La compréhension des processus cellulaires au niveau membranaire est un domaine d’étude important en recherche biomédicale. Contourner la limite de diffraction en microscopie de fluorescence est maintenant devenu possible en exploitant les transitions moléculaires du fluorophore. Ce travail présente le développement instrumental de deux techniques complémentaires permettant d’atteindre une résolution nanométrique, grâce à l'émission stimulée (STimulated Emission Depletion - STED) d’une part, et la microscopie de fluorescence aux angles supercritiques (Supercritical Angle Fluorescence, SAF) d’autre part. La microscopie STED est une méthode permettant de surpasser la barrière de diffraction et d’atteindre des résolutions latérales de l'ordre de 40 nm dans des échantillons biologiques. Ce dispositif de microscopie exploite les transitions moléculaires des marqueurs fluorescents pour surmonter la limite de résolution due à la diffraction. L'amélioration de la résolution est obtenue par déplétion de l'état excité du fluorophores dans les régions périphériques de l'espace du volume focal. Cependant, malgré l'amélioration importante de la résolution latérale avec la technique STED, cette dernière présente une réelle complexité de mise en œuvre qui a par conséquence un impact important sur le cout des instruments STED commerciaux. Dans ce contexte, la réalisation instrumentale et la performance en imagerie d'un dispositif STED sont présentées dans ce manuscrit. Bien que les microscopes STED classiques offrent une meilleure résolution latérale, la résolution axiale est toujours limitée par la diffraction. L’amélioration de la résolution dans cette direction implique une certaine complexité instrumentale. Dans ce cadre, nous démontrons une nouvelle approche utilisant l’imagerie SAF permettant d'obtenir un sectionnement axial de l'ordre de 150 nm. L’approche se base sur la propriété d'une molécule à émettre dans les angles supercritiques uniquement lorsqu’elle se rapproche de l'interface verre-eau. Le sectionnement axial est obtenu dans une configuration simple en détectant uniquement les composantes de l’émission supercritique. La combinaison de ces techniques d'imagerie donne un outil puissant pour étudier les phénomènes moléculaires sur les membranes biologiques. / Understanding cellular processes on membranes has been a key area of biomedical research. Circumventing the diffraction limit in fluorescence microscopy has now become possible by exploiting the molecular transitions of the fluorophore. In this context, this work presents the instrumental development of two complementary techniques for realizing nanometric all-optical resolution and axial sectioning, namely STimulated Emission Depletion (STED) and Supercritical Angle Fluorescence (SAF) microscopy. STED microscopy is an elegant method that has allowed us to break the diffraction barrier with light microscopes and has achieved resolutions of the order of 40 nm (transverse) in biological samples. In this technique, we exploit the molecular transitions of the fluorescent marker to overcome the resolution limit due to diffraction. Resolution enhancement is achieved by efficient depletion of the excited state of the marker in the peripheral spatial regions of the focal volume by using depletion beams in addition to the excitation beam. Despite the major resolution improvement demonstrated, the technique is not well spread out, mainly due to its apparent complexity; and the cost and limited tunability of the commercial system. In this context, the instrumental realization and the imaging performance of a cost-effective home-built STED microscope is presented in this manuscript. While conventional STED microscopes offer improved lateral resolution, an isotropic gain in resolution usually comes at the cost of complex instrumentation. In this regard, we demonstrate SAF microscopy as a powerful tool that achieves an axial sectioning of the order of 150 nm. This is done by exploiting the property of a molecule to emit into the supercritical anglesonly when near the glass-water interface. Axial sectioning is obtained in a simple configuration by detecting solely the supercritical components of radiation. A combination of these imaging techniques offer a powerful tool to study molecular phenomena on the biological membranes.

