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Overton JA, Vita R, Dunn P, Burel JG, Bukhari SAC, Cheung KH, Kleinstein SH, Diehl AD, Peters B. Reporting and connecting cell type names and gating definitions through ontologies. BMC Bioinformatics 2019; 20:182. [PMID: 31272390 PMCID: PMC6509839 DOI: 10.1186/s12859-019-2725-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background Human immunology studies often rely on the isolation and quantification of cell populations from an input sample based on flow cytometry and related techniques. Such techniques classify cells into populations based on the detection of a pattern of markers. The description of the cell populations targeted in such experiments typically have two complementary components: the description of the cell type targeted (e.g. ‘T cells’), and the description of the marker pattern utilized (e.g. CD14−, CD3+). Results We here describe our attempts to use ontologies to cross-compare cell types and marker patterns (also referred to as gating definitions). We used a large set of such gating definitions and corresponding cell types submitted by different investigators into ImmPort, a central database for immunology studies, to examine the ability to parse gating definitions using terms from the Protein Ontology (PRO) and cell type descriptions, using the Cell Ontology (CL). We then used logical axioms from CL to detect discrepancies between the two. Conclusions We suggest adoption of our proposed format for describing gating and cell type definitions to make comparisons easier. We also suggest a number of new terms to describe gating definitions in flow cytometry that are not based on molecular markers captured in PRO, but on forward- and side-scatter of light during data acquisition, which is more appropriate to capture in the Ontology for Biomedical Investigations (OBI). Finally, our approach results in suggestions on what logical axioms and new cell types could be considered for addition to the Cell Ontology.
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Affiliation(s)
| | - Randi Vita
- Division for Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA
| | - Patrick Dunn
- ImmPort Curation Team, NG Health Solutions, Rockville, MD, USA
| | - Julie G Burel
- Division for Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA
| | | | - Kei-Hoi Cheung
- Department of Emergency Medicine and Yale Center for Medical Informatics, Yale School of Medicine, New Haven, CT, USA
| | - Steven H Kleinstein
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut, USA.,Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Alexander D Diehl
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Bjoern Peters
- Division for Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA, USA. .,Department of Medicine, University of California San Diego, La Jolla, CA, USA.
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Abstract
BACKGROUND Within the cancer domain, ontologies play an important role in the integration and annotation of data in order to support numerous biomedical tools and applications. This work seeks to leverage existing standards in immunophenotyping cell types found in hematologic malignancies to provide an ontological representation of them to aid in data annotation and analysis for patient data. RESULTS We have developed the Cancer Cell Ontology according to OBO Foundry principles as an extension of the Cell Ontology. We define classes in Cancer Cell Ontology by using a genus-differentia approach using logical axioms capturing the expression of cellular surface markers in order to represent types of hematologic malignancies. By adopting conventions used in the Cell Ontology, we have created human and computer-readable definitions for 300 classes of blood cancers, based on the EGIL classification system for leukemias, and relying upon additional classification approaches for multiple myelomas and other hematologic malignancies. CONCLUSION We have demonstrated a proof of concept for leveraging the built-in logical axioms of the ontology in order to classify patient surface marker data into appropriate diagnostic categories. We plan to integrate our ontology into existing tools for flow cytometry data analysis to facilitate the automated diagnosis of hematologic malignancies.
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Affiliation(s)
- Lucas M Serra
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
| | - William D Duncan
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, 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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He Y, Duncan WD, Cooper DJ, Hansen J, Iyengar R, Ong E, Walker K, Tibi O, Smith S, Serra LM, Zheng J, Sarntivijai S, Schürer S, O'Shea KS, Diehl AD. OSCI: standardized stem cell ontology representation and use cases for stem cell investigation. BMC Bioinformatics 2019; 20:180. [PMID: 31272389 PMCID: PMC6509805 DOI: 10.1186/s12859-019-2723-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2023] Open
Abstract
Background Stem cells and stem cell lines are widely used in biomedical research. The Cell Ontology (CL) and Cell Line Ontology (CLO) are two community-based OBO Foundry ontologies in the domains of in vivo cells and in vitro cell line cells, respectively. Results To support standardized stem cell investigations, we have developed an Ontology for Stem Cell Investigations (OSCI). OSCI imports stem cell and cell line terms from CL and CLO, and investigation-related terms from existing ontologies. A novel focus of OSCI is its application in representing metadata types associated with various stem cell investigations. We also applied OSCI to systematically categorize experimental variables in an induced pluripotent stem cell line cell study related to bipolar disorder. In addition, we used a semi-automated literature mining approach to identify over 200 stem cell gene markers. The relations between these genes and stem cells are modeled and represented in OSCI. Conclusions OSCI standardizes stem cells found in vivo and in vitro and in various stem cell investigation processes and entities. The presented use cases demonstrate the utility of OSCI in iPSC studies and literature mining related to bipolar disorder.
