拉曼散射
线性判别分析
癌细胞
拉曼光谱
主成分分析
细胞
材料科学
多元统计
癌症
生物系统
化学
生物
人工智能
计算机科学
光学
生物化学
遗传学
机器学习
物理
作者
Yuan Fang,Taifeng Lin,Zheng Dang,Ye Zhu,Limin Wang,Yingying Fu,Huiqin Wang,Xihao Wu,Ping Zhang
摘要
Abstract Rapid detection and classification of cancer cells with label‐free and non‐destructive methods are helpful for rapid screening of cancer patients in clinical settings. Here, surface‐enhanced Raman scattering (SERS) was used for rapid, unlabeled, and non‐destructive detection of seven different cell types, including human cancer cells and non‐tumorous cells. Au nanoparticles were used as enhanced substrates and directly added to cell surfaces. The single cellular SERS signals could be easily and stably collected in several minutes, and the cells maintained structural integrity over one hour. Different types of cells had unique Raman phenotypes. By applying multivariate statistical analysis to the Raman phenotypes, the cancer cells and non‐tumorous cells were accurately identified. The high sensitivity enabled this method to discriminate subtle molecular changes in different cell types, and the accuracy reached 81.2% with principal components analysis and linear discriminant analysis. The technique provided a rapid, unlabeled, and non‐destructive method for the detection and identification of various cancer types.
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