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

Victor Olariu, Mary A. Yui, Pawel Krupinski, Wen Zhou, Julia Deichmann, Emil Andersson, Ellen V. Rothenberg, Carsten Peterson

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number108622
JournalCell Reports
Volume34
Issue number2
DOIs
Publication statusPublished - 2021

Subject classification (UKÄ)

  • Cell and Molecular Biology
  • Bioinformatics and Systems Biology
  • Cell Biology
  • Other Physics Topics

Free keywords

  • epigenetic modeling
  • experimental validations
  • kinetic measurements
  • population modeling
  • proliferation measurements
  • single-cell measurements
  • stochastic simulations
  • T cell development
  • transcriptional modeling

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