1. Hematopoietic Cell Types in the Cell Ontology Alexander D. Diehl 6/12/12

2. The Cell Ontology An ontology of cell types built by biologists for the needs of data annotation and analysis. The Cell Ontology covers in vivo cell types from all of biology. It also provides high level terms to describe in vitro and experimental cell types. The Cell Ontology is not – A list of specific cell lines, immortal or otherwise, although the Cell Ontology may be used to describe such cells if they correspond to an in vivo cell type. – A list of in vitro methods for preparing cell cultures, although the Cell Ontology may be used to describe the resulting cells if they correspond to an in vivo cell type.

3. OBO Reference Ontologies RELATION TO TIME GRANULARITY CONTINUANTOCCURRENT INDEPENDENTDEPENDENT ORGAN AND ORGANISM Organism (NCBI Taxonomy?) Anatomical Entity (FMA, CARO) Organ Function (FMP, CPRO) Phenotypic Quality (PaTO) Organism-Level Process (GO) CELL AND CELLULAR COMPONENT Cell (CL) Cellular Component (FMA, GO) Cellular Function (GO) Cellular Process (GO) MOLECULE Molecule (ChEBI, SO, RnaO, PrO) Molecular Function (GO) Molecular Process (GO) Smith et al., Nature Biotechnology 25, 1251 - 1255 (2007)

5. Overview of the Cell Ontology Initially developed in 2003 by Jonathan Bard, David States, Michael Ashburner, and Seung Rhee. First described in 2005, Genome Biology, 6:R21. Available as the file cl.obo at http://obofoundry.org.http://obofoundry.org The Cell Ontology is sometimes called the Cell Type Ontology, and often referred to as the “CL”, based on its identifier space. For instance the term “B cell” has the ID “CL:0000236” The CL currently has 1922 cell type terms.

6. Overview of the Cell Ontology As initially developed, the ontology was built solely with is_a and develops_from relationships, with extensive multiple inheritance. The exception is the hematopoietic cells which are now arranged as a single-inheritance hierarchy with limited multiple inheritance. The representation of hematopoietic cells has been enhanced with extensive logical/computable definitions and this approach is being extended to neurons and other cell types.

7. ARRA Grant to Support the Cell Ontology We received an ARRA “stimulus” competitive revision to the main Gene Ontology Consortium grant awarded September 30, 2009, with a 23 month term until August 31, 2011 for the purpose of revising and extending the Cell Ontology. Project Leaders – Alexander Diehl, The Jackson Laboratory/University at Buffalo – Christopher Mungall, LBNL

8. Grant Outcomes Total terms nearly doubled 1022  1922. Textual definitions exist for 81% of terms vs. 58% prior to the grant. Logical definitions exist for 49% of terms vs. 0% prior to the grant. Imported many terms from FMA and KUPO, with added logical definitions. Supported two workshops: – May 2010, Jax, General issues around CL development. – April 2011, Atlanta, neurons.

9. Immune/Hematopoietic Cell Types Currently there are about 450 terms for hematopoietic or immune cell types in the CL, all with logical definitions. These terms represent the most highly curated terms in the CL.

10. 10 Three Rounds of Revision of Hematopoietic Cells Round 1, 2006 – As an adjunct to major improvements to the representation of immunological processes in the Gene Ontology. – 80 new cell types introduced. – is_a parentage provided for all hematopoietically derived cells. – develops_from parentage provided for all major hematopoietic lineages. – The B and T cell lineages were completely revised and greatly expanded. – Nomenclature reflected literature better.

11. 11 Three Rounds of Revision of Hematopoietic Cells Round 2, 2008 - 2009 – NIAID Cell Ontology Workshop (May 2008). Gathering of NIAID experts on immune system cells with an interest in ontology. Two day meeting focusing on: – Ontology best practices – T cells – B cells – Dendritic cells – Macrophages Resulted in another 150 new terms.

12. 12 Three Rounds of Revision of Hematopoietic Cells Round 3, 2009 – 2011 – ARRA funded improvements to Cell Ontology. Built upon outcome of the NIAID Cell Ontology Workshop and work by Masci et al. on dendritic cells. Used the hematopoietic cells as a test case for redevelopment of the entire Cell Ontology with logical definitions, also known as cross-products or computable definitions. Over 450 hematopoietic cell terms have been given logical definitions.

13. 13 Many Immune Cell Types Wide range of cell types: – T-helper 17 cell – Tr1 cell – plasmablast – inflammatory macrophage Precisely specified cell types : – CD4-negative, CD8-negative type I NK T cell secreting interleukin-4 – immature CD8_alpha-positive CD11b-negative dendritic cell – IgA memory B cell

14. 14 Removal of Asserted Multiple Inheritance

15. 15 Ontology now built with logical definitions name: CD4-positive, CD25-positive, alpha-beta regulatory T cell def: "A CD4-positive, CD25-positive, alpha-beta T cell that regulates overall immune responses as well as the responses of other T cell subsets through direct cell-cell contact and cytokine release.” name: induced T-regulatory cell def: "CD4-positive alpha-beta T cell with the phenotype CD25-positive, CTLA-4-positive, and FoxP3-positive with regulatory function."

16. 16 Ontology now built with logical definitions

17. 17 Ontology now built with logical definitions

18. 18 CD56-bright cytokine secreting natural killer cell

19. 19 Ontologies Utilized for Logical Definitions

20. 20 Benefits of Logical Definitions Logical Definitions allow for the definition of terms in an ontology based on relationships to previously curated terms in other ontologies. This allows for modularity in ontology building by reusing terms already curated by other ontology developers. This ensures univocality of language within OBO Foundry ontologies and provides for interoperabilty across the core OBO ontologies and application ontologies based on them. Logical definitions allow for ontological reasoners to infer additional parentage for terms in an ontology and allow terms to be viewed according to particular differentia.

