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Abstract

Lattice light-sheet microscopy provides large amounts high-dimensional, high-spatiotemporal resolution imaging data of cell surface receptors across the 3D surface of live cells, but user-friendly analysis pipelines are lacking. In this thesis, lattice light-sheet microscopy multi-dimensional analyses (LaMDA) is described, which is an end-to-end pipeline comprised of publicly-available software packages that combines machine learning, dimensionality reduction, and diffusion maps to analyze transmembrane receptor dynamics and classify cellular signaling states without the need for complex biochemical measurements or other prior information. LaMDA is used to analyze images of TCR microclusters on the surface of live primary T cells under resting and stimulated conditions. LaMDA accurately differentiates stimulated cells from unstimulated cells, precisely predicts attenuated T-cell signaling after CD4 and CD28 receptor blockades, provides interesting insight to checkpoint inhibitors, reliably discriminates between structurally similar TCR ligands, and presents coordinated global spatial and temporal changes of TCRs across the 3D cell surface. Finally, in response to the global COVID-19 pandemic that arose in 2019, a nanoparticle entitled “Nanotraps” is presented that inhibits entry of SARS-CoV-2 to ACE2-expressing cells and triggers subsequent phagocytosis by macrophages to clear the virus. The Nanotraps showed a neutralizing capacity of more than 10 times that of its soluble ACE2 or neutralizing antibody counterparts.

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