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Bialy N, Alber F, Andrews B, Angelo M, Beliveau B, Bintu L, Boettiger A, Boehm U, Brown CM, Maina MB, Chambers JJ, Cimini BA, Eliceiri K, Errington R, Faklaris O, Gaudreault N, Germain RN, Goscinski W, Grunwald D, Halter M, Hanein D, Hickey JW, Lacoste J, Laude A, Lundberg E, Ma J, Malacrida L, Moore J, Nelson G, Neumann EK, Nitschke R, Onami S, Pimentel JA, Plant AL, Radtke AJ, Sabata B, Schapiro D, Schöneberg J, Spraggins JM, Sudar D, Adrien Maria Vierdag WM, Volkmann N, Wählby C, Wang SS, Yaniv Z, Strambio-De-Castillia C. Harmonizing the Generation and Pre-publication Stewardship of FAIR Image data. ARXIV 2024:arXiv:2401.13022v4. [PMID: 38351940 PMCID: PMC10862930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
Together with the molecular knowledge of genes and proteins, biological images promise to significantly enhance the scientific understanding of complex cellular systems and to advance predictive and personalized therapeutic products for human health. For this potential to be realized, quality-assured image data must be shared among labs at a global scale to be compared, pooled, and reanalyzed, thus unleashing untold potential beyond the original purpose for which the data was generated. There are two broad sets of requirements to enable image data sharing in the life sciences. One set of requirements is articulated in the companion White Paper entitled "Enabling Global Image Data Sharing in the Life Sciences," which is published in parallel and addresses the need to build the cyberinfrastructure for sharing the digital array data (arXiv:2401.13023 [q-bio.OT], https://doi.org/10.48550/arXiv.2401.13023). In this White Paper, we detail a broad set of requirements, which involves collecting, managing, presenting, and propagating contextual information essential to assess the quality, understand the content, interpret the scientific implications, and reuse image data in the context of the experimental details. We start by providing an overview of the main lessons learned to date through international community activities, which have recently made considerable progress toward generating community standard practices for imaging Quality Control (QC) and metadata. We then provide a clear set of recommendations for amplifying this work. The driving goal is to address remaining challenges, and democratize access to common practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.
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Affiliation(s)
- Nikki Bialy
- Morgridge Institute for Research, Madison, USA
| | | | | | | | | | | | | | | | | | | | | | - Beth A Cimini
- Broad Institute of MIT and Harvard, Imaging Platform, Cambridge, USA
| | - Kevin Eliceiri
- Morgridge Institute for Research, Madison, USA
- University of Wisconsin-Madison, Madison, USA
| | | | | | | | - Ronald N Germain
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, USA
| | | | | | - Michael Halter
- National Institute of Standards and Technology, Gaithersburg, USA
| | | | | | | | - Alex Laude
- Newcastle University, Newcastle upon Tyne, UK
| | - Emma Lundberg
- Stanford University, Palo Alto, USA
- SciLifeLab, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Jian Ma
- Carnegie Mellon University, Pittsburgh, USA
| | - Leonel Malacrida
- Institut Pasteur de Montevideo, & Universidad de la República, Montevideo, Uruguay
| | - Josh Moore
- German BioImaging-Gesellschaft für Mikroskopie und Bildanalyse e.V., Constance, Germany
| | - Glyn Nelson
- Newcastle University, Newcastle upon Tyne, UK
| | | | | | - Shuichi Onami
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | | | - Anne L Plant
- National Institute of Standards and Technology, Gaithersburg, USA
| | - Andrea J Radtke
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, USA
| | | | | | | | | | - Damir Sudar
- Quantitative Imaging Systems LLC, Portland, USA
| | | | | | | | | | - Ziv Yaniv
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, USA
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Pan H, Bian X, Yang S, He Y, Yang X, Liu Y. The cell line ontology-based representation, integration and analysis of cell lines used in China. BMC Bioinformatics 2019; 20:179. [PMID: 31272367 PMCID: PMC6509802 DOI: 10.1186/s12859-019-2724-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The Chinese National Infrastructure of Cell Line stores and distributes cell lines for biomedical research in China. This study aims to represent and integrate the information of NICR cell lines into the community-based Cell Line Ontology (CLO). RESULTS We have aligned, represented, and added all identified 2704 cell line cells in NICR to CLO. We also proposed new ontology design patterns to represent the usage of cell line cells as disease models by inducing tumor formation in model organisms, and the relations between cell line cells and their expressed or overexpressed genes or proteins. The resulting CLO-NICR ontology also includes the Chinese representation of the NICR cell line information. CLO-NICR was merged into the general CLO. To serve the cell research community in China, the Chinese version of CLO-NICR was also generated and deposited in the OntoChina ontology repository. The usage of CLO-NICR was demonstrated by DL query and knowledge extraction. CONCLUSIONS In summary, all identified cell lines from NICR are represented by the semantics framework of CLO and incorporated into CLO as a most recent update. We also generated a CLO-NICR and its Chinese view (CLO-NICR-Cv). The development of CLO-NICR and CLO-NIC-Cv allows the integration of the cell lines from NICR into the community-based CLO ontology and provides an integrative platform to support different applications of CLO in China.
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Affiliation(s)
- Hongjie Pan
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, China
| | - Xiaocui Bian
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, China
| | - Sheng Yang
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, China
| | - Yongqun He
- University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - Xiaolin Yang
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, China
| | - Yuqin Liu
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, China
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Sarntivijai S, He Y, Diehl AD. Cells in ExperimentaL Life Sciences (CELLS-2018): capturing the knowledge of normal and diseased cells with ontologies. BMC Bioinformatics 2019; 20:183. [PMID: 31272374 PMCID: PMC6509796 DOI: 10.1186/s12859-019-2721-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Cell cultures and cell lines are widely used in life science experiments. In conjunction with the 2018 International Conference on Biomedical Ontology (ICBO-2018), the 2nd International Workshop on Cells in ExperimentaL Life Science (CELLS-2018) focused on two themes of knowledge representation, for newly-discovered cell types and for cells in disease states. This workshop included five oral presentations and a general discussion session. Two new ontologies, including the Cancer Cell Ontology (CCL) and the Ontology for Stem Cell Investigations (OSCI), were reported in the workshop. In another representation, the Cell Line Ontology (CLO) framework was applied and extended to represent cell line cells used in China and their Chinese representation. Other presentations included a report on the application of ontologies to cross-compare cell types and marker patterns used in flow cytometry studies, and a presentation on new experimental findings about novel cell types based on single cell RNA sequencing assay and their corresponding ontological representation. The general discussion session focused on the ontology design patterns in representing newly-discovered cell types and cells in disease states.
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Affiliation(s)
| | - Yongqun He
- University of Michigan Medical School, Ann Arbor, MI USA
| | - Alexander D. Diehl
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY USA
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