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Moutakanni, T.* ; Couprie, C.* ; Yi, S.* ; Doron, M.* ; Chen, Z.S.* ; Moshkov, N.* ; Gardes, E.* ; Caron, M.* ; Touvron, H.* ; Joulin, A.* ; Bojanowski, P.* ; Pernice, W.M.*

Cell-DINO: Self-supervised image-based embeddings for cell fluorescent microscopy.

PLoS Comput. Biol. 21:e1013828 (2026)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Accurately quantifying cellular morphology at scale could substantially empower existing single-cell approaches. However, measuring cell morphology remains an active field of research, which has inspired multiple computer vision algorithms over the years. Here, we show that DINOv2, a vision-transformer based, self-supervised algorithm, has a remarkable ability for learning rich representations of cellular morphology without manual annotations or any other type of supervision. We apply DINOv2 to cell phenotyping problems, and compare the performance of resulting models, called Cell-DINO models, on a wide variety of tasks across two publicly available imaging datasets of diverse specifications and biological focus. Compared to supervised and other self-supervised baselines, Cell-DINO models demonstrate improved performance, especially in low annotation regimes. For instance, to classify protein localization using only 1% of annotations on a challenging single-cell dataset, Cell-DINO performs 70% better than a supervised strategy, and 24% better than another self-supervised alternative. The results show that Cell-DINO can support the study of unknown biological variation, including single-cell heterogeneity and relationships between experimental conditions, making it an excellent tool for image-based biological discovery.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Nucleus Segmentation
ISSN (print) / ISBN 1553-734X
e-ISSN 1553-7358
Journal PLoS Computational Biology
Quellenangaben Volume: 21, Issue: 12, Pages: , Article Number: e1013828 Supplement: ,
Publisher Public Library of Science (PLoS)
Publishing Place 1160 Battery Street, Ste 100, San Francisco, Ca 94111 Usa
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Grants National Science Foundation
Muscular Dystrophy Association
Hungarian Ministry of Innovation
National Institutes of Health