Developing a data analysis tool to understand phenotypic heterogeneity from live imaging of 3-Dimensional tumour cell cultures

Freckmann, Eva C. (2022) Developing a data analysis tool to understand phenotypic heterogeneity from live imaging of 3-Dimensional tumour cell cultures. PhD thesis, University of Glasgow.

Abstract

Intra-tumour heterogeneity creates a major challenge in cancer treatment; while some cells in the tumour population are responsive to therapy, others may be resistant. This variable response to treatment leads to acquired treatment-resistance in the cell population, and eventual relapse for the patient. Single cell profiling by genetic, proteomic and imaging methods has expanded the ability to identify programmes that regulate distinct cell states. The 3-dimensional (3D) culture of cells or tissue fragments provides a system to study how such states contribute to multicellular morphogenesis. Due to a lack of tools for the detection and interrogation of heterogeneity, whether cells cultured in 3D exhibit a singular phenotype or whether multiple biologically distinct phenotypes arise in parallel remains an open question.

In this thesis I present a computational method I developed for the analysis of label-free multi-day time-lapse imaging to identify heterogeneous states in 3D culture and how these give rise to distinct temporal behaviours. With the help of collaborators I use this to identify the temporal landscape of states of cancer cell lines varying in metastatic potential and drug resistance, using this information to identify drug combinations that inhibit such heterogeneity. This approach is therefore an important companion tool to single-cell technologies by facilitating real-time identification via live-imaging of distinct temporal phenotypes that occur in parallel in 3D culture.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Colleges/Schools: College of Medical Veterinary and Life Sciences > School of Cancer Sciences > Beatson Institute of Cancer Research
• Supervisor's Name: Bryant, Dr. David and Miller, Professor Crispin
• Date of Award: 2022
• Depositing User: Theses Team
• Unique ID: glathesis:2022-83249
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 07 Nov 2022 15:15
• Last Modified: 07 Nov 2022 15:25
• Thesis DOI: 10.5525/gla.thesis.83249
• URI: https://theses.gla.ac.uk/id/eprint/83249

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