We demonstrate a hard-x-ray microscope that does not use a lens and is not limited to a small field of view or an object of finite size. The method does not suffer any of the physical constraints, convergence problems, or defocus ambiguities that often arise in conventional phase-retrieval diffractive imaging techniques. Calculation times are about a thousand times shorter than in current iterative algorithms. We need no a priori knowledge about the object, which can be a transmission function with both modulus and phase components. The technique has revolutionary implications for x-ray imaging of all classes of specimen.

We examine the spectral dependence in the visible frequency range of the polarization rotation of two-dimensional gratings consisting of chiral gold nanostructures with subwavelength features. The gratings, which do not diffract, are shown to exhibit giant specific rotation (approximately 10(4) degrees/mm) of polarization in direct transmission at normal incidence. The rotation is the same for light incident on the front and back sides of the sample. Such reciprocity indicates three dimensionality of the structure arising from the asymmetry of light-plasmon coupling at the air-metal and substrate-metal interfaces. The structures thus enable polarization control with quasi-two-dimensional planar objects. However, in contradiction with recently suggested interpretation of experiments on larger scale but otherwise similar structures, the observed polarization phenomena violate neither reciprocity nor time-reversal symmetry.

Biosensing with nanoholes is one of the most promising applications of nanoplasmonic devices. The sensor properties, however, are complex due to coupled resonances through propagating and localized surface plasmons. This Full Paper demonstrates experimental and simulation studies on different plasmonic hole systems, namely various patterns of circular holes in gold films. In contrast to most previous work, here, the challenging situation of optically thin films is considered. The refractive-index-sensing properties, such as sensitive locations in the nanostructure and sensitive spectral features, are investigated. The multiple multipole program provides the complete field distribution in the nanostructure for different wavelengths. It is shown that the spectral feature most sensitive to refractive-index changes is the extinction minimum, rather than the maximum. The results are consistent with theory for perfect electrical conductors. The spectral response is investigated for molecular adsorption at different positions inside or outside a hole. Furthermore, the optical properties of nanohole arrays with long-range and short-range order are compared and found to demonstrate remarkable similarities. Our results help to predict the resonance wavelengths of nanoholes with arbitrary patterns, including short-range order. The results presented here are highly important since they extend and challenge several aspects of the current understanding of plasmon resonances in nanohole arrays. These theoretical models, simulation results, and experimental data together help provide the understanding necessary for the development of efficient biomolecular analysis tools based on metallic nanoholes.

Single-cell level measurements are necessary to characterize the intrinsic biological variability in a population of cells. In this study, we demonstrate that, with the microarrays for mass spectrometry platform, we are able to observe this variability. We monitor environmentally (2-deoxy-D-glucose) and genetically (ΔPFK2) perturbed Saccharomyces cerevisiae cells at the single-cell, few-cell, and population levels. Correlation plots between metabolites from the glycolytic pathway, as well as with the observed ATP/ADP ratio as a measure of cellular energy charge, give biological insight that is not accessible from population-level metabolomic data.single-cell measurements | MALDI mass spectrometry | baker's yeast E ven genetically identical cells present in the same microenvironment can express different phenotypes, for a number of reasons: cell-to-cell heterogeneity can stem from differences in the cell age and differences in the cell cycle stage, and stochastic effects together with feedback mechanisms can lead to distinctively different phenotypes, too (1-6). As population-level measurement techniques inherently average out such cell-to-cell differences, biochemical mechanisms underlying a studied system cannot be deduced from such measurements. Thus, to detect and exploit this heterogeneity, new analytical platforms with a sensitivity at the single-cell level and the ability to perform quantitative analyses must be developed and validated.Motivated by advances of mass spectrometry (MS) in metabolomics, the analytical chemistry community has stepped up its efforts toward realizing MS-based single-cell metabolomics (1, 2). A number of analytical approaches were developed with detection limits low enough for single-cell metabolite analyses [e.g., nanostructured surfaces (7, 8), postionization techniques (9, 10), modified laser optics (11), the use of microsampling tools (12, 13), microarrays for MS measurements (14, 15), etc.]. Until now, however, most MS studies targeting single-cell metabolite analysis have only shown the analytical capabilities, but have not demonstrated that true biological information can be retrieved from studying the metabolism of single cells.Here, using the unicellular eukaryotic model organism Saccharomyces cerevisiae, we present an analytical validation of a single-cell metabolite analysis using the microarrays for mass spectrometry (MAMS) platform. This validation concerns both the analytical methodology and the biological information, by monitoring expected cellular responses upon an environmental and a genetic perturbation. Furthermore, we present examples of biological insight that are only accessible with a platform such as MAMS. Specifically, we unravel metabolite-metabolite correlations, and visualize coexisting subpopulations in an isogenic cell culture. This technology can now be used to reveal metabolic differences in cells of isogenic cell populations, such as differences caused by cell cycles stages, cell ages, or stochastically induced phenotypic differences. Results and ...

No abstract