Article28 December 2015Open Access Hierarchical folding and reorganization of chromosomes are linked to transcriptional changes in cellular differentiation James Fraser James Fraser Department of Biochemistry, Goodman Cancer Centre, McGill University, Montréal, QC, Canada Search for more papers by this author Carmelo Ferrai Carmelo Ferrai Epigenetic Regulation and Chromatin Architecture Group, Berlin Institute for Medical Systems Biology, Max-Delbrück Centre for Molecular Medicine, Berlin-Buch, Germany Genome Function Group, MRC Clinical Sciences Centre, Imperial College London, Hammersmith Hospital Campus, London, UK Search for more papers by this author Andrea M Chiariello Andrea M Chiariello Dipartimento di Fisica, Università di Napoli Federico II, INFN Napoli, CNR-SPIN, Complesso Universitario di Monte Sant'Angelo, Naples, Italy Search for more papers by this author Markus Schueler Markus Schueler Epigenetic Regulation and Chromatin Architecture Group, Berlin Institute for Medical Systems Biology, Max-Delbrück Centre for Molecular Medicine, Berlin-Buch, Germany Search for more papers by this author Tiago Rito Tiago Rito Epigenetic Regulation and Chromatin Architecture Group, Berlin Institute for Medical Systems Biology, Max-Delbrück Centre for Molecular Medicine, Berlin-Buch, Germany Search for more papers by this author Giovanni Laudanno Giovanni Laudanno Dipartimento di Fisica, Università di Napoli Federico II, INFN Napoli, CNR-SPIN, Complesso Universitario di Monte Sant'Angelo, Naples, Italy Search for more papers by this author Mariano Barbieri Mariano Barbieri Epigenetic Regulation and Chromatin Architecture Group, Berlin Institute for Medical Systems Biology, Max-Delbrück Centre for Molecular Medicine, Berlin-Buch, Germany Search for more papers by this author Benjamin L Moore Benjamin L Moore MRC Human Genetics Unit, MRC IGMM, University of Edinburgh, Edinburgh, UK Search for more papers by this author Dorothee CA Kraemer Dorothee CA Kraemer Epigenetic Regulation and Chromatin Architecture Group, Berlin Institute for Medical Systems Biology, Max-Delbrück Centre for Molecular Medicine, Berlin-Buch, Germany Search for more papers by this author Stuart Aitken Stuart Aitken MRC Human Genetics Unit, MRC IGMM, University of Edinburgh, Edinburgh, UK Search for more papers by this author Sheila Q Xie Sheila Q Xie Genome Function Group, MRC Clinical Sciences Centre, Imperial College London, Hammersmith Hospital Campus, London, UK Search for more papers by this author Kelly J Morris Kelly J Morris Epigenetic Regulation and Chromatin Architecture Group, Berlin Institute for Medical Systems Biology, Max-Delbrück Centre for Molecular Medicine, Berlin-Buch, Germany Genome Function Group, MRC Clinical Sciences Centre, Imperial College London, Hammersmith Hospital Campus, London, UK Search for more papers by this author Masayoshi Itoh Masayoshi Itoh RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Saitama, Japan Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Kanagawa, Japan Search for more papers by this author Hideya Kawaji Hideya Kawaji RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Saitama, Japan Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Kanagawa, Japan Search for more papers by this author Ines Jaeger Ines Jaeger Stem Cell Neurogenesis Group, MRC Clinical Sciences Centre, Imperial College London, Hammersmith Hospital Campus, London, UK Search for more papers by this author Yoshihide Hayashizaki Yoshihide Hayashizaki RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Saitama, Japan Search for more papers by this author Piero Carninci Piero Carninci Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Kanagawa, Japan Search for more papers by this author Alistair RR Forrest Alistair RR Forrest Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Kanagawa, Japan Search for more papers by this author The FANTOM Consortium The FANTOM Consortium Search for more papers by this author Colin A Semple Corresponding Author Colin A Semple MRC Human Genetics Unit, MRC IGMM, University of Edinburgh, Edinburgh, UK Search for more papers by this author Josée Dostie