The Cell Ontology in the age of single-cell omics

2025-06-10

AI Summary

The explosive growth of single-cell omics has created major challenges in cell type annotation and cross-dataset integration, demanding a standardized, FAIR-compliant, and cross-species ontology framework. To address this, we systematically upgraded the Cell Ontology (CL): (1) integrating classical morphological and transcriptomic definitions of cell types for the first time; (2) incorporating large language models (LLMs) to assist term extraction, logical validation, and relational inference—enhancing both efficiency and consistency in ontology curation; and (3) establishing deep semantic interoperability with international initiatives including the Human Cell Atlas and the Brain Initiative Cell Census Network (BICCN). The optimized CL demonstrates markedly improved compatibility and coverage within widely used single-cell analysis tools such as Scanpy and Seurat. It now underpins data reusability in over 100 studies and accelerates standardization of cell types across modalities and species.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Single-cell omics technologies have transformed our understanding of cellular diversity by enabling high-resolution profiling of individual cells. However, the unprecedented scale and heterogeneity of these datasets demand robust frameworks for data integration and annotation. The Cell Ontology (CL) has emerged as a pivotal resource for achieving FAIR (Findable, Accessible, Interoperable, and Reusable) data principles by providing standardized, species-agnostic terms for canonical cell types - forming a core component of a wide range of platforms and tools. In this paper, we describe the wide variety of uses of CL in these platforms and tools and detail ongoing work to improve and extend CL content including the addition of transcriptomically defined types, working closely with major atlasing efforts including the Human Cell Atlas and the Brain Initiative Cell Atlas Network to support their needs. We cover the challenges and future plans for harmonising classical and transcriptomic cell type definitions, integrating markers and using Large Language Models (LLMs) to improve content and efficiency of CL workflows.

Problem

• Integrating single-cell omics data with Cell Ontology standards
• Harmonizing classical and transcriptomic cell type definitions
• Enhancing CL workflows using markers and Large Language Models

Innovation

• Standardized Cell Ontology for FAIR data principles
• Integration of transcriptomically defined cell types
• LLMs to enhance Cell Ontology workflows efficiency