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Related Experiment Video

Updated: Jan 11, 2026

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
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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
PubMed
Summary
This summary is machine-generated.

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.

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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.