Notingher I, Green C, Dyer C, Perkins E, Hopkins N, Lindsay C, Hench L L
Department of Materials, Imperial College London, Exhibition Road, London, SW7 2AZ, UK.
J R Soc Interface. 2004 Nov 22;1(1):79-90. doi: 10.1098/rsif.2004.0008.
A Raman spectroscopy cell-based biosensor has been proposed for rapid detection of toxic agents, identification of the type of toxin and prediction of the concentration used. This technology allows the monitoring of the biochemical properties of living cells over long periods of time by measuring the Raman spectra of the cells non-invasively, rapidly and without use of labels (Notingher et al. 2004 doi:10.1016/j.bios.2004.04.008). Here we show that this technology can be used to distinguish between changes induced in A549 lung cells by the toxin ricin and the chemical warfare agent sulphur mustard. A multivariate model based on principal component analysis (PCA) and linear discriminant analysis (LDA) was used for the analysis of the Raman spectra of the cells. The leave-one-out cross-validation of the PCA-LDA model showed that the damaged cells can be detected with high sensitivity (98.9%) and high specificity (87.7%). High accuracy in identifying the toxic agent was also found: 88.6% for sulphur mustard and 71.4% for ricin. The prediction errors were observed mostly for the ricin treated cells and the cells exposed to the lower concentration of sulphur mustard, as they induced similar biochemical changes, as indicated by cytotoxicity assays. The concentrations of sulphur mustard used were also identified with high accuracy: 93% for 200 microM and 500 microM, and 100% for 1,000 microM. Thus, biological Raman microspectroscopy and PCA-LDA analysis not only distinguishes between viable and damaged cells, but can also discriminate between toxic challenges based on the cellular biochemical and structural changes induced by these agents and the eventual mode of cell death.
一种基于拉曼光谱细胞的生物传感器已被提出用于快速检测有毒物质、识别毒素类型以及预测所使用的浓度。该技术通过非侵入性、快速且不使用标记物的方式测量细胞的拉曼光谱,从而能够长时间监测活细胞的生化特性(诺廷格等人,2004年,doi:10.1016/j.bios.2004.04.008)。在此我们表明,该技术可用于区分毒素蓖麻毒素和化学战剂硫芥对A549肺细胞诱导的变化。基于主成分分析(PCA)和线性判别分析(LDA)的多变量模型被用于分析细胞的拉曼光谱。PCA-LDA模型的留一法交叉验证表明,受损细胞能够以高灵敏度(98.9%)和高特异性(87.7%)被检测到。在识别有毒物质方面也发现了高精度:硫芥为88.6%,蓖麻毒素为71.4%。预测误差主要出现在蓖麻毒素处理的细胞和暴露于较低浓度硫芥的细胞中,因为细胞毒性试验表明它们诱导了相似的生化变化。所使用的硫芥浓度也能被高精度识别:200微摩尔和500微摩尔时为93%,1000微摩尔时为100%。因此,生物拉曼显微光谱和PCA-LDA分析不仅能区分活细胞和受损细胞,还能根据这些物质诱导的细胞生化和结构变化以及最终的细胞死亡模式来区分不同的毒性挑战。