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Related Experiment Video

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Multi-scale Dynamical Modeling of T Cell Development from an Early Thymic Progenitor State to Lineage Commitment.

Victor Olariu1, Mary A Yui2, Pawel Krupinski1

  • 1Computational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.

Cell Reports
|January 13, 2021
PubMed
Summary

This study models T-cell commitment dynamics, revealing how gene regulation, chromatin state, and cell proliferation interact to establish cell identity during T-cell development. The model predicts key kinetic features of this crucial developmental process.

Keywords:
T cell developmentepigenetic modelingexperimental validationskinetic measurementspopulation modelingproliferation measurementssingle-cell measurementsstochastic simulationstranscriptional modeling

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Area of Science:

  • * Developmental immunology
  • * Molecular systems biology
  • * Computational biology

Background:

  • * T-cell development involves complex transitions from hematopoietic stem cells to committed T-cell progenitors.
  • * Understanding the molecular mechanisms governing T-cell commitment is crucial for immunology and regenerative medicine.
  • * Previous studies highlighted cis-acting chromatin effects and kinetics but lacked a unified mechanistic model.

Purpose of the Study:

  • * To develop a multi-scale dynamic model of T-cell commitment.
  • * To integrate gene regulatory networks, chromatin state dynamics, and cell proliferation.
  • * To mechanistically explain the programmed gene expression changes and commitment kinetics observed during T-cell development.

Main Methods:

  • * Developed a three-level dynamic model incorporating gene regulatory network (GRN) architecture, a stochastic chromatin-state gate, and a single-cell proliferation model.
  • * Utilized transcription factor (TF) perturbation data to define the core GRN.
  • * Employed RNA fluorescence in situ hybridization (FISH) and bulk population dynamics for model validation against clonal growth and commitment kinetics.

Main Results:

  • * The model successfully predicts state-switching kinetics during T-cell commitment.
  • * Validated model predictions using experimental data on clonal proliferation and commitment times.
  • * Demonstrated the interplay between gene regulation, chromatin accessibility, and proliferation in driving cell fate decisions.

Conclusions:

  • * The developed multi-scale model provides a mechanistic framework for dissecting T-cell commitment dynamics.
  • * This approach offers insights into how environmental signals and internal circuitry establish cell identity.
  • * The model serves as a foundation for further investigation into T-cell differentiation and potential therapeutic targets.