CLO: The cell line ontology
- Sirarat Sarntivijai1, 2Email author,
- Yu Lin2,
- Zuoshuang Xiang2,
- Terrence F Meehan3,
- Alexander D Diehl4,
- Uma D Vempati5,
- Stephan C Schürer5,
- Chao Pang6,
- James Malone3,
- Helen Parkinson3,
- Yue Liu2,
- Terue Takatsuki7,
- Kaoru Saijo7,
- Hiroshi Masuya7,
- Yukio Nakamura7,
- Matthew H Brush8,
- Melissa A Haendel8,
- Jie Zheng9,
- Christian J Stoeckert9,
- Bjoern Peters10,
- Christopher J Mungall11,
- Thomas E Carey2,
- David J States12,
- Brian D Athey2 and
- Yongqun He2Email author
© Sarntivijai et al.; licensee BioMed Central Ltd. 2014
Received: 24 June 2013
Accepted: 24 June 2014
Published: 13 August 2014
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.
KeywordsCell line Cell line cell Immortal cell line cell Mortal cell line cell Cell line cell culturing Anatomy
Cell culturing dates back to as early as 1911 when Alexis Carrel attempted to grow living cells outside an organism. The establishment of the first human cell line, HeLa, in 1951 has since brought the fruitful development of other cell lines from different organisms. Cell lines have been commonly used in many aspects of biomedical research and experimentation. Mass production of cell line culture of animal cells is fundamental to the manufacture of viral vaccines and other products in biotechnology such as enzymes, synthetic hormones, anti-cancer agents, and immunobiologicals (e.g., monoclonal antibodies, interleukins, and lymphokines). However, it has been realized that cell lines are often contaminated by other lines – for example, the robust HeLa line has been shown to have widely contaminated many cell lines [1–3].
In addition to the cross-contamination, other issues exist in the domain of cell line representation. Due in large part to a history of bottom-up naming practices, cell line nomenclature has not been standardized or controlled by any centralized authority. This has made management and tracking of cell line information a difficult task, despite the existence of various public repositories and indexed catalogues available for open access. Moreover, cell line related terms are loosely interchangeable and inconsistently used across communities, such that terms like ‘primary cells’ , ‘primary cell culture’ , and ‘cell line’ have become loaded with conflated and ambiguous meaning. Confusion can also come from variability in how cell lines are categorized. This results in part from the wide range of methods for generating and modifying cell lines confer diverse attributes used in their classification. As we move toward the establishment of a centralized resource for cell lines, the ambiguity of cell line-associated terms needs to be clarified.
Many of the challenges can be addressed by the development of an ontology for cell lines, wherein the various cell line attributes can be normalized and based on agreement between users in the community. The different aspects of describing a cell line can be modularized by their corresponding source organism and anatomical part, modifications, and culturing methods, or related diseases.
The Cell Line Ontology (CLO) is a community-based ontology that covers the biological cell line domain. The CLO was originally presented in the International Conference on Biomedical Ontology (ICBO) in 2011 . The original CLO was developed cooperatively by ontology editors from the University of Michigan Cell Line Knowledge base (CLKB) team, the EBI Functional Genomics Production Team, Cell Ontology (CL)  team, and the Bioassay Ontology (BAO) development team at the University of Miami. Subsequently, the Cell Bank of RIKEN BioResource Center (BRC) in Japan, the Ontology for Biomedical Investigation , and Reagent Ontology  joined the CLO development consortium. The CLO Consortium aims to unify publicly available cell line data from multiple sources to a standardized format based on a consensus design pattern derived from the establishment of CLO. This manuscript focuses on introducing recent updates on the CLO development.
