AI-based approach to dissect the variability of mouse stem cell-derived embryo models

Paolo Caldarelli, Luca Deininger, Shi Zhao, Pallavi Panda, Changhuei Yang, Ralf Mikut () and Magdalena Zernicka-Goetz ()
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Paolo Caldarelli: California Institute of Technology
Luca Deininger: Karlsruhe Institute of Technology
Shi Zhao: California Institute of Technology
Changhuei Yang: California Institute of Technology
Ralf Mikut: Karlsruhe Institute of Technology
Magdalena Zernicka-Goetz: California Institute of Technology

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Recent advances in stem cell-derived embryo models have transformed developmental biology, offering insights into embryogenesis without the constraints of natural embryos. However, variability in their development challenges research standardization. To address this, we use deep learning to enhance the reproducibility of selecting stem cell-derived embryo models. Through live imaging and AI-based models, we classify 900 mouse post-implantation stem cell-derived embryo-like structures (ETiX-embryos) into normal and abnormal categories. Our best-performing model achieves 88% accuracy at 90 h post-cell seeding and 65% accuracy at the initial cell-seeding stage, forecasting developmental trajectories. Our analysis reveals that normally developed ETiX-embryos have higher cell counts and distinct morphological features such as larger size and more compact shape. Perturbation experiments increasing initial cell numbers further supported this finding by improving normal development outcomes. This study demonstrates deep learning’s utility in improving embryo model selection and reveals critical features of ETiX-embryo self-organization, advancing consistency in this evolving field.

Date: 2025
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DOI: 10.1038/s41467-025-56908-5

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