Scalable nanolaminated SERS multiwell cell culture assay

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. Researchers in the USA have developed a cell culture platform which enhances spectroscopic analysis, making it possible to carry out molecular profiling of biological samples without any labeling. A team led by Masoud Agah and Wei Zhou of Virginia Tech incorporated nanolaminated plasmonic structures into the surface of a multi-well cell culture plate. This modification improves the performance of Raman spectroscopy, facilitating more sensitive and precise biochemical profiling, and the resulting spectra were analyzed via machine learning. The team tested their approach by using it to discriminate samples of normal breast tissue, breast cancer cells, and prostate cancer cells. The analysis distinguished the cell types with over 90% accuracy. This approach can be used for label-free analysis of diagnostic biomarkers to identify specific cancer subtypes, and further work may make it possible to analyze samples of mixed cells.