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(Notingher et al. doi:10.1016(Notingher et al. /j.bios.2004). 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.

Surface-enhanced Raman scattering (SERS) with deep-UV excitation is of particular interest because a large variety of biomolecules such as amino acids exhibit electronic transitions in the UV spectral range and resonant excitation dramatically increases the cross section of the associated vibrational modes. Despite its potential, UV-SERS is still little-explored. We present a novel straightforward scalable route to fabricate aluminum nanovoids for reproducible SERS in the deep-UV without the need of expensive lithographic techniques. We adopt a modified template stripping method utilizing a soluble template and self-assembled polymer spheres to create nanopatterned aluminum films. We observe high surface enhancement of approximately 6 orders of magnitude, with excitation in the deep-UV (244 nm) on structures optimized for this wavelength. This work thus enables sensitive detection of organics and biomolecules, normally nonresonant at visible wavelengths, with deep-UV surface-enhanced resonant Raman scattering on reproducible and scalable substrates.

To use porous silicon as an optical interferometric biosensor, the pores must be sufficiently large to allow easy ingress of reagents and the layer must also display Fabry-Perot optical cavity modes. Here the detection antibody is rabbit IgG and the analyte is a-rabbit IgG conjugated to horseradish peroxidase (HRP). For this model system, the pores should be >50 nm in diameter. Such diameters have been obtained in 0.05 W cm n-type silicon using anodisation followed by chemical etching in ethanolic KOH and also by anodising 0.005 W cm p-type material. The latter also displays optical cavity modes. The silicon surface is oxidised in ozone, silanised using aminopropylmethoxysilanes with one, two or three methoxy groups, and cross linked to IgG using glutaraldehyde. High specific binding is found for mono-, di-and tri-methoxy silanes, but the lowest nonspecific binding is found for silanisation with the tri-methoxy silane.

The sensitivity of the optical reflectivity of porous silicon structures to the refractive index of liquid within the pores is studied for a single layer, a Bragg mirror and a microcavity. Sucrose solutions of concentration in the range 0.05 to 1.0% by weight are introduced into the pores within a flow cell in order to change the refractive index of the liquid in the pores from 1.3330 to 1.3344. Optimum wavelengths for detection via reflectivity changes are determined based on a signal to noise analysis. The optical thickness of the single layer is also monitored by measuring the fringe spacing via a Fourier transform technique. It is just possible to detect the effect of a change in refractive index of liquid in the pores of 0.00007 for both the reflectivity and optical thickness approaches.

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