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Label-free detection of cell-cycling polyploid cells in osteosarcoma

Almagwashi, Basmah 2022. Label-free detection of cell-cycling polyploid cells in osteosarcoma. PhD Thesis, Cardiff University.
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The current work investigates the prospect of detecting live cell-cycling polyploid cells (in osteosarcoma through endocycling) based on their label-free scatter and images. Human polyploid cells are cells that carry multiples of their diploid DNA content. In many types of tumours, this DNA irregularity makes these cells one of the triggers behind genome instability, particularly when such cells commit to a ploidy cell cycle. Studies conducted on viable polyploid cells are often limited by classic fluorescent stoichiometric DNA labels for detection. These labels are only appropriate for end-point assays; thus prompts the need for label-free detection methods. In the current work, polyploidy in osteosarcoma populations has been induced via drug treatment with the reversible topoisomerase inhibitor ICRF-193, where the generation of a rare endoreduplicating population can be enriched through different drug treatment doses and regimen. Different flow cytometer platforms are then used to detect and measure this cell cohort using label-free parameters of interest: the forward and side scatter on conventional flow cytometry (CFC), and brightfield and darkfield images on imaging flow cytometry (IFC). The current work showed that both the forward and side scatter intensity (area) measurements can be used in the detection of the target polyploid cells with low diploid contamination (approximately 0.4% of the original diploidy population) through automated clustering analysis on the data collected on the CFC platforms. For label-free image data collected on IFC, six supervised machine learning models were able to classify up to 75% of the original target cells accurately, where as low as 0.3% of the diploidy population were mislabelled as target cells. The work also recommended the detection specifications of the target cells, as well as protocols for sample handling, sample controls, and data analysis methods to facilitate the reproducibility of the findings on any flow cytometric platform.

Item Type: Thesis (PhD)
Date Type: Completion
Status: Unpublished
Schools: Physics and Astronomy
Subjects: Q Science > QC Physics
Uncontrolled Keywords: Label-free Polyploid Cells Detection Osteosarcoma Label-free Detection Label-free Cell Detection Flow Cytometry Imaging Flow Cytometry Light Scattering of Single Cells Forward Scatter Side Scatter Brightfield Cell Image Classification Darkfield Cell Image Classification Automated Flow Cytometry Gating Supervised Machine Learning Biophysics
Funders: Overseas government
Date of First Compliant Deposit: 1 September 2022
Last Modified: 01 Sep 2022 09:26

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