Construction and content
Summary of ontology terms in CLO and major source ontologies used in CLO as of November 21 st , 2013
CLO (Cell Line Ontology) specific
Imported upper-level ontologies
BFO (Basic Formal Ontology)
RO (Relation Ontology)
BSPO (Spatial Ontology)
SIO (SemanticScience Integrated Ontology)
IAO (Information Artifact Ontology)
Imported entities from other external ontologies
OBI (Ontology for Biomedical Investigation)
EFO (Experimental Factor Ontology)
CL (Cell Ontology)
NCBITaxon (NCBI Taxonomy)
PATO (Phenotypic Quality Ontology)
GO (Gene Ontology)
PR (Protein Ontology)
DOID (Human Disease Ontology)
ChEBI (Chemical Entities of Biological Interest)
In summary, CLO consists of over 38,000 terms for various cell line cells. These are mostly cell line information obtained from cell line records deposited at four cell line resources: the ATCC and HyperCLDB cell lines stored in the CLKB from the University of Michigan, Corriell Cell Lines processed by EBI, and the cell lines from the Cell Bank of RIKEN BioResource Center (BRC). Almost 300 cell line entry descriptors of cell types were imported from CL and over 1,300 anatomical entities were imported from UBERON . CLO currently contains information of cell lines derived from more than 350 species (NCBITaxon entities). Biomedical experiment related terms were imported from OBI and EFO . When applicable, components from the following resources: Gene Ontology (GO), Phenotypic Quality Ontology (PATO), Protein Ontology (PRO) , Chemical Entity of Biological Interest (ChEBI), and Human Disease Ontology (DOID) are imported into CLO based on available information in the cell line cell records.
The CLO was developed using the format of W3C standard Web Ontology Language (OWL2) (http://www.w3.org/TR/owl-guide/). The latest CLO is available for public view and download at http://code.google.com/p/clo-ontology/. The latest version of the CLO is also available for visualization and downloading from Ontobee (http://www.ontobee.org/browser/index.php?o=CLO) or NCBO’s BioPortal: (http://purl.bioontology.org/ontology/CLO). The source code of CLO is open and freely available under the Apache License 2.0.
Alignment of core domain concepts between CLO, OBI, and CL
As part of the CLO refactoring process, a working group was established between members of several key open biomedical ontologies where cell line-related entities are represented, including the Cell Ontology (CL) and the Ontology for Biomedical Investigation . The goal of this group was to align modelling related to cultured cells in accordance with OBO Foundry principles of orthogonality and re-use. One key outcome of this work was the integration of inconsistent representations into a single shared model. Classes representing key concepts were implemented in the CL, CLO, and OBI - with the CL as a home for high-level in vitro cell modelling (e.g. cultured cell), the CLO as a home for more specific cell line cell and cell line classes, and the OBI as a home for experimental entities related to these cell lines (e.g. cell line culture and establishing cell line classes). As a result, each term has a single representation that is re-used between ontologies through established import mechanisms. A second key outcome of this alignment work was the crafting of clear consensus definitions for common but ambiguous domain terminology, including a careful characterization of the term 'cell line' itself. Updated definitions for a selection of key CLO classes resulting from this work are detailed below.
A cell line is defined as a genetically stable and homogenous population of cultured cells that shares a common propagation history (i.e. has been successively passaged together in culture). This view clarifies two key confusions surrounding the term ‘cell line’. The first relates to the scale at which the term applies, here referring to discrete experimental populations rather than maximal collections representing an entire lineage (e.g. the collection of all HeLa cells that exist). The second concerns the criteria that establish when a cultured cell population qualifies as a ‘line’. By applying ‘cell line’ to experimental populations with a shared culture history, we define the term consistently with its most prevalent usage in domain discourse, and in a way that is most fit for data annotation needs, as it represents populations that are actually cultured, experimented upon, and shared in the practice of science.
A clonal cell line is defined as a cell line that derives from a single cell that is expanded in culture. Feedback from community experts and stakeholders initiated the representation of this specific type of cell line as a key experimental resource with unique and valuable attributes.
Finally, a cell line culture represents an actual physical culture of cell line cells that is an input to experimental processes, and is comprised cell line cells and the media along with any added components. This term is intended to cover actively propagated cultures as well as those kept frozen.
Through the alignment efforts summarized above, we have increased the utility of the CLO as a community resource for standardizing reference to domain concepts and facilitating the exchange and discovery of cell line related information.
Basic CLO cell line design pattern
The basic design model can be extended in different aspects. In addition to having an organism acting as a ‘bearer of’ a cancer, or a disease, the Coriell cell line repository has often provided the information that associates a cell line to a specific disease using the relation ‘is model for’. With this relation, there is no need to connect a cell line to a disease through the organism. One drawback of directly applying this design pattern is that the cell line layout in the original CLO structure was mostly one-layer with one level of subclassing where majority of the cell line cell classes are all immediate children of the parent class ‘mortal’ or ‘immortal’ cell line cell. Logical reasoning is the primary approach to infer a possible hierarchy. However, such inferential reasoning of this one-level subclassing information approach is not optimal as the scalability of computational resources and power can be problematic in processing a large ontology. Another possibility to ease the situation is to introduce a pre-composed differentia criteria assertion for classification. However, if we consider pre-composing all possible differentia parents in CLO (e.g. by organism, by cell type, by anatomical entity, or by culturing methods), construction of an asserted hierarchy based on all criteria will lead to scalability and maintenance issues and, we would face the situation of exponential growth of the pre-composed differentia parent hierarchy and risks the generation of logical, but biologically nonsensical class creation. A proposal for the solution to overcome this challenge has been addressed in the updated version as outlined below.
