Raman spectral imaging in tissue engineering & regenerative medicine applications

The label-free nature of Raman spectroscopy makes it a valuable tool for cellular and tissue characterisation. Its ability to probe molecular vibrations within biological structures without affecting their biochemistry offers an advantage over conventional histological and biochemical assays. Providing a pure investigation of unperturbed biological processes, without the need for introduction of exogenous molecules for labelling, makes the information Raman spectroscopy offers very valuable in deciphering complex biological functions and mechanisms. Raman spectral signatures are unique "fingerprints" of each biomolecule probed and can be used for cellular phenotype characterisation, tissue composition, disease development in a cellular or tissue level and much more. This thesis focuses on the use of Raman spectral imaging in novel biological applications displaying its flexibility across the fields of tissue engineering and regenerative medicine. Bone regeneration was the first biological process investigated, where Raman spectral imaging was used to characterise bioactive glass-assisted bone repair using standard and novel glass compositions. Newly-formed bone quality was assessed using multivariate analysis, showing similar quality between glass compositions and existing bone. Morphological analysis after in vivo implantation of bioactive glass particles showed distinct spectral zones confirming results from existing in vitro models. The second application, focused on the development of a novel Raman-based gene delivery tracking methodology. Viral particles, containing modified viral-nucleotides with alkyne bonds were produced were successfully detected using Raman spectral imaging in cells after infection. The implications of this technology offer a new cell screening methodology for gene therapy. Finally, the potential of Raman spectral imaging as a complementary technique for 3D cell culture systems was explored. A computational framework was developed which allows for the visualisation and quantification of subcellular structures. The accurate 3D reconstruction of whole cells of known architecture from a volumetric hyperspectral Raman dataset was reported here for the first time. Moreover, using spectral unmixing algorithms to quantify subcellular components, revealed an unprecedented molecular specificity. This allowed imaging of cells within hydrogel-based 3D cell culture systems. The synergy of Raman spectral imaging, multivariate and image analysis to answer complex biological questions offers objective biomolecular characterisation, quantification and visualisation of molecular architecture. This work demonstrates the potential of Raman spectroscopy as a valuable complementary tool in tissue engineering and regenerative medicine applications.Open Acces

Quantification of the plant endoplasmic reticulum

One of the challenges of quantitative approaches to biological sciences is the lack of understanding of the interplay between form and function. Each cell is full of complex-shaped objects, which moreover change their form over time. To address this issue, we exploit recent advances in confocal microscopy, by using data collected from a series of optical sections taken at short regular intervals along the optical axis to reconstruct the Endoplasmic Reticulum (ER) in 3D, obtain its skeleton, then associate to each of its edges key geometric and dynamic characteristics obtained from the original filled in ER specimen. These properties include the total length, surface area, and volume of the ER specimen, as well as the length surface area, and volume of each of its branches. In a view to benefit from the well established graph theory algorithms, we abstract the obtained skeleton by a mathematical entity that is a graph. We achieve this by replacing the inner points in each edge in the skeleton by the line segment connecting its end points. We then attach to this graph the ER geometric properties as weights, allowing therefore a more precise quantitative characterisation, by thinning the filled in ER to its essential features. The graph plays a major role in this study and is the final and most abstract quantification of the ER. One of its advantages is that it serves as a geometric invariant, both in static and dynamic samples. Moreover, graph theoretic features, such as the number of vertices and their degrees, and the number of edges and their lengths are robust against different kinds of small perturbations. We propose a methodology to associate parameters such as surface areas and volumes to its individual edges and monitor their variations with time. One of the main contributions of this thesis is the use of the skeleton of the ER to analyse the trajectories of moving junctions using confocal digital videos. We report that the ER could be modeled by a network of connected cylinders (0.87μm±0.36 in diameter) with a majority of 3-way junctions. The average length, surface area and volume of an ER branch are found to be 2.78±2.04μm, 7.53±5.59μm2 and 1.81±1.86μm3 respectively. Using the analysis of variance technique we found that there are no significant differences in four different locations across the cell at 0.05 significance level. The apparent movement of the junctions in the plant ER consists of different types, namely: (a) the extension and shrinkage of tubules, and (b) the closing and opening of loops. The average velocity of a junction is found to be 0.25μm/sec±0.23 and lies in the range 0 to 1.7μm/sec which matches the reported actin filament range