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Title: Human neutrophil elastase phenotyping : classifying neutrophils by function with novel imaging agents
Author: Campbell, Thane Allardyce
ISNI:       0000 0004 8509 2708
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2020
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This thesis describes approaches taken to classify the functionally diverse neutrophil by its different functions in a live-cell imaging tool. Human neutrophil elastase (HNE) cleaves over 30 substrates, its release is controlled with varying extents of degranulation and its activity is subject to complex modulation. HNE regulation is necessary for the maintenance of health via HNE's multiplicity of functions. Although neutrophils are routinely quantified in assessments of chronic inflammation, we do not have a clinically translatable, integrated tool for measuring HNE in human tissues. Two novel probes have been characterised and multiplexed to form the basis of an imaging technique for understanding the functional implications of neutrophil activity in human tissues, in real-time. Neutrophil Activation Probe (NAP) and VE200 are novel probes for HNE activity and presence. These probes are characterised in vitro as sensitive and HNE-specific imaging agents for live-cell imaging and image cytometry. Multiplexed NAP and HNE are detect neutrophil activation, apoptosis and necrosis. These imaging agents can inform deep profiling techniques to separate neutrophils into untreated, primed, activated and primed-activated states and endogenous vs. exogenous stimulus sub-states. Finally, sections of adenocarcinomatous human lung and whole human lungs, ventilated ex vivo, demonstrate the applicability of HNEbased, multiparametric profiling and neutrophil activation detection to clinically relevant platforms.
Supervisor: Dhaliwal, Kanwaldeep ; Bradley, Mark ; Dweck, Marc Sponsor: Engineering and Physical Sciences Research Council (EPSRC)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
Keywords: neutrophil ; neutrophil elastase ; probe ; fibred confocal endomicroscopy ; machine learning