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      The Human Cell Atlas

      discussion
      1 , 2 , 3 , , 4 , 5 , 6 , , 1 , 2 , 7 , , 8 , 9 , 5 , 5 , 10 , 4 , 11 , 6 , 12 , 13 , 14 , 15 , 5 , 16 , 17 , 18 , 19 , 20 , 21 , 11 , 22 , 1 , 23 , 24 , 4 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 4 , 5 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 1 , 42 , 43 , 44 , 4 , 45 , 46 , 1 , 47 , 48 , 49 , 50 , 1 , 51 , 52 , 53 , 54 , 12 , 5 , 4 , 4 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 3 , 62 , 63 , 64 , 65 , 1 , 66 , 67 , 68 , 52 , 60 , Human Cell Atlas Meeting Participants
      eLife
      eLife Sciences Publications, Ltd
      single-cell genomics, lineage, cell atlas, science forum, Human, Mouse

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.

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          Fast unfolding of communities in large networks

          We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection method in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2.6 million customers and by analyzing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad-hoc modular networks. .
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            Comparative Analysis of Single-Cell RNA Sequencing Methods.

            Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq, and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq, and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq, and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods, and it provides a framework for benchmarking further improvements of scRNA-seq protocols.
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              Reconstruction and Simulation of Neocortical Microcircuitry.

