Label-Free Exosomal SERS Detection Assisted by Machine Learning for Accurately Discriminating Cell Cycle Stages and Revealing the Molecular Mechanisms during the Mitotic Process.

Cell cycle analysis is crucial for disease diagnosis and treatment, especially for investigating cell heterogeneity and regulating cell behaviors. Exosomes are highly appealing as noninvasive biomarkers for monitoring real-time changes in the cell cycle due to their abundant molecular information inherited from their metrocyte cells and reflecting the state of these cells to some extent. However, to our knowledge, the relationship between exosomes and the cell cycle has not been reported. Herein, we successfully monitored the variation of exosomal surface-enhanced Raman spectroscopy (SERS) spectra to discriminate different cell cycle stages (G/G, S, and G/M phases) based on label-free surface-enhanced Raman spectroscopy (SERS) combined with the machine learning method of linear discriminant analysis (LDA). An average accuracy of 85% based on the trained SERS spectra of exosomes from different cell cycle stages confirmed the high reliability of the support vector machine (SVM) algorithm for analyzing dynamic changes in the cell cycle at different time points. Importantly, the related molecular mechanisms among mitotic processes (prometaphase, metaphase, and anaphase/telophase) and unique biomolecular events between cancerous (HeLa) and normal (H8) cells were also revealed by the present label-free SERS detection platform. Based on SERS analysis, the content of phenylalanine (Phe) within HeLa cells increased, and some structures of proteins containing Phe and tryptophan (Trp) residues may be transformed during the mitotic process. Notably, the α-helix and β-sheet of proteins coexisted in HeLa cells; meanwhile, the α-helix of the proteins was more dominant in H8 cells than in HeLa cells. The strategy is effective for discriminating cell cycle stages and elucidating the associated molecular events during the cell mitotic process and will provide potential application value for guiding the cell cycle treatment strategies of cancer in the future.