The Cell Ontology in the age of single-cell omics
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908832716816384 |
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| author | Tan, Shawn Zheng Kai Puig-Barbe, Aleix Goutte-Gattat, Damien Eastwood, Caroline Aevermann, Brian Avola, Alida Balhoff, James P Bayindir, Ismail Ugur Belfiore, Jasmine Caron, Anita Reane Fischer, David S George, Nancy Gyori, Benjamin M Haendel, Melissa A Hoyt, Charles Tapley Kir, Huseyin Lubiana, Tiago Matentzoglu, Nicolas Overton, James A Peng, Beverly Peters, Bjoern Quardokus, Ellen M Ray, Patrick L Roncaglia, Paola Rivera, Andrea D Stefancsik, Ray Teh, Wei Kheng Toro, Sabrina Vasilevsky, Nicole Xu, Chuan Zhang, Yun Scheuermann, Richard H Mungall, Christopher J Diehl, Alexander D Osumi-Sutherland, David |
| author_facet | Tan, Shawn Zheng Kai Puig-Barbe, Aleix Goutte-Gattat, Damien Eastwood, Caroline Aevermann, Brian Avola, Alida Balhoff, James P Bayindir, Ismail Ugur Belfiore, Jasmine Caron, Anita Reane Fischer, David S George, Nancy Gyori, Benjamin M Haendel, Melissa A Hoyt, Charles Tapley Kir, Huseyin Lubiana, Tiago Matentzoglu, Nicolas Overton, James A Peng, Beverly Peters, Bjoern Quardokus, Ellen M Ray, Patrick L Roncaglia, Paola Rivera, Andrea D Stefancsik, Ray Teh, Wei Kheng Toro, Sabrina Vasilevsky, Nicole Xu, Chuan Zhang, Yun Scheuermann, Richard H Mungall, Christopher J Diehl, Alexander D Osumi-Sutherland, David |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_10037 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Cell Ontology in the age of single-cell omics Tan, Shawn Zheng Kai Puig-Barbe, Aleix Goutte-Gattat, Damien Eastwood, Caroline Aevermann, Brian Avola, Alida Balhoff, James P Bayindir, Ismail Ugur Belfiore, Jasmine Caron, Anita Reane Fischer, David S George, Nancy Gyori, Benjamin M Haendel, Melissa A Hoyt, Charles Tapley Kir, Huseyin Lubiana, Tiago Matentzoglu, Nicolas Overton, James A Peng, Beverly Peters, Bjoern Quardokus, Ellen M Ray, Patrick L Roncaglia, Paola Rivera, Andrea D Stefancsik, Ray Teh, Wei Kheng Toro, Sabrina Vasilevsky, Nicole Xu, Chuan Zhang, Yun Scheuermann, Richard H Mungall, Christopher J Diehl, Alexander D Osumi-Sutherland, David Other Quantitative Biology Information Retrieval 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. |
| title | The Cell Ontology in the age of single-cell omics |
| topic | Other Quantitative Biology Information Retrieval |
| url | https://arxiv.org/abs/2506.10037 |