (locus, n = 8995), DNA-PAINT (H3, n = 23023), and oligoSTORM and DNA-PAINT beads (n = 135 and n = 158 localization tracks, respectively).

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Extended Data Fig. 2 Restraint-based model reproduces cell-to-cell structural variability.

a Evolution of the number of input distance restraint violations from the experimental median distance matrix (Fig. 2a, left panel) when adding subsequent modeled structures to the ensemble obtained with the restraint-based approach. b End-to-end distance distributions for the experimental (gray) and modeled (red) ensembles. The boxes highlight the first, second and third quartiles, while the whiskers extend 1.5 times the interquartile range away from the box edges. Outliers are omitted. The plots come from 3,496 experimental and 70 modeled conformations. p = 0.16, two-sided Mann-Whitney test. c Root mean square deviation (rmsd) of bead/probe positions for best fitted modeled structures against each experimental structure (red, Nexp-model = 3,496), and null distribution of rmsd values between all experimental structures, after fitted / aligned (gray, Nnull = 6,109,260). p < 1e−16, two-sided Mann-Whitney test. The boxes highlight the first, second and third quartiles, while the whiskers extend 1.5 times the interquartile range away from the box edges. Outliers are omitted. d Variance from the first 10 principal components from PCA. e Projection of the displacement vectors onto the first 2 principal components from PCA. f 3D distance matrices for single structures extracted from experimental microscopy data18 (left) and from the ensemble obtained with the restraint-based model (right). The color scale ranges from 200 nm to 850 nm.

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Extended Data Fig. 3 Contact matrices and correlation analyses between capture Hi-C replicates in hFibs, hiPSCs and published datasets.

a-c Contact matrices for the region chr12:6,140,000:8,460,000 in hFibs, displayed at 5-kb resolution, for (a) Hi-C data from Rao et al. (2014), (b) capture Hi-C for replica 1, and (c) capture Hi-C for replica 2. Plotted values are log10 of iteratively corrected interaction counts scaled to sum 1 million. The position of genes GAPDH-IFFO1 (magenta) and of STELLA and NANOG (green) are marked on X and Y axes. d, e Replicates 1 and 2, respectively, of the capture Hi-C from hiPSCs. Plotted values are the same as in (a-c). f Pearson correlation coefficient of the contact matrices between every pair of experiments: Rao et al. 2014, in-house hFibs (replicates 1 and 2), and hiPSCs (replicates 1 and 2).

Extended Data Fig. 4 Parameter selection and structure overlap for restraint-based models.

a, b Tuning of α parameter used in the distance restraint-based model for hFibs (a) or hiPSCs (b). For each α, correlation between experimental Hi-C interaction matrix and the modeled contact matrix (left) and mean absolute error between Hi-C derived average distances and predicted ensemble mean distances (right). Both Spearman and stratum-adjusted (HiCRep)70,71 correlation coefficients are shown. c Representation of the 2.3 Mb region of human chr12 segment from hiPSCs (cyan) and hFibs (yellow) cells. The GAPDH-IFFO1, NANOG, and STELLA loci are colored in pink, red, and orange, respectively. d Close-up of the GAPDH-IFFO1 region. e, f Close-ups of the STELLA/NANOG region highlighting the relative location of the two genes with respect to TAD formation in hiPSCs (cyan circle) and hFibs (yellow circles) cells.

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Extended Data Fig. 5 Fittings using iOS localizations, capture Hi-C contacts, and the restraint-based model of chromatin.

Two specific cells with a given distance between both gene regions (GAPDH-IFFO1 and NANOG) are shown. a Structure from the simulated ensemble (distance GAPDH-IFFO1 to NANOG fixed at 1.191 µm) that best fits the iOS localizations within the confocal plane (considering a depth of 0.260 µm) of one hFib cell. Note that this single structure of the chr12 segment connecting the genes of interest fit to 52.8% of the iOS localizations (5-kb beads fitted are shown as orange spheres) and fulfills 43.9% of the Hi-C contacts simultaneously. Zoom-in of the genes showing the beads fitted to iOS localizations. b Same as (a) for a hiPS cell, where the GAPDH-IFFO1 to NANOG distance was fixed to 1.082 µm, fulfilling 72.7% of iOS localizations and 42.4% of the Hi-C contacts.

