Holroyd, Natalie Aroha;
(2023)
Multi-Fluorescence Large Volume Imaging for Investigating the Tumour Micro-environment.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
The tumour microenvironment has increasingly become an area of interest in cancer research as a therapeutic target and an important biomarker for monitoring cancer progression and therapeutic response. However, current imaging techniques struggle to visualise the tumour microenvironment at sufficiently high resolution to provide information about cellular features and microvascular networks, without sacrificing field of view. High Resolution Episcopic Microscopy (HREM) is an automated block-face imaging technique that has the potential to produce cellular-resolution three-dimensional images over volumes up to 1 cm3. Current applications of HREM uses eosin-derived contrast, which lacks specificity and hence does not provide the structural and functional information that can be gained from targeted fluorescence labelling. In this project, protocols for Multi-fluorescence HREM (MF-HREM) imaging were developed on healthy tissues, and then applied to murine tumour models. HREM-compatible fluorescence labels were identified, and the sample preparation pipeline was optimised. Additionally, a novel machine learning algorithm was developed to allow quantitative analysis of the large 3D datasets produced by HREM. It was found that microvascular networks in healthy and tumour tissues could be labelled with intravenous fluorophore-conjugated lectin. Technovit 8100 resin was identified as the optimal resin for embedding samples whilst preserving fluorescence signal, and saponin treatment was found to increase the penetration of labels into large tissue samples. The ‘bleed-through’ artefact associated with HREM imaging was addressed with a combination of opaque resin and computational post-processing. A Convolutional Neural Network with a U-Net architecture was developed for vessel segmentation, and it was found the pre-training on vessel data from different imaging modalities, as well as computer-generated simulated vessel data, improved the accuracy of the network on unseen images. These results confirm that MF-HREM has the capability to provide 3D micrometer-scale information which cannot be visualised easily using current methods, and paves the way for the development of multi-fluorescence HREM for cancer imaging in the future.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Multi-Fluorescence Large Volume Imaging for Investigating the Tumour Micro-environment |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2023. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| Keywords: | Tumour Microenvironment, HREM, Machine Learning, Episcopic Imaging |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Department of Imaging |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10171127 |
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