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Affiliation(s)
- Yongqun He
- University of Michigan Medical School, Ann Arbor, MI, USA.
| | | | | | - Jens Hansen
- Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,SBCNY, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ravi Iyengar
- Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,SBCNY, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edison Ong
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Kendal Walker
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Omar Tibi
- John Hopkins Unversity, Baltimore, MD, USA
| | | | - Lucas M Serra
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Jie Zheng
- University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - K Sue O'Shea
- 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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Abstract
Cell cultures used in biomedical experiments come in the form of both sample biopsy primary cells, and maintainable immortalised cell lineages. The rise of bioinformatics and high-throughput technologies has led us to the requirement of ontology representation of cell types and cell lines. The Cell Ontology (CL) and Cell Line Ontology (CLO) have long been established as reference ontologies in the OBO framework. We have compiled a series of the challenges and the proposals of solutions in this CELLS (Cells in ExperimentaL Life Sciences) thematic series that cover the grounds of standing issues and the directions, which were discussed in the First International Workshop on CELLS at the the International Conference on Biomedical Ontology (ICBO). This workshop focused on the extension of the current CL and CLO to cover a wider set of biological questions and challenges needing semantic infrastructure for information modeling. We discussed data-driven use cases that leverage linkage of CL, CLO and other bio-ontologies. This is an established approach in data-driven ontologies such as the Experimental Factor Ontology (EFO), and the Ontology for Biomedical Investigation (OBI). The First International Workshop on CELLS at the International Conference on Biomedical Ontology has brought together experimental biologists and biomedical ontologists to discuss solutions to organizing and representing the rapidly evolving knowledge gained from experimental cells. The workshop has successfully identified the areas of challenge, and the gap in connecting the two domains of knowledge. The outcome of this workshop yielded practical implementation plans to filled in this gap.This CELLS workshop also provided a venue for panel discussions of innovative solutions as well as challenges in the development and applications of biomedical ontologies to represent and analyze experimental cell data.
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Affiliation(s)
- Sirarat Sarntivijai
- European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD UK
| | - Alexander D. Diehl
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, New York 14203 USA
| | - Yongqun He
- Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, and Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109 USA
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Bakken T, Cowell L, Aevermann BD, Novotny M, Hodge R, Miller JA, Lee A, Chang I, McCorrison J, Pulendran B, Qian Y, Schork NJ, Lasken RS, Lein ES, Scheuermann RH. Cell type discovery and representation in the era of high-content single cell phenotyping. BMC Bioinformatics 2017; 18:559. [PMID: 29322913 PMCID: PMC5763450 DOI: 10.1186/s12859-017-1977-1] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Background A fundamental characteristic of multicellular organisms is the specialization of functional cell types through the process of differentiation. These specialized cell types not only characterize the normal functioning of different organs and tissues, they can also be used as cellular biomarkers of a variety of different disease states and therapeutic/vaccine responses. In order to serve as a reference for cell type representation, the Cell Ontology has been developed to provide a standard nomenclature of defined cell types for comparative analysis and biomarker discovery. Historically, these cell types have been defined based on unique cellular shapes and structures, anatomic locations, and marker protein expression. However, we are now experiencing a revolution in cellular characterization resulting from the application of new high-throughput, high-content cytometry and sequencing technologies. The resulting explosion in the number of distinct cell types being identified is challenging the current paradigm for cell type definition in the Cell Ontology. Results In this paper, we provide examples of state-of-the-art cellular biomarker characterization using high-content cytometry and single cell RNA sequencing, and present strategies for standardized cell type representations based on the data outputs from these cutting-edge technologies, including “context annotations” in the form of standardized experiment metadata about the specimen source analyzed and marker genes that serve as the most useful features in machine learning-based cell type classification models. We also propose a statistical strategy for comparing new experiment data to these standardized cell type representations. Conclusion The advent of high-throughput/high-content single cell technologies is leading to an explosion in the number of distinct cell types being identified. It will be critical for the bioinformatics community to develop and adopt data standard conventions that will be compatible with these new technologies and support the data representation needs of the research community. The proposals enumerated here will serve as a useful starting point to address these challenges.
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Affiliation(s)
- Trygve Bakken
- Allen Institute for Brain Science, Seattle, Washington, 98103, USA
| | - Lindsay Cowell
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, USA
| | - Brian D Aevermann
- J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA
| | - Mark Novotny
- J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA
| | - Rebecca Hodge
- Allen Institute for Brain Science, Seattle, Washington, 98103, USA
| | - Jeremy A Miller
- Allen Institute for Brain Science, Seattle, Washington, 98103, USA
| | - Alexandra Lee
- J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA
| | - Ivan Chang
- J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA
| | - Jamison McCorrison
- J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA
| | - Bali Pulendran
- Department of Pathology and Laboratory Medicine, Emory University, 201 Dowman Dr, Atlanta, GA, 30322, USA
| | - Yu Qian
- J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA
| | - Nicholas J Schork
- J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA
| | - Roger S Lasken
- J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, Washington, 98103, USA
| | - Richard H Scheuermann
- J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA. .,Department of Pathology, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
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