[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121917-en":3,"doc-seo-121917-105":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},121917,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Label-free detection of exosomes from different cellular sources based on surface-enhanced Raman spectroscopy combined with machine learning models","Exosomes facilitate inter-cellular communication and can reveal cell-to-cell interactions, signaling pathways, regulatory mechanisms, and disease diagnostics. Existing exosome identification workflows often require large datasets, limiting efficient and timely mechanism studies and diagnostic workflows. This work presents a machine-learning assisted SERS strategy to detect exosomes from six cell lines using a small dataset. Using PCA-SVM on 100 SERS spectra per exosome type, the approach achieves 94.4% accuracy for predicting cellular origin, supporting rapid downstream biological and disease investigations.","Label-free detection of exosomes from different cellular sources based on surface-enhanced Raman spectroscopy combined with machine learning models  \nYang Lia,b,c, 1, * , Xiaoming Lyub,1 , Kuo Zhana, Haoyu Jid, Lei Qinb, JianAn Huanga  \na. Research Unit of Health Sciences and Technology (HST), Faculty of Medicine University of Oulu, Finland  \nb. Research Center for Innovative Technology of Pharmaceutical Analysis, College of Pharmacy， Harbin Medical University， Heilongjiang 150081, PR China  \nc. National Key Laboratory of Frigid Zone Cardiovascular Diseases (NKLFZCD), College of Pharmacy, Harbin Medical University, Heilongjiang 150081, PR China;  \nd. Department of Pharmacy at The Second Affiliated Hospital, and Department of Pharmacology at College of Pharmacy (The Key Laboratory of Cardiovascular Medicine Research, Ministry of Education), Harbin Medical University, Harbin 150081, PR China  \n* Corresponding authors:  \nEmail address: [liy@hrbmu.edu.cn](liy@hrbmu.edu.cn)  \n1 These authors contributed equally to this work.  \nABSTRACT  \nExosomes are significant facilitators of inter-cellular communication that can unveil cell-cell interactions, signaling pathways, regulatory mechanisms and disease diagnostics. Nonetheless, current analysis required large amount of data for exosome identification that it hampers efficient and timely mechanism study and diagnostics. Here, we used a machine-learning assisted Surface-enhanced Raman spectroscopy (SERS) method to detect exosomes derived from six distinct cell lines (HepG2, Hela, 143B, LO-2, BMSC, and H8) with small amount of data. By employing sodium borohydride-reduced silver nanoparticles and sodium borohydride solution as an aggregating agent, 100 SERS spectra of the each types of exosomes were collected and then subjected to multivariate and machine learning analysis. By integrating Principal Component Analysis with Support Vector Machine (PCA-SVM) models, our analysis achieved a high accuracy rate of 94.4% in predicting exosomes originating from various cellular sources. In comparison to other machine learning analysis, our method used small amount of SERS data to allow a simple and rapid exosome detection, which enables a timely subsequent study of cell-cell interactions, communication mechanisms, and disease mechanisms in life sciences.  \nKEYWORDS: Cell-derived exosomes; Surface-enhanced Raman spectroscopy (SERS); Principal component analysis（PCA）; Hierarchical Cluster analysis（HCA）; Machine learning.  \n1. Introduction  \nExosomes are extracellular vesicles that are actively released by living cells, and their diameters usually range from 30-150 nm. Exosomes contain nucleic acids, proteins, and lipids, and their function as an important communication medium between cells has attracted much attention(Hessvik and Llorente 2018; Špilak et al. 2021; Staubach et al. 2021) . Initially, exosomes were thought to be carriers of cellular wastes and were widely found in human fluids, such as saliva, sweat, blood, and urine(Jara-Acevedo et al. 2019) . However, more and more studies have demonstrated that the content of specific components of exosomes can reflect the pathophysiological status of parental cells, and the protein, nucleic acids, and lipid content vary with cellular composition and function(Teng and Fussenegger 2021) . Thus, exosomesplay an important role in intercellular communication, tumour development, and metastasis(Hu et al. 2020; Meldolesi 2018; Regev-Rudzki et al. 2013; Vyas and Dhawan 2017) . Liquid biopsy methods forexosomes reduce patient harm and lower testing costs compared with traditional invasive histopathology  \nbiopsies. Therefore, exosomes have been widely studied as biomarkers for tumours(Han et al. 2022; Liet al. 2022a; Li et al. 2022b; Pan et al. 2021; Zhou et al. 2020) . Traditional methods for exosome detection include transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA), protein immunoblotting (Western blot), and enzyme-linked immun","cbCaigD9jPh75sDR","https://ap.wps.com/l/cbCaigD9jPh75sDR","pdf",1037554,1,19,"English","en",105,"# Abstract\n# Introduction\n## Exosomes and current detection methods\n## SERS for exosome analysis and label-free detection","[{\"question\":\"Why is label-free exosome detection important compared with traditional methods?\",\"answer\":\"Traditional exosome detection methods such as TEM, NTA, Western blot, and ELISA are time-consuming and cumbersome. Label-free approaches can simplify workflow and improve speed for diagnostics and mechanistic studies.\"},{\"question\":\"How does the proposed method detect exosomes from different cellular sources?\",\"answer\":\"The method combines surface-enhanced Raman spectroscopy with machine learning. SERS spectra are collected from exosomes derived from six cell lines, then analyzed with multivariate and machine learning models.\"},{\"question\":\"What machine learning strategy achieves the reported performance?\",\"answer\":\"Principal Component Analysis combined with Support Vector Machine (PCA-SVM) is used. It achieves a 94.4% accuracy rate in predicting exosome origin from various cellular sources using relatively small SERS datasets.\"}]","Label-free detection of exosomes from different cellular sources based on surface-enhanced Raman spectroscopy combined with machine learning models | PDF",1785807729,48,{"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},"label-free-detection-of-exosomes-from-different-cellular-sources-based-on-surface-enhanced-raman-spectroscopy-combined-with-machine-learning-models","",{"@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/label-free-detection-of-exosomes-from-different-cellular-sources-based-on-surface-enhanced-raman-spectroscopy-combined-with-machine-learning-models/121917/",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","2026-08-04",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 label-free exosome detection important compared with traditional methods?","Question",{"text":75,"@type":76},"Traditional exosome detection methods such as TEM, NTA, Western blot, and ELISA are time-consuming and cumbersome. Label-free approaches can simplify workflow and improve speed for diagnostics and mechanistic studies.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method detect exosomes from different cellular sources?",{"text":80,"@type":76},"The method combines surface-enhanced Raman spectroscopy with machine learning. SERS spectra are collected from exosomes derived from six cell lines, then analyzed with multivariate and machine learning models.",{"name":82,"@type":73,"acceptedAnswer":83},"What machine learning strategy achieves the reported performance?",{"text":84,"@type":76},"Principal Component Analysis combined with Support Vector Machine (PCA-SVM) is used. It achieves a 94.4% accuracy rate in predicting exosome origin from various cellular sources using relatively small SERS datasets.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]