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Related Experiment Video

Updated: Jan 13, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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Decoding cell state transitions driven by dynamic cell-cell communication in spatial transcriptomics.

Lulu Yan1, Dongyan Zhang1, Xiaoqiang Sun2,3

  • 1School of Mathematics, Sun Yat-sen University, Guangzhou, China.

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|January 6, 2026
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Summary
This summary is machine-generated.

We developed CCCvelo, a new method to map cell fate transitions driven by cell-cell communication. This approach reconstructs spatiotemporal dynamics, revealing how communication orchestrates development and disease.

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Area of Science:

  • Computational Biology
  • Genomics
  • Developmental Biology

Background:

  • Cell fate determination relies on complex intracellular and intercellular signaling.
  • Spatial transcriptomics (ST) offers insights into these processes, but modeling cell state transitions (CSTs) driven by cell-cell communication (CCC) is challenging.

Purpose of the Study:

  • To introduce CCCvelo, a novel computational framework for reconstructing CCC-driven CST dynamics.
  • To integrate intercellular signaling gradients and intracellular transcription factor cascades for modeling gene expression dynamics.

Main Methods:

  • Developed CCCvelo, a unified multiscale nonlinear kinetic model.
  • Devised PINN-CELL, a physics-informed neural-network coevolution learning algorithm for parameter optimization and pseudotemporal ordering.
  • Applied CCCvelo to high-resolution ST datasets from mouse cortex, embryonic development, and human prostate cancer.

Main Results:

  • CCCvelo successfully reconstructed known morphogenetic trajectories.
  • The method uncovered dynamic CCC signaling rewiring during CST progression.
  • Demonstrated the capability to infer spatiotemporal dynamics of cell state transitions governed by CCC.

Conclusions:

  • CCCvelo provides a powerful tool for understanding CCC-driven cell fate determination.
  • The framework advances the analysis of spatial transcriptomics data for developmental and disease studies.
  • Highlights the importance of dynamic signaling network rewiring in orchestrating cell state transitions.