Voorbij de kroniek : over het belang van theatertijdschriften

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Direct Translocation as Major Cellular Uptake for CADY Self-Assembling Peptide-Based Nanoparticles

Cell penetrating peptides constitute a potent approach to overcome the limitations of in vivo siRNA delivery. We recently proposed a peptide-based nanoparticle system, CADY, for efficient delivery of siRNA into numerous cell lines. CADY is a secondary amphipathic peptide that forms stable complexes with siRNA thereby improving both their cellular uptake and biological response. With the aim of understanding the cellular uptake mechanism of CADY:siRNA complexes, we have combined biochemical, confocal and electron microscopy approaches. In the present work, we provide evidence that the major route for CADY:siRNA cellular uptake involves direct translocation through the membrane but not the endosomal pathway. We have demonstrated that CADY:siRNA complexes do not colocalize with most endosomal markers and remain fully active in the presence of inhibitors of the endosomal pathway. Moreover, neither electrostatic interactions with cell surface heparan sulphates nor membrane potential are essential for CADY:siRNA cell entry. In contrast, we have shown that CADY:siRNA complexes clearly induce a transient cell membrane permeabilization, which is rapidly restored by cell membrane fluidity. Therefore, we propose that direct translocation is the major gate for cell entry of CADY:siRNA complexes. Membrane perturbation and uptake are driven mainly by the ability of CADY to interact with phospholipids within the cell membrane, followed by rapid localization of the complex in the cytoplasm, without affecting cell integrity or viability

Microscopie computationnelle hyperspectrale par feuillet de lumière structurée et réseaux de neurones convolutionnels profonds

Fluorescence microscopy is a powerful tool to study living organisms that allow to image objects of microscopic size, from the small living organism (1 to 10 millimetres) to the cell (10 to 100 micrometres). This type of microscopy allows to produce high contrast within a dark background with differentiated targeting of the parts of the object being studied thanks to the use of different fluorochromes. Today, there are many types of microscopes that have been developed to increase the type of data that can be measured and its resolution (super-resolution, volume microscopy, intrinsic labelling, etc...). These new instruments have increased our capacity to study living organisms. In this thesis, we developed a hyperspectral light sheet microscope. This type of instrument allows the measurement of a hyperspectral data cube which corresponds to the spectra of the fluorescence emitted by a sample at any point of the sample. However, a four-dimensional high-resolution sensor is not available to measure the four dimensions of the hyperspectral data cube (three spatial dimensions and one spectral dimension). Nevertheless, the hyperspectral cube can be acquired by scanning the dimensions, but this will be at the cost of a trade-off between acquisition time and spatial and spectral resolution. In order to optimise this trade-off, we have chosen to perform Hadamard spectroscopy, so as to maximise the amount of signal collected. We also chose to subsample the measurements to reduce the acquisition time. However, measurements obtained by Hadamard spectroscopy require reconstruction and sub-sampling leads to a loss in resolution. We have therefore integrated physics-informed deep learning into our reconstruction algorithms to improve the quality of the images reconstructed. During my thesis, I developed two experimental setups based on the concept of computational acquisition by structured illumination. The first setup allowed us to validate this concept and thus produce the first hyperspectral computational microscope by light sheet. Nevertheless, this set-up has a limited spatial resolution. Therefore, I developed a second setup based on another method of generating the structured light sheet, I also added a convolutional neural network architecture in the image reconstruction algorithms. Thanks to this approach, we have improved the spatial resolution of our acquisition system. Furthermore, the latest version of the experimental setup allows us to structure the illumination beam along an additional dimension. This would allow us to reduce the acquisition time by one more dimension. In future work, it would be interesting to direct the developments towards an application to a specific type of organism or a biological problem.Thus, on the basis of this work, a work on the power and structuring of the illumination would be necessary. It would also be relevant to continue the development of reconstruction algorithms in order to improve the quality of the reconstructions.La microscopie en fluorescence est un puissant outil de l'étude du vivant qui permet d'étudier des objets de taille microscopique : du petit organisme vivant (1 à 10 millimètres) à la cellule (10 à 100 micromètres). L'avantage principal de ce type de microscopie est qu'il permet d'obtenir des images à fort contraste en fond sombre avec un ciblage différencié des structures d'intérêt grâce à l'utilisation de fluorochromes. Aujourd'hui, il existe de nombreux types de microscopes qui ont été développés afin d'augmenter le type de données mesurables et la résolution de celle-ci (super-résolution, microscopie volumique, marquage intrinsèque...).Dans le cadre de cette thèse, nous avons développé un microscope à feuillet de lumière hyperspectral. Ce type d'instrument permet de mesurer un cube de données hyperspectrales qui correspond aux spectres de la fluorescence émise par un échantillon en tout point de celui-ci. Or, on ne dispose pas de capteur quadridimensionnel à haute résolution pour mesurer les quatre dimensions du cube de données hyperspectrales (trois dimensions spatiales et une dimension spectrale). Néanmoins on peut faire l'acquisition du cube hyperspectral par balayage des dimensions, mais cela se fera au prix d'un compromis entre temps d'acquisition et résolution spatiale et spectrale. Afin d'optimiser ce compromis, nous avons choisi de faire de la spectroscopie d'Hadamard, de sorte à maximiser le rapport signal à bruit. Nous avons aussi choisi de sous-échantillonner les mesures pour réduire les temps d'acquisition. Or les mesures obtenues par spectroscopie d'Hadamard nécessitent d'être reconstruites et le sous-échantillonnage induit une perte en résolution. Nous avons donc intégrer du "Physics-informed deep learning" à nos algorithmes de reconstruction pour améliorer la qualité des images. Durant ma thèse, j'ai développé deux montages expérimentaux basés sur le concept d'acquisition computationnelle par illumination structurée qui permet de faire de la spectroscopie d'Hadamard. Le premier montage nous a permis de valider ce concept et ainsi de produire le premier microscope computationnel hyperspectral par feuillet de lumière. Néanmoins, ce montage présente une résolution spatiale limitée. C'est pourquoi j'ai mis au point un second montage basé sur une autre méthode de génération du feuillet de lumière structurée, j'ai également ajouté dans les algorithmes de reconstruction d'images une architecture de réseaux de neurones convolutionnelle. Grâce à cette approche, nous avons amélioré la résolution spatiale de notre système d'acquisition. De plus, la dernière version du montage expérimental permet de structurer le faisceau d'illumination selon une dimension supplémentaire. Ce qui permettrait de réduire les temps d'acquisition selon une dimension supplémentaire. Dans des travaux ultérieurs, il serait intéressant d'orienter les développements en vue d'une application à un type d'organisme précis ou une problématique biologique. Pour ce faire, sur la base de ces travaux un travail sur la puissance et la structuration de l'illumination serait nécessaire. Il serait aussi pertinent de poursuivre le développement des algorithmes de reconstruction afin d'améliorer la qualité des reconstructions

Computational hyperspectral light-sheet microscopy

International audienceWe describe a computational light-sheet microscope designed for hyperspectral acquisition at high spectral resolution. The fluorescence light emitted from the full field-of-view is focused along the entrance slit of an imaging spectrometer using a cylindrical lens. To acquire the spatial dimension orthogonal to the slit of the spectrometer, we propose to illuminate the specimen with a sequence of structured light patterns and to solve the image reconstruction problem. Beam shaping is obtained simply using a digital micromirror device in conjunction with a traditional selective plane illumination microscopy setup. We demonstrate the feasibility of this method and report the first results in vivo in hydra specimens labeled using two fluorophores

Microscope computationnel hyper spectral par feuillet de lumière structurée

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A computational hyperspectral structured light sheet microscope

International audienceWe describe here a hyperspectral light sheet microscope that is based on beam shaping and image reconstruction. Our approach has no moving parts and can be easily combined with a standard microscope

Subsidence dynamics of the Montney Formation (Early Triassic, Western Canada Sedimentary Basin): insights for its geodynamic setting and wider implications

International audienceThis study presents the first published subsidence analysis during the deposition of the Montney Formation. It was deposited during the Early Triassic (ca. 252.2–245 Ma) in the Western Canadian Sedimentary Basin (WCSB) located along the western margin of the North American craton. Subsidence analyses of six representative wells and two outcrop sections along a proximal to distal transect are presented using a backstripping method integrating recent high-resolution stratigraphic correlations for the Montney Formation. The entire Paleozoic to Cenozoic sedimentary column of the WCSB was backstripped to put the deposition of the Montney Formation into a broader context and provide results regarding the type of subsidence and geodynamic setting for the Montney Formation.The spatial and temporal evolution of the subsidence during the deposition of the Montney Formation indicates that the most likely basin setting is a foreland. The tectonic subsidence during the Triassic is herein interpreted as a combination of the topographic loading of the orogenic wedge (flexure) and the sublithospheric “loading” caused by slab load-driven subsidence (dynamic subsidence). This suggests that the retro-foreland basin setting was associated with an eastward dipping subduction during the deposition of the Montney Fm.Three foreland stages are thus recorded in the whole WCSB, with evidence for: 1) a Late Permian (fore-arc) pro-foreland setting; then 2) a Triassic collisional retro-foreland basin prior to the well-known; 3) Jurassic-Cenozoic collisional retro-foreland

Modelling the effects of temperature and leaf wetness on monocyclic infection in a tropical fungal pathosystem

International audienceModelling the epidemiology of water yam anthracnose (Dioscorea alata) caused by the fungus Colletotrichum gloeosporioides is an important research goal, as it will allow the investigation of a wide range of scenarios of new practices to reduce the disease impact before experimentation in the field. Developing such a model requires a prior knowledge of the fungus’s response to the environmental conditions, which will be affected by pest management. In this work, we first measured the response of the fungus to the main physical environmental factors controlling its development, namely temperature (ranging from 18 °C to 36 °C) and wetness duration (from 2 h to 72 h). As response variables, we measured the percentage of formed appressoria (relative to the total number of spores), the length of the latent period (time lag between inoculation and first symptoms observed), and the rate of necrotic lesion extension (percentage of diseased leaf surface at different time steps). These variables allow us to estimate the effects of temperature and wetness duration on the success of infection (appressoria formation) and the subsequent rate of disease development (latent period length and lesion extension rate). The data were fitted to non-linear models chosen for their ability to describe the observed patterns. From our data and model analyses, we were able to estimate parameters such as the optimal and maximal temperatures (25–28 °C and 36 °C, respectively), the required wetness duration to reach 20 % of infection success and the time to reach 5 % disease severity as a function of temperature