Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.678465
Title: Nanoparticles in medicine : automating the analysis process of high-throughput microscopy data
Author: Tonkin, James
Awarding Body: Swansea University
Current Institution: Swansea University
Date of Award: 2013
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Abstract:
Automated tracking of cells across timelapse microscopy image sequences typically employs complex segmentation routines and/or bio-staining of the tracking objective. Often accurate identification of a cell's morphology is not of interest and the accurate segmentation of cells in pursuit of non-morphological parameters is complex and time consuming. This thesis explores the potential of internalized quantum dot nanoparticles as alternative, bio- and photo-stable optical markers for tracking the motions of cells through time. CdTe/ZnS core-shell quantum dots act as nodes in moving light display networks within A549, epithelial, lung cancer cells over a 40 hour time period. These quantum dot fluorescence sources are identified and interpreted using simplistic algorithms to find consistent, non-subjective centroids that represent cell centre locations. The presented tracking protocols yield an approximate 91% success rate over 24 hours and 78% over the full 40 hours. The nanoparticle moving light displays also provide simultaneous collection of cell motility data, resolution of mitotic traversal dynamics and identification of familial relationships enabling the construction of multi-parameter lineage trees. This principle is then developed further through inclusion of 3 different coloured quantum dots to create cell specific colour barcodes and reduce the number of time points necessary to successfully track cells through time. The tracking software and identification of parameters without detailed morphological knowledge is also demonstrated through automated extraction of DOX accumulation profiles and Cobalt agglomeration accruement statistics from two separate toxicology assays without the need for cell segmentation.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.678465  DOI: Not available
Keywords: Nanoparticles ; Microscopy ; Quantum dots ; Biochemical markers
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