Corresponding Author Josée Dostie Department of Biochemistry, Goodman Cancer Centre, McGill University, Montréal, QC, Canada Search for more papers by this author Ana Pombo Corresponding Author Ana Pombo Epigenetic Regulation and Chromatin Architecture Group, Berlin Institute for Medical Systems Biology, Max-Delbrück Centre for Molecular Medicine, Berlin-Buch, Germany Genome Function Group, MRC Clinical Sciences Centre, Imperial College London, Hammersmith Hospital Campus, London, UK Search for more papers by this author Mario Nicodemi Corresponding Author Mario Nicodemi Dipartimento di Fisica, Università di Napoli Federico II, INFN Napoli, CNR-SPIN, Complesso Universitario di Monte Sant'Angelo, Naples, Italy Search for more papers by this author James Fraser James Fraser Department of Biochemistry, Goodman Cancer Centre, McGill University, Montréal, QC, Canada Search for more papers by this author Carmelo Ferrai Carmelo Ferrai Epigenetic Regulation and Chromatin Architecture Group, Berlin Institute for Medical Systems Biology, Max-Delbrück Centre for Molecular Medicine, Berlin-Buch, Germany Genome Function Group, MRC Clinical Sciences Centre, Imperial College London, Hammersmith Hospital Campus, London, UK Search for more papers by this author Andrea M Chiariello Andrea M Chiariello Dipartimento di Fisica, Università di Napoli Federico II, INFN Napoli, CNR-SPIN, Complesso Universitario di Monte Sant'Angelo, Naples, Italy Search for more papers by this author Markus Schueler Markus Schueler Epigenetic Regulation and Chromatin Architecture Group, Berlin Institute for Medical Systems Biology, Max-Delbrück Centre for Molecular Medicine, Berlin-Buch, Germany Search for more papers by this author Tiago Rito Tiago Rito Epigenetic Regulation and Chromatin Architecture Group, Berlin Institute for Medical Systems Biology, Max-Delbrück Centre for Molecular Medicine, Berlin-Buch, Germany Search for more papers by this author Giovanni Laudanno Giovanni Laudanno Dipartimento di Fisica, Università di Napoli Federico II, INFN Napoli, CNR-SPIN, Complesso Universitario di Monte Sant'Angelo, Naples, Italy Search for more papers by this author Mariano Barbieri Mariano Barbieri Epigenetic Regulation and Chromatin Architecture Group, Berlin Institute for Medical Systems Biology, Max-Delbrück Centre for Molecular Medicine, Berlin-Buch, Germany Search for more papers by this author Benjamin L Moore Benjamin L Moore MRC Human Genetics Unit, MRC IGMM, University of Edinburgh, Edinburgh, UK Search for more papers by this author Dorothee CA Kraemer Dorothee CA Kraemer Epigenetic Regulation and Chromatin Architecture Group, Berlin Institute for Medical Systems Biology, Max-Delbrück Centre for Molecular Medicine, Berlin-Buch, Germany Search for more papers by this author Stuart Aitken Stuart Aitken MRC Human Genetics Unit, MRC IGMM, University of Edinburgh, Edinburgh, UK Search for more papers by this author Sheila Q Xie Sheila Q Xie Genome Function Group, MRC Clinical Sciences Centre, Imperial College London, Hammersmith Hospital Campus, London, UK Search for more papers by this author Kelly J Morris Kelly J Morris Epigenetic Regulation and Chromatin Architecture Group, Berlin Institute for Medical Systems Biology, Max-Delbrück Centre for Molecular Medicine, Berlin-Buch, Germany Genome Function Group, MRC Clinical Sciences Centre, Imperial College London, Hammersmith Hospital Campus, London, UK Search for more papers by this author Masayoshi Itoh Masayoshi Itoh RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Saitama, Japan Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Kanagawa, Japan Search for more papers by this author Hideya Kawaji Hideya Kawaji RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Saitama, Japan Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Kanagawa, Japan Search for more papers by this author Ines Jaeger Ines Jaeger Stem Cell Neurogenesis Group, MRC Clinical Sciences Centre, Imperial College London, Hammersmith Hospital Campus, London, UK Search for more papers by this author Yoshihide Hayashizaki Yoshihide Hayashizaki RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Saitama, Japan