1. Gene Expression and Cell Identity Alexander Diehl ImmPort Science Talk 3/20/14

2. Understanding the Nature of Entities in Reality

3. Depends on What Parts We See

4. Reality is Often More Complex Than at First Glance

5. And Perspective is Important

6. What are Cells? Cell are physical entities that exist in reality. We understand aspects of cells based on results of experimental assays. Our knowledge of cell types is necessarily incomplete even as we attempt to understand their nature.

7. How We Represent Cells in the Cell Ontology Morphology Surface marker expression, singly or in combination Transcription factor expression or expression of other internal protein By lineage By function or capability

8. Types of Evidence Behind the Representation of Cells in CL Microscopy, with or without staining (histology). Immunofluorescence in situ or in vitro Flow cytometry or CyTOF Colony formation assays In vivo/in vitro lineage tracking Directs assays of cellular function, typically in vitro Indirect assays of cellular function in vivo And rarely, assays of gene expression.

9. 9 Experimental Data from Multiple Sources Is Synthesized into a Single Definition in CL

10. Challenges in Ontology Building We want to represent both general cell types and specific cell types. Many cell types are considered equivalent across species in their general characteristics such as surface marker expression or functions. 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.

11. Challenges in Ontology Building We need to provide unique representations via logical definitions for each cell type. We need to recognize that in some cases, different combinations of markers may identify the same cell type.

12. The HIPC Lyoplate Project

13. Standardization of human PBMC immunophenotyping to enhance reproducibility across different facilities. Use of eight color flow cytometry with standardized antibody panels Use of standardized sample preparation Use of standardized instrument settings Use of standardized data analysis

14. The HIPC Lyoplate Project

16. HIPC-Defined Cell Types in CL

22. “effector CD8+ T cell”

25. A request to CL…

29. TEMRA = a memory T cell without CD45RO expression.

30. Are These the Same Cell Type?

33. But… “effector CD4+ cell”?

34. The Gene Expression Part Questions: Can we use the structure of the Cell Ontology as a framework for comparing gene expression data tied to specific cell types? Can we use the CL framework to identify genes that distinguish one cell type from closely related cell types?

36. The Immunological Genome Project

37. Linking IGP data to CL cell types. IGP provides gene expression data based on sorted mouse immune cell types developed according to standardized methods. We mapped IGP cell types to cell type terms in the Cell Ontology. The CL structure was used to guide comparisons between gene expression profiles of different cell types.

38. Linking IGP data to CL cell types. 88 cell types were compared in a pairwise fashion. Separate gene sets were created for genes whose expression differed by greater than or less than 1.5 fold, respectively, for each pairwise comparison. 7656 gene sets resulted. An ontological framework was created to map these gene sets to Cell Ontology classes.

39. Workflow of CL-IGP Project

40. Searching for Pairwise Comparisons Mapped to CL

42. Summary of Upregulated and Downregulated Genes by Cell Type

43. Novel Genes Identified for Specific Cell Types and Confirmed by IGP Scd1 is an enzyme involved in biosynthesis of monounsaturated fatty acids whose expression is restricted to mature B cells types in comparison to other immune cell types. I830077J02Rik, a single-pass transmembrane protein, otherwise uncharacterized, is widely expressed among myeloid cells. In lymphocytes, expression of this protein is restricted to marginal zone B cells.

44. Novel Genes Identified for Specific Cell Types and Confirmed by IGP Scd1 is an enzyme involved in biosynthesis of monounsaturated fatty acids whose expression is restricted to mature B cells types in comparison to other immune cell types. I830077J02Rik, a single-pass transmembrane protein, otherwise uncharacterized, is widely expressed among myeloid cells. In lymphocytes, expression of this protein is restricted to marginal zone B cells.

45. Candidate Genes Involved in the Unique Functions of Germinal Center B cells

46. Conclusions Gene expression comparisons placed in an ontology framework can provide details about genes uniquely expression in particular immune cell subtypes. Results of our approach has been validated against non-ontologically based analyses of IGP data, for instance for NK cells, and similar results are seen.

47. Acknowledgements Barry Smith Alan Ruttenberg Ryan Brinkman Raphael Gottardo Richard Scheuermann David Dougall Holden Maeckler Philip McCoy Terry Meehan Nicole Vasilevsky Chris Mungall Melissa Haendel Judy Blake And many other contributors to the CL