Multi-scale Dynamical Modeling of T Cell Development from an Early Thymic Progenitor State to Lineage Commitment
Abstract
Intrathymic development of committed progenitor (pro)-T cells from multipotent hematopoietic precursors offers an opportunity to dissect the molecular circuitry establishing cell identity in response to environmental signals. This transition encompasses programmed shutoff of stem/progenitor genes, upregulation of T cell specification genes, proliferation, and ultimately commitment. To explain these features in light of reported cis-acting chromatin effects and experimental kinetic data, we develop a three-level dynamic model of commitment based upon regulation of the commitment-linked gene Bcl11b. The levels are (1) a core gene regulatory network (GRN) architecture from transcription factor (TF) perturbation data, (2) a stochastically controlled chromatin-state gate, and (3) a single-cell proliferation model validated by experimental clonal growth and commitment kinetic assays. Using RNA fluorescence in situ hybridization (FISH) measurements of genes encoding key TFs and measured bulk population dynamics, this single-cell model predicts state-switching kinetics validated by measured clonal proliferation and commitment times. The resulting multi-scale model provides a mechanistic framework for dissecting commitment dynamics.
Additional Information
© 2020 The Author(s). Under a Creative Commons license - Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0). Received 15 October 2019, Revised 24 April 2020, Accepted 18 December 2020, Available online 12 January 2021. The authors thank Dr. Long Cai for support for the smFISH analysis; Dr. Jeffrey Longmate for data analysis; Dr. Hao Yuan Kueh for helpful discussions and advice on imaging and analysis; Kenneth Ng for technical help; Diana Perez, Jamie Tijerina, and Rochelle Diamond of the Caltech Flow Cytometry Facility for fluorescence-activated cell sorting (FACS); and Dr. Andreas Collazo and the Caltech Biological Imaging Facility for microscopy assistance. The authors gratefully acknowledge the support of the US National Institutes of Health (USPHS grant R01HL119102 to E.V.R. and C.P.) and the Albert Billings Ruddock Professorship (to E.V.R.). Author Contributions. V.O., M.A.Y., E.V.R., and C.P. designed the study. V.O., M.A.Y., E.V.R., and C.P. wrote most of the manuscript. M.A.Y. performed the CTV and kinetics experiments. W.Z. performed the FISH experiments and wrote part of the manuscript. V.O. developed the transcriptional and epigenetic models and analyzed the data. P.K. developed the population model. E.A. conducted parameter optimization and confidence bounds calculations. J.D. implemented the pseudo-time-series and multi-scale models. The modeling results shown in Figures 1, 4, and 5 along with the ones in the Figures S2, S4, and S7 were obtained using MATLAB version 9..3.0.713579 R (2017b), The Mathworks, Inc. Available at https://www.mathworks.com. The authors declare no competing interests.Attached Files
Published - 1-s2.0-S2211124720316119-main.pdf
Accepted Version - nihms-1670076.pdf
Submitted - 667709.full.pdf
Supplemental Material - 1-s2.0-S2211124720316119-mmc1.pdf
Supplemental Material - 1-s2.0-S2211124720316119-mmc2.xlsx
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Additional details
- Alternative title
- Multi-scale dynamical modelling of T-cell development from an early thymic progenitor state to lineage commitment
- PMCID
- PMC7943435
- Eprint ID
- 96306
- Resolver ID
- CaltechAUTHORS:20190612-072821066
- NIH
- R01HL119102
- Albert Billings Ruddock Professorship
- Created
-
2019-06-12Created from EPrint's datestamp field
- Updated
-
2023-07-21Created from EPrint's last_modified field
- Caltech groups
- Division of Biology and Biological Engineering (BBE)