AI-based approach to dissect the variability of mouse stem cell-derived embryo models
Creators
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.
Copyright and License
© The Author(s) 2025.
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Acknowledgement
We thank Wenqi Hu for the annotation of the dataset. This work was supported by the MZG.PIONEER.1.NIHP (HD104575A to M.Z.-G.), NOMIS Foundation (12540449 to M.Z.-G.), the Wellcome Trust (207415/Z/17/Z to M.Z.-G.), the Open Philanthropy Project (to M.Z.-G.), the Helmholtz Association under the joint research school “HIDSS4Health” – Helmholtz Information and Data Science School for Health to L.D., and the Helmholtz program NACIP to R.M. and L.D. This work is supported by the Helmholtz Association Initiative and Networking Fund on the HAICORE@KIT partition.
Funding
This work was supported by the MZG.PIONEER.1.NIHP (HD104575A to M.Z.-G.), NOMIS Foundation (12540449 to M.Z.-G.), the Wellcome Trust (207415/Z/17/Z to M.Z.-G.), the Open Philanthropy Project (to M.Z.-G.), the Helmholtz Association under the joint research school “HIDSS4Health” – Helmholtz Information and Data Science School for Health to L.D., and the Helmholtz program NACIP to R.M. and L.D. This work is supported by the Helmholtz Association Initiative and Networking Fund on the HAICORE@KIT partition.
Data Availability
The data generated in this study have been deposited in the Zenodo database available at https://doi.org/10.5281/zenodo.1460509335. Source data are provided with this paper.
Code Availability
The custom code developed for this study is publicly accessible on GitHub at https://github.com/deiluca/StembryoNet and co-deposited on Zenodo at https://doi.org/10.5281/zenodo.1460517736.
Contributions
These authors contributed equally: Paolo Caldarelli, Luca Deininger.
The project was originally conceptualized by P.C. and M.Z.-G and developed by P.C., M.Z.-G, L.D., R.M., C.Y. and P.C. designed and performed experiments with help from P.P. and L.D. developed the deep learning model with help from S.Z., P.C. and L.D. analyzed the data. P.C., L.D., and M.Z.-G. wrote the manuscript with comments from the other co-authors. M.Z.-G., R.M., and C.Y. supervised the project and acquired funding.
Conflict of Interest
The authors declare no competing interests.
Supplemental Material
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Additional details
Identifiers
- PMID
- 39971935
- PMCID
- PMC11839995
Funding
- National Institutes of Health
- MZG.PIONEER.1.NIHP HD104575A
- Nomis Foundation
- 12540449
- Wellcome Trust
- 207415/Z/17/Z
- Open Philanthropy Project
- Helmholtz Association of German Research Centres
Dates
- Submitted
-
2024-08-30
- Accepted
-
2025-02-05