Khatun, Suniya; (2024) Computational Tool and Experimental Method Development for Unveiling Cell Competition with Mass Spectrometry. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Cell competition, a phenomenon observed initially in Drosophila melanogaster, plays a pivotal role in maintaining tissue integrity. Through this mechanism, cells deemed less fit are eliminated. Yet, the precise molecular mechanisms underlying this observation remain unclear. This research delves into the competitive interaction between normal (MDCKWT, ’winner’) and modified (ScribKD (tet+), ’loser’) MDCK cells using mass spectrometry-based proteomics techniques, aiming to monitor protein expression changes across distinct competition phases to achieve a comprehensive understanding of the process. Chapter 2 commences with a thorough meta-analysis of sample preparation workflows for bottom-up proteomics, highlighting key contributors to increasing proteome coverage. Further, it culminated in the development of ProteoGuide, a web-based platform to aid researchers in making optimal decisions for sample preparation to enhance proteome coverage. Moreover, it functions as a resource hub, fostering knowledge-sharing and collaboration. Chapter 3 investigates the intricacies of MDCKWT and ScribKD (tet+) cellular dynamics during competition. By monitoring these cells over time, this chapter elucidates how cells respond and adapt to competition. A detailed timeline is presented, highlighting the survival tactics, adaptations, and vulnerabilities of ’winner’ and ’loser’ cells. These findings suggest that adaptive responses of cells in competitive environments arise from complex interplays of various cellular processes, potentially reshaping our understanding of tissue homeostasis and development. Recognising the potential role glycosylation may play in cell competition, Chapter 4 introduces GlycoRT. This computational tool, constructed using CNN and LSTM enhanced with an attention mechanism, predicts glycopeptide retention time. Used as orthogonal information during database searching, it can increase the accuracy and confidence of glycosylated peptide identification. Overall, this research integrates proteomic methodologies with computational tools, shedding light on the molecular dynamics of cell competition, offering an enriched framework for understanding cellular interactions in competitive landscapes - an essential principle in tissue homeostasis and growth.

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