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Nanoparticles in medicine: Automating the analysis process of high-throughput microscopy data. / James Tonkin

Swansea University Author: James Tonkin

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...

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Published: 2013
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
URI: https://cronfa.swan.ac.uk/Record/cronfa42602
first_indexed 2018-08-02T18:55:06Z
last_indexed 2018-08-03T10:10:35Z
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recordtype RisThesis
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spelling 2018-08-02T16:24:29.8057994 v2 42602 2018-08-02 Nanoparticles in medicine: Automating the analysis process of high-throughput microscopy data. 1587dd2583916c71214909d5a9f9a443 NULL James Tonkin James Tonkin true true 2018-08-02 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. E-Thesis Biomedical engineering.;Nanotechnology. 31 12 2013 2013-12-31 COLLEGE NANME Engineering COLLEGE CODE Swansea University Doctoral Ph.D 2018-08-02T16:24:29.8057994 2018-08-02T16:24:29.8057994 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised James Tonkin NULL 1 0042602-02082018162507.pdf 10805360.pdf 2018-08-02T16:25:07.3870000 Output 25775571 application/pdf E-Thesis true 2018-08-02T16:25:07.3870000 false
title Nanoparticles in medicine: Automating the analysis process of high-throughput microscopy data.
spellingShingle Nanoparticles in medicine: Automating the analysis process of high-throughput microscopy data.
James Tonkin
title_short Nanoparticles in medicine: Automating the analysis process of high-throughput microscopy data.
title_full Nanoparticles in medicine: Automating the analysis process of high-throughput microscopy data.
title_fullStr Nanoparticles in medicine: Automating the analysis process of high-throughput microscopy data.
title_full_unstemmed Nanoparticles in medicine: Automating the analysis process of high-throughput microscopy data.
title_sort Nanoparticles in medicine: Automating the analysis process of high-throughput microscopy data.
author_id_str_mv 1587dd2583916c71214909d5a9f9a443
author_id_fullname_str_mv 1587dd2583916c71214909d5a9f9a443_***_James Tonkin
author James Tonkin
author2 James Tonkin
format E-Thesis
publishDate 2013
institution Swansea University
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
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description 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.
published_date 2013-12-31T15:04:30Z
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score 11.08899

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