[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121815-en":3,"doc-seo-121815-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},121815,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Analysis and monitoring of single HaCaT cells using volumetric Raman mapping and machine learning","Biomedical manufacturing demands rigorous validation to build confidence in positive outcomes, from drugs to bioengineered tissues. This thesis develops a dynamic Raman-spectroscopy experimental platform that supports mammalian cells in a Raman spectroscope, enabling automated adaptation of culture conditions and real-time spectral monitoring. The work refines volumetric Raman mapping (VRM) through k-means-directed shading, depth-distortion exploration, and calibration, then uses machine learning with PCA to classify VRM and quantify single-cell HaCaT morphological adaptations.","Analysis and monitoring of single HaCaT cells using volumetric Raman mapping and machine learning  \nMichael James Greenop  \nThis thesis is submitted for the degree of Doctor of Philosophy Lancaster University – School of Engineering September 2023  \nAbstract  \nNo explorer reached a pole without a map, no chef served a meal without tasting , and no surgeon implants untested devices. Higher accuracy maps, more sensitive taste buds, and more rigorous tests increase confidence in positive outcomes. Biomedical manufacturing necessitates rigour, whether developing drugs or creating bioengineered tissues [1]–[4] . By designing a dynamic environment that supports mammalian cells during experiments within a Raman spectroscope, this project provides a platform that more closely replicates in vivo conditions. The platform also adds the opportunity to automate the adaptation of the cell culture environment, alongside spectral monitoring of cells with machine learning and three-dimensional Raman mapping, called volumetric Raman mapping (VRM) . Previous research highlighted key areas for refinement, like a structured approach for shading Raman maps [5], [6], and the collection of VRM [7] . Refining VRM shading and collection was the initial focus, k-means directed shading for vibrational spectroscopy map shading was developed in Chapter 3 and exploration of depth distortion and VRM calibration (Chapter 4) .“Cage” scaffolds, designed using the findings from Chapter 4 were then utilised to influence cell behaviour by varying the number of cage beams to change the scaffold porosity. Altering the porosity facilitated spectroscopy investigation into previously observed changes in cell biology alteration in response to porous scaffolds [8] . VRM visualised changed single human keratinocyte (HaCaT) cell morphology, providing a complementary technique for machine learning classification. Increased technical rigour justified progression onto in-situ flow chamber for Raman spectroscopy development in Chapter 6, using a Psoriasis (dithranol-HaCaT) model on unfixed cells. K-means-directed shading and principal component analysis (PCA) revealed HaCaT cell adaptations aligning with previous publications [5] and earlier thesis sections. The k-means-directed Raman maps and PCA score plots verified the drug-supplying capacity of the flow chamber, justifying future investigation into VRM and machine learning for monitoring single cells within the flow chamber.  \nAcknowledgements  \nWriting this thesis provided a unique learning and development experience, made impossible without the opportunity, guidance, and support I received through the project. I am forever indebted to all who contributed, not only during the doctorate but the years prior encompassing housemates, coursemates, colleagues, lecturers, teammates, and the Nottingham lot.  \nSpecifically, I thank my supervisors, Prof. Rehman, and Dr. Ashton whose research and guidance helped shape this project. I am incredibly fortunate to have had both of you as my supervisors and have your continued support. Prof. Rehman, who brought me into the Bioengineering research group, who I thank for their advice, training , and friendship. Whilst in the group I completed my MSc dissertation , resulting in this Lancaster University School of Engineering funded PhD opportunity. I acknowledge the support of the School of Engineering, both financially and in providing a world-leading engineering environment to develop skills, knowledge, and resilience.  \nThe incredible support, phenomenal experiences, and tolerance provided by my family are the foundation for any successes I have. Thanking my parents and sister for their help and my childhood is therefore critical. To Mitch, I am grateful for the friendship and distraction of watching the Garibaldi Reds. Finally, but significantly, Ellie, first defence against stress, forgiver of my quirks and geographical distance, and enabler of my dreams, I thank you for everything.  \n","cbCaik6b4VgsWlso","https://ap.wps.com/l/cbCaik6b4VgsWlso","pdf",6181973,1,249,"English","en",105,"# Chapter 1-Introduction\n## Introduction\n## Raman spectroscopy theoretical background\n### Raman theory\n### Volumetric Raman mapping (VRM)\n### Live-cell Raman\n## Confocal Raman optics theoretical background\n### Confocal systems\n### Refraction\n# Chapter 2-Literature review and methods\n## Introduction\n## Literature review (Part A: Cells and scaffolds)\n### Direct laser writing\n### Directing cells using substrate surfaces\n### Cell response to 3D scaffolds\n### 3D scaffolds used to test cell behaviour\n### VRM of cells on DLW scaffolds","[{\"question\":\"What problem does the thesis address in biomedical Raman experiments?\",\"answer\":\"It targets the need for more accurate and rigorous mapping and monitoring of cells during Raman spectroscopy to increase confidence in positive experimental outcomes relevant to biomedical manufacturing.\"},{\"question\":\"How does the project support mammalian cells during Raman measurement?\",\"answer\":\"It designs a dynamic environment that supports mammalian cells within a Raman spectroscope, enabling conditions to be adapted automatically alongside spectral monitoring.\"},{\"question\":\"What methods are used to improve and interpret volumetric Raman mapping?\",\"answer\":\"It refines VRM shading and collection using k-means-directed shading, explores depth distortion and VRM calibration, and applies machine learning with PCA to reveal and verify single HaCaT cell adaptations.\"}]","Analysis and monitoring of single HaCaT cells using volumetric Raman mapping and machine learning | 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problem does the thesis address in biomedical Raman experiments?","Question",{"text":75,"@type":76},"It targets the need for more accurate and rigorous mapping and monitoring of cells during Raman spectroscopy to increase confidence in positive experimental outcomes relevant to biomedical manufacturing.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the project support mammalian cells during Raman measurement?",{"text":80,"@type":76},"It designs a dynamic environment that supports mammalian cells within a Raman spectroscope, enabling conditions to be adapted automatically alongside spectral monitoring.",{"name":82,"@type":73,"acceptedAnswer":83},"What methods are used to improve and interpret volumetric Raman mapping?",{"text":84,"@type":76},"It refines VRM shading and collection using k-means-directed shading, explores depth distortion and VRM calibration, and applies machine learning with PCA to reveal and verify single HaCaT cell 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