Restructuring of cell line cell hierarchy
There are approximately 38,000 immortal cell line cells in the CLO. To list them all as a single layer under immortal cell line cell is problematic. For example, a one-layer structure would miss a classification that may be useful to have as intermediate layer terms (e.g., immortal epithelial cell), which have been frequently used. These intermediate layer terms need to be added to the CLO, and specific cell lines can be asserted under these terms to support direct usage. After intermediate terms are added, specific cell line cell terms can be asserted to under these intermediate terms, supporting effective query and data analysis. The accurately asserted hierarchy can also prevent time consuming reasoning process given the large number of cell line cells in the CLO.
'derives from' some
and ('is part of' some ('uterine cervix'
and ('is part of' some 'Homo sapiens')))) The ‘epithelial cell’ (CL_0000066) defined in the CL includes parent terms ‘animal cell’, ‘eukaryotic cell’, and then ‘native cell’. Based on this hierarchy, the CLO includes the first three layers of superclasses of ‘HeLa cell’ under ‘immortal cell line cell’ (Figure 3A). Based on the UBERON ‘uterine cervix’ linkage, a new term ‘immortal uterine cervix-derived epithelial cell line cell’ is generated. Lastly, the term ‘immortal human uterine cervix-derived epithelial cell line cell’ is generated as a new immediate superclass of ‘HeLa cell’ (Figure 3A). It is noted that the CLO includes a new term called ‘immortal human epithelial cell line cell’ that is not an asserted superclass of the ‘immortal uterine cervix-derived epithelial cell line cell’ (Figure 3A). However, a reasoning process infers such an is_a relation (Figure 3C), based on the related equivalent class definitions (Figure 3D and E).
Adding new cell line cells in Japan RIKEN Cell Bank to CLO
RIKEN cell lines are associated with unique IDs managed in the BioResource Web Catalog (http://www.brc.riken.jp/lab/cell/english/search.shtml). When integrating these cell lines into CLO, they are merged and assigned new CLO IDs. First, an Excel worksheet with a fixed template was generated to represent 1622 cell lines from the Riken Cell Bank. It is noted that these cell lines do not include any stem cells stored in the RIKEN Cell Bank. The data prepared in the worksheet was used as input for the Ontorat tool to generate an OWL file that can be directly imported and merged to CLO. The Ontorat program (http://ontorat.hegroup.org/)  acts by following ontology design patterns. Specifically, the immediate parent terms of individual RIKEN cell line cells were manually identified based on the reconstructed cell line cell hierarchy described above, and inserted into the Excel worksheet. A new CLO 'label' was generated by combining a unique RIKEN cell line number (e.g., RCB0871) with the word 'cell', for example, 'RCB0871 cell'. The RIKEN cell line names are defined as alternative terms. Other information such as cell line originators and registers are also included in the CLO annotations for these new RIKEN cell lines.
Utility and discussion
The European Bioinformatics Institute (EBI) Functional Genomics Group has developed ArrayExpress and an ontology-based linked data system for direct processing and query of microarray data from different studies, including cell line cell studies. Given the usage of cell lines in microarray studies, the development of a comprehensive cell line cell ontology is needed to support for efficient query of functional genomics studies. EBI has been using an internally developed Coriell Cell Line Ontology for ArrayExpress microarray data analysis . Since the contents of the Coriell Cell Line Ontology are now incorporated into CLO and the CLO structure and its alignment with existing ontologies have significantly improved and EBI will utilize the updated CLO for their future array data analysis. A clear CLO use case lies in the usability of the ArrayExpress database. Researchers often query cell lines with cross-reference information to the cell lines’ corresponding tissues and cell types. The reconstructed CLO intermediate term hierarchy provides an effective mechanism to support this type of query.