Triangle-mesh modeling, as one of the approaches for representing images based on nonuniform sampling, has become quite popular and beneficial in many applications. In this thesis, image representation using triangle-mesh models and its application in image scaling are studied. Consequently, two new methods, namely, the SEMMG and MIS methods are proposed, where each solves a different problem. In particular, the SEMMG method is proposed to address the problem of image representation by producing effective mesh models that are used for representing grayscale images, by minimizing squared error. The MIS method is proposed to address the image-scaling problem for grayscale images that are approximately piecewise-smooth, using triangle-mesh models. The SEMMG method, which is proposed for addressing the mesh-generation problem, is developed based on an earlier work, which uses a greedy-point-insertion (GPI) approach to generate a mesh model with explicit representation of discontinuities (ERD). After in-depth analyses of two existing methods for generating the ERD models, several weaknesses are identified and specifically addressed to improve the quality of the generated models, leading to the proposal of the SEMMG method. The performance of the SEMMG method is then evaluated by comparing the quality of the meshes it produces with those obtained by eight other competing methods, namely, the error-diffusion (ED) method of Yang, the modified Garland-Heckbert (MGH) method, the ERDED and ERDGPI methods of Tu and Adams, the Garcia-Vintimilla-Sappa (GVS) method, the hybrid wavelet triangulation (HWT) method of Phichet, the binary space partition (BSP) method of Sarkis, and the adaptive triangular meshes (ATM) method of Liu. For this evaluation, the error between the original and reconstructed images, obtained from each method under comparison, is measured in terms of the PSNR. Moreover, in the case of the competing methods whose implementations are available, the subjective quality is compared in addition to the PSNR. Evaluation results show that the reconstructed images obtained from the SEMMG method are better than those obtained by the competing methods in terms of both PSNR and subjective quality. More specifically, in the case of the methods with implementations, the results collected from 350 test cases show that the SEMMG method outperforms the ED, MGH, ERDED, and ERDGPI schemes in approximately 100%, 89%, 99%, and 85% of cases, respectively. Moreover, in the case of the methods without implementations, we show that the PSNR of the reconstructed images produced by the SEMMG method are on average 3.85, 0.75, 2, and 1.10 dB higher than those obtained by the GVS, HWT, BSP, and ATM methods, respectively. Furthermore, for a given PSNR, the SEMMG method is shown to produce much smaller meshes compared to those obtained by the GVS and BSP methods, with approximately 65% to 80% fewer vertices and 10% to 60% fewer triangles, respectively. Therefore, the SEMMG method is shown to be capable of producing triangular meshes of higher quality and smaller sizes (i.e., number of vertices or triangles) which can be effectively used for image representation. Besides the superior image approximations achieved with the SEMMG method, this work also makes contributions by addressing the problem of image scaling. For this purpose, the application of triangle-mesh mesh models in image scaling is studied. Some of the mesh-based image-scaling approaches proposed to date employ mesh models that are associated with an approximating function that is continuous everywhere, which inevitably yields edge blurring in the process of image scaling. Moreover, other mesh-based image-scaling approaches that employ approximating functions with discontinuities are often based on mesh simplification where the method starts with an extremely large initial mesh, leading to a very slow mesh generation with high memory cost. In this thesis, however, we propose a new mesh-based image-scaling (MIS) method which firstly employs an approximating function with selected discontinuities to better maintain the sharpness at the edges. Secondly, unlike most of the other discontinuity-preserving mesh-based methods, the proposed MIS method is not based on mesh simplification. Instead, our MIS method employs a mesh-refinement scheme, where it starts from a very simple mesh and iteratively refines the mesh to reach a desirable size. For developing the MIS method, the performance of our SEMMG method, which is proposed for image representation, is examined in the application of image scaling. Although the SEMMG method is not designed for solving the problem of image scaling, examining its performance in this application helps to better understand potential shortcomings of using a mesh generator in image scaling. Through this examination, several shortcomings are found and different techniques are devised to address them. By applying these techniques, a new effective mesh-generation method called MISMG is developed that can be used for image scaling. The MISMG method is then combined with a scaling transformation and a subdivision-based model-rasterization algorithm, yielding the proposed MIS method for scaling grayscale images that are approximately piecewise-smooth. The performance of our MIS method is then evaluated by comparing the quality