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Liu A, Peng B, Pankajam AV, Duong TE, Pryhuber G, Scheuermann RH, Zhang Y. Discovery of optimal cell type classification marker genes from single cell RNA sequencing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.22.590194. [PMID: 38712147 PMCID: PMC11071431 DOI: 10.1101/2024.04.22.590194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
The use of single cell/nucleus RNA sequencing (scRNA-seq) technologies that quantitively describe cell transcriptional phenotypes is revolutionizing our understanding of cell biology, leading to new insights in cell type identification, disease mechanisms, and drug development. The tremendous growth in scRNA-seq data has posed new challenges in efficiently characterizing data-driven cell types and identifying quantifiable marker genes for cell type classification. The use of machine learning and explainable artificial intelligence has emerged as an effective approach to study large-scale scRNA-seq data. NS-Forest is a random forest machine learning-based algorithm that aims to provide a scalable data-driven solution to identify minimum combinations of necessary and sufficient marker genes that capture cell type identity with maximum classification accuracy. Here, we describe the latest version, NS-Forest version 4.0 and its companion Python package (https://github.com/JCVenterInstitute/NSForest), with several enhancements to select marker gene combinations that exhibit highly selective expression patterns among closely related cell types and more efficiently perform marker gene selection for large-scale scRNA-seq data atlases with millions of cells. By modularizing the final decision tree step, NS-Forest v4.0 can be used to compare the performance of user-defined marker genes with the NS-Forest computationally-derived marker genes based on the decision tree classifiers. To quantify how well the identified markers exhibit the desired pattern of being exclusively expressed at high levels within their target cell types, we introduce the On-Target Fraction metric that ranges from 0 to 1, with a metric of 1 assigned to markers that are only expressed within their target cell types and not in cells of any other cell types. NS-Forest v4.0 outperforms previous versions on its ability to identify markers with higher On-Target Fraction values for closely related cell types and outperforms other marker gene selection approaches at classification with significantly higher F-beta scores when applied to datasets from three human organs - brain, kidney, and lung.
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Affiliation(s)
- Angela Liu
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA, United States of America
| | - Beverly Peng
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA, United States of America
| | - Ajith V Pankajam
- Intramural Research Program, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States of America
| | - Thu Elizabeth Duong
- Department of Pediatrics, Division of Respiratory Medicine, University of California, San Diego, La Jolla, CA, United States of America
| | - Gloria Pryhuber
- Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, United States of America
| | - Richard H Scheuermann
- Intramural Research Program, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States of America
| | - Yun Zhang
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA, United States of America
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2
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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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3
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Börner K, Blood PD, Silverstein JC, Ruffalo M, Teichmann SA, Pryhuber G, Misra R, Purkerson J, Fan J, Hickey JW, Molla G, Xu C, Zhang Y, Weber G, Jain Y, Qaurooni D, Kong Y, Bueckle A, Herr BW. Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.27.587041. [PMID: 38826261 PMCID: PMC11142047 DOI: 10.1101/2024.03.27.587041] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
The Human BioMolecular Atlas Program (HuBMAP) aims to construct a reference 3D structural, cellular, and molecular atlas of the healthy adult human body. The HuBMAP Data Portal (https://portal.hubmapconsortium.org) serves experimental datasets and supports data processing, search, filtering, and visualization. The Human Reference Atlas (HRA) Portal (https://humanatlas.io) provides open access to atlas data, code, procedures, and instructional materials. Experts from more than 20 consortia are collaborating to construct the HRA's Common Coordinate Framework (CCF), knowledge graphs, and tools that describe the multiscale structure of the human body (from organs and tissues down to cells, genes, and biomarkers) and to use the HRA to understand changes that occur at each of these levels with aging, disease, and other perturbations. The 6th release of the HRA v2.0 covers 36 organs with 4,499 unique anatomical structures, 1,195 cell types, and 2,089 biomarkers (e.g., genes, proteins, lipids) linked to ontologies. In addition, three workflows were developed to map new experimental data into the HRA's CCF. This paper describes the HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interface (APIs), flexible hybrid cloud infrastructure, and demonstrates first atlas usage applications and previews.
