{"ID": "PMID:28818938", "lab": {"@type": ["Lab", "Item"], "status": "current", "@id": "/labs/jay-ashok-shendure-lab/", "display_title": "Jay Ashok Shendure, UW", "title": "Jay Ashok Shendure, UW", "correspondence": [{"contact_email": "c2hlbmR1cmVAdXcuZWR1", "@id": "/users/56682066-d5c0-48b5-aec9-9234c1135b22/", "display_title": "Jay Ashok Shendure"}], "uuid": "f9d7e470-cde0-4847-b629-2b70ae4e31cf", "pi": {"error": "no view permissions"}, "principals_allowed": {"view": ["system.Everyone"], "edit": ["group.admin", "role.lab_submitter", "submits_for.f9d7e470-cde0-4847-b629-2b70ae4e31cf"]}}, "url": "https://www.ncbi.nlm.nih.gov/pubmed/28818938", "award": {"center_title": "NOFIC - Shendure", "uuid": "fcc7f634-9252-499f-b79c-380795af2ddd", "project": "4DN", "@id": "/awards/1U54DK107979-01/", "description": "NOFIC: A current grand challenge in genomics involves accurately assaying, at all relevant scales, the 3D conformation of DNA in vivo and then linking conformational changes to dynamic processes such as the cell cycle, differentiation and disease. Here we propose to create the University of Washington Center for Nuclear Organization and Function, bringing together an interdisciplinary team of investigators whose diverse areas of expertise - technology development, computational modeling, and mouse and human biology - make them ideally suited to this challenge. Our overall hypothesis is that characterizing and understanding changes in genome architecture over time (the 4D nucleome) will lead to fundamental insights into human biology and disease. We will address this hypothesis by developing a combination of experimental and computational methods development, coupled with their systematic biological validation and application to development- and disease-relevant systems. On the experimental side, we will further optimize our recently developed DNase Hi- C assay, including combinatorial methods for single cells, ultimately aiming to concurrently assay nuclear architecture and gene expression within each of many single cells. On the computational side, we will extend our existing 3D modeling algorithms to account for diploidy, cell-to-cell variabilit, the hierarchical nature of genome architecture, and to explicitly model architectural changes over cell cycle and cell differentiation time scales. We will then employ several complementary computational methods to link our 4D nucleome models to existing, 1D genomics data sets. The outputs of these new experimental and computational technologies will be subjected to orthogonal validation in several well-understood model systems: human cell lines, in vivo tissues from interspecific F1 hybrid mice, mouse embryonic stem cells (ESCs) and skeletal myoblasts. We will also test specific predictions of the models in response to targeted (genome editing) or large-scale (chromosome silencing) perturbations. After initial validation and in parallel with further methods development, we will apply our new tools to the analysis of three biological systems: we will characterize the dynamics of nuclear architecture during the directed differentiation of na\u00efve human ESCs into cardiomyocytes and endothelial cells; we will test the hypothesis that cardiomyopathy-inducing mutations in the nuclear scaffolding protein, lamin A, are associated with derangements in cardiomyocyte nuclear architecture; and we will determine the changes in human cardiomyocyte nuclear architecture induced by trisomy 21. The proposed center will produce new experimental protocols for ascertaining 4D nucleome architecture, two new software toolkits for modeling the 4D nucleome and linking features of the nucleome to other types of genomic data, a variety of publicly available, large-scale 4D nucleome data sets in mouse and human systems, and fundamental insights into human biology and disease. In all of this work, we will work closely and openly with NOFIC and the 4DN Network to maximize the impact of our center and the overall program.", "@type": ["Award", "Item"], "name": "1U54DK107979-01", "status": "current", "display_title": "UNIVERSITY OF WASHINGTON CENTER FOR NUCLEAR ORGANIZATION AND FUNCTION", "pi": {"error": "no view permissions"}, "principals_allowed": {"view": ["system.Everyone"], "edit": ["group.admin"]}}, "title": "Comprehensive single-cell transcriptional profiling of a multicellular organism.", "status": "current", "aliases": ["4dn-dcic-lab:pmid_28818938_sci_rnaseq"], "authors": ["Cao J", "Packer JS", "Ramani V", "Cusanovich DA", "Huynh C", "Daza R", "Qiu X", "Lee C", "Furlan SN", "Steemers FJ", "Adey A", "Waterston RH", "Trapnell C", "Shendure J"], "journal": "Science (New York, N.Y.)", "abstract": "To resolve cellular heterogeneity, we developed a combinatorial indexing strategy to profile the transcriptomes of single cells or nuclei, termed sci-RNA-seq (single-cell combinatorial indexing RNA sequencing). We applied sci-RNA-seq to profile nearly 50,000 cells from the nematode Caenorhabditis elegans at the L2 larval stage, which provided >50-fold \"shotgun\" cellular coverage of its somatic  cell composition. From these data, we defined consensus expression profiles for 27 cell types and recovered rare neuronal cell types corresponding to as few as one or two cells in the L2 worm. We integrated these profiles with whole-animal chromatin immunoprecipitation sequencing data to deconvolve the cell type-specific effects of transcription factors. The data generated by sci-RNA-seq constitute a powerful resource for nematode biology and foreshadow similar atlases for other organisms.", "categories": ["technology development"], "date_created": "2020-04-01T17:51:36.358468+00:00", "published_by": "4DN", "submitted_by": {"error": "no view permissions"}, "last_modified": {"modified_by": {"error": "no view permissions"}, "date_modified": "2020-04-01T17:52:02.370213+00:00"}, "date_published": "2017-08-18", "public_release": "2020-04-01", "schema_version": "2", "project_release": "2020-04-01", "@id": "/publications/d58cbe95-227c-4e33-8d54-6265a23de74b/", "@type": ["Publication", "Item"], "uuid": "d58cbe95-227c-4e33-8d54-6265a23de74b", "principals_allowed": {"view": ["system.Everyone"], "edit": ["group.admin"]}, "display_title": "Cao J et al. (2017) PMID:28818938", "external_references": [], "short_attribution": "Cao J et al. (2017)", "@context": "/terms/", "aggregated-items": {}, "validation-errors": []}