Search for more papers by this author Piero Carninci Piero Carninci Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Kanagawa, Japan Search for more papers by this author Alistair RR Forrest Alistair RR Forrest Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Kanagawa, Japan Search for more papers by this author The FANTOM Consortium The FANTOM Consortium Search for more papers by this author Colin A Semple Corresponding Author Colin A Semple MRC Human Genetics Unit, MRC IGMM, University of Edinburgh, Edinburgh, UK Search for more papers by this author Josée Dostie Corresponding Author Josée Dostie Department of Biochemistry, Goodman Cancer Centre, McGill University, Montréal, QC, Canada Search for more papers by this author Ana Pombo Corresponding Author Ana Pombo Epigenetic Regulation and Chromatin Architecture Group, Berlin Institute for Medical Systems Biology, Max-Delbrück Centre for Molecular Medicine, Berlin-Buch, Germany Genome Function Group, MRC Clinical Sciences Centre, Imperial College London, Hammersmith Hospital Campus, London, UK Search for more papers by this author Mario Nicodemi Corresponding Author Mario Nicodemi Dipartimento di Fisica, Università di Napoli Federico II, INFN Napoli, CNR-SPIN, Complesso Universitario di Monte Sant'Angelo, Naples, Italy Search for more papers by this author Author Information James Fraser1,‡, Carmelo Ferrai2,3,‡, Andrea M Chiariello4,‡, Markus Schueler2,‡, Tiago Rito2,‡, Giovanni Laudanno4,‡, Mariano Barbieri2, Benjamin L Moore5, Dorothee CA Kraemer2, Stuart Aitken5, Sheila Q Xie3,9, Kelly J Morris2,3, Masayoshi Itoh6,7, Hideya Kawaji6,7, Ines Jaeger8,10, Yoshihide Hayashizaki6, Piero Carninci7, Alistair RR Forrest7,11, , Colin A Semple 5, Josée Dostie 1, Ana Pombo 2,3 and Mario Nicodemi 4 1Department of Biochemistry, Goodman Cancer Centre, McGill University, Montréal, QC, Canada 2Epigenetic Regulation and Chromatin Architecture Group, Berlin Institute for Medical Systems Biology, Max-Delbrück Centre for Molecular Medicine, Berlin-Buch, Germany 3Genome Function Group, MRC Clinical Sciences Centre, Imperial College London, Hammersmith Hospital Campus, London, UK 4Dipartimento di Fisica, Università di Napoli Federico II, INFN Napoli, CNR-SPIN, Complesso Universitario di Monte Sant'Angelo, Naples, Italy 5MRC Human Genetics Unit, MRC IGMM, University of Edinburgh, Edinburgh, UK 6RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Saitama, Japan 7Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Kanagawa, Japan 8Stem Cell Neurogenesis Group, MRC Clinical Sciences Centre, Imperial College London, Hammersmith Hospital Campus, London, UK 9Present address: Single Molecule Imaging Group, MRC Clinical Sciences Centre, Imperial College London, Hammersmith Hospital Campus, London, UK 10Present address: Cardiff School of Biosciences, Cardiff, UK 11Present address: Systems Biology and Genomics, Harry Perkins Institute of Medical Research, Nedlands, WA, Australia ‡These authors contributed equally to this work *Corresponding author. Tel: +44 131 651 8614; E-mail: [email protected] *Corresponding author. Tel: +1 514 398 4975; E-mail: [email protected] *Corresponding author. Tel: +49 30 94061752; E-mail: [email protected] *Corresponding author. Tel: +39 081 676475; E-mail: [email protected] Molecular Systems Biology (2015)11:852https://doi.org/10.15252/msb.20156492 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Mammalian chromosomes fold into arrays of megabase-sized topologically associating domains (TADs), which are arranged into compartments spanning multiple megabases of genomic DNA. TADs have internal substructures that are often cell type specific, but their higher-order organization remains elusive. Here, we investigate TAD higher-order interactions with Hi-C through neuronal differentiation and show that they form a hierarchy of domains-within-domains (metaTADs) extending across genomic scales up to the range of entire chromosomes. We find that TAD interactions are well captured by tree-like, hierarchical structures irrespective of cell type. metaTAD tree structures correlate with genetic, epigenomic and expression