Baresch, G. (2021). Nanoscale characterization of T cell receptor microclusters [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2021.85023
Single molecule localization microscopy; T cell; T cell receptor; microcluster; immunological synapse; superrcitical angle fluorescence
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Abstract:
The interaction between the T cell receptor (TCR) and the peptide in major histocompatibility complex (pMHC) molecules is a crucial step in T cell activation. The entire process of antigen recognition control and the relative contributions of TCR–MHC binding energy are still not well-understood. In order to analyse the protein clustering at the nanoscales upon T cell activation in animal model TCR, single molecule localization microscopy (SMLM) with direct stochastic optical reconstruction microscopy (dSTORM) based on total internal reflection fluorescence (TIRF) was used. The beta chain of TCR was labelled with a single chain fragment of antibody scFv-AF647. With the mentioned microscopic techniques, a different subset of fluorescent probes was activated at different times, allowing these fluorophores to be imaged without substantial spatial overlap and to be localized with high precision in xyz-coordinates. The axial precision was achieved using the defocussing approach with the help of the near field of a dipole, using supercritical angle fluorescence (SAF). SAF light only occurs near the coverslip, which gives the advantage of gathering information for determining the axial position of the fluorophore by using interference to cause a change in the shape of the image of a fluorophore point spread function (PSF) on the camera. The interference was controlled and optimized using the focus position. Using this method allowed the analysis of microclusters in the nanoscale with three dimensional (3D) super resolution technique. The aim of this thesis was to compare various parameters of TCR microclusters such as: size, density, and z-position depending on different concentrations of pMHC, using density based spatial clustering of applications with noise (DBSCAN) as clustering algorithm.