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dc.contributor.advisorHang, Lu
dc.contributor.advisorKemp, Melissa
dc.contributor.authorHe, Luye
dc.date.accessioned2019-01-16T17:21:22Z
dc.date.available2019-01-16T17:21:22Z
dc.date.created2017-12
dc.date.issued2017-11-13
dc.date.submittedDecember 2017
dc.identifier.urihttp://hdl.handle.net/1853/60713
dc.description.abstractCell-mediated immunity is a critical component of an adaptive immune system, where immune functions are mostly mediated through the coordination of T cells. The understanding of fundamental T cell signaling processes involved in cell-mediated immunity has underlined recent advancement of cancer immunotherapy. While conventional methods (including lipid bilayer, biomembrane force probe (BFP) and microwell, etc) remain important tools in studying these fundamental signaling processes, the lack of either control (e.g. microwell) or throughput (e.g. BFP) poses substantial challenges in effective study of highly dynamic and heterogeneous cellular signaling processes. This thesis responded to these challenges through development of microfluidic tools and multivariate data analysis methods to solve problems related to immune cell signaling study. In Chapter 2, a microfluidic platform able to deliver programmable dynamic stimulus to interrogate T cell signaling was developed. This platform was applied to probe system property of T cell signal transduction pathways, which showed T cell calcium signaling pathway behaved as low-path filter and was highly heterogeneous among T cell population. In Chapter 3, a microfluidic system to precisely manipulate and synchronize cell interaction of large number of cell pairs was developed. This system provided simultaneous real-time signaling imaging and organelle tracking at temporal density with single cell resolution. In addition, new image-derived metrics were developed to quantify calcium response and mitochondria movement. In Chapter 4, the cell interaction microfluidic system was applied to study how subtle differences in antigen structures translate to distinct T-cell effector functions through early signaling processes such as calcium and mitochondria during T-cell antigen recognition. Using an altered peptide ligands (APLs) and a hybridoma cell line model, this work recapitulated prior findings of T cell calcium dynamics responding to differences in antigen potency with sensitivity of single amino acid change, while remained inert to changes in antigen concentration. Lastly, a partial least square regression model was developed, which highlighted mitochondrial positioning as a strong predictor of calcium response during T-cell antigen recognition. In summary, this thesis, microfluidic tools were developed to provide precise control of cell microenvironment and interaction, which enabled in vitro real-time live-imaging of cell signaling events at large sample size with single cell resolution. Combined with advanced statistical techniques to interpret these data, this work shed new insights into signaling processes relevant to immune cells, which had not been resolved using conventional techniques and population-based statistics. These results demonstrated the new methodology of using microfluidic tools and multivariate analysis to investigate fundamental cellular and molecular process, which was critical for understanding of immune system and future engineering of rational therapies.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectMicrofluidic platform
dc.subjectSystem automation
dc.subjectImmunology
dc.subjectCell signaling
dc.subjectImage processing
dc.subjectData analysis
dc.titleMicrofluidic platforms and multivariate analysis for immune cell signaling study
dc.title.alternativeMicrofluidic platforms and mulivariate analysis for immune cell signaling study
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentChemical and Biomolecular Engineering
thesis.degree.levelDoctoral
dc.contributor.committeeMemberZhu, Cheng
dc.contributor.committeeMemberStyczynski, Mark
dc.contributor.committeeMemberBreedveld, Victor
dc.date.updated2019-01-16T17:21:22Z


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