Diao Xingkang, Qi GuoHua, Li Xinli, Tian Yu, Li Jing, Jin Yongdong
State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, P. R. China.
School of Applied Chemistry and Engineering, University of Science and Technology of China, Hefei 230026, P. R. China.
Anal Chem. 2025 Mar 11;97(9):5093-5101. doi: 10.1021/acs.analchem.4c06240. Epub 2025 Feb 25.
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.
细胞周期分析对于疾病的诊断和治疗至关重要，特别是在研究细胞异质性和调节细胞行为方面。外泌体作为非侵入性生物标志物极具吸引力，可用于监测细胞周期的实时变化，因为它们从母细胞继承了丰富的分子信息，并在一定程度上反映了这些细胞的状态。然而，据我们所知，外泌体与细胞周期之间的关系尚未见报道。在此，我们成功监测了外泌体表面增强拉曼光谱（SERS）光谱的变化，基于无标记表面增强拉曼光谱（SERS）结合线性判别分析（LDA）的机器学习方法来区分不同的细胞周期阶段（G₀/G₁、S和G₂/M期）。基于来自不同细胞周期阶段的外泌体训练后的SERS光谱，平均准确率达到85%，证实了支持向量机（SVM）算法在分析不同时间点细胞周期动态变化方面的高可靠性。重要的是，本无标记SERS检测平台还揭示了有丝分裂过程（前中期、中期和后期/末期）中的相关分子机制以及癌细胞（HeLa）和正常细胞（H8）之间独特的生物分子事件。基于SERS分析，HeLa细胞内苯丙氨酸（Phe）的含量增加，并且在有丝分裂过程中，一些含有Phe和色氨酸（Trp）残基的蛋白质结构可能会发生转变。值得注意的是，HeLa细胞中蛋白质的α-螺旋和β-折叠共存；同时，H8细胞中蛋白质的α-螺旋比HeLa细胞中更占主导地位。该策略对于区分细胞周期阶段和阐明细胞有丝分裂过程中的相关分子事件是有效的，并将为未来指导癌症的细胞周期治疗策略提供潜在的应用价值。
