Decoding cell fate: integrated experimental and computational analysis at the single-cell level.
Yutong Zhou1, Shuyang Hou1, Xinhao Miao1
1Integrated Science Program, Yuanpei College, Peking University, Beijing, 100871, China.
Bioinformatics (Oxford, England)
|November 8, 2025
Summary
Single-cell technologies reveal complex cell fate decisions. This review highlights experimental and AI advances for predicting and manipulating cell fates, crucial for developmental biology and regenerative medicine.
Area of Science:
- Developmental Biology
- Regenerative Medicine
- Computational Biology
Background:
- Cell fate determination is key in developmental biology and regenerative medicine.
- Traditional methods obscure cellular heterogeneity and lack systematic cell fate control views.
- Single-cell technologies offer high resolution, revealing cell fate decision complexity.
Purpose of the Study:
- To review experimental and computational advances in cell fate determination.
- To assess the role of AI and foundation models in cell fate studies.
- To highlight challenges and future directions in predicting and manipulating cell fates.
Main Methods:
- Review of single-cell multi-omics, lineage tracing, and perturbation techniques.
- Discussion of modeling paradigms for cell fate studies.
- Assessment of AI tools for perturbation modeling and foundation models.
Main Results:
- Experimental advances enable dynamic tracking of cell fate transitions.
- AI tools show potential in perturbation modeling.
- Case studies demonstrate cell fate prediction and manipulation.
Conclusions:
- Sophisticated computational methods are needed for complex cell fate decisions.
- Emerging AI and foundation models offer new avenues for research.
- Further research is required to address key challenges in the field.
