CLO: The cell line ontology - PubMed (original) (raw)
doi: 10.1186/2041-1480-5-37. eCollection 2014.
Yu Lin 2, Zuoshuang Xiang 2, Terrence F Meehan 3, Alexander D Diehl 4, Uma D Vempati 5, Stephan C Schürer 5, Chao Pang 6, James Malone 3, Helen Parkinson 3, Yue Liu 2, Terue Takatsuki 7, Kaoru Saijo 7, Hiroshi Masuya 7, Yukio Nakamura 7, Matthew H Brush 8, Melissa A Haendel 8, Jie Zheng 9, Christian J Stoeckert 9, Bjoern Peters 10, Christopher J Mungall 11, Thomas E Carey 2, David J States 12, Brian D Athey 2, Yongqun He 2
Affiliations
- PMID: 25852852
- PMCID: PMC4387853
- DOI: 10.1186/2041-1480-5-37
CLO: The cell line ontology
Sirarat Sarntivijai et al. J Biomed Semantics. 2014.
Abstract
Background: Cell lines have been widely used in biomedical research. The community-based Cell Line Ontology (CLO) is a member of the OBO Foundry library that covers the domain of cell lines. Since its publication two years ago, significant updates have been made, including new groups joining the CLO consortium, new cell line cells, upper level alignment with the Cell Ontology (CL) and the Ontology for Biomedical Investigation, and logical extensions.
Construction and content: Collaboration among the CLO, CL, and OBI has established consensus definitions of cell line-specific terms such as 'cell line', 'cell line cell', 'cell line culturing', and 'mortal' vs. 'immortal cell line cell'. A cell line is a genetically stable cultured cell population that contains individual cell line cells. The hierarchical structure of the CLO is built based on the hierarchy of the in vivo cell types defined in CL and tissue types (from which cell line cells are derived) defined in the UBERON cross-species anatomy ontology. The new hierarchical structure makes it easier to browse, query, and perform automated classification. We have recently added classes representing more than 2,000 cell line cells from the RIKEN BRC Cell Bank to CLO. Overall, the CLO now contains ~38,000 classes of specific cell line cells derived from over 200 in vivo cell types from various organisms.
Utility and discussion: The CLO has been applied to different biomedical research studies. Example case studies include annotation and analysis of EBI ArrayExpress data, bioassays, and host-vaccine/pathogen interaction. CLO's utility goes beyond a catalogue of cell line types. The alignment of the CLO with related ontologies combined with the use of ontological reasoners will support sophisticated inferencing to advance translational informatics development.
Keywords: Anatomy; Cell line; Cell line cell; Cell line cell culturing; Immortal cell line cell; Mortal cell line cell.
Figures
Figure 1
The top level CLO hierarchical structure and key ontology terms. Terms imported from other ontologies are indicated by the ontology abbreviations inside parentheses. Terms without an identified source are CLO terms. All the arrows indicate the ‘is_a’ relation except the explicitly labelled ‘has grain’ relation.
Figure 2
CLO cell line design pattern and an example. (A) Generic CLO design pattern. Terms imported from external ontologies are displayed in blue boxes, while CLO-specific terms are displayed in yellow boxes. (B) An example of HeLa cell representation by applying CLO design pattern with extended information obtained from the BAO development (shown in dashed orange boxes) with details of culturing method, cell line modification, and STR profiling. Boxes represent instances, some labelled by the class they are an instance of. The relations used to link the boxes are instance-level relations. In several cases the parent class is also noted in the box using an is_a relation.
Figure 3
Demonstration of restructured CLO cell line cell hierarchy and the application of reasoning. (A) asserted HeLa cell hierarchy. Five intermediate terms between ‘immortal cell line cell’ and ‘HeLa cell’ have been generated in CLO. (B) The SubClass axioms of ‘HeLa cell’. (C) Part of an inferred CLO hierarchy. Compared to the asserted hierarchy in Figure 3A, the term ‘immortal human uterine cervix-derived epithelial cell line cell’ has been inferred as a subclass of ‘immortal human epithelial cell line cell’. (D) The equivalent class definition for ‘immortal human epithelial cell line cell’. (E) The equivalent class definition for ‘immortal human uterine cervix-derived epithelial cell line cell’. The OntoFox tool [9] was used to generate a subset of CLO that includes HeLa cell-related terms. The screenshots were generated using the Protégé OWL editor. The ELK version 0.3.2 ontology reasoner was used for reasoning.
Figure 4
CLO application in modelling the cell line cell-vaccine/pathogen interactions. As reported in a reference [16], the live attenuated B. abortus cattle vaccine strain RB51 positively regulated programmed cell death of RAW264.7 cell, a macrophage cell line cell. However, its wild type virulent strain 2308 negatively regulated the programmed cell death. The infected cells underwent different cellular processes after the interaction with the vaccine or pathogen. All the boxes represent instances, labelled by the class they are an instance of.
Similar articles
- The Cell Ontology 2016: enhanced content, modularization, and ontology interoperability.
Diehl AD, Meehan TF, Bradford YM, Brush MH, Dahdul WM, Dougall DS, He Y, Osumi-Sutherland D, Ruttenberg A, Sarntivijai S, Van Slyke CE, Vasilevsky NA, Haendel MA, Blake JA, Mungall CJ. Diehl AD, et al. J Biomed Semantics. 2016 Jul 4;7(1):44. doi: 10.1186/s13326-016-0088-7. J Biomed Semantics. 2016. PMID: 27377652 Free PMC article. - Comparison, alignment, and synchronization of cell line information between CLO and EFO.
