多路复用
拉曼散射
人工智能
乳腺癌
材料科学
拉曼光谱
机器学习
癌症
算法
计算机科学
纳米技术
生物
生物信息学
物理
光学
遗传学
作者
Alberto Luis Rodriguez-Nieves,Mitchell Lee Taylor,Raymond Edward Wilson,Brinton King Eldridge,Samadhi Nawalage,Assam Annamer,H. Miller,Madhusudhan Alle,Saghar Gomrok,Dongmao Zhang,Yongmei Wang,Xiaohua Huang
标识
DOI:10.1021/acsami.3c13921
摘要
Cancer is the second leading cause of death attributed to disease worldwide. Current standard detection methods often rely on a single cancer marker, which can lead to inaccurate results, including false negatives, and an inability to detect multiple cancers simultaneously. Here, we developed a multiplex method that can effectively detect and classify surface proteins associated with three distinct types of breast cancer by utilizing gap-enhanced Raman scattering nanotags and machine learning algorithm. We synthesized anisotropic magnetic core-gold shell gap-enhanced Raman nanotags incorporating three different Raman reporters. These multicolor Raman nanotags were employed to distinguish specific surface protein markers in breast cancer cells. The acquired signals were deconvoluted and analyzed using classical least-squares regression to generate a surface protein profile and characterize the breast cancer cells. Furthermore, computational data obtained via finite-difference time-domain and discrete dipole approximation showed the amplification of the electric fields within the gap region due to plasmonic coupling between the two gold layers. Finally, a random forest classifier achieved an impressive classification and profiling accuracy of 93.9%, enabling effective distinguishing between the three different types of breast cancer cell lines in a mixed solution. With the combination of immunomagnetic multiplex target specificity and separation, gap-enhancement Raman nanotags, and machine learning, our method provides an accurate and integrated platform to profile and classify different cancer cells, giving implications for identification of the origin of circulating tumor cells in the blood system.
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