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Single-cell analysis of cell competition using quantitative microscopy and machine learning

Day, Nathan Joshua; (2023) Single-cell analysis of cell competition using quantitative microscopy and machine learning. Doctoral thesis (Ph.D), UCL (University College London). Green open access

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Abstract

Cell competition is a widely conserved, fundamental biological quality control mechanism. The cell competition assay of MDCK wild-type versus mutant MDCK Scribble-knockdown (ScribKD) relies on a mechanical mechanism of competition, which posits that the emergence of compressing stresses within the tissue at high confluency drive the competitive outcome. According to this mechanism, proliferating wild-type cells out-compete mutant ScribKD cells, resulting in their apoptosis and apical extrusion. Previous studies show that there is an increased division rate of wild-type cells in neighbourhoods with high numbers of ScribKD cells, but what still remains a mystery is whether this is a cause or consequence of increased apoptosis in the “loser” cell population. This project also interrogated the competitive assay of wild-type versus RasV12 , which is hypothesized to operate on a biochemical mechanism and results in the apical extrusion (but not apoptosis) of the loser RasV12 population. For both these mechanisms of competition it is still unknown which population of cells are driving the winner/loser outcome. Is the winner cell proliferation prompting the loser cell demise? Or is an autonomous loser elimination prompting a subsequent winner cell proliferation? In my research, I have employed multi-modal, time-lapse microscopy to image competition assays continuously for several days. These data were then segmented into wild-type or mutant instances using a Convolutional Neural Network (CNN) that can differentiate between the cell types, after which they were tracked across cellular generations using a Bayesian multi-object tracker. A conjugate analysis of fluorescent cell-cycle indicator probes was then utilised to automatically identify key time points of cellular fate commitment using deep-learning image classification. A spatio-temporal analysis was then conducted in order to quantify any correlation between wild-type proliferation and mutant cell demise. For the case of wild-type versus ScribKD , there was no clear evidence for the wild-type cells mitoses directly impacting upon the ScribKD cell apoptotic elimination. Instead, a subsequent analysis found that a more subtle mechanism of pre-emptive, local density increases around the apoptosis site appeared to be determining the eventual ScribKD fate. On the other hand, there was clear evidence of a direct impact of wild-type mitoses on the subsequent apical extrusion and competitive elimination of RasV12 cells. Both of these conclusions agree with the prevailing classification of cell competition types: mechanical interactions are more diffuse and occur over a larger spatio-temporal domain, whereas biochemical interactions are constrained to nearest neighbour cells. The hypothesized density-dependency of ScribKD elimination was further quantified on a single-cell scale by these analyses, as well as a potential new understanding of RasV12 extrusion. Most interestingly, it appears that there is a clear biophysical mechanism to the elimination in the biochemical RasV12 cell competition. This suggests that perhaps a new semantic approach is needed in the field of cell competition in order to accurately classify different mechanisms of elimination.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: Single-cell analysis of cell competition using quantitative microscopy and machine learning
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: Copyright © The Author 2022. 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.
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 Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences
URI: https://discovery.ucl.ac.uk/id/eprint/10165838
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