Cell lines were not tested for mycoplasma contamination. Commonly misidentified lines (See ICLAC register) No commonly misidentified cell lines were used.

Higher-order chromatin organization such as A/B compartments, TADs, and chromatin loops are temporarily disrupted during mitosis 1,2. Since these structures are thought to influence gene regulation, it is important to understand how they are re-established after mitosis. We examined the dynamics of chromosome reorganization by Hi-C after mitosis in highly purified, synchronous cell populations. We observed rapid establishment, gradual intensification, and expansion of A/B compartments. Contact domains form from the "bottom-up" with smaller subTADs forming initially, followed by convergence into multi-domain TAD structures. CTCF is partially retained on mitotic chromosomes and immediately resumes full binding at ana/telophase. In contrast, cohesin is completely evicted from mitotic chromosomes and regains focal binding with delayed Reprints and permissions information is available at www.nature.com/reprints.Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://

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During mammalian embryogenesis, differential gene expression gradually builds the identity and complexity of each tissue and organ system 1. Here we systematically quantified mouse polyA-RNA from day 10.5 of embryonic development to birth, sampling 17 tissues and organs. The resulting developmental transcriptome is globally structured by dynamic cytodifferentiation, body-axis and cell-proliferation gene sets that were further characterized by the transcription factor motif codes of their promoters. We decomposed the tissue-level transcriptome using single-cell RNA-seq (sequencing of RNA reverse transcribed into cDNA) and found that neurogenesis and haematopoiesis dominate at both the gene and cellular levels, jointly accounting for one-third of differential gene expression and more than 40% of identified cell types. By integrating promoter sequence motifs with companion ENCODE epigenomic profiles, we identified a prominent promoter de-repression mechanism in neuronal expression clusters that was attributable to known and novel repressors. Focusing on the developing limb, single-cell RNA data identified 25 candidate cell types that included progenitor and differentiating states with computationally inferred lineage relationships. We extracted cell-type transcription factor networks and complementary sets of candidate enhancer elements by using single-cell RNA-seq to decompose integrative cis-element (IDEAS) models that were derived from whole-tissue epigenome chromatin data. These ENCODE reference data, computed network components and IDEAS chromatin segmentations are companion resources to the matching epigenomic developmental matrix, and are available for researchers to further mine and integrate. Hierarchical transcription programs regulate mammalian histogenesis, a spatiotemporally coordinated process of changing cell identities, numbers and locations 1. Contemporary RNA-seq time-courses can comprehensively quantify expression trajectories, including the transcriptional regulators that drive patterning, cell-type specification and differentiation and their regulatory targets. Here we systematically map the mouse polyadenylated RNA transcriptome, tracking 12 major tissues from embryonic day (E) 10.5 to birth (postnatal day (P) 0) (Fig. 1a, b, Extended Data Fig. 1a) to cover much of organogenesis and histogenesis. Pertinent to integrative regulatory analysis and modelling, these RNA expression data are part of the ENCODE Consortium mouse embryo project, which provides companion genome-wide microRNA, DNA methylation, histone mark, and chromatin accessibility datasets for the same sample matrix 2. To better interpret the core sample set, we added five additional organs at P0, sampling seventeen tissues in all. As these whole-tissue data are intended for community use, including integration with high-resolution single-cell transcriptomes, we chose a widely used RNA-seq method that is robust at both bulk sample and single-cell scales 3 and has been used for other single-cell RNA-seq (scRNA-seq) experiments in ENCODE...