Ong E, Sarntivijai S, Jupp S, Parkinson H, He Y. Ong E, et al. BMC Bioinformatics. 2017 Dec 21;18(Suppl 17):557. doi: 10.1186/s12859-017-1979-z. BMC Bioinformatics. 2017. PMID: 29322915 Free PMC article. - OSCI: standardized stem cell ontology representation and use cases for stem cell investigation.
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. He Y, et al. BMC Bioinformatics. 2019 Apr 25;20(Suppl 5):180. doi: 10.1186/s12859-019-2723-7. BMC Bioinformatics. 2019. PMID: 31272389 Free PMC article. - Ontology-based Precision Vaccinology for Deep Mechanism Understanding and Precision Vaccine Development.
Xie J, Zi W, Li Z, He Y. Xie J, et al. Curr Pharm Des. 2021;27(7):900-910. doi: 10.2174/1381612826666201125112131. Curr Pharm Des. 2021. PMID: 33238868 Review. - Ontology-Based Vaccine Adverse Event Representation and Analysis.
Xie J, He Y. Xie J, et al. Adv Exp Med Biol. 2017;1028:89-103. doi: 10.1007/978-981-10-6041-0_6. Adv Exp Med Biol. 2017. PMID: 29058218 Review.
Cited by
- Feasibility and barriers to rapid establishment of patient-derived primary osteosarcoma cell lines in clinical management.
Chow T, Humble W, Lucarelli E, Onofrillo C, Choong PF, Di Bella C, Duchi S. Chow T, et al. iScience. 2024 Jun 19;27(9):110251. doi: 10.1016/j.isci.2024.110251. eCollection 2024 Sep 20. iScience. 2024. PMID: 39286504 Free PMC article. Review. - An ontology-based knowledge graph for representing interactions involving RNA molecules.
Cavalleri E, Cabri A, Soto-Gomez M, Bonfitto S, Perlasca P, Gliozzo J, Callahan TJ, Reese J, Robinson PN, Casiraghi E, Valentini G, Mesiti M. Cavalleri E, et al. Sci Data. 2024 Aug 22;11(1):906. doi: 10.1038/s41597-024-03673-7. Sci Data. 2024. PMID: 39174566 Free PMC article. - Functional implications of glycans and their curation: insights from the workshop held at the 16th Annual International Biocuration Conference in Padua, Italy.
Martinez K, Agirre J, Akune Y, Aoki-Kinoshita KF, Arighi C, Axelsen KB, Bolton E, Bordeleau E, Edwards NJ, Fadda E, Feizi T, Hayes C, Ives CM, Joshi HJ, Krishna Prasad K, Kossida S, Lisacek F, Liu Y, Lütteke T, Ma J, Malik A, Martin M, Mehta AY, Neelamegham S, Panneerselvam K, Ranzinger R, Ricard-Blum S, Sanou G, Shanker V, Thomas PD, Tiemeyer M, Urban J, Vita R, Vora J, Yamamoto Y, Mazumder R. Martinez K, et al. Database (Oxford). 2024 Aug 13;2024:baae073. doi: 10.1093/database/baae073. Database (Oxford). 2024. PMID: 39137905 Free PMC article. - Integration of chromosome locations and functional aspects of enhancers and topologically associating domains in knowledge graphs enables versatile queries about gene regulation.
Mulero-Hernández J, Mironov V, Miñarro-Giménez JA, Kuiper M, Fernández-Breis JT. Mulero-Hernández J, et al. Nucleic Acids Res. 2024 Aug 27;52(15):e69. doi: 10.1093/nar/gkae566. Nucleic Acids Res. 2024. PMID: 38967009 Free PMC article. - The Immunopeptidomics Ontology (ImPO).
Faria D, Eugénio P, Contreiras Silva M, Balbi L, Bedran G, Kallor AA, Nunes S, Palkowski A, Waleron M, Alfaro JA, Pesquita C. Faria D, et al. Database (Oxford). 2024 Jun 7;2024:baae014. doi: 10.1093/database/baae014. Database (Oxford). 2024. PMID: 38857186 Free PMC article.
References
- Drexler HG, Quentmeier H, Dirks WG, Uphoff CC, MacLeod RA. DNA profiling and cytogenetic analysis of cell line WSU-CLL reveal cross-contamination with cell line REH (pre B-ALL) Leukemia. 2002;16(9):1868–1870. - PubMed
- Drexler HG, Uphoff CC, Dirks WG, MacLeod RA. Mix-ups and mycoplasma: the enemies within. Leuk Res. 2002;26(4):329–333. - PubMed
- Sarntivijai S, Xiang Z, Meehan TF, Diehl AD, Vempati U, Schurer S, Pang C, Malone J, Parkinson H, Athey BD, Yongqun H. International Conference on Biomedical Ontologies (ICBO), July 26–30, 2011. NY, USA: University at Buffalo; 2011. Cell Line Ontology: Redesigning Cell Line Knowledgebase to Aid Integrative Translational Informatics; pp. 25–32.
LinkOut - more resources
Full Text Sources
Other Literature Sources
Research Materials