Nanolaminate Plasmonic Substrates for High-Throughput Living Cell SERS Measurements and Artificial Neural Network Classification of Cellular Drug Responses

Rapid in situ bio-analysis of cellular behaviors in response to external stimuli remains a formidable challenge but can open crucial opportunities in biology and medicine. The standard label-based end point assays suffer from invasiveness and complex sample handling. In this regard, label-free surface-enhanced Raman spectroscopy (SERS) has emerged as a promising non-invasive in situ bio-analysis technique for living cells. Nevertheless, achieving rapid in situ SERS bio-analysis still faces challenges in reliable high-throughput measurements and accurate multivariate analysis, which requires significant innovations in bio-interfaced SERS devices and machine learning (ML) methods. Here, we exploit cell-interfaced nanolaminate SERS substrates to demonstrate reliable high-throughput SERS measurements using well-studied living cancer cells with four drug dosages. Artificial neural network (ANN) for multiclass classification of cellular drug responses provides high accuracy (94%). Uniquely, nanolaminate SERS substrates with a high SERS enhancement factor (>107) can rapidly generate big SERS data sets with rich molecular information on living cells (10,000 spectra within 3 min) that can enable the utilization of data-hungry ML methods (e.g., ANN). By capturing additional hidden features in high-dimensional spectroscopic data, ANN is more powerful for multiclass classification than five other popular ML methods, including principal component analysis combined with linear discriminant analysis (PCA-LDA), partial least-squares discriminant analysis (PLSDA), classification tree (CT), k-nearest neighbor (KNN), and support vector machine (SVM). On the basis of the proof-of-concept demonstration using drugs on living cells, we anticipate that the nanolaminate SERS substrates can potentially monitor living cell responses to other external stimuli in a label-free and non-invasive manner.