Peter Knief

Raman spectroscopy – a potential platform for the rapid measurement of carbon nanotube-induced cytotoxicity

In this study the suitability of Raman spectroscopy for the determination of carbon nanotube mediated toxicity on human alveolar carcinoma epithelial cells (A549) is explored. The exposure of this cell line represents the primary pathway of exposure in humans, that of inhalation. Peak ratio analysis demonstrates a dose-dependent response which correlates to previous toxicological studies. Principal component analysis is employed to further classify cellular response as a function of dose and to examine differences between spectra as a function of exposed concentration. To further illustrate the potential of Raman spectroscopy in this field, Partial Least Squares (PLS) regression and genetic algorithm feature selection have been utilised to demonstrate that clonogenic endpoints, and therefore toxic response, can be potentially predicted from spectra of cells exposed to un-determined doses, removing the need for costly and time consuming biochemical assays. This preliminary study demonstrates the potential of Raman spectroscopy as a probe of cytotoxicity to nanoparticle exposure.

Spectral Pre and Post Processing for Infrared and Raman Spectroscopy of Biological Tissues and Cells

Vibrational spectroscopy, both infrared absorption and Raman spectroscopy, have attracted increasing attention for biomedical applications, from in vivo and ex vivo disease diagnostics and screening, to in vitro screening of therapeutics. There remain, however, many challenges related to the accuracy of analysis of physically and chemically inhomogeneous samples, across heterogeneous sample sets. Data preprocessing is required to deal with variations in instrumental responses and intrinsic spectral backgrounds and distortions in order to extract reliable spectral data. Data postprocessing is required to extract the most reliable information from the sample sets, based on often very subtle changes in spectra associated with the targeted pathology or biochemical process. This review presents the current understanding of the factors influencing the quality of spectra recorded and the pre-processing steps commonly employed to improve on spectral quality. It further explores some of the most common techniques which have emerged for classification and analysis of the spectral data for biomedical applications. The importance of sample presentation and measurement conditions to yield the highest quality spectra in the first place is emphasised, as is the potential of model simulated datasets to validate both pre- and post-processing protocols.

Three dimensional collagen gels as a cell culture matrix for the study of live cells by Raman spectroscopy

Three dimensional collagen gels are evaluated as matrices for the study of live cells by Raman spectroscopy. The study is conducted on a human lung adenocarcinoma (A549) and a spontaneously immortalized human epithelial keratinocyte (HaCaT) cell line. It is demonstrated, using the Alamar Blue assay, that both cell models exhibit enhanced viability in collagen matrices compared to quartz substrates, commonly used for Raman spectroscopy. Using principal component analysis, it is shown that the Raman spectral analysis of cells in collagen matrices is minimally contaminated by substrate contributions and the cell to cell spectral variations are greatly reduced compared to those measured on quartz substrates. Furthermore, the spectral measurements are seen to have little contribution from the cell culture medium, implying that cultures can be kept viable over prolonged measurement or mapping procedures.

Processing ThinPrep cervical cytological samples for Raman spectroscopic analysis

Raman microspectroscopy has been proven to be a promising technique for diagnosis and early detection of pathologies. The data collected delivers a chemical fingerprint allowing the identification of specific biomarkers indicating the presence of abnormalities. Label free, fast and cost effective, Raman spectroscopy has already been proposed as the new generation of diagnostic tool with a strong potential but has not emerged in the medical field as yet. Notably, it is crucial to improve and adapt the protocols used to reach suitable reproducibility for screening large cohorts of patients. In this study, it is demonstrated that the variability existing in the data sets collected can be limiting. Notably, when working on cervical ThinPrep samples, the presence of blood residue can be detected by Raman spectroscopy swamping the cellular signal. However, combining a washing of the slides using H2O2 and alcohol (70% ethanol and 100% Industrial Methylated Spirits), the blood features are removed from the data without altering either the cell morphology or the spectral features. Ultimately, this work demonstrates the improved potential of Raman spectroscopy for ThinPrep analysis based on improved protocols for sample preparation. Therefore, the screening of cervical cells for the detection of abnormalities and identification of patients with Cervical Intraepithelial Neoplasia (CIN) is achievable.

Collagen matrices as an improved model for in vitro study of live cells using Raman microspectroscopy

Due to its high lateral resolution, Raman microspectrsocopy is rapidly becoming an accepted technique for the subcellular imaging of single cells. Although the potential of the technique has frequently been demonstrated, many improvements have still to be realised to enhance the relevancy of the data collected. Although often employed, chemical fixation of cells can cause modifications to the molecular composition and therefore influence the observations made. However, the weak contribution of water to Raman spectra offers the potential to study live cells cultured in vitro using an immersion lens, giving the possibility to record highly specific spectra from cells in their original state. Unfortunately, in common 2-D culture models, the contribution of the substrates to the spectra recorded requires significant data pre-processing causing difficulties in developing automated methods for the correction of the spectra. Moreover, the 2-D in vitro cell model is not ideal and dissimilarities between different optical substrates within in vitro cell cultures results in morphological and functional changes to the cells. The interaction between the cells and their microenvironment is crucial to their behavior but also their response to different external agents such as radiation or anticancer drugs. In order to create an experimental model closer to the real conditions encountered by the cell in vivo, 3-D collagen gels have been evaluated as a substrate for the spectroscopic study of live cells. It is demonstrated that neither the medium used for cell culture nor the collagen gels themselves contribute to the spectra collected. Thus the background contributions are reduced to that of the water. Spectral measurements can be made in full cell culture medium, allowing prolonged measurement times. Optimizations made in the use of collagen gels for live cells analysis by Raman spectroscopy are encouraging and studying live cells within a collagenous microenvironment seems perfectly accessible.

Functional and pathological analysis of biological systems using vibrational spectroscopy with chemometric and heuristic approaches

Vibrational spectroscopy (Raman and FTIR microspectroscopy) is an attractive modality for the analysis of biological samples since it provides a complete non-invasive acquisition of the biochemical fingerprint of the sample. Studies in our laboratory have applied vibrational spectroscopy to the analysis of biological function in response to external agents (chemotherapeutic drugs, ionising radiation, nanoparticles), together with studies of the pathology of tissue (skin and cervix) in health and disease. Genetic algorithms have been used to optimize spectral treatments in tandem with the analysis of the data (using generalized regression neural networks (GRNN), artificial neural networks (ANN), partial least squares modelling (PLS), and support vector machines (SVM)), to optimize the complete analytical scheme and maximize the predictive capacity of the spectroscopic data. In addition we utilise variable selection techniques to focus on spectral features that provide maximal classification or regression of the spectroscopic data against analytical targets. This approach has yielded interesting insights into the variation of biochemical features of the biological system with its state, and has also provided the means to develop further the analytical and predictive capabilities of vibrational spectroscopy in biological analysis.