              We present a first-draft digital reconstruction of the microcircuitry of somatosensory cortex of juvenile rat. The reconstruction uses cellular and synaptic organizing principles to algorithmically reconstruct detailed anatomy and physiology from sparse experimental data. An objective anatomical method defines a neocortical volume of 0.29 ± 0.01 mm(3) containing ~31,000 neurons, and patch-clamp studies identify 55 layer-specific morphological and 207 morpho-electrical neuron subtypes. When digitally reconstructed neurons are positioned in the volume and synapse formation is restricted to biological bouton densities and numbers of synapses per connection, their overlapping arbors form ~8 million connections with ~37 million synapses. Simulations reproduce an array of in vitro and in vivo experiments without parameter tuning. Additionally, we find a spectrum of network states with a sharp transition from synchronous to asynchronous activity, modulated by physiological mechanisms. The spectrum of network states, dynamically reconfigured around this transition, supports diverse information processing strategies.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                05 December 2017
                2017
                : 6
                : e27041
                Affiliations
                [1 ]Broad Institute of MIT and Harvard CambridgeUnited States
                [2 ]deptDepartment of Biology Massachusetts Institute of Technology CambridgeUnited States
                [3 ]Howard Hughes Medical Institute Chevy ChaseUnited States
                [4 ]Wellcome Trust Sanger Institute, Wellcome Genome Campus HinxtonUnited Kingdom
                [5 ]deptEMBL-European Bioinformatics Institute Wellcome Genome Campus HinxtonUnited Kingdom
                [6 ]deptCavendish Laboratory, Department of Physics University of Cambridge CambridgeUnited Kingdom
                [7 ]deptDepartment of Systems Biology Harvard Medical School BostonUnited States
                [8 ]deptDepartment of Immunology Weizmann Institute of Science RehovotIsrael
                [9 ]deptDivision of Immunology, Department of Microbiology and Immunobiology Harvard Medical School BostonUnited States
                [10 ]deptInstitute of Molecular Life Sciences University of Zürich ZürichSwitzerland
                [11 ]deptDepartment of Haematology University of Cambridge CambridgeUnited Kingdom
                [12 ]deptDivision of Genomic Technologies RIKEN Center for Life Science Technologies YokohamaJapan
                [13 ]deptMolecular Immunity Unit, Department of Medicine, MRC Laboratory of Molecular Biology University of Cambridge CambridgeUnited Kingdom
                [14 ]Hubrecht Institute, Princess Maxima Center for Pediatric Oncology and University Medical Center Utrecht UtrechtThe Netherlands
                [15 ]deptInstitute of Bioengineering, School of Life Sciences Swiss Federal Institute of Technology (EPFL) LausanneSwitzerland
                [16 ]deptDepartment of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine, University of Pennsylvania PhiladelphiaUnited States
                [17 ]deptDivision of Theoretical Bioinformatics (B080) German Cancer Research Center (DKFZ) HeidelbergGermany
                [18 ]deptDepartment for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant Heidelberg University HeidelbergGermany
                [19 ]deptDepartment of Biology II Ludwig Maximilian University Munich MartinsriedGermany
                [20 ]Takara Bio United States, Inc. Mountain ViewUnited States
                [21 ]deptOxford Centre for Neuroinflammation, Nuffield Department of Clinical Neurosciences, and MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine John Radcliffe Hospital, University of Oxford OxfordUnited Kingdom
                [22 ]deptWellcome Trust-MRC Cambridge Stem Cell Institute University of Cambridge CambridgeUnited Kingdom
                [23 ]Massachusetts General Hospital Cancer Center BostonUnited States
                [24 ]deptInstitute of Cellular Medicine Newcastle University Newcastle upon TyneUnited Kingdom
                [25 ]deptDepartments of Developmental Biology and of Medicine Stanford University School of Medicine StanfordUnited States
                [26 ]deptPeter Medawar Building for Pathogen Research and the Translational Gastroenterology Unit, Nuffield Department of Clinical Medicine University of Oxford OxfordUnited Kingdom
                [27 ]deptOxford NIHR Biomedical Research Centre John Radcliffe Hospital OxfordUnited Kingdom
                [28 ]deptEli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research University of California, San Francisco San FranciscoUnited States
                [29 ]Allen Institute for Brain Science SeattleUnited States
                [30 ]deptLaboratory for Molecular Neurobiology, Department of Medical Biochemistry and Biophysics Karolinska Institutet StockholmSweden
                [31 ]deptScience for Life Laboratory, School of Biotechnology KTH Royal Institute of Technology StockholmSweden
                [32 ]deptDepartment of Genetics Stanford University StanfordUnited States
                [33 ]deptScience for Life Laboratory, Department of Gene Technology KTH Royal Institute of Technology StockholmSweden
                [34 ]National Institute of Biomedical Genomics KalyaniIndia
                [35 ]deptCancer Research UK Cambridge Institute University of Cambridge CambridgeUnited Kingdom
                [36 ]deptPrecision Immunology Institute Icahn School of Medicine at Mount Sinai New YorkUnited States
                [37 ]deptDivision of Chemical, Systems & Synthetic Biology, Institute for Infectious Disease & Molecular Medicine (IDM), Department of Integrative Biomedical Sciences, Faculty of Health Sciences University of Cape Town Cape TownSouth Africa
                [38 ]deptDepartment of Pathology and Medical Biology, GRIAC Research Institute University of Groningen, University Medical Center Groningen GroningenThe Netherlands
                [39 ]deptDepartment of Internal Medicine and Radboud Center for Infectious Diseases Radboud University Medical Center NijmegenThe Netherlands
                [40 ]deptDepartment of Microbiology and Immunology Stanford University StanfordUnited States
                [41 ]deptComputational and Systems Biology Program Sloan Kettering Institute New YorkUnited States
                [42 ]deptMRC Human Genetics Unit, MRC Institute of Genetics & Molecular Medicine University of Edinburgh EdinburghUnited Kingdom
                [43 ]deptDepartment of Applied Physics and Department of Bioengineering Stanford University StanfordUnited States
                [44 ]Chan Zuckerberg Biohub San FranciscoUnited States
                [45 ]deptEpigenetics Programme The Babraham Institute CambridgeUnited Kingdom
                [46 ]deptCentre for Trophoblast Research University of Cambridge CambridgeUnited Kingdom
                [47 ]deptCenter for Brain Science and Department of Molecular and Cellular Biology Harvard University CambridgeUnited States
                [48 ]deptDepartment of Biology New York University New YorkUnited States
                [49 ]deptNew York Genome Center New York University New YorkUnited States
                [50 ]deptDivision of Immunology The Netherlands Cancer Institute AmsterdamThe Netherlands
                [51 ]deptInstitute for Medical Engineering & Science (IMES) and Department of Chemistry Massachusetts Institute of Technology CambridgeUnited States
                [52 ]Ragon Institute of MGH, MIT and Harvard CambridgeUnited States
                [53 ]deptDepartment of Computer Science and Department of Biomolecular Sciences Weizmann Institute of Science RehovotIsrael
                [54 ]deptDepartment of Genitourinary Medical Oncology, Department of Immunology, MD Anderson Cancer Center University of Texas HoustonUnited States
                [55 ]deptInstitute of Computational Biology German Research Center for Environmental Health, Helmholtz Center Munich NeuherbergGermany
                [56 ]deptDepartment of Mathematics Technical University of Munich GarchingGermany
                [57 ]deptScience for Life Laboratory and Department of Proteomics KTH Royal Institute of Technology StockholmSweden
                [58 ]deptNovo Nordisk Foundation Center for Biosustainability Danish Technical University LyngbyDenmark
                [59 ]Hubrecht Institute and University Medical Center Utrecht UtrechtThe Netherlands
                [60 ]deptDepartment of Electrical Engineering and Computer Science and the Center for Computational Biology University of California, Berkeley BerkeleyUnited States
                [61 ]deptCentre for Stem Cells and Regenerative Medicine King's College London LondonUnited Kingdom
                [62 ]deptDepartment of Cellular & Molecular Pharmacology University of California, San Francisco San FranciscoUnited States
                [63 ]deptCalifornia Institute for Quantitative Biomedical Research University of California, San Francisco San FranciscoUnited States
                [64 ]deptCenter for RNA Systems Biology University of California, San Francisco San FranciscoUnited States
                [65 ]deptDivision of Biology and Biological Engineering California Institute of Technology PasadenaUnited States
                [66 ]deptCenter for Computational and Integrative Biology Massachusetts General Hospital BostonUnited States
                [67 ]deptGastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease Massachusetts General Hospital BostonUnited States
                [68 ]deptCenter for Microbiome Informatics and Therapeutics Massachusetts Institute of Technology CambridgeUnited States
                Cold Spring Harbor Laboratory United States
                Cold Spring Harbor Laboratory United States
                Author information
                http://orcid.org/0000-0003-3293-3158
                https://orcid.org/0000-0001-7202-7243
                https://orcid.org/0000-0001-9935-843X
                https://orcid.org/0000-0002-4056-0550
                https://orcid.org/0000-0001-6302-5705
                https://orcid.org/0000-0002-3927-2084
                https://orcid.org/0000-0001-9012-6552
                https://orcid.org/0000-0003-0202-7816
                https://orcid.org/0000-0001-8926-8836
                https://orcid.org/0000-0001-9448-8833
                https://orcid.org/0000-0001-9151-5154
                https://orcid.org/0000-0003-2445-670X
                https://orcid.org/0000-0001-9004-1225
                Article
                27041
                10.7554/eLife.27041
                5762154
                29206104
                8e174bd7-c8b0-43b7-928b-38da4c503588
                © 2017, Regev et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 28 March 2017
                : 30 November 2017
                Funding
                The authors declare that there was no funding for this work
                Categories
                Feature Article
                Cell Biology
                Computational and Systems Biology
                Science Forum
                Custom metadata
                Advances in techniques for analysing single cells and tissues have inspired an international effort to create comprehensive reference maps of all human cells - the fundamental units of life - as a basis for both understanding human health and diagnosing, monitoring and treating disease.

                Life sciences
                single-cell genomics,lineage,cell atlas,science forum,human,mouse
                Life sciences
                single-cell genomics, lineage, cell atlas, science forum, human, mouse

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