of the scaled images it produces with those obtained from five well-known raster-based methods, namely, bilinear interpolation, bicubic interpolation of Keys, the directional cubic convolution interpolation (DCCI) method of Zhou et al., the new edge-directed image interpolation (NEDI) method of Li and Orchard, and the recent method of super-resolution using convolutional neural networks (SRCNN) by Dong et al.. Since our main goal is to produce scaled images of higher subjective quality with the least amount of edge blurring, the quality of the scaled images are first compared through a subjective evaluation followed by some objective evaluations. The results of the subjective evaluation show that the proposed MIS method was ranked best overall in almost 67\% of the cases, with the best average rank of 2 out of 6, among 380 collected rankings with 20 images and 19 participants. Moreover, visual inspections on the scaled images obtained with different methods show that the proposed MIS method produces scaled images of better quality with more accurate and sharper edges. Furthermore, in the case of the mesh-based image-scaling methods, where no implementation is available, the MIS method is conceptually compared, using theoretical analysis, to two mesh-based methods, namely, the subdivision-based image-representation (SBIR) method of Liao et al. and the curvilinear feature driven image-representation (CFDIR) method of Zhou et al.. / Graduate

As técnicas de super-resolução possibilitam combinar várias imagens de uma mesma cena para se obter uma imagem com resolução radiométrica e geométrica aumentada, denominada de imagem de super-resolução. Nessa imagem são realçadas características importantes possibilitando recuperar detalhes e informações. As aplicações envolvem diferentes áreas, tais como: na agricultura para identificar possíveis desmatamentos e controle de pragas, na área médica para a detecção de doenças em estágios iniciais, identificação facial de pessoas suspeitas em imagens de circuito fechado, reconstrução de filmes, identificação de placas de veículos, entre outras. No presente trabalho, é proposta uma metodologia para a geração de imagens de super-resolução a partir de uma região selecionada de um mosaico. Embora existam vários trabalhos publicados relacionados à geração de imagens de super-resolução, as metodologias não se aplicam para uma região específica do mosaico. E grande parte dos trabalhos utiliza uma imagem de referência, a partir da qual é gerada a imagem de super-resolução. Na metodologia proposta, inicialmente, é gerado um mosaico a partir de um conjunto de imagens baseando-se nos algoritmos SIFT ou SURF, BBF e RANSAC e é criada uma estrutura de dados, que organiza os pontos correlacionáveis das imagens com sobreposição, facilitando e simplificando o processo de fusão desses pontos para a obtenção da imagem de super-resolução. A ferramenta implementada a partir dessa metodologia, possibilita ao operador selecionar a região de interesse no mosaico, a partir da qual, é gerada a imagem de super-resolução utilizando as técnicas SIFT (ou SURF), interpolação Bicúbica e a fusão pelo valor mediano dos pontos da área com sobreposição das imagens da sequência. Para validar a metodologia, foram utilizados quatro conjuntos de imagens, que incluem imagens simuladas, obtidas com câmeras de baixa e alta resolução, imagens aéreas de áreas urbanas e rurais, coloridas e em escalas de cinza, e imagens contendo elementos textuais. Nas imagens simuladas foram adicionados ruídos e avaliada a imagem de super-resolução gerada por meio de duas métricas: raiz do erro médio quadrático (RMSE) e o índice de similaridade estrutural (SSIM). Os resultados mostraram que mesmo com valor de RMSE elevado, o SSIM foi acima de 70%, evidenciando o alto grau de similaridade. As imagens de super-resolução obtidas a partir de uma região dos mosaicos gerados foram comparadas com imagens superamostradas por meio de interpolações e avaliadas confrontando as imagens extrapoladas para verificação visual dos elementos da cena. Os resultados apresentados concluem que as imagens de super-resolução geradas, apresentam melhorias no que diz respeito à restauração das mesmas para futura análise de alvos de interesse, sem ter o retrabalho de adquirir novas imagens da cena, pois dependendo da cena analisada não será possível nova aquisição. O presente trabalho contribui com a geração de imagem de super-resolução, a partir de uma região do mosaico e com estruturas de dados e algoritmos que possibilitam a análise de regiões específicas do mosaico, sem que o mesmo tenha que ser processado integralmente. / Super-resolution techniques allow combining several images of the same scene in order to obtain an image with increased geometric and radiometric resolution, called super-resolution image. In this image are enhanced features allowing to recover important details and information. The applications involve different areas, such as: in the agriculture to identify possible deforestation and pest control, in the medical area to detect diseases in early stages, facial identification in images of suspects in closed loop, movies reconstruction, license plates recognition, among others. In this work, we propose a methodology for generating super-resolution images from a selected region of a mosaic. Although there are