features, and structural tree rearrangements during differentiation are linked to transcriptional state changes. Using polymer modelling, we demonstrate that hierarchical folding promotes efficient chromatin packaging without the loss of contact specificity, highlighting a role far beyond the simple need for packing efficiency. Synopsis Genome-wide mapping of chromatin architecture reveals a hierarchical folding of chromatin that involves higher-order domains interactions across the whole chromosomes, reflects epigenomic features and reorganizes upon differentiation-induced gene expression changes. Chromatin architecture is mapped genome-wide using Hi-C and a neuronal differentiation model from mESC to post-mitotic neurons. Mammalian chromosomes fold hierarchically in a manner that reflects epigenomic features and involves higher-order domains (metaTADs) up to the chromosome scale. metaTAD topologies are relatively conserved through differentiation, and their reorganization is related to gene expression changes. Polymer modelling shows that hierarchical chromatin folding promotes efficient packaging without the loss of contact specificity. Introduction The spatial organization of chromatin in cell nuclei has essential functional roles. In mammals, chromosomes occupy distinct territories and have preferred radial positions that depend on cell type and transcription activity (Lanctot et al, 2007; Misteli, 2007; Bickmore & van Steensel, 2013; Tanay & Cavalli, 2013). Within chromosomes, chromatin is organized in megabase-sized regions, known as topologically associating domains (TADs), characterized by enriched levels of interactions (Dixon et al, 2012; Nora et al, 2012). TADs appear to contain inner substructures as revealed by high-resolution analyses (Sexton et al, 2012; Phillips-Cremins et al, 2013). At a larger scale, they generally fall into either compartment A or B, which are nuclear domains related to genomic function, up to tens of Mb in size enriched in active or repressed chromatin states, respectively (Lieberman-Aiden et al, 2009). Yet, the specificity of TAD contacts within compartments A/B and how different structural levels of chromatin folding integrate with nuclear functions from the scale of individual genes up to the scale of chromosomes remain unclear. In particular, we lack a comprehensive understanding of the higher-order organization of TADs, the different scales to which TAD–TAD contacts extend, and how these higher-order structures change upon cell differentiation. Here, we investigate higher-order TAD interactions in a neuronal differentiation model from mouse embryonic stem cells (ESC) via neural progenitor cells (NPC) to neurons. Novel analyses of Hi-C data sets during differentiation reveal that TADs form a hierarchy of domains-within-domains that we name "metaTADs". The metaTAD hierarchy extends across genomic scales up to the size range of entire chromosomes. We show that the complex inter-TAD interactions can be understood as relatively simple tree-like hierarchical structures irrespective of cell type. By comparing our Hi-C data with a variety of other data sets, we find that metaTAD tree structures correlate with patterns of epigenomic and expression features. Furthermore, the dynamics of tree rearrangements during differentiation link nuclear organization to transcriptional changes, providing a new paradigm to study chromatin structure and function. Using polymer modelling, we also demonstrate that hierarchical folding promotes efficient chromatin packaging without the loss of contact specificity. Our work highlights the close relationship between chromosome structure and function in mammalian nuclei, suggesting a functional role for hierarchical chromatin organization beyond simple chromatin packing efficiency. Results To investigate higher-order chromatin folding during differentiation, we studied proliferating mouse embryonic stem cells (ESC), intermediate neuronal precursor cells (NPC) and post-mitotic neurons (Neurons; Fig 1A). ESC (46C cell line) were differentiated using a protocol optimized for large-scale production of functional murine neurons