The Bioassay Ontology (BAO) describes bioassays and results obtained from small molecule perturbations, such as those in the PubChem database . To describe and annotate cell-based PubChem assays and screening results comprehensively, the BAO is being extended through collaborative development of the CLO. By integrating the BAO with the CLO, those cell lines that are typically used in cellular assays are added into the CLO. Based on the demands of BAO bioassay modelling, extended parameters are being added to the CLO, including different sources of cell lines (normal/healthy tissue, pathological tissue, or tumor), cell modification methods (plasmid transfection, viral transduction, cell fusion, etc.), culture conditions (composition of culture medium), morphology (epithelial, lymphoblast, etc.), growth properties (adherent or suspension), short tandem repeat (STR) profiling and other properties that are relevant for cellular screening . As a demonstration of the use of CLO in BAO bioassay modelling, the STR profiling analysis with the HeLa cell line has been modelled in the context of a PubChem assay (AID 1611) (Figure 2B). In the PubChem assay, HeLa cells were modified by stable transfection with a heat shock promoter driven-luciferase reporter gene construct. In this assay, the modified HeLa cells were used to screen for compounds that could induce heat shock transcriptional response as a potential therapeutic for Huntington’s disease and amyotrophic lateral sclerosis (ALS) (http://pubchem.ncbi.nlm.nih.gov/assay/assay.cgi?aid=1611).
As shown in the use cases above, integrating a formal representation of cell lines will benefit researchers in interpreting and analysing host-pathogen interactions and cell-based screening results, and better understanding the mechanisms of genotype-phenotype mappings. Formally described cell lines and related experimental conditions can help researchers better understand cellular and immunological pathways, and support rational design of novel assays, for example, with respect to choosing the best cellular model system, in identifying which modified cell lines are available and in demonstrating which ones work best in existing assays.
Discussion and perspectives
The initial attempt to represent cell line information systematically in the CLKB has been performed by imposing ontological semantics and structure on the CLKB and transferring it to the CLO. The CLO now provides a better organization of cell line related information. This, in turn, aids further ontology application in modern translational bioinformatics, especially in the domain of concept mapping and alignment to represent knowledge in a complex biological system. As shown in this paper, contributions by the community collaborators have customized the CLO to accommodate the various use cases in the biomedical domain. The CLO aligns with existing OBO Foundry ontologies including CL, OBI, and UBERON. The governance of the CLO is achieved by importing upper-ontology classes from other OBO Foundry ontologies, namely, BFO, RO, OBI, and OGMS. Based on the alignment and new CLO design pattern, intermediate cell line cell terms were generated and new cell line cells from Japan RIKEN Cell Bank were included in the CLO. This paper also introduces many use cases utilizing CLO ontological features.
The recent development of the Cell Culture Ontology (CCONT) has resulted in an ontology with overlapping cell line information to CLO . CCONT inherits a parent cell line definition from the Experimental Factor Ontology (EFO)  that describes a cell line as a population of cells cultured in vitro. This shifts the focus of cell line representation from biologically-defined individual cells to the experimental perspective of a cell line population culturing description. CCONT has not yet aligned with CL and OBI and intermediate terms of tissue and organism have not been implemented. CCONT, however, introduces many useful cell line-associated properties such as safety classification and cytogenetics. With the complementary components to describe a cell line related information shared between the CLO and CCONT, it is possible that future collaboration may be initiated for the benefit of the community.
The current primary focus of the CLO does not yet encompass the consideration of stem cell derived cell lines. A generally accepted definition of a stem cell line is a self-renewing population of cells with the ability to differentiate into multiple distinct cell types . In this definition, human stem cells have their origin in a variety of cell types ranging from pluripotent cells derived from embryos (human embryonic stem cell – hESC) to adult stem cells derived from various fully developed tissues such as blood, or bone marrow. The ability to differentiate into distinct cell types is more limited in adult stem cells in comparison to the pluripotency in hESC. In addition to having multiple cell type origins, stem cells can also differentiate into multiple distinct cell types under different conditions. Therefore, defining stem cell-derived cell lines based on their origin tissue/cell where they are created (as mandated by CLO’s design pattern) may not be sufficient to yield the information that experimental researchers need. Compared to stem cells, regular immortal cell line cells have largely unchanged cell line property characteristics in the passaging process. The issues surrounding the correct ontological representation of stem cell line cells are currently under investigation at a community level.