21. 21 Benefits: Connections to Other Ontologies OntologyRelationship Yellow = PATOD = develops_from Green = GO-bpC = capable_of Blue = CLQ = has_quality P = part_of

22. 22 Benefits: Ontological Reasoning Pre-reasoning Post-reasoning

23. 23 Benefits: Reasoner flags errors Red = GO-cellular component Blue = Hemo_CL = has_plasma_membrane_part Declared disjoint_from each other

24. 24 Benefits: Reasoner flags errors Corrected logical definition

25. 25 Benefits: Unexpected Associations

26. Challenges in Ontology Building We have tried to model the way immune system cell types are described by immunologists. Sometimes the reasoner suggests cell type relationships that immunologists don’t recognize. For instance, at various points, the reasoner suggested that all the following are types of “helper T cell”: – activated type II NKT T cell – activated CD4-negative, CD8-negative type I NKT T cell – CD8-positive, alpha-beta cytokine secreting effector T cell – CD4-positive helper T cell

27. Challenges in Ontology Building We have tried to model the way immune system cell types are described by immunologists. Sometimes the reasoner suggests cell type relationships that immunologists don’t recognize. For instance, at various points, the reasoner suggested that all the following are types of “helper T cell”: – activated type II NKT T cell – activated CD4-negative, CD8-negative type I NKT T cell – CD8-positive, alpha-beta cytokine secreting effector T cell – CD4-positive helper T cell

28. Challenges in Ontology Building Hematopoietic cell types in different species, such as mouse and human, sometimes are called the same name but are defined by different sets of surface markers. This is particular problem with dendritic cells and developmental cell types.

29. Challenges in Ontology Building

30. Hematopoietic Cell Types Summary Representation matches literature description quite closely. Use of logical definitions allows for integration with other ontologies and use of ontology reasoner to infer polyhierarchy. Model for the rest of the Cell Ontology.

31. Use Cases for the Cell Ontology Data Annotation (ongoing for GO, future use for IEDB, IDO, and VO). Cross product term formation with GO, MP, and other ontologies (ongoing). Representation of flow cytometry results. Immune system modeling. Nervous system modeling.

32. [Term] id: CL:0002106 name: IgD-positive CD38-positive IgG memory B cell namespace: cell def: "An IgD-positive CD38-positive IgG memory B cell is a CD38-positive IgG-positive class switched memory B cell that has class switched and expresses IgD on the cell surface with the phenotype IgD- positive, CD38-positive, and IgG-positive." [GOC:dsd, GOC:rhs, GOC:tfm, PMID:20123131] is_a: CL:0002105 ! CD38-positive IgG memory B cell intersection_of: CL:0002105 ! CD38-positive IgG memory B cell intersection_of: has_plasma_membrane_part GO:0071738 ! IgD immunoglobulin complex relationship: has_plasma_membrane_part GO:0071738 ! IgD immunoglobulin complex [Term] id: CL:0002107 name: IgD-negative CD38-positive IgG memory B cell namespace: cell def: "An IgD-negative CD38-positive IgG memory B cell is a CD38-positive IgG-positive that has class switched and lacks expression of IgD on the cell surface with the phenotype IgD-negative, CD38- positive, and IgG-positive." [GOC:dsd, GOC:rhs, GOC:tfm, PMID:20123131] is_a: CL:0002105 ! CD38-positive IgG memory B cell intersection_of: CL:0002105 ! CD38-positive IgG memory B cell intersection_of: lacks_plasma_membrane_part GO:0071738 ! IgD immunoglobulin complex relationship: lacks_plasma_membrane_part GO:0071738 ! IgD immunoglobulin complex

33. The Cell Type Knowledgebase Proposed RDF triple-store to capture knowledge about cell types: – Marker/Protein expression – Gene expression – Images – References Would include both manually and computation curated information.

34. The Cell Type Knowledgebase Assessable via web interface and web services. Outside tools could query freely.

35. 35 Acknowledgements MGI Judith Blake Terry Meehan David Hill Morgan Hightshoe Wade Lyman LBNL Christopher Mungall Suzanna Lewis Duke University Anna Maria Masci UT Southwestern Richard Scheuermann Jamie Lee Lindsay Cowell University of Cambridge David Osumi-Sutherland Science Commons/Buffalo/INCF Alan Ruttenberg Buffalo Barry Smith

36. PRO Driving Biomedical Project Utilize clinical flow cytometry data as a driver of ontology development in the PRO and the CL by assessing current standard clinical assays and recent approaches based on automated gating of multidimensional flow cytometry. 1Ensure that relevant protein isoforms and post-translationally modified forms identified by flow cytometry typing reagents are represented in the PRO 2Ensure that cell types defined by expression of particular sets of PRO terms are represented in the CL. 3Ensure that clinical flow cytometry results can be properly mapped to defined PRO terms and defined cell types in the CL. 4Promote standardization in interpretation and integration of clinical flow cytometry data.

37. Broader Goals Assessing representation requirements for Flow Cytometry in PRO, CL, IEDB, OBI, ImmPort, and Immune System Modeling. Development of a data store to collect extended cell type-protein relationships. Defining a tool wish-list for CL-linked flow cytometry analysis and CL-assisted marker selection for cell type analysis.

40. Broader Goals Assessing representation requirements for Flow Cytometry in PRO, CL, IEDB, OBI, ImmPort, and Immune System Modeling. Development of a data store to collect extended cell type-protein relationships. Defining a tool wish-list for CL-linked flow cytometry analysis and CL-assisted marker selection for cell type analysis.