ArticleComputational and structural biotechnology journal2026
OsteoNet: A deep learning framework linking cellular morphology to molecular markers for quantifying osteogenic differentiation.
Article in Computational and structural biotechnology journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- A cellular automaton model of osteogenic differentiation reveals identifiability limits of endpoint assays.Frontiers in systems biology · 2026Article
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21 authors.
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Abstract
Mesenchymal stem cells (MSCs) are widely applied in regenerative medicine, but conventional osteogenic induction assays are time-consuming and rely on destructive endpoint measurements. Here, we propose OsteoNet, a deep learning framework that predicts osteogenic differentiation from bright-field images and generates an Osteogenic Score (OsScore) reflecting differentiation dynamics. The predictive performance of OsteoNet was evaluated across multiple time points using an independent test set, achieving an AUC of 0.94 on day 0 and 0.98 on day 5, demonstrating robust early-stage detection capability. The OsScore increased progressively with induction time and showed strong positive correlations with both early and late osteogenic markers, including RUNX2, OCN, and OSX, at the RNA and protein levels. Morphological analysis of immunofluorescence images further confirmed significant increases in cell size during early differentiation, supporting the model's sensitivity to subtle morphological cues. Collectively, OsteoNet enables non-invasive, quantitative, and early monitoring of osteogenic differentiation in hAMSCs, offering a powerful tool to accelerate research and reduce reliance on destructive endpoint assays.
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