Classification of T cell movement tracks allows for prediction of cell function

Reka K. Kelemen, Gengen F. He, Hannah L. Woo, Thomas Lane, Caroline Rempe, Jun Wang, Ian A. Cockburn, Rogerio Amino, Vitaly V. Ganusov, Michael W. Berry*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    3 Citations (Scopus)

    Abstract

    Using a unique combination of visual, statistical, and data mining methods, we tested the hypothesis that an immune cell's movement pattern can convey key information about the cell's function, antigen specificity, and environment.We applied clustering, statistical tests, and a support vector machine (SVM) to assess our ability to classify different datasets of imaged flouresently labelled T cells in mouse liver.We additionally sawclusters of differentmovement patterns of T cells of identical antigenic specificity.We found that the movement patterns of T cells specific and non-specific for malaria parasites are differentiable with 72% accuracy, and that specific cells have a higher tendency to move towards the parasite than non-specific cells. Movements of antigen-specific T cells in uninfected mice vs. infected mice were differentiable with 69.8% accuracy. Wxe additionally saw clusters of different movement patterns of T cells of identical antigenic specificity. We concluded that our combination of methods has the potential to advance the understanding of cell movements in vivo.

    Original languageEnglish
    Pages (from-to)113-129
    Number of pages17
    JournalInternational Journal of Computational Biology and Drug Design
    Volume7
    Issue number2-3
    DOIs
    Publication statusPublished - 2014

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