The Cell Ontology in the age of single-cell omics

TL;DR

This paper discusses how the Cell Ontology supports single-cell omics data integration, highlights ongoing improvements, and explores future directions including harmonization and AI integration.

Contribution

It details the current uses of the Cell Ontology in single-cell data platforms and describes efforts to extend and improve its content and utility.

Findings

CL is widely used in single-cell data platforms.

Ongoing work includes adding transcriptomic cell types.

Future plans involve harmonizing cell type definitions and using LLMs.

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 transcriptomic 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.