Extended Data Fig. 6 Nucleosome positioning in hiPSC and hFibs cells determined from capture MNase-seq.

a–e Comparison of fuzziness score obtained with nucleR (0:well-positioned - 1:fuzzy nucleosome) between hiPSCs and hFibs for nucleosomes detected in the complete captured region (chr12:6140000–8460000) (a) and at the individual genes (b–e); Replica 1 (R1) and 2 (R2) are shown. Box plots include a marker for the median of the data and a box indicating the interquartile range. Whiskers show minimum and maximum values. Wilcoxon rank sum test; (a) p < 2.22e-16 (188 vs 172 nucleosomes over two independent experiments, in hiPSCs and hFibs, respectively), (b) R1: p = 0.17, R2: p = 0.0012 (35 vs 34 nucleosomes over two independent experiments, in hiPSCs and hFibs, respectively) (c) R1: p = 4.8e-8, R2: p = 4.2e-10 (92 vs 83 nucleosomes over two independent experiments, in hiPSCs and hFibs, respectively), (d) R1: p = 0.04, R2: p = 0.0069 (32 vs 29 nucleosomes over two independent experiments, in hiPSCs and hFibs, respectively), (e) R1: p = 0.00022, R2: p = 0.0014 (29 vs 26 nucleosomes over two independent experiments, in hiPSCs and hFibs, respectively). f–i Nucleosome positioning around the following genes: (f) GAPDH, (g) IFFO1, (h) NANOG, and (i) STELLA. Black lines represent normalized (0–1) nucleosome coverage. Blue boxes are the nucleosome positions detected by nucleR60. Changes in nucleosome organization from hiPSCs to hFibs, detected with NucDyn41 are represented as color-coded boxes for inclusion (green), eviction (red), positive shifts (purple), and negative shifts (yellow).

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Extended Data Fig. 7 Amplified views of the bottom-up first-principle coarse-grained model of the nucleosome fiber and the distribution of fitting values when the sampled conformations are confronted to iOS localizations.

a A representative folded GAPDH gene is amplified until consecutives centroids are shown. Each individual DNA centroid is located at the base pair reference frame (BPRF) following Cambridge and Tsukuba conventions72,73,74. These centroids represent the monomer length defined in our implementation, whose arbitrariness was based on a detailed knowledge on the structure and dynamics of B-DNA at the atomistic level42,75,76. In the first amplification, each DNA centroid is roughly represented considering its spherical exclusion volume (radius of van der Waals) centered at the BPRF. In the last two amplifications, the DNA-excluded volume is no longer depicted, and only the center of the BPRF is shown. Note that on average, being B-DNA, the distance between two consecutives base pairs is ~3.3 Å, although the experimentally falsifiable resolution of our predictions ranges from nucleosome clutches to near single nucleosome particles. b Distribution of the fitting values obtained from the filtered ensembles of GAPDH and NANOG folded conformations in hFibs and hiPSCs when confronted to iOS localizations. The top 10 structures with the highest fitting numbers, for which physical descriptors were computed and reported in Fig. 7 and Supplementary Table 2, are found at the right of the vertical dashed lines.

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Supplementary information

Supplementary Information

Supplementary Note (related to Methods) and Tables 1, 2, 4 and 5.

Reporting Summary

Supplementary Video 1

Video of the overlap of structures obtained with the restraint-based simulations. Representation of the 2.3-Mb region of human chr12 segment from hiPSCs (cyan) and hFibs (yellow) cells. The GAPDH-IFFO1, NANOG and STELLA loci are colored in pink, red and orange, respectively. The video was made using the Molywood tool76.

Supplementary Table 3

List of primary probes Oligopaints.

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Neguembor, M.V., Arcon, J.P., Buitrago, D. et al. MiOS, an integrated imaging and computational strategy to model gene folding with nucleosome resolution. Nat Struct Mol Biol 29, 1011–1023 (2022). https://doi.org/10.1038/s41594-022-00839-y

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