several published papers related to the generation of super-resolution images, the existing methodologies do not apply to a specific region of the mosaic. And the majority of studies use a reference image, from which is generated the super-resolution image. In the proposed methodology, initially, a mosaic is generated from a set of images based on the algorithms SIFT or SURF, BBF and RANSAC and creates a data structure that organizes the points correlate of the images with overlapping, facilitating and simplifying the fusion process of these points to obtain the super-resolution image. The tool implemented from this methodology allows to the operator to select the region of interest in the mosaic, from which is generated the image using super-resolution techniques SIFT (or SURF), Bicubic interpolation and fusion process by the median value of the points with overlapping area from the images of the sequence. In order to validate the methodology, we used four sets of images, including simulated images taken with cameras of low and high resolution, aerial images of urban and rural areas, color and grayscale images and images containing texts. In the simulated images were added noise and were evaluated the super-resolution image generated by two metrics: root mean square error (RMSE) and the structural similarity index (SSIM). The results showed that even with high RMSE value, the SSIM was above 70%, reflecting the high degree of similarity. The super-resolution images obtained from a region of the mosaics were compared with images generated by super-sampled interpolation and evaluated by comparing the images extrapolated to visual inspection of elements of the scene. From the results it can be concluded that the super-resolution images generated present improvements with regard to restoration of images for further analysis of targets of interest, without reworking to acquire new images of the scene, because depending on the analyzed scene it would not be possible a new acquisition. This work contributes to the generation of super-resolution image from a region of the mosaic and with data structures and algorithms which enable the analysis of specific regions of the mosaic without it has to be fully processed.

Les techniques de super-résolution offrent un nouvel aperçu de la description de l'organisation moléculaire dynamique de la membrane plasmique. Parmi ces techniques, la microscopie par déplétion d'émission stimulée (stimulated emission depletion, STED) dépasse la limite de diffraction optique et atteint une résolution de quelques dizaines de nanomètres. Il est une technique polyvalente qui peut être combinée avec d'autres techniques telles que la spectroscopie par corrélation de fluorescence (fluorescence correlation spectroscopy, FCS), fournissant des résolutions spatiales et temporelles élevées pour explorer les processus dynamiques qui se produisent dans les cellules vivantes. Ce projet de doctorat vise à mettre en œuvre un microscope STED, puis à combiner ce module STED avec la technique FCS pour les applications biologiques. Des études théoriques du STED et de la technique combinant STED et FCS ont permis dans les aspects spatio-temporels. Une solution analytique pour la fonction d'autocorrélation FCS a été dérivée dans l'état de déplétion STED incomplet. et un nouveau modèle d'ajustement FCS a été proposé. La méthode de variation du volume d’observation FCS (spot variation FCS, svFCS) a démontré sa capacité à identifier la présence de nanodomaines limitant la diffusion latérale des molécules dans la membrane plasmique. L’approche STED-FCS permet d’étendre l’application de la svFCS à l'échelle nanométrique afin d’évaluer la persistance plus ou moins importante de tels nanodomaines. Dans ce contexte, des simulations préliminaires de Monte Carlo ont été réalisées figurant des molécules diffusant en présence d'auto-assemblage/désassemblage dynamique des nanodomaines. / Super-resolution techniques offer new insight into the description of the dynamic molecular organization at the plasma membrane. Among these techniques, the stimulated emission depletion (STED) microscopy breaks the optical diffraction limit and reaches the resolution of tens of nanometer. It is a versatile setup that can be combined with other techniques such as fluorescence correlation spectroscopy (FCS), providing both high spatial and temporal resolutions to explore dynamic processes occurring in live cells. This PhD project aims at implementing a STED microscope, and then at combining this STED module with FCS technique for biological applications. Detailed theoretical studies on STED and the combined STED-FCS technique in spatio-temporal aspects were performed. An analytical solution for FCS autocorrelation function was derived in the condition of incomplete STED depletion and a new FCS fitting model was proposed to overcome this problem. The spot variation FCS (svFCS) method has demonstrated its capability to identify the presence of nanodomains constraining the lateral diffusion of molecules at the plasma membrane. The STED-FCS can extend the svFCS approach to the nanoscale evaluating the long-lasting existence of such nanodomains. Within this frame, preliminary Monte Carlo simulations were conducted mimicking molecules diffusing in the presence of dynamic self-assembling/disassembling nanodomains.

Using Time of Arrival (TOA) as ranging metric is the most popular technique for accurate indoor positioning. Accuracy of measuring the distance using TOA is sensitive to the bandwidth of the system and the multipath condition between the wireless terminal and the access point. In a telecommunication-specific application, the channel is divided into Line of Sight (LOS) and Obstructed Line of Sight (OLOS) based on the existence of physical obstruction between the transmitter and receiver. In indoor geolocation application, with extensive multipath conditions, the emphasis is placed on the behavior of the first path and the channel conditions are classified as Dominant Direct Path (DDP), Nondominant Direct Path (NDDP) and Undetected Direct Path (UDP). In general, as the bandwidth increases the distance measurement error decreases. However, for the so called UDP conditions the system exhibits substantially high distance measurement errors that can not be eliminated with the increase in the bandwidth of the system. Based on existing measurements performed in CWINS, WPI a measurement database that contains adequate number of measurement samples of all the different classification is created. Comparative analysis of TOA estimation in different multipath conditions is carried out using the measurement database. The performance of super-resolution and traditional TOA estimation algorithms are then compared in LOS, OLOS DDP, NDDP and UDP conditions. Finally, the analysis of the effect of system bandwidth on the behavior of the TOA of the first path is presented.

There has been significant progress in the optical resolution of microscopes over the last two decades. However, the majority of currently used methods (e.g. STED, PALM, STORM) have a number of drawbacks, including high intensities of light that result in damage to living specimens in STED, and long data acquisition time leading to limitations on live-cell imaging. Therefore, there is a niche for faster image acquisition at lower intensities while maintaining resolution beyond the diffraction limit. Here, we have developed a new methodology - Quantum Dot-based Optical Spectral Separation (QDOSS) - which relies on using Quantum Dots (QDs) as fluorophores, and on their separation and localisation based on their spectral signatures. We utilise the key advantages of QDs over the usual organic fluorophores: broad excitation, narrow emission spectra and high resistance to photobleaching. Besides, since QDOSS is based on spectral differences for separation, QDs can be deterministically localised in a relatively short time - milliseconds and, potentially, microseconds. Last but not least, QDOSS is suitable for obtaining super-resolution images using a standard confocal fluorescence microscope equipped with a single laser excitation wavelength and capable of spectral signal separation (e.g. Leica TCS SP series or Zeiss LSM series). First, we demonstrated resolution down to 60 nm using triangular DNA origami as a reference. Furthermore, we labelled and imaged the alpha-tubulin structure in HEK293T cells. We showed that using a mixture of standard off-the-shelf QDs of different sizes, resolution down to 40 nm could be achieved via spectroscopic separation of QDs. Finally, we demonstrated that QDOSS could also be used for multicolour imaging of synaptic proteins distributed around synapsis in neurons within diffraction limit. All in all, we believe that these features of QDOSS make it a potential method for long-term live super-resolution imaging, which is going to have a high impact in biological sciences.

As técnicas de super-resolução possibilitam combinar várias imagens de uma mesma cena para se obter uma imagem com resolução radiométrica e geométrica aumentada, denominada de imagem de super-resolução. Nessa imagem são realçadas características importantes possibilitando recuperar detalhes e informações. As aplicações envolvem diferentes áreas, tais como: na agricultura para identificar possíveis desmatamentos e controle de pragas, na área médica para a detecção de doenças em estágios iniciais, identificação facial de pessoas suspeitas em imagens de circuito fechado, reconstrução de filmes, identificação de placas de veículos, entre outras. No presente trabalho, é proposta uma metodologia para a geração de imagens de super-resolução a partir de uma região selecionada de um mosaico. Embora existam vários trabalhos publicados relacionados à geração de imagens de super-resolução, as metodologias não se aplicam para uma região específica do mosaico. E grande parte dos trabalhos utiliza uma imagem de referência, a partir da qual é gerada a imagem de super-resolução. Na metodologia proposta, inicialmente, é gerado um mosaico a partir de um conjunto de imagens baseando-se nos algoritmos SIFT ou SURF, BBF e RANSAC e é criada uma estrutura de dados, que organiza os pontos correlacionáveis das imagens com sobreposição, facilitando e simplificando o processo de fusão desses pontos para a obtenção da imagem de super-resolução. A ferramenta implementada a partir dessa metodologia, possibilita ao operador selecionar a região de interesse no mosaico, a partir da qual, é gerada a imagem de super-resolução utilizando as técnicas SIFT (ou SURF), interpolação Bicúbica e a fusão pelo valor mediano dos pontos da área com sobreposição das imagens da sequência. Para validar a metodologia, foram utilizados quatro conjuntos de imagens, que incluem imagens simuladas, obtidas com câmeras de baixa e alta resolução, imagens aéreas de áreas urbanas e rurais, coloridas e em escalas de cinza, e imagens contendo elementos textuais. Nas imagens simuladas foram adicionados ruídos e avaliada a imagem de super-resolução gerada por meio de duas métricas: raiz do erro médio quadrático (RMSE) e o índice de similaridade estrutural (SSIM). Os resultados mostraram que mesmo com valor de RMSE elevado, o SSIM foi acima de 70%, evidenciando o alto grau de similaridade. As imagens de super-resolução obtidas a partir de uma região dos mosaicos gerados foram comparadas com imagens superamostradas por meio de interpolações e avaliadas confrontando as imagens extrapoladas para verificação visual dos elementos da cena. Os resultados apresentados concluem que as imagens de super-resolução geradas, apresentam melhorias no que diz respeito à restauração das mesmas para futura análise de alvos de interesse, sem ter o retrabalho de adquirir novas imagens da cena, pois dependendo da cena analisada não será possível nova aquisição. O presente trabalho contribui com a geração de imagem de super-resolução, a partir de uma região do mosaico e com estruturas de dados e algoritmos que possibilitam a análise de regiões específicas do mosaico, sem que o mesmo tenha que ser processado integralmente. / Super-resolution techniques allow combining several images of the same scene in order to obtain an image with increased geometric and radiometric resolution, called super-resolution image. In this image are enhanced features allowing to recover important details and information. The applications involve different areas, such as: in the agriculture to identify possible deforestation and pest control, in the medical area to detect diseases in early stages, facial identification in images of suspects in closed loop, movies reconstruction, license plates recognition, among others. In this work, we propose a methodology for generating super-resolution images from a selected region of a mosaic. Although there are several published papers related to the generation of super-resolution images, the existing methodologies do not apply to a specific region of the mosaic. And the majority of studies use a reference image, from which is generated the super-resolution image. In the proposed methodology, initially, a mosaic is generated from a set of images based on the algorithms SIFT or SURF, BBF and RANSAC and creates a data structure that organizes the points correlate of the images with overlapping, facilitating and simplifying the fusion process of these points to obtain the super-resolution image. The tool implemented from this methodology allows to the operator to select the region of interest in the mosaic, from which is generated the image using super-resolution techniques SIFT (or SURF), Bicubic interpolation and fusion process by the median value of the points with overlapping area from the images of the sequence. In order to validate the methodology, we used four sets of images, including simulated images taken with cameras of low and high resolution, aerial images of urban and rural areas, color and grayscale images and images containing texts. In the simulated images were added noise and were evaluated the super-resolution image generated by two metrics: root mean square error (RMSE) and the structural similarity index (SSIM). The results showed that even with high RMSE value, the SSIM was above 70%, reflecting the high degree of similarity. The super-resolution images obtained from a region of the mosaics were compared with images generated by super-sampled interpolation and evaluated by comparing the images extrapolated to visual inspection of elements of the scene. From the results it can be concluded that the super-resolution images generated present improvements with regard to restoration of images for further analysis of targets of interest, without reworking to acquire new images of the scene, because depending on the analyzed scene it would not be possible a new acquisition. This work contributes to the generation of super-resolution image from a region of the mosaic and with data structures and algorithms which enable the analysis of specific regions of the mosaic without it has to be fully processed.

No description available.

Natural Killer (NK) cells are cytotoxic lymphocytes that can recognize and kill virally infected or tumour transformed cells by the secretion of cytolytic granules containing perforin. An individual NK cell can kill several target cells sequentially. Each target cell can trigger NK cell activation via different activating ligands and here we report that the order in which ligands are encountered affects the NK cell response. When NK cells are repeatedly activated via their Fc receptor CD16, with the therapeutic antibody rituximab, perforin secretion decreases with each stimulation. However, perforin secretion is restored to its initial level upon subsequent activation by MICA, which ligates NKG2D. Repeated stimulation of NK cells via MICA also decreases the degranulation capacity of NK cells but, strikingly, this effect cannot be rescued by a subsequent stimulation with rituximab. The strength of perforin secretion is also translated to killing of Daudi target cells, expressing different ligands. When Daudi, opsonised with rituximab is the first target NK cell encounters, the sequential killing of another opsonised rituximab or Daudi, expressing MICA will not be affected. But, when Daudi-MICA is met first, the consecutive killing of Daudi-MICA as well as Daudi-rituximab will be impaired. We found that the mechanism underlying these differential outcomes involves shedding of CD16, which occurs upon NK cell activation through both, CD16 and NKG2D. Shedding of CD16 renders the cells insensitive to further activation via that receptor but they remain competent for further activation through NKG2D. Interestingly, however, we also identified the beneficial role of CD16 shedding for NK cell serial killing. NK cells are more motile on rituximab-coated surfaces than on MICA-coated surfaces and their migration speed decreases upon inhibition of CD16 shedding. Moreover, the inhibition of CD16 shedding also prevents the NK cell detachment from rituximab opsonised Daudi cells. Thus, the shedding of the receptor can serve to augment NK cell motility to move between target cells. Efficient NK cell detachment also correlated with their increased survival. Finally, we report that CD16 is constitutively organised in small, dense nanoclusters and that the ligation with rituximab does not affect their spatial distribution. Despite the shedding of the receptor, leading to less protein molecules at the surface, the area of these clusters remains the same. Together these data suggest that CD16 shedding hinders NK cell cytotoxicity against opsonised targets, but promotes their movements between different targets. Thus, receptor shedding is important for efficient NK cell serial killing. Manipulation of CD16 shedding, perhaps by boosting its recovery, might therefore represent an important target for NK cell-based therapies including treatments with therapeutic antibodies.

Optical cell manipulation allows precise and non-invasive exploration of mammalian cell function and physiology for medical applications. Plants, however, represent a vital component of the Earth's ecosystem and the knowledge gained from using optical tools to study plant cells can help to understand and manipulate useful agricultural and ecological traits. This thesis explores the potential of several biophotonic techniques in plant cells and tissue. Laser-mediated introduction of nucleic acids and other membrane impermeable molecules into mammalian cells is an important biophotonic technique. Optical injection presents a tool to deliver dyes and drugs for diagnostics and therapy of single cells in a sterile and interactive manner. Using femtosecond laser pulses increases the tunability of multiphoton effects and confines the damage volume, providing sub-cellular precision and high viability. Extending current femtosecond photoporation knowledge to plant cells could have sociological and environmental benefits, but presents different challenges to mammalian cells. The effects of varying optical and biological parameters on optical injection of a model plant cell line were investigated. A reconfigurable optical system was designed to allow easy switching between different spatial modes and pulse durations. Varying the medium osmolarity and optoinjectant size and type affected optoinjection efficacy, allowing optimisation of optical delivery of relevant biomolecules into plant cells. Advanced optical microscopy techniques that allow imaging beyond the diffraction limit have transformed biological studies. An ultimate goal is to merge several biophotonic techniques, creating a plant cell workstation. A step towards this was demonstrated by incorporating a fibre-based optical trap into a commercial super-resolution microscope for manipulation of cells and organelles under super-resolution. As proof-of-concept, the system was used to optically induce and quantify an immunosynapse. The capacity of the super-resolution microscope to resolve structure in plant organelles in aberrating plant tissue was critically evaluated.