拉曼显微光谱(RMS)是一种强大的非标记工具，可以在细胞/亚细胞水平研究药物的影响。然而，能够提取与特定细胞响应相关的生化和动力学光谱特征十分重要。本研究使用多元曲线分析-交替最小二乘法(MCR-ALS)和主成分分析(PCA)相结合的方法，对拉曼显微光谱数据进行分析，以曝露原发性口腔鳞癌细胞(OSCC)对化疗药物顺铂的实例为例。通过细胞毒性试验确定了给药方案，并利用细胞凋亡试验监测了药物作用后16至72小时内的细胞光谱特征，从而确定药物处理后存活(V)、早期凋亡(EA)和晚期凋亡/死亡(LA/D)细胞的相对比例。基于细胞状态从V > EA > D的动力学模型，MCR-ALS回归分析能够提取与细胞响应的每个阶段相关的光谱特征，实现对各药物处理响应速率的定量比较。此外，PCA被用于比较接触药物的存活细胞的光谱特征。光谱差异在早期阶段(16小时接触)被突出显示，表明对药物处理的初始细胞响应，并且在晚期阶段(48-72小时接触)也有差异，代表了细胞死亡途径。本研究表明，RMS结合多元分析可以定量监测细胞对不同药物的响应进展，为无标记、体外的临床前筛选研究提供了未来应用的方向。

Raman MicroSpectroscopy (RMS) is a powerful label-free tool to probe the effects of drugs at a cellular/subcellular level. It is important, however, to be able to extract relevant biochemical and kinetic spectroscopic signatures of the specific cellular responses. In the present study, a combination of Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and Principal Component Analysis (PCA) is used to analyse the RMS data for the example of exposure of primary Oral Squamous Carcinoma Cells (OSCC) to the chemotherapeutic agent cisplatin. Dosing regimens were established by cytotoxicity assays, and the effects of the drug on cellular spectral profiles were monitored from 16 to 72 hours post-exposure using an apoptosis assay, to establish the relative populations of viable (V), early (EA) and late apoptotic/dead (LA/D) cells after the drug treatment. Based on a kinetic model of the progression from V > EA > D, MCR-ALS regression analysis of the RMS responses was able to extract spectral profiles associated with each stage of the cellular responses, enabling a quantitative comparison of the response rates for the respective drug treatments. Moreover, PCA was used to compare the spectral profiles of the viable cells exposed to the drug. Spectral differences were highlighted in the early stages (16 hours exposure), indicative of the initial cellular response to the drug treatment, and also in the late stages (48-72 hours exposure), representing the cell death pathway. The study demonstrates that RMS coupled with multivariate analysis can be used to quantitatively monitor the progression of cellular responses to different drugs, towards future applications for label-free, in vitro, pre-clinical screening.