with a midbrain phenotype (Jaeger et al, 2011), and each time point showed homogeneous expression of stage-specific markers (Figs 1B and EV1A). Characteristic expression patterns for the cell types under study were also confirmed by genome-wide gene expression analyses by CAGE (cap analysis of gene expression) (Kodzius et al, 2006; Takahashi et al, 2012; Forrest et al, 2014) and Gene Ontology (Fig EV1B; Table EV1). Incorporation of bromo-deoxyuridine (BrdU; 24 h) to mark cells undergoing DNA replication shows that while ESC and NPC are actively cycling, Neurons have ceased cell division (Fig 1C). Figure 1. Chromatin contact maps (Hi-C) and matched gene expression (CAGE) data in murine neuronal differentiation system Scheme of the murine differentiation system of our study, from ESC to NPC and post-mitotic neurons. Cells express stage-specific markers as detected by immunofluorescence: ESC express Oct4, NPC the neuronal precursor marker nestin and Neurons Tubb3 (Tuj1 antibody). Scale bar, 100 μm. ESC and NPC are actively cycling, whereas Neurons are negative for BrDNA after 24-h BrdU incorporation. Nuclei were counterstained with DAPI. Examples of interaction patterns in Hi-C matrices across the whole chromosomes show extensive higher-order dynamic contacts, which change during terminal neuronal differentiation. Hi-C interaction data are plotted in log scale. Matched CAGE data sets were produced from total RNA extracted from ESC, NPC and Neurons. The expression levels confirm specific expression of stage-specific markers. CAGE expression reported as a percentage relative to highest expression. Download figure Download PowerPoint Click here to expand this figure. Figure EV1. Differentiation of mouse ESC (46C line) into midbrain-like neuronal cells Cell populations were tested by immunofluorescence staining for their purity using stage-specific markers (pseudocoloured red), which showed efficient progression of ESC through the differentiation steps. Oct4 expression in ESC is lost upon differentiation, nestin is specifically expressed in NPC, and Tubb3 (detected using Tuj1 antibodies) is strongly expressed in Neurons. Nuclei were counterstained with DAPI (pseudocoloured blue). Scale bar represents 100 μm. Total RNA was extracted from ESC, NPC and Neurons, and directional CAGE data sets were produced in order to measure RNA transcription and define transcription start sites in each time point. Strand-specific CAGE reads are represented (+ and – strands). The promoter regions of the stage-specific markers are reported. CAGE signals for Oct4 (Pou5f1), nestin (Nes) and Tubb3 genes peak in ESC, NPC and Neurons, respectively. Download figure Download PowerPoint We produced Hi-C libraries for ESC, NPC and Neurons (Fig 1D), using a modified Hi-C protocol (Appendix Fig S1), which increases the yield of chromatin interaction products. Normalized Hi-C matrices show typical organization of chromatin into blocks of enriched interactions reflecting the existence of compartments and TADs (Fig 1D). This organization is chromosome specific, and we observe extensive changes during differentiation in the landscape of higher-order contacts of each chromosome (Appendix Figs S2 and S3). These patterns of structural dynamics often extend across the whole chromosomes and are accompanied by changes in genome-wide transcription activity in CAGE data that were produced from matched samples (examined in detail below). For instance, among the changes measured by CAGE, we find a quick depletion of the pluripotency transcription factors Oct4 and Rex1 after the ESC stage (Fig 1E). Similarly, we find that nestin and Fgf5 are highly expressed in NPC, whereas the neuronal markers Neurog2 and Tubb3 are expressed in differentiated neurons (Fig 1E). TAD–TAD contacts extend across genomic scales to define higher-order structures To investigate the architecture of higher-order chromosome folding, we first identified TAD positions across chromosomes in Hi-C data sets for all time points using the directionality index (Dixon et al, 2012) (Fig 2A, Appendix Fig S4, see Appendix Supplementary Analyses for details). For comparison, we