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Sun H, Yu S, Casals AM, Bäckström A, Lu Y, Lindskog C, Ruffalo M, Lundberg E, Murphy RF
Cell Syst 16 (9) 101374 [2025-09-17; online 2025-09-08]
Identifying cell types in highly multiplexed images is essential for understanding tissue spatial organization. Current cell-type annotation methods often rely on extensive reference images and manual adjustments. In this work, we present a tool, the Robust Image-Based Cell Annotator (RIBCA), that enables accurate, automated, unbiased, and fine-grained cell-type annotation for images with a wide range of antibody panels without requiring additional model training or human intervention. Our tool has successfully annotated over 3 million cells, revealing the spatial organization of various cell types across more than 40 different human tissues. It is open source and features a modular design, allowing for easy extension to additional cell types.
PubMed 40925369
DOI 10.1016/j.cels.2025.101374
Crossref 10.1016/j.cels.2025.101374
mid: NIHMS2120417
pmc: PMC12728825
pii: S2405-4712(25)00207-8