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Affiliation(s)
- Katy Börner
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
- CIFAR MacMillan Multiscale Human program, CIFAR, Toronto, ON, Canada
| | - Philip D. Blood
- Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Jonathan C. Silverstein
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Matthew Ruffalo
- Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Sarah A. Teichmann
- CIFAR MacMillan Multiscale Human program, CIFAR, Toronto, ON, Canada
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | | | - Ravi Misra
- University of Rochester Medical Center, Rochester, NY, USA
| | | | - Jean Fan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD, USA
| | - John W. Hickey
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | | | - Chuan Xu
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Yun Zhang
- J. Craig Venter Institute, La Jolla, CA, USA
| | - Griffin Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Yashvardhan Jain
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | - Danial Qaurooni
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | - Yongxin Kong
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | | | - Andreas Bueckle
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | - Bruce W. Herr
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
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4
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Steyn C, Mishi R, Fillmore S, Verhoog MB, More J, Rohlwink UK, Melvill R, Butler J, Enslin JMN, Jacobs M, Sauka-Spengler T, Greco M, Quiñones S, Dulla CG, Raimondo JV, Figaji A, Hockman D. Cell type-specific gene expression dynamics during human brain maturation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.29.560114. [PMID: 37808657 PMCID: PMC10557738 DOI: 10.1101/2023.09.29.560114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
The human brain undergoes protracted post-natal maturation, guided by dynamic changes in gene expression. Most studies exploring these processes have used bulk tissue analyses, which mask cell type-specific gene expression dynamics. Here, using single nucleus (sn)RNA-seq on temporal lobe tissue, including samples of African ancestry, we build a joint paediatric and adult atlas of 75 cell subtypes, which we verify with spatial transcriptomics. We explore the differences between paediatric and adult cell types, revealing the genes and pathways that change during brain maturation. Our results highlight excitatory neuron subtypes, including the LTK and FREM subtypes, that show elevated expression of genes associated with cognition and synaptic plasticity in paediatric tissue. The new resources we present here improve our understanding of the brain during its development and contribute to global efforts to build an inclusive brain cell map.
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Affiliation(s)
- Christina Steyn
- Division of Cell Biology, Department of Human Biology, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Ruvimbo Mishi
- Division of Cell Biology, Department of Human Biology, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Stephanie Fillmore
- Division of Cell Biology, Department of Human Biology, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Matthijs B Verhoog
- Division of Cell Biology, Department of Human Biology, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Jessica More
- Division of Cell Biology, Department of Human Biology, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Ursula K Rohlwink
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Division of Neurosurgery, Department of Surgery, University of Cape Town, Cape Town, South Africa
| | - Roger Melvill
- Division of Neurosurgery, Department of Surgery, University of Cape Town, Cape Town, South Africa
| | - James Butler
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Division of Neurology, Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Johannes M N Enslin
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Division of Neurosurgery, Department of Surgery, University of Cape Town, Cape Town, South Africa
| | - Muazzam Jacobs
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
- Division of Immunology, Department of Pathology University of Cape Town
- National Health Laboratory Service, South Africa
| | - Tatjana Sauka-Spengler
- Radcliffe Department of Medicine, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- Stowers Institute for Medical Research, Kansas City, MO, USA
| | - Maria Greco
- Single Cell Facility, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Sadi Quiñones
- Department of Neuroscience, Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA, USA
- Graduate School of Biomedical Science, Tufts University School of Medicine, Boston, MA, USA
| | - Chris G Dulla
- Department of Neuroscience, Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA, USA
| | - Joseph V Raimondo
- Division of Cell Biology, Department of Human Biology, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Anthony Figaji
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Division of Neurosurgery, Department of Surgery, University of Cape Town, Cape Town, South Africa
| | - Dorit Hockman
- Division of Cell Biology, Department of Human Biology, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
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5
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Ling E, Nemesh J, Goldman M, Kamitaki N, Reed N, Handsaker RE, Genovese G, Vogelgsang JS, Gerges S, Kashin S, Ghosh S, Esposito JM, Morris K, Meyer D, Lutservitz A, Mullally CD, Wysoker A, Spina L, Neumann A, Hogan M, Ichihara K, Berretta S, McCarroll SA. A concerted neuron-astrocyte program declines in ageing and schizophrenia. Nature 2024; 627:604-611. [PMID: 38448582 PMCID: PMC10954558 DOI: 10.1038/s41586-024-07109-5] [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: 12/04/2022] [Accepted: 01/23/2024] [Indexed: 03/08/2024]
Abstract
Human brains vary across people and over time; such variation is not yet understood in cellular terms. Here we describe a relationship between people's cortical neurons and cortical astrocytes. We used single-nucleus RNA sequencing to analyse the prefrontal cortex of 191 human donors aged 22-97 years, including healthy individuals and people with schizophrenia. Latent-factor analysis of these data revealed that, in people whose cortical neurons more strongly expressed genes encoding synaptic components, cortical astrocytes more strongly expressed distinct genes with synaptic functions and genes for synthesizing cholesterol, an astrocyte-supplied component of synaptic membranes. We call this relationship the synaptic neuron and astrocyte program (SNAP). In schizophrenia and ageing-two conditions that involve declines in cognitive flexibility and plasticity1,2-cells divested from SNAP: astrocytes, glutamatergic (excitatory) neurons and GABAergic (inhibitory) neurons all showed reduced SNAP expression to corresponding degrees. The distinct astrocytic and neuronal components of SNAP both involved genes in which genetic risk factors for schizophrenia were strongly concentrated. SNAP, which varies quantitatively even among healthy people of similar age, may underlie many aspects of normal human interindividual differences and may be an important point of convergence for multiple kinds of pathophysiology.
