Raman spectroscopy is an optical technique based on inelastic scattering of light by vibrating molecules and can provide chemical fingerprints of cells, tissues or biofluids. The high chemical specificity, minimal or lack of sample preparation and the ability to use advanced optical technologies in the visible or near-infrared spectral range (lasers, microscopes, fibre-optics) have recently led to an increase in medical diagnostic applications of Raman spectroscopy. The key hypothesis underpinning this field is that molecular changes in cells, tissues or biofluids, that are either the cause or the effect of diseases, can be detected and quantified by Raman spectroscopy. Furthermore, multivariate calibration and classification models based on Raman spectra can be developed on large "training" datasets and used subsequently on samples from new patients to obtain quantitative and objective diagnosis. Historically, spontaneous Raman spectroscopy has been known as a low signal technique requiring relatively long acquisition times. Nevertheless, new strategies have been developed recently to overcome these issues: non-linear optical effects and metallic nanoparticles can be used to enhance the Raman signals, optimised fibre-optic Raman probes can be used for real-time in-vivo single-point measurements, while multimodal integration with other optical techniques can guide the Raman measurements to increase the acquisition speed and spatial accuracy of diagnosis. These recent efforts have advanced Raman spectroscopy to the point where the diagnostic accuracy and speed are compatible with clinical use. This paper reviews the main Raman spectroscopy techniques used in medical diagnostics and provides an overview of various applications.

Raman spectroscopy is an increasingly popular technique in many areas, including biology and medicine. It is based on Raman scattering, a phenomenon in which incident photons lose or gain energy via interactions with vibrating molecules in a sample. These energy shifts can be used to obtain information regarding molecular composition of the sample with very high accuracy. Applications of Raman spectroscopy in the life sciences have included quantification of biomolecules, hyperspectral molecular imaging of cells and tissue, medical diagnosis, and others. This review briefly presents the physical origin of Raman scattering, explaining the key classical and quantum mechanical concepts. Variations of the Raman effect will also be considered, including resonance, coherent, and enhanced Raman scattering. We discuss the molecular origins of prominent bands often found in the Raman spectra of biological samples. Finally, we examine several variations of Raman spectroscopy techniques in practice, looking at their applications, strengths, and challenges. This review is intended to be a starting resource for scientists new to Raman spectroscopy, providing theoretical background and practical examples as the foundation for further study and exploration.

One of the main challenges faced by biodetection systems is the ability to detectand identify a large range of toxins at low concentrations and in short times. Cell-basedbiosensors rely on detecting changes in cell behaviour, metabolism, or induction of celldeath following exposure of live cells to toxic agents. Raman spectroscopy is a powerfultechnique for studying cellular biochemistry. Different toxic chemicals have differenteffects on living cells and induce different time-dependent biochemical changes related tocell death mechanisms. Cellular changes start with membrane receptor signalling leading tocytoplasmic shrinkage and nuclear fragmentation. The potential advantage of Ramanspectroscopy cell-based systems is that they are not engineered to respond specifically to asingle toxic agent but are free to react to many biologically active compounds. Ramanspectroscopy biosensors can also provide additional information from the time-dependentchanges of cellular biochemistry. Since no cell labelling or staining is required, the specifictime dependent biochemical changes in the living cells can be used for the identificationand quantification of the toxic agents. Thus, detection of biochemical changes of cells byRaman spectroscopy could overcome the limitations of other biosensor techniques, withrespect to detection and discrimination of a large range of toxic agents. Furtherdevelopments of this technique may also include integration of cellular microarrays forhigh throughput in vitro toxicological testing of pharmaceuticals and in situ monitoring ofthe growth of engineered tissues.

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 (Notingheret 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 μM and 500 μM, and 100% for 1000 μM. 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.