Journal of microscopy最新文献

Electron energy loss spectra collected from fresh and corroded silver nanoparticles are compared with those from a number of reference materials, focusing on the M_4,5 edge. Chemical shifts and changes in the energy loss near edge structure (ELNES) are described and found to be sufficient to distinguish metallic silver from chemically oxidised silver. The measurements, in conjunction with electron energy loss spectrum imaging, are used to assess the mechanisms for atmospheric corrosion of silver nanoparticles. We unambiguously assign the corrosion product under atmospheric conditions to be silver sulphide, but show the reaction process to be distinctly inhomogeneous, producing a variety of types of corroded particles. LAY DESCRIPTION: >Here, we use analytical electron microscopy to track the corrosion of silver nanoparticles and present chemical maps of the corrosion products. We show clear spectroscopic differences between metallic and corroded silver using the M_4,5 electron energy loss spectral feature, which is not commonly studied. Our study shows that corrosion is due to interactions with sulphur in the atmosphere; and the corrosion is not uniform, but appears to develop from specific points on the surface of the nanoparticles.

In this study, the effects of different sizes of reinforcing particles on the corrosion behaviour and mechanical properties of aluminium (Al)-based composites produced by spark plasma sintering (SPS) are analysed. In the study, the effects of SPS parameters, including electrical power, applied pressure and sintering temperature, on the consolidation process and microstructure evolution of the composite are closely investigated. The results reveal a nuanced relationship between the sintering conditions and the properties of the particles, which in turn determine the sintering dynamics and the formation of the microstructural features. The evaluation of mechanical properties indicates a remarkable influence of particle size distribution on the hardness of the composites, showing an initial improvement with the introduction of nanoparticles, followed by a slight decrease as the balance between nano- and micron-sized Al₂O₃ particles shifts. A scanning electron microscopy (SEM) study demonstrates the influence of particle dimensions on the change of grain boundaries and the spatial arrangement of the composite matrix. Electrochemical experiments in a 0.1 M NaCl solution show a consistent corrosion potential (E_corr) across all samples, while the current densities associated with corrosion (i_corr) show considerable variation. The presence of nano-sized Al₂O₃ particles was found to increase corrosion resistance, in contrast to the detrimental effects observed with larger microparticles. In particular, composites with a bimodal distribution of particle sizes showed a 3.5-fold increase in corrosion resistance compared to pure Al. The specific Al-2n8mAl₂O₃ composite that exhibited active electrochemical properties at elevated potentials without a defined passivation range emphasises the significant role of particle size. This study draws attention to bimodal microstructures as a promising route to achieving uniformity and improved corrosion resistance in Al matrix composites, while pointing to the need for further research to fully elucidate the operative mechanisms.

Single Molecule Localisation Microscopy (SMLM) is becoming a widely used technique in