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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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Mendeley readers

Mendeley readers

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%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 August 2014.
All research outputs
#22,953,184
of 25,593,129 outputs
Outputs from PLoS Computational Biology
#8,615
of 9,006 outputs
Outputs of similar age
#207,743
of 241,959 outputs
Outputs of similar age from PLoS Computational Biology
#151
of 163 outputs
Altmetric has tracked 25,593,129 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
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