T-cell commitment inheritance—an agent-based multi-scale model
Abstract
T-cell development provides an excellent model system for studying lineage commitment from a multipotent progenitor. The intrathymic development process has been thoroughly studied. The molecular circuitry controlling it has been dissected and the necessary steps like programmed shut off of progenitor genes and T-cell genes upregulation have been revealed. However, the exact timing between decision-making and commitment stage remains unexplored. To this end, we implemented an agent-based multi-scale model to investigate inheritance in early T-cell development. Treating each cell as an agent provides a powerful tool as it tracks each individual cell of a simulated T-cell colony, enabling the construction of lineage trees. Based on the lineage trees, we introduce the concept of the last common ancestors (LCA) of committed cells and analyse their relations, both at single-cell level and population level. In addition to simulating wild-type development, we also conduct knockdown analysis. Our simulations predicted that the commitment is a three-step process that occurs on average over several cell generations once a cell is first prepared by a transcriptional switch. This is followed by the loss of the Bcl11b-opposing function approximately two to three generations later. This is when our LCA analysis indicates that the decision to commit is taken even though in general another one to two generations elapse before the cell actually becomes committed by transitioning to the DN2b state. Our results showed that there is decision inheritance in the commitment mechanism.
Copyright and License
© The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Acknowledgement
The authors thank Dr. Wen Zhou and Dr. Mary A. Yui for helpful discussions. The authors gratefully acknowledge the support of the United States Public Health Service (NIH) (USPHS grant R01HL119102 to E.V.R. and C.P.). This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.
Funding
Open access funding provided by Lund University.
Contributions
E.A., V.O., E.V.R. and C.P. designed the study. E.A. implemented the agent-based multi-scale model and performed all the analysis. E.A. and V.O. wrote the manuscript. All authors provided inputs and comments on the manuscript.
Code Availability
The original code is available at https://github.com/Emil-cbbp/agent-based_multi-scale_model.git.
Conflict of Interest
The authors declare no competing interests.
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Additional details
- PMCID
- PMC11024127
- National Institutes of Health
- R01HL119102
- Knut and Alice Wallenberg Foundation
- Lund University
- Caltech groups
- Division of Biology and Biological Engineering