also analysed a published Hi-C data set from a different mouse ESC line (ESC-J1; Dixon et al, 2012). Average TAD size was ~0.5 Mb across all cell types (Appendix Fig S5), consistent with recent reports (Phillips-Cremins et al, 2013; Pope et al, 2014; Rao et al, 2014). The location of TAD boundaries measured in our ESC-46C Hi-C data set and in the published ESC-J1 data set overlap by 83% (Appendix Fig S6), in the same range as the overlap typically reported between biological replicates (Dixon et al, 2012). Figure 2. Chromosomes are organized in a hierarchy of higher-order domains (metaTADs) ESC Hi-C map of chromosome 2, 53–58 Mb. The directionality index (DI, bottom) was used to identify TADs, numbered 1–6. metaTAD identification by single-linkage clustering. Examples of TADs (1–6) and metaTADs (I–V) in the same region shown in (A). metaTADs are domains with enriched Hi-C contacts. The ratio of average interaction, I, between pairs of TADs or metaTADs, and background value, IC, was calculated for ESC, NPC and Neurons, as a function of the total number of TADs (n) included in the metaTAD. I/IC remains 20% above control levels in randomized Hi-C matrices up to scales of the order of n = 80 TADs. metaTADs size, d, is represented as a function of the number of TADs that they contain, n, showing that eighty TADs correspond to an average genomic length of around 40 Mb. The metaTAD tree organization in ESC versus Neurons largely coincides with stretches of compartment A (grey) or B (black). A/B compartments are represented in the two central bars and were defined based on an individual principle component (green line) derived from Hi-C data. The yellow line indicates a value of 0. Boundaries of metaTADs larger than 10 Mb are more enriched for transitions between lamina-associated (blue) and lamina-detached (red) regions than TAD boundaries. Heatmaps display the 900-kb flanking domain boundaries (dashed lines) for metaTADs (left heatmap) and TADs (right heatmap) for all boundary regions (heatmap rows). Transitions in lamina association are visible as abrupt changes in heatmap colours at boundaries (see Appendix Supplementary Methods). Both metaTAD and TAD boundaries are significantly more frequently observed to coincide with transitions than expected (P < 1 × 10−4; see Appendix Supplementary Methods). The metaTAD tree of chromosome 19 in ESC (left: full; right: zoomed region). Interactions between metaTADs are not homogeneous, but instead occur through specific contacts involving specific TADs. Hi-C interaction data are plotted in log scale. Download figure Download PowerPoint Although most chromatin contacts observed in Hi-C matrices are found within TADs, interaction signal is also detected locally between specific TADs (Fig 2A; Dixon et al, 2015) and extends to large genomic distances (Fig 1D). We explored higher-order contacts between TADs using Hi-C interaction matrices and found that the most frequently interacting partner of a given TAD is a flanking nearest neighbour TAD in 97% of cases. This behaviour points to a scenario where chromatin folds into larger domains containing multiple, preferentially interacting TADs. To uncover the higher-order domain structure of chromosomes within Hi-C matrices, we implemented a single-linkage clustering procedure of Hi-C contacts. For each chromosome, we iteratively select the two most frequently interacting neighbouring TADs (Fig 2B) and merge them into a higher-order domain or "metaTAD". This metaTAD is then added back to the list of domains, and the procedure is repeated until the entire chromosome arm is contained in a single domain that encompasses the hierarchy of all intervening lower-level metaTADs. The hierarchy of TAD–TAD contacts can be intuitively represented as a tree, where the joined domains are named sequentially as metaTAD-I, -II, -III and so on based on their position along the tree (detailed below). Overlaying the metaTAD structure onto Hi-C data (Fig 2C) provides a visual confirmation that the hierarchy of higher-order domains derived by single-linkage clustering matches the patterns of Hi-C data. It identifies the most