Title |
Harnessing the Heterogeneity of T Cell Differentiation Fate to Fine-Tune Generation of Effector and Memory T Cells
|
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Published in |
Frontiers in immunology, January 2014
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DOI | 10.3389/fimmu.2014.00057 |
Pubmed ID | |
Authors |
Chang Gong, Jennifer J. Linderman, Denise Kirschner |
Abstract |
Recent studies show that naïve T cells bearing identical T cell receptors experience heterogeneous differentiation and clonal expansion processes. The factors controlling this outcome are not well characterized, and their contributions to immune cell dynamics are similarly poorly understood. In this study, we develop a computational model to elaborate mechanisms occurring within and between two important physiological compartments, lymph nodes and blood, to determine how immune cell dynamics are controlled. Our multi-organ (multi-compartment) model integrates cellular and tissue level events and allows us to examine the heterogeneous differentiation of individual precursor cognate naïve T cells to generate both effector and memory T lymphocytes. Using this model, we simulate a hypothetical immune response and reproduce both primary and recall responses to infection. Increased numbers of antigen-bearing dendritic cells (DCs) are predicted to raise production of both effector and memory T cells, and distinct "sweet spots" of peptide-MHC levels on those DCs exist that favor CD4+ or CD8+ T cell differentiation toward either effector or memory cell phenotypes. This has important implications for vaccine development and immunotherapy. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Switzerland | 1 | 25% |
United States | 1 | 25% |
United Kingdom | 1 | 25% |
Unknown | 1 | 25% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 50% |
Science communicators (journalists, bloggers, editors) | 2 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 2 | 2% |
Greece | 1 | <1% |
Germany | 1 | <1% |
Singapore | 1 | <1% |
Unknown | 112 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 30 | 26% |
Researcher | 24 | 21% |
Student > Doctoral Student | 12 | 10% |
Other | 8 | 7% |
Professor > Associate Professor | 8 | 7% |
Other | 15 | 13% |
Unknown | 20 | 17% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 32 | 27% |
Immunology and Microbiology | 21 | 18% |
Biochemistry, Genetics and Molecular Biology | 9 | 8% |
Engineering | 8 | 7% |
Mathematics | 7 | 6% |
Other | 19 | 16% |
Unknown | 21 | 18% |