Since the discovery of Raman scattering, physicists have analyzed different materials based on the changes in photons caused by the scattering effect in order to get the desired information. Surface-enhanced Raman scattering (SERS) is an ultra-sensitive optical analysis method based on Raman scattering, which introduces signal-enhancing molecules such as gold and silver nanoparticles to adsorb on rough metal surfaces to enhance the signal intensity of Raman scattering.
In order to realize the label-free detection of HER2 in cells, Xie and colleagues developed a novel machine learning-driven SERS method by utilizing the intrinsic SERS signal features of cancer cells (Fig.1). Before the detection, the Au nanostars were firstly internalized into HER2+ cancer cells to amplify the intrinsic SERS signals of cells. Subsequently, they introduced multivariate curve resolution - alternating least squares (MCR-ALS) and threshold segmentation to analyze the obtained intracellular SERS signals in detail. Meanwhile, the combination of linear discriminant analysis and principal component analysis can effectively distinguish the three breast cancer cells with different HER2 expression levels, which can achieve more accurate cell analysis.
Fig.1 Strategy for label-free spectroscopic detection of the HER2 status. (Xie, 2022)
To verify the effectiveness of the method, they used this approach to monitor the HER2 content in Hapt-treated SKBR-3 cells (high HER2 expression) in real-time. HApt is a newly discovered anti-cancer drug that can be applied to the treatment of breast cancer by promoting the lysosomal degradation of HER2 and then inducing apoptosis. They treated SKBR-3 cells with HApt for 24, 48, and 96 h and monitored the expression of HER2 in the cells treated at different times during the whole process.
Fig.2 Therapeutic treatment of HER2+ SKBR-3 cells with HApt. (Xie, 2022)
As shown in Fig.2, the expression level of HER2 was extremely high in the original SKBR-3 cells. However, after the addition of HApt, the expression level of HER2 became lower and lower with the increase of time. Meanwhile, the WB data also showed consistency with the detection results of the SERS system. The above data confirm that HApt can effectively reduce the expression level of HER2 in cancer cells, and the machine learning-driven SERS strategy established by the authors can effectively realize the dynamic monitoring of the the expression level of HER2 in cells, which can provide a powerful tool for evaluating the prognostic effect of drug therapy for cancer.
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