[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119131-en":3,"doc-seo-119131-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119131,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","A Morphodynamic Map of Cell Division Using Machine Learning","The thesis investigates how cells undergo dynamic morphological changes throughout the cell cycle and how these behaviors can be quantified at scale. It targets nuclear division in the fission yeast Schizosaccharomyces pombe, where a closed mitosis process produces reproducible morphological transformations. A machine learning framework is built using live-cell fluorescence imaging to generate segmentation masks and single-cell tracks, then unsupervised deep learning extracts latent features that encode multi-channel shape changes. A 2D latent landscape reveals condition-dependent trajectory patterns, supporting data-driven insight and future prediction in biology.","A morphodynamic map of cell division using machine learning  \nTchyn Lang Laure Ho  \nA thesis submitted to University College London (UCL) in partial fulfillment of the requirements for the degree of Doctor of Philosophy.  \nSupervisors: Dr . Alan R . Lowe, Prof. Buzz Baum  \nStructural and Molecular Biology  \nUniversity College London  \n16 February 2024  \nDeclaration  \nI, Tchyn Lang Laure Ho, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, Iconfirm that this has been indicated in the thesis.  \nTchyn Lang Laure Ho  \n16 February 2024  \nAbstract  \nAll cells have to undergo dynamic morphological changes throughout the cell cycle, a series of ordered events through which a cell grows and divides into two new cells. Analysis of the observed behaviors is canonically done in subsets of cells by quantifying a set of manually chosen features deemed relevant to the process of interest. Machine learning and image analysis techniques allow to scale this up in an automated and unbiased way, offering descriptions of cell populations as well as individual cells.  \nNuclear division is the process by which eukaryotes duplicate their nuclear compartment before cell division. In the fission yeast Schizosaccharomyces pombe, this occurs in a closed mitosis form during which an intact nuclear envelope undergoes remarkably reproducible morphological changes. This work presents a machine learning based framework to explore thousands of fission yeast nuclear division trajectories, with the aim of discovering the rules that govern this essential cell cycle process. First, we performed live-cell fluorescence imaging using markers of the nuclear envelope and microtubules to capture wild-type behavior of nuclear division as well as aberrant behavior following genetic mutations and treatment with pharmacological components. We then developed animage processing pipeline that produces segmentation masks and singlecell tracks, allowing us to follow individual nuclear divisions in time. Next, we performed unsupervised feature extraction using a deep neural network and showed that the resultant latent features are able to encode two-channel changes that significantly overlap with manually extracted shape features. Finally, when representing nuclear division trajectories in a 2D latent landscape, cells under different conditions are seen traveling through different parts of the space, suggesting that this data-driven approach can yield interesting insight into dynamic biological processes. Overall, this proof-of-concept work lays the foundation for the development of machine learning based prediction in biology.  \nImpact Statement  \nThe research presented in this thesis aims to be a proof-of-concept computational approach to the study of a fundamental biological process such as mitosis. It contributes an extensive library of curated fluorescence timelapse microscopy movies of Saccharomyces pombe nuclear divisions with endogenously tagged nuclear envelope and tubulin. This dataset represents an important and novel resource for the cell biology community, and serves as a good starting point for further studies of fission yeast nuclear division. The contributed work also contrasts between automated and manual feature extraction, and shows that deep neural networks are able to learn compact representations from raw data, which can be assigned certain biophysical meaning. Lastly, it shows on a 2D map in dimensionality reduction space that robust patterns of normal nuclear division can be established and certain divergent patterns can be identified. Studying mechanisms governing normal mitotic progression as well as errors that can or cannot be corrected in this process is essential for increasing understanding of various human diseases, such as cancer or developmental disorders. More broadly, this work contributes to the research and development of machine learning technology to problems in biology.  \nThe ","cbCaieU893E3scOT","https://ap.wps.com/l/cbCaieU893E3scOT","pdf",30515071,1,167,"English","en",105,"# Abstract\n# Impact Statement\n# Acknowledgements","[{\"question\":\"What biological process and organism does the thesis focus on?\",\"answer\":\"It focuses on nuclear division in the fission yeast Schizosaccharomyces pombe, a closed mitosis form in which the nuclear envelope shows reproducible morphological changes.\"},{\"question\":\"How does the thesis capture and analyze nuclear division behavior?\",\"answer\":\"It uses live-cell fluorescence imaging with nuclear envelope and microtubule markers, then processes images to produce segmentation masks and time-resolved single-cell tracks.\"},{\"question\":\"What role do machine learning methods play in the results?\",\"answer\":\"A deep neural network performs unsupervised feature extraction, producing latent features that overlap significantly with manually extracted shape features and can organize trajectories in a 2D latent landscape.\"}]","A Morphodynamic Map of Cell Division Using Machine Learning | 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biological process and organism does the thesis focus on?","Question",{"text":76,"@type":77},"It focuses on nuclear division in the fission yeast Schizosaccharomyces pombe, a closed mitosis form in which the nuclear envelope shows reproducible morphological changes.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the thesis capture and analyze nuclear division behavior?",{"text":81,"@type":77},"It uses live-cell fluorescence imaging with nuclear envelope and microtubule markers, then processes images to produce segmentation masks and time-resolved single-cell tracks.",{"name":83,"@type":74,"acceptedAnswer":84},"What role do machine learning methods play in the results?",{"text":85,"@type":77},"A deep neural network performs unsupervised feature extraction, producing latent features that overlap significantly with manually extracted shape features and can organize trajectories in a 2D latent 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