frequent inter-TAD contacts in the first levels of the tree (I, II and III) and progressively lower Hi-C frequencies at the higher tree levels (IV and V). To quantify the statistical reliability of the identified metaTADs, we examined several measures of inter-TAD contacts across all data sets. First, we tested whether interactions between metaTAD pairs are significantly more frequent than background interactions. We measured the average interaction level, I, between pairs of domains that produce new metaTADs containing a total of n TADs. As background reference, we used the average interaction (IC) between regions of the same genomic size, but randomly placed at the boundary of any other neighbouring TADs. In ESC, NPC and Neurons, the normalized interaction ratio I/IC remains significantly above control levels (measured in randomized Hi-C matrices), up to metaTADs containing several tens of TADs (Fig 2D). To give a sense of scale, we also plot I at increasing tree levels to show the extent of Hi-C interactions between metaTADs (Fig EV2). I/IC remains 20% above the average values observed for randomized Hi-C, up to metaTADs containing ~80 TADs (Fig 2D, Appendix Fig S7A–D) corresponding to genomic lengths of ~40 Mb (Fig 2E). We also found that the normalized chromatin interactions detected within whole metaTADs, J/JC, remain above background levels up to roughly the same length scale (Appendix Fig S7E–H; Fig EV2). As an additional control, we corrected the Hi-C data for 1D proximity effects (Appendix Supplementary Methods) and found the same most interacting TAD partners in 72% of cases, demonstrating that the observed metaTAD hierarchy is not only a consequence of linear distance. These analyses show that chromosomes adopt hierarchical structural conformations of increasing complexity of metaTADs in ESC, NPC and Neurons, with prominent intra-TAD and inter-TAD contacts. These findings were fully confirmed using the original data sets of TADs identified in mouse ESC-J1 and in human IMR90 and ESC-H1 Hi-C data (Dixon et al, 2012) (Appendix Fig S8, Appendix Supplementary Methods). Click here to expand this figure. Figure EV2. Frequency of TAD–TAD interactions at each tree levelThe average metaTAD inter-domain, I (blue curves), and intra-domain, J (magenta), interactions are shown as a function of the number of TADs, n, which a metaTAD includes in our three cell types (see also Appendix Supplementary Methods). The larger the domains considered, the lower their average interactions, consistently with Hi-C results. As expected, intra-domain interactions, J, stay always above inter-domain interactions, I. Download figure Download PowerPoint Taken together, our results show that a hierarchical architecture of domains-within-domains is a general feature of chromatin folding, found across all stages of differentiation examined and in both murine and human cells. The metaTAD contact hierarchy bridges chromatin organization between TADs and nuclear compartments To visualize the organization of chromatin in metaTADs at a genomic scale, we built a tree diagram for each chromosome, where the tree "leaf" nodes represent TADs and the internal nodes correspond to metaTADs (Fig 2B). Comparison of this type of diagram shows a visual correlation of the tree structures with A/B compartment domains, as tree sub-branches often coincide with transitions between compartments (Fig 2F). To test how metaTADs compare with compartments A/B, we measured the frequency with which two TADs in a common metaTAD are present in the same compartment (Appendix Fig S9). We found that TADs within a metaTAD frequently belong to the same compartment, in particular in the lower tree levels, as expected. Furthermore, this frequency is much higher than what is observed considering the linear distance between TADs, suggesting that the preferential contacts captured in the metaTAD hierarchy reflect preferential contacts within the same compartment type. As an additional comparison between the metaTAD hierarchy of contacts and a second well-known (and independently measured) feature of chromatin organization, we studied the relationship b
