[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125035-en":3,"doc-seo-125035-105":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},125035,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Molecular Insights into α-Synuclein Fibrillation - A Raman Spectroscopy and Machine Learning Approach","α-synuclein aggregation drives Lewy body diseases, including Parkinson’s disease and dementia with Lewy bodies. The aggregation pathway follows nucleation, elongation, and secondary nucleation with prion-like spreading. Raman spectroscopy combined with a machine learning pipeline was used to quantify biomolecular changes during fibrillation of purified recombinant wild-type α-synuclein. Raman stages were validated by transmission electron microscopy, mass spectrometry, and light scattering, differentiating aggregation groups via key spectral peak shifts.","This article is licensed under CC-BY 4.0   \n[pubs.acs.org/chemneuro](pubs.acs.org/chemneuro)  Research Article   \nMolecular Insights into α‑Synuclein Fibrillation: A Raman Spectroscopy and Machine Learning Approach  \nNathan P. Coles, Suzan Elsheikh, Agathe Quesnel, Lucy Butler, Claire Jennings, Chaimaa Tarzi, Ojodomo J. Achadu, Meez Islam, Karunakaran Kalesh, Annalisa Occhipinti, Claudio Angione, Jon Marles-Wright, David J. Koss, Alan J. Thomas, Tiago F. Outeiro, Panagiota S. Filippou, and Ahmad A. Khundakar *  \nDownloaded via TEESSIDE UNIV on February 5, 2025 at 11:54:26 (UTC) . See [https://pubs.acs.org/sharingguidelines](https://pubs.acs.org/sharingguidelines) for options on how to legitimately share published articles.  \n Cite This: [https://doi.org/10.1021/acschemneuro.4c00726](https://doi.org/10.1021/acschemneuro.4c00726)  \nRead Online  \n\n|  |  |  |  |\n| --- | --- | --- | --- |\n| ACCESS   | Metrics & More |  |  Article Recommendations |\n\nABSTRACT: The aggregation of α-synuclein is crucial to the development of Lewy body diseases, including Parkinson’s disease and dementia with Lewy bodies. The aggregation pathway of α -synuclein typically involves a defined sequence of nucleation, elongation, and secondary nucleation, exhibiting prion-like spreading. This study employed Raman spectroscopy and machine learning analysis, alongside complementary techniques, to characterize the biomolecular changes during the fibrillation of purified recombinant wild-type α-synuclein protein. Monomeric α-synuclein was produced, purified, and subjected to a 7-day fibrillation assay to generate preformed fibrils. Stages of α-synuclein fibrillation were analyzed using Raman spectroscopy, with aggregation  \nconfirmed through negative staining transmission electron microscopy, mass spectrometry, and light scattering analyses. A machine learning pipeline incorporating principal component analysis and uniform manifold approximation and projection was used to analyze the Raman spectral data and identify significant peaks, resulting in differentiation between sample groups. Notable spectral shifts in α-synuclein were found in various stages of aggregation. Early changes (D1) included increases in α-helical structures (1303, 1330 cm−1) and β-sheet formation (1045 cm−1), with reductions in COO − and CH2 bond regions (1406, 1445 cm−1). By D4, these structural shifts persist with additional β-sheet features. At D7, a decrease in β-sheet H-bonding (1625 cm−1) and tyrosine ring breathing (830 cm−1) indicates further structural stabilization, suggesting a shift from initial helical structures to stabilized β-sheets and aggregated fibrils. Additionally, alterations in peaks related to tyrosine, alanine, proline, and glutamic acid were identified, emphasizing the role of these amino acids in intramolecular interactions during the transition from α-helical to β-sheet conformational states in α-synuclein fibrillation. This approach offers insight into α-synuclein aggregation, enhancing the understanding of its role in Lewy body disease pathophysiology and potential diagnostic relevance.  \nKEYWORDS: α-synuclein aggregation, Lewy body diseases, Raman spectroscopy, machine learning analysis, β-sheet formation, fibrillation pathway  \n■ INTRODUCTION sheet formation10, 11 and implicated in protein aggregation; 12  \nThe aberrant accumulation of α-synuclein is the key defining pathological feature of Lewy body diseases, such as Parkinson’s disease (PD) and dementia with Lewy bodies (DLB). 1−3 This culminates in the formation of Lewy bodies and neurites, disrupting cellular function and driving neurodegeneration asthe protein transitions from a soluble monomer to insoluble fibrils, a hallmark of disease progression.4−6 In its native form,α-synuclein is a soluble, monomeric protein with a molecular weight of 14,460.16 Da, composed of 140 amino acids.7 Its structure includes three distinct regions: the N-terminal domain (amino acids 1−60), which contains several repeat mo","cbCairMqOS76ReKs","https://ap.wps.com/l/cbCairMqOS76ReKs","pdf",3615745,1,12,"English","en",105,"# Abstract\n# Introduction\n## Lewy body disease background and α-synuclein structure\n## Aggregation cascade and disease progression","[{\"question\":\"Why is α-synuclein aggregation important for Lewy body diseases?\",\"answer\":\"α-synuclein aggregation is a defining pathological feature underlying Lewy body diseases such as Parkinson’s disease and dementia with Lewy bodies. Aggregation disrupts cellular function and drives neurodegeneration as the protein transitions from soluble monomer to insoluble fibrils.\"},{\"question\":\"What methods were used to study α-synuclein fibrillation stages?\",\"answer\":\"Raman spectroscopy was used to analyze biomolecular changes across fibrillation stages. Complementary validation was performed with negative staining transmission electron microscopy, mass spectrometry, and light scattering.\"},{\"question\":\"How did machine learning contribute to the Raman spectral analysis?\",\"answer\":\"A machine learning pipeline using principal component analysis and uniform manifold approximation and projection was applied to the Raman spectra. It identified significant peaks and differentiated between the sample groups, revealing structural shifts across early and late aggregation stages.\"}]","Molecular Insights into α-Synuclein Fibrillation - A Raman Spectroscopy and Machine Learning Approach | PDF",1785896285,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"molecular-insights-into-synuclein-fibrillation-a-raman-spectroscopy-and-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/molecular-insights-into-synuclein-fibrillation-a-raman-spectroscopy-and-machine-learning-approach/125035/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is α-synuclein aggregation important for Lewy body diseases?","Question",{"text":75,"@type":76},"α-synuclein aggregation is a defining pathological feature underlying Lewy body diseases such as Parkinson’s disease and dementia with Lewy bodies. Aggregation disrupts cellular function and drives neurodegeneration as the protein transitions from soluble monomer to insoluble fibrils.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What methods were used to study α-synuclein fibrillation stages?",{"text":80,"@type":76},"Raman spectroscopy was used to analyze biomolecular changes across fibrillation stages. Complementary validation was performed with negative staining transmission electron microscopy, mass spectrometry, and light scattering.",{"name":82,"@type":73,"acceptedAnswer":83},"How did machine learning contribute to the Raman spectral analysis?",{"text":84,"@type":76},"A machine learning pipeline using principal component analysis and uniform manifold approximation and projection was applied to the Raman spectra. It identified significant peaks and differentiated between the sample groups, revealing structural shifts across early and late aggregation stages.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]