微泡
拉曼光谱
表面增强拉曼光谱
细胞外小泡
纳米粒子跟踪分析
纳米技术
材料科学
癌细胞
分子
外体
纳米颗粒
光谱学
人工神经网络
计算机科学
生物系统
人工智能
计算生物学
化学
小RNA
癌症
拉曼散射
细胞生物学
生物化学
生物
物理
基因
光学
有机化学
量子力学
遗传学
作者
Uğur Parlatan,Mehmet Ozgun Ozen,Ibrahim Keçoğlu,Batuhan Koyuncu,Hülya Torun,Davod Khalafkhany,İrem Loç,Mehmet Giray Ogut,Fatih İnci,Demir Akin,İhsan Solaroğlu,Nesrin Özören,Mehmet Burçin Ünlü,Utkan Demirci
出处
期刊:Small
[Wiley]
日期:2023-01-15
卷期号:19 (9)
被引量:40
标识
DOI:10.1002/smll.202205519
摘要
Abstract Exosomes, nano‐sized extracellular vesicles (EVs) secreted from cells, carry various cargo molecules reflecting their cells of origin. As EV content, structure, and size are highly heterogeneous, their classification via cargo molecules by determining their origin is challenging. Here, a method is presented combining surface‐enhanced Raman spectroscopy (SERS) with machine learning algorithms to employ the classification of EVs derived from five different cell lines to reveal their cellular origins. Using an artificial neural network algorithm, it is shown that the label‐free Raman spectroscopy method's prediction ratio correlates with the ratio of HT‐1080 exosomes in the mixture. This machine learning‐assisted SERS method enables a new direction through label‐free investigation of EV preparations by differentiating cancer cell‐derived exosomes from those of healthy. This approach will potentially open up new avenues of research for early detection and monitoring of various diseases, including cancer.
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