Computational staining of CD3/CD20 positive lymphocytes in human tissues with experimental confirmation in a genetically engineered mouse model
These results demonstrate that CD3/CD20 positive lymphocytes can be accurately detected and characterized on H&E by deep learning and generalized across species. Collectively, these data suggest that our understanding of complex biological systems may benefit from computationally-derived spatial analysis, as well as integration of computational science and basic science.
Authors
- Rico Castillo
- Ashlyn G. Rickard (ORCID: https://orcid.org/0000-0002-0752-8129)
- Tammara L. Watts (ORCID: https://orcid.org/0000-0001-7814-4491)
- Laura Barisoni (ORCID: https://orcid.org/0000-0003-0848-9683)
- Carlo Heirman
- Jeffrey I. Everitt (ORCID: https://orcid.org/0000-0003-0273-6284)
- Kyle J. Lafata (ORCID: https://orcid.org/0000-0002-4513-6249)
- Yvonne Marie Mowery (ORCID: https://orcid.org/0000-0002-9839-2414)
- Andrew Janowczyk (ORCID: https://orcid.org/0000-0003-2982-4321)
- Gina Sotolongo (ORCID: https://orcid.org/0000-0003-0530-6335)
- Jeffery B. Hodgin
- Xiang Li (ORCID: https://orcid.org/0000-0003-0569-2176)
- Temitayo Adanlawo
Institutions
- Georgia Institute of Technology (US)
- Emory University (US)
- Duke University (US)
- University of Michigan (US)
- University Hospital of Geneva (CH)
- UPMC Hillman Cancer Center (US)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2024-10-28
- DOI
- https://doi.org/10.3389/fimmu.2024.1451261
- Citations
- 4
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.94
Funders
- Damon Runyon Cancer Research Foundation
- National Institutes of Health
- National Cancer Institute
- National Institute of Allergy and Infectious Diseases
- National Institute of Diabetes and Digestive and Kidney Diseases
- National Institute of Dental and Craniofacial Research
- Congressionally Directed Medical Research Programs