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Affiliation(s)
- Emi Ling
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Genetics, Harvard Medical School, Boston, MA, USA.
| | - James Nemesh
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Melissa Goldman
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Nolan Kamitaki
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Nora Reed
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Robert E Handsaker
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Giulio Genovese
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Jonathan S Vogelgsang
- McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Sherif Gerges
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Seva Kashin
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Sulagna Ghosh
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | | | | | - Daniel Meyer
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Alyssa Lutservitz
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Christopher D Mullally
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Alec Wysoker
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Liv Spina
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Anna Neumann
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Marina Hogan
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Kiku Ichihara
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Sabina Berretta
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- McLean Hospital, Belmont, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
- Program in Neuroscience, Harvard Medical School, Boston, MA, USA.
| | - Steven A McCarroll
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Genetics, Harvard Medical School, Boston, MA, USA.
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6
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Ling E, Nemesh J, Goldman M, Kamitaki N, Reed N, Handsaker RE, Genovese G, Vogelgsang JS, Gerges S, Kashin S, Ghosh S, Esposito JM, French K, Meyer D, Lutservitz A, Mullally CD, Wysoker A, Spina L, Neumann A, Hogan M, Ichihara K, Berretta S, McCarroll SA. Concerted neuron-astrocyte gene expression declines in aging and schizophrenia. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.07.574148. [PMID: 38260461 PMCID: PMC10802483 DOI: 10.1101/2024.01.07.574148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Human brains vary across people and over time; such variation is not yet understood in cellular terms. Here we describe a striking relationship between people's cortical neurons and cortical astrocytes. We used single-nucleus RNA-seq to analyze the prefrontal cortex of 191 human donors ages 22-97 years, including healthy individuals and persons with schizophrenia. Latent-factor analysis of these data revealed that in persons whose cortical neurons more strongly expressed genes for synaptic components, cortical astrocytes more strongly expressed distinct genes with synaptic functions and genes for synthesizing cholesterol, an astrocyte-supplied component of synaptic membranes. We call this relationship the Synaptic Neuron-and-Astrocyte Program (SNAP). In schizophrenia and aging - two conditions that involve declines in cognitive flexibility and plasticity 1,2 - cells had divested from SNAP: astrocytes, glutamatergic (excitatory) neurons, and GABAergic (inhibitory) neurons all reduced SNAP expression to corresponding degrees. The distinct astrocytic and neuronal components of SNAP both involved genes in which genetic risk factors for schizophrenia were strongly concentrated. SNAP, which varies quantitatively even among healthy persons of similar age, may underlie many aspects of normal human interindividual differences and be an important point of convergence for multiple kinds of pathophysiology.
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Affiliation(s)
- Emi Ling
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - James Nemesh
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Melissa Goldman
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Nolan Kamitaki
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Nora Reed
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Robert E. Handsaker
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Giulio Genovese
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Jonathan S. Vogelgsang
- McLean Hospital, Belmont, MA 02478, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA
| | - Sherif Gerges
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Seva Kashin
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Sulagna Ghosh
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | | | | | - Daniel Meyer
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Alyssa Lutservitz
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Christopher D. Mullally
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Alec Wysoker
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Liv Spina
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Anna Neumann
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Marina Hogan
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Kiku Ichihara
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Sabina Berretta
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McLean Hospital, Belmont, MA 02478, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA
- Program in Neuroscience, Harvard Medical School, Boston, MA 02215, USA
| | - Steven A. McCarroll
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
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7
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Martone ME. The past, present and future of neuroscience data sharing: a perspective on the state of practices and infrastructure for FAIR. Front Neuroinform 2024; 17:1276407. [PMID: 38250019 PMCID: PMC10796549 DOI: 10.3389/fninf.2023.1276407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 10/31/2023] [Indexed: 01/23/2024] Open
Abstract
Neuroscience has made significant strides over the past decade in moving from a largely closed science characterized by anemic data sharing, to a largely open science where the amount of publicly available neuroscience data has increased dramatically. While this increase is driven in significant part by large prospective data sharing studies, we are starting to see increased sharing in the long tail of neuroscience data, driven no doubt by journal requirements and funder mandates. Concomitant with this shift to open is the increasing support of the FAIR data principles by neuroscience practices and infrastructure. FAIR is particularly critical for neuroscience with its multiplicity of data types, scales and model systems and the infrastructure that serves them. As envisioned from the early days of neuroinformatics, neuroscience is currently served by a globally distributed ecosystem of neuroscience-centric data repositories, largely specialized around data types. To make neuroscience data findable, accessible, interoperable, and reusable requires the coordination across different stakeholders, including the researchers who produce the data, data repositories who make it available, the aggregators and indexers who field search engines across the data, and community organizations who help to coordinate efforts and develop the community standards critical to FAIR. The International Neuroinformatics Coordinating Facility has led efforts to move neuroscience toward FAIR, fielding several resources to help researchers and repositories achieve FAIR. In this perspective, I provide an overview of the components and practices required to achieve FAIR in neuroscience and provide thoughts on the past, present and future of FAIR infrastructure for neuroscience, from the laboratory to the search engine.
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Affiliation(s)
- Maryann E. Martone
- Department of Neurosciences, University of California, San Diego, CA, United States
- San Francisco Veterans Administration Hospital, San Francisco, CA, United States
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