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Random Migration and Signal Integration Promote Rapid and Robust T Cell Recruitment

Overview of attention for article published in PLoS Computational Biology, August 2014
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Title
Random Migration and Signal Integration Promote Rapid and Robust T Cell Recruitment
Published in
PLoS Computational Biology, August 2014
DOI 10.1371/journal.pcbi.1003752
Pubmed ID
Authors

Johannes Textor, Sarah E. Henrickson, Judith N. Mandl, Ulrich H. von Andrian, Jürgen Westermann, Rob J. de Boer, Joost B. Beltman

Abstract

To fight infections, rare T cells must quickly home to appropriate lymph nodes (LNs), and reliably localize the antigen (Ag) within them. The first challenge calls for rapid trafficking between LNs, whereas the second may require extensive search within each LN. Here we combine simulations and experimental data to investigate which features of random T cell migration within and between LNs allow meeting these two conflicting demands. Our model indicates that integrating signals from multiple random encounters with Ag-presenting cells permits reliable detection of even low-dose Ag, and predicts a kinetic feature of cognate T cell arrest in LNs that we confirm using intravital two-photon data. Furthermore, we obtain the most reliable retention if T cells transit through LNs stochastically, which may explain the long and widely distributed LN dwell times observed in vivo. Finally, we demonstrate that random migration, both between and within LNs, allows recruiting the majority of cognate precursors within a few days for various realistic infection scenarios. Thus, the combination of two-scale stochastic migration and signal integration is an efficient and robust strategy for T cell immune surveillance.

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The data shown below were compiled from readership statistics for 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Spain 1 1%
Netherlands 1 1%
Unknown 73 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 24 31%
Student > Ph. D. Student 17 22%
Student > Master 8 10%
Student > Bachelor 5 6%
Professor > Associate Professor 4 5%
Other 9 12%
Unknown 10 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 32%
Immunology and Microbiology 8 10%
Biochemistry, Genetics and Molecular Biology 8 10%
Medicine and Dentistry 8 10%
Computer Science 8 10%
Other 6 8%
Unknown 14 18%