Confusion created by mislabelling/misidentification due to cross contamination and naming ambiguity has led to the need for cell line authentication and management, especially when cell lines are used as disease models in drug-discovery screening projects. A few attempts to resolve the situation have been introduced to the community. The ATCC initiated the ATCC Standards Development Organization (ATCC SDO) in 2007, leading to new consensus standards to authenticate cell lines by short tandem repeat (STR) DNA profiling (http://www.atcc.org/~/media/PDFs/STR_Profiling.ashx). Although STR profiling provides an online service for human cell line authentication, there exists no unified cataloguing data source that lends itself as a structured information system with the ontological capability to manage the information, transfer knowledge, and assist in knowledge discovery. We expect that a future collaboration between the CLO , the ATCC and other partners will provide a more effective way to support such an effort.
The CLO will contribute to the wider dissemination of cell line information for improving access and promoting the common use of cell lines as biological resources. Recently, “Linked Open Data (LOD)”, a set of methodologies for exposing, sharing, and connecting pieces of data, information, and knowledge on the World Wide Web (WWW) using URI and Resource Description Framework (RDF) coding has been proposed as a strategy forming the collective intelligence by the integration of data throughout the entire Internet. The CLO is fully compatible with the LOD strategy and has the potential to promote wider dissemination, easier use in local data analyses, and the expansion of public data sources by enabling distributed (non-centralized) efforts by the user community for cell line data. In RIKEN, the RDF/OWL based integrated database (SciNetS: https://database.riken.jp/)  and open data archive (BioLOD: http://www.w3.org/wiki/HCLSIG/LODD/Data), will utililize CLO for the data format of cell lines. Furthermore, the coordinating efforts in CLO developments with other biomedical ontologies such as those in the OBO Foundry will promote broader integration of cell line information with other domains in life science.
Capabilities embedded within the CLO’s structure facilitate knowledge transfer and discovery that a simple catalogue of cell line cells could not achieve. Automated reasoning and alignment with other related ontologies study will expand the network of knowledge much needed for the future translational informatics development. Not only will the ontology backbone of CLO assist in this development, but the uniform knowledge base of over 38,000 cell line cells in CLO also makes CLO a reference resource for translational informatics. Ambiguity introduced by mislabelling of cell lines from various factors can also be minimized by using the CLO as a reference. A uniform representation of each cell line’s properties along with the curation of verifying information from other knowledge sources will minimize errors in experimental reporting.
The CLO Consortium initiative is the first collaborative attempt among our partner institutes as listed here to facilitate cell line data discovery and knowledge transfer to aid integrative translational biomedical research. As the CLO is an on-going community-driven project, it will continue to grow and evolve to overcome challenges that surface in the translational domain. We encourage all parties to participate by contributing their domain knowledge and expertise in this collaborative movement.
Basic formal ontology
Riken Bioresource Cell Bank
Cell culture ontology
Chemical entities of biological interest
Cell line knowledgebase
Cell line ontology
Human disease ontology
European Molecular Laboratory (EMBL) European Bioinformatics Institute
Experimental factor ontology
International Conference on Biomedical Ontology
Infectious disease ontology
NCBI Organismal Classification
Ontology for Biomedical Investigations
- OBO Foundry:
Open Biology Ontology Foundry
Ontology for General Medical Science
Phenotypic quality ontology
The work described is funded in part by the National Institutes of Health (NIH) grants 1R01AI081062 (YH) from the National Institute of Allergy and Infectious Diseases (NIAID), U54 DA021519 for the National Center for Integrative Biomedical Informatics (BDA), 1R24OD011883 from the NIH Office of the Director (MAH and MHB), NIH grant 1R01GM093132-01 (JZ and CJS), NIH contract HHSN272201200010C (BP), U01HL111561 (SCS) as part of the Library of Integrated Network-based Cellular Signatures (LINCS) program, and RC2HG005668 (SCS) supported by the National Human Genome Research Institute (NHGRI), European Molecular Biology Laboratory (EMBL-EBI) core funds (HP, JM), EC FP7 Funds Grant number: 200754 Gen2Phen (CP), and funding from the National Bioscience Database Center (NBDC) of the Japan Science and Technology Agency (JST) for BRC . The content of this paper is solely the responsibility of the author and does not necessarily represent the official views of the NIAID, NHGRI, the NIH or other funding organizations. The article-processing charge for this article was paid by a discretionary fund from Dr. Robert Dysko, the director of the Unit for Laboratory Animal Medicine (ULAM) in the University of Michigan.
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