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Kong Y, Börner K. Publication, funding, and experimental data in support of Human Reference Atlas construction and usage. Sci Data 2024; 11:574. [PMID: 38834597 PMCID: PMC11150433 DOI: 10.1038/s41597-024-03416-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 05/24/2024] [Indexed: 06/06/2024] Open
Abstract
Experts from 18 consortia are collaborating on the Human Reference Atlas (HRA) which aims to map the 37 trillion cells in the healthy human body. Information relevant for HRA construction and usage is held by experts, published in scholarly papers, and captured in experimental data. However, these data sources use different metadata schemas and cannot be cross-searched efficiently. This paper documents the compilation of a dataset, named HRAlit, that links the 136 HRA v1.4 digital objects (31 organs with 4,279 anatomical structures, 1,210 cell types, 2,089 biomarkers) to 583,117 experts; 7,103,180 publications; 896,680 funded projects, and 1,816 experimental datasets. The resulting HRAlit has 22 tables with 20,939,937 records including 6 junction tables with 13,170,651 relationships. The HRAlit can be mined to identify leading experts, major papers, funding trends, or alignment with existing ontologies in support of systematic HRA construction and usage.
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Affiliation(s)
- Yongxin Kong
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA.
- School of Information Management, Sun Yat-sen University, Guangzhou, 510006, China.
| | - Katy Börner
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA.
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Klotz L, Antel J, Kuhlmann T. Inflammation in multiple sclerosis: consequences for remyelination and disease progression. Nat Rev Neurol 2023; 19:305-320. [PMID: 37059811 DOI: 10.1038/s41582-023-00801-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/15/2023] [Indexed: 04/16/2023]
Abstract
Despite the large number of immunomodulatory or immunosuppressive treatments available to treat relapsing-remitting multiple sclerosis (MS), treatment of the progressive phase of the disease has not yet been achieved. This lack of successful treatment approaches is caused by our poor understanding of the mechanisms driving disease progression. Emerging concepts suggest that a combination of persisting focal and diffuse inflammation within the CNS and a gradual failure of compensatory mechanisms, including remyelination, result in disease progression. Therefore, promotion of remyelination presents a promising intervention approach. However, despite our increasing knowledge regarding the cellular and molecular mechanisms regulating remyelination in animal models, therapeutic increases in remyelination remain an unmet need in MS, which suggests that mechanisms of remyelination and remyelination failure differ fundamentally between humans and demyelinating animal models. New and emerging technologies now allow us to investigate the cellular and molecular mechanisms underlying remyelination failure in human tissue samples in an unprecedented way. The aim of this Review is to summarize our current knowledge regarding mechanisms of remyelination and remyelination failure in MS and in animal models of the disease, identify open questions, challenge existing concepts, and discuss strategies to overcome the translational roadblock in the field of remyelination-promoting therapies.
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Affiliation(s)
- Luisa Klotz
- Department of Neurology with Institute of Translational Neurology, University Hospital Münster, Münster, Germany
| | - Jack Antel
- Neuroimmunology Unit, Montreal Neurological Institute, McGill University, Québec, Canada
| | - Tanja Kuhlmann
- Neuroimmunology Unit, Montreal Neurological Institute, McGill University, Québec, Canada.
- Institute of Neuropathology, University Hospital Münster, Münster, Germany.
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Herr BW, Hardi J, Quardokus EM, Bueckle A, Chen L, Wang F, Caron AR, Osumi-Sutherland D, Musen MA, Börner K. Specimen, biological structure, and spatial ontologies in support of a Human Reference Atlas. Sci Data 2023; 10:171. [PMID: 36973309 PMCID: PMC10043028 DOI: 10.1038/s41597-023-01993-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 01/30/2023] [Indexed: 03/29/2023] Open
Abstract
The Human Reference Atlas (HRA) is defined as a comprehensive, three-dimensional (3D) atlas of all the cells in the healthy human body. It is compiled by an international team of experts who develop standard terminologies that they link to 3D reference objects, describing anatomical structures. The third HRA release (v1.2) covers spatial reference data and ontology annotations for 26 organs. Experts access the HRA annotations via spreadsheets and view reference object models in 3D editing tools. This paper introduces the Common Coordinate Framework (CCF) Ontology v2.0.1 that interlinks specimen, biological structure, and spatial data, together with the CCF API that makes the HRA programmatically accessible and interoperable with Linked Open Data (LOD). We detail how real-world user needs and experimental data guide CCF Ontology design and implementation, present CCF Ontology classes and properties together with exemplary usage, and report on validation methods. The CCF Ontology graph database and API are used in the HuBMAP portal, HRA Organ Gallery, and other applications that support data queries across multiple, heterogeneous sources.
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Affiliation(s)
- Bruce W Herr
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA
| | - Josef Hardi
- Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, 94305, USA
| | - Ellen M Quardokus
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA
| | - Andreas Bueckle
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA.
| | - Lu Chen
- Department of Computer Science, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Fusheng Wang
- Department of Computer Science, Stony Brook University, Stony Brook, NY, 11794, USA
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Anita R Caron
- European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
| | | | - Mark A Musen
- Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, 94305, USA
| | - Katy Börner
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA.
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Chaudhri VK, Baru C, Chittar N, Dong XL, Genesereth M, Hendler J, Kalyanpur A, Lenat DB, Sequeda J, Vrandečić D, Wang K. Knowledge graphs: Introduction, history, and perspectives. AI MAG 2022. [DOI: 10.1002/aaai.12033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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