Reporting and connecting cell type names and gating definitions through ontologies

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.

Pipeline for retrieval of COVID-19 immune signatures

“…Both the author response rate and the quality of authors responses are important for the utility of our pipeline approach. To reduce author burden and enable direct linkage of the signature to text in the manuscript, we requested authors report information as it appeared in the paper rather than attempting to translate terms to a standardized form (e.g., leveraging the Cell Ontology for cell types that are reported as response components) [32]. Thus, signatures included references to “NK cell”, “CD3 T cells” and “gammadelta T cells,” which could then be mapped to terms from the Cell Ontology: “natural killer cell” (CL:0000623), “T cell” (CL:0000084), and “gamma-delta T cell” (CL:0000798), respectively.…”

A curated collection of human vaccination response signatures

“…IFNG+ T cells). This same naming convention is used to describe the tissue in which the signature was observed 16 . To annotate the immune challenges driving each signature, we utilized the Immune Exposure model 17 , which provides a standardized description of a broad range of potential and actual exposures to different immunological agents (e.g., vaccination, laboratory confirmed infection, living in an endemic area, etc.).…”

“…Cell types -Cell types as response components were first curated from the publications as published using a combination of cell type terms and additional descriptive terms, such as protein marker expression. This information was then mapped to a combination of Cell Ontology and Protein Ontology terms, according to a published model 16 . Note that cell types can appear in two different contexts, either as response components themselves, or as the cell type isolated for gene expression experiments.…”

Cells in ExperimentaL Life Sciences (CELLS-2018): capturing the knowledge of normal and diseased cells with ontologies

“…CD14−/CD3+). In a CELLS-2018 presentation, Vita et al applied ontologies to connect cell population descriptions and gating definitions [16]. In their study, ontologies were used to cross-compare cell types and marker patterns in the ImmPort Immunology Database and Analysis Portal [17].…”