Quantitative Study of T-cell Polarization

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DESCRIPTION (provided by applicant): The objective of this project is to put our understanding of T cell polarization on a firm quantitative basis. T cells of the immune system polarize, i.e. orient themselves, toward their targets, such as infected or tumor cells to be eliminated. The crucial component of the T cell polarization is reorientation of the centrosome toward the target. Although a number of molecular pathways involved in regulation of this process have been identified, even a general understanding is lacking as to the mechanism of the process itself. New data lead us to hypothesize, contrary to the accepted notion, that the basic mechanism is whole-cell movement and deformation on contact with the target, a process directed toward whole-cell structural optimization. First, we will employ a simplified experimental system of T cells stimulated with planar biomimetic substrates to elucidate the basic features of the centrosome reorientation. This will be achieved through combination of computational modeling with quantitative, multidimensional, live-cell microscopy. Next, the contribution of specific force-generating intracellular processes (molecular motors, cytoskeletal dynamics) to the centrosome translocation will be determined by enriching the basic model with specific numerical models of these processes and comparing the predictions with experiments in which the specific molecular activities are modulated. Lastly, we will use the achieved understanding of the simplified system amenable to high- throughput measurements to extrapolate our knowledge computationally to the realistic case of the T cell- target cell interaction. The predictions will be tested by means of quantitative microscopy and the additional features found in the cell-cell interactions added to the computer model. The outcome of the project will be not only rigorous understanding of the biomedically significant phenomenon of centrosome polarization in the immune cells, formalized in a predictive computer model, but also a substantial empirical improvement of the new methodology of quantitative biomedical research.