Automated Force Volume Image Processing for Biological Samples

Atomic force microscopy (AFM) has now become a powerful technique for investigating on a molecular level, surface forces, nanomechanical properties of deformable particles, biomolecular interactions, kinetics, and dynamic processes. This paper specifically focuses on the analysis of AFM force curves collected on biological systems, in particular, bacteria. The goal is to provide fully automated tools to achieve theoretical interpretation of force curves on the basis of adequate, available physical models. In this respect, we propose two algorithms, one for the processing of approach force curves and another for the quantitative analysis of retraction force curves. In the former, electrostatic interactions prior to contact between AFM probe and bacterium are accounted for and mechanical interactions operating after contact are described in terms of Hertz-Hooke formalism. Retraction force curves are analyzed on the basis of the Freely Jointed Chain model. For both algorithms, the quantitative reconstruction of force curves is based on the robust detection of critical points (jumps, changes of slope or changes of curvature) which mark the transitions between the various relevant interactions taking place between the AFM tip and the studied sample during approach and retraction. Once the key regions of separation distance and indentation are detected, the physical parameters describing the relevant interactions operating in these regions are extracted making use of regression procedure for fitting experiments to theory. The flexibility, accuracy and strength of the algorithms are illustrated with the processing of two force-volume images, which collect a large set of approach and retraction curves measured on a single biological surface. For each force-volume image, several maps are generated, representing the spatial distribution of the searched physical parameters as estimated for each pixel of the force-volume image

Performance Demonstration Statement for Sunburst Sensors SAMI-CO2

The plant glutathione peroxidase (GPX) family consists of multiple isoenzymes with distinct subcellular locations which exhibit different tissue-specific expression patterns and environmental stress responses. Contrary to most of their counterparts in animal cells, plant GPXs contain cysteine instead of selenocysteine in their active site and while some of them have both glutathione peroxidase and thioredoxin peroxidase functions, the thioredoxin regenerating system is much more efficient in vitro than the glutathione system. At present, the function of these enzymes in plants is not completely understood. The occurrence of thiol-dependent activities of plant GPX isoenzymes suggests that - besides detoxification of H2O2 and organic hydroperoxides - they may be involved in regulation of the cellular redox homeostasis by maintaining the thiol/disulfide or NADPH/NADP+ balance. GPXs may represent a link existing between the glutathione- and the thioredoxin-based system. The various thiol buffers, including Trx, can affect a number of redox reactions in the cells most probably via modulation of thiol status. It is still required to identify the in vivo reductant for particular GPX isoenzymes and partners that GPXs interact with specifically. Recent evidence suggests that plant GPXs does not only protect cells from stress induced oxidative damage but they can be implicated in plant growth and development. Following a more general introduction, this study summarizes present knowledge on plant GPXs, highlighting the results on gene expression analysis, regulation and signaling of Arabidopsis thaliana GPXs and also suggests some perspectives for future research