Tutorial: Time series hyperspectral image analysis

A hyperspectral image is a large dataset in which each pixel corresponds to a spectrum, thus providing high-quality detail of a sample surface. Hyperspectral images are thus characterised by dual information, spectral and spatial, which allows for the acquisition of both qualitative and quantitative information from a sample. A hyperspectral image, commonly known as a 'hypercube', comprises two spatial dimensions and one spectral dimension. The data of such a file contain both chemical and physical information. Such files need to be analysed with a computational 'chemometric' approach in order to reduce the dimensionality of the data, while retaining the most useful spectral information. Time series hyperspectral imaging data comprise multiple hypercubes, each presenting the sample at a different time point, requiring additional considerations in the data analysis. This paper provides a step-by-step tutorial for time series hyperspectral data analysis, with detailed command line scripts in the Matlab and R computing languages presented in the supplementary data. The example time series data, available for download, are a set of time series hyperspectral images following the setting of a cement-based biomaterial. Starting from spectral pre-processing (image acquisition, background removal, dead pixels and spikes, masking) and pre-treatments, the typical steps encountered in time series hyperspectral image processing are then presented, including unsupervised and supervised chemometric methods. At the end of the tutorial paper, some general guidelines on hyperspectral image processing are proposed.European Commission - Seventh Framework Programme (FP7)European Research Counci

Multivariate Chemical Image Fusion of Vibrational Spectroscopic Imaging Modalities

Chemical image fusion refers to the combination of chemical images from different modalities for improved characterisation of a sample. Challenges associated with existing approaches include: difficulties with imaging the same sample area or having identical pixels across microscopic modalities, lack of prior knowledge of sample composition and lack of knowledge regarding correlation between modalities for a given sample. In addition, the multivariate structure of chemical images is often overlooked when fusion is carried out. We address these challenges by proposing a framework for multivariate chemical image fusion of vibrational spectroscopic imaging modalities, demonstrating the approach for image registration, fusion and resolution enhancement of chemical images obtained with IR and Raman microscopy

Characterisation of titanium oxide layers using Raman spectroscopy and optical profilometry: Influence of oxide properties

This study evaluates the use of a combination of Raman spectroscopy and optical profilometry as a surface characterisation technique for the examination of oxide layers grown on titanium metal substrates. The titanium oxide layers with thickness of up to 8 µm, were obtained using a low-pressure oxygen microwave plasma treatment of the titanium metal substrate. The effect of the microwave plasma processing conditions (input power, pressure and treatment time) on the Raman bandwidth, intensity and peak position was evaluated. Also, the effect of these processing conditions on the surface roughness parameters (Sa, Sdq, Ssk and Sku) of the oxide layers was investigated. Analysis of the peak positions of Eg and A1g modes indicated that the effects of input power and chamber pressure was to induce a shift towards the lower frequency with increasing input power and pressure (1–2 kPa). The intensity of the Raman bands was found to be dependent on the morphology and surface chemistry of the oxide layer. The intensity of Raman band (A1g), was found to be particularly influenced by the average surface roughness (Sa) and the crystallite size. Exponential and polynomial relations were found to correlate with these properties. A two-latent variable Partial Least Squares Regression model developed on Raman spectral data could predict surface roughness with a coefficient of determination (R2) of approx. 0.87 when applied to the testing of an independent set of titanium oxide test coatings.European Commission - Seventh Framework Programme (FP7)Science Foundation Irelan

Annexin-enriched osteoblast-derived vesicles act as an extracellular site of mineral nucleation within developing stem cell cultures

This is an Open Access Article. It is published by Nature under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/The application of extracellular vesicles (EVs) as natural delivery vehicles capable of enhancing tissue regeneration could represent an exciting new phase in medicine. We sought to define the capacity of EVs derived from mineralising osteoblasts (MO-EVs) to induce mineralisation in mesenchymal stem cell (MSC) cultures and delineate the underlying biochemical mechanisms involved. Strikingly, we show that the addition of MO-EVs to MSC cultures significantly (P < 0.05) enhanced the expression of alkaline phosphatase, as well as the rate and volume of mineralisation beyond the current gold-standard, BMP-2. Intriguingly, these effects were only observed in the presence of an exogenous phosphate source. EVs derived from non-mineralising osteoblasts (NMO-EVs) were not found to enhance mineralisation beyond the control. Comparative label-free LC-MS/MS profiling of EVs indicated that enhanced mineralisation could be attributed to the delivery of bridging collagens, primarily associated with osteoblast communication, and other non-collagenous proteins to the developing extracellular matrix. In particular, EV-associated annexin calcium channelling proteins, which form a nucleational core with the phospholipid-rich membrane and support the formation of a pre-apatitic mineral phase, which was identified using infrared spectroscopy. These findings support the role of EVs as early sites of mineral nucleation and demonstrate their value for promoting hard tissue regeneration