This paper presents a new cell culture platform enabling label-free surface-enhanced Raman spectroscopy (SERS) analysis of biological samples. The platform integrates a multilayered metal-insulator-metal nanolaminated SERS substrate and polydimethylsiloxane (PDMS) multiwells for the simultaneous analysis of cultured cells. Multiple cell lines, including breast normal and cancer cells and prostate cancer cells, were used to validate the applicability of this unique platform. The cell lines were cultured in different wells. The Raman spectra of over 100 cells from each cell line were collected and analyzed after 12 h of introducing the cells to the assay. The unique Raman spectra of each cell line yielded biomarkers for identifying cancerous and normal cells. A kernel-based machine learning algorithm was used to extract the high-dimensional variables from the Raman spectra. Specifically, the nonnegative garrote on a kernel machine classifier is a hybrid approach with a mixed nonparametric model that considers the nonlinear relationships between the higher-dimension variables. The breast cancer cell lines and normal breast epithelial cells were distinguished with an accuracy close to 90%. The prediction rate between breast cancer cells and prostate cancer cells reached 94%. Four blind test groups were used to evaluate the prediction power of the SERS spectra. The peak intensities at the selected Raman shifts of the testing groups were selected and compared with the training groups used in the machine learning algorithm. The blind testing groups were correctly predicted 100% of the time, demonstrating the applicability of the multiwell SERS array for analyzing cell populations for cancer research.

本文介绍了一种新的细胞培养平台，能够对生物样品进行无标记表面增强拉曼光谱 (SERS) 分析。该平台集成了多层金属-绝缘体-金属纳米层压 SERS 基板和聚二甲基硅氧烷 (PDMS) 多孔孔，用于同时分析培养细胞。多种细胞系，包括乳腺正常细胞和癌细胞以及前列腺癌细胞，被用来验证这个独特平台的适用性。细胞系在不同的孔中培养。在将细胞引入测定 12 小时后，收集并分析来自每个细胞系的 100 多个细胞的拉曼光谱。每个细胞系的独特拉曼光谱产生了用于识别癌细胞和正常细胞的生物标志物。使用基于内核的机器学习算法从拉曼光谱中提取高维变量。具体来说，核机器分类器上的非负绞索是一种混合非参数模型的混合方法，该模型考虑了高维变量之间的非线性关系。乳腺癌细胞系和正常乳腺上皮细胞的区分准确率接近90%。乳腺癌细胞与前列腺癌细胞的预测率达到94%。四个盲测试组用于评估 SERS 光谱的预测能力。选择测试组选定拉曼位移的峰值强度，并与机器学习算法中使用的训练组进行比较。盲测组在 100% 的时间内被正确预测，