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

Authors: 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

Addresses: UT-ORNL Graduate School of Genome Science and Technology, Center for Environmental Biotechnology, and Departments of Civil and Environmental Engineering, Geography, and Microbiology, University of Tennessee, F337 Walters Life Science, Knoxville, TN 37996-0840, USA ' UT-ORNL Graduate School of Genome Science and Technology, Center for Environmental Biotechnology, and Departments of Civil and Environmental Engineering, Geography, and Microbiology, University of Tennessee, F337 Walters Life Science, Knoxville, TN 37996-0840, USA ' UT-ORNL Graduate School of Genome Science and Technology, Center for Environmental Biotechnology, and Departments of Civil and Environmental Engineering, Geography, and Microbiology, University of Tennessee, F337 Walters Life Science, Knoxville, TN 37996-0840, USA ' UT-ORNL Graduate School of Genome Science and Technology, Center for Environmental Biotechnology, and Departments of Civil and Environmental Engineering, Geography, and Microbiology, University of Tennessee, F337 Walters Life Science, Knoxville, TN 37996-0840, USA ' UT-ORNL Graduate School of Genome Science and Technology, Center for Environmental Biotechnology, and Departments of Civil and Environmental Engineering, Geography, and Microbiology, University of Tennessee, F337 Walters Life Science, Knoxville, TN 37996-0840, USA ' UT-ORNL Graduate School of Genome Science and Technology, Center for Environmental Biotechnology, and Departments of Civil and Environmental Engineering, Geography, and Microbiology, University of Tennessee, F337 Walters Life Science, Knoxville, TN 37996-0840, USA ' Department of Pathogens and Immunity, John Curtin School of Medical Research, Australian National University, GPO Box 334, Canberra City ACT 2600, Australia ' Unité de Biologie et Génétique du Paludisme, Institut Pasteur, 75724, Paris Cedex 15, France ' Department of Microbiology, University of Tennessee, M409 Walters Life Science Building, 1414 West Cumberland Avenue, Knoxville, TN 37996-0845, USA ' EECS Department, University of Tennessee, 401 Min H. Kao Building, Knoxville, TN 37996, USA

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 saw clusters of different movement 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. We 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.

Keywords: computational biology; data mining; support vector machines; SVM; T cells; T cell movement tracks; pattern classification; cell function prediction; immune cells; clustering; mouse liver; malaria parasites; antigen specificity; cell movements.

DOI: 10.1504/IJCBDD.2014.061655

International Journal of Computational Biology and Drug Design, 2014 Vol.7 No.2/3, pp.113 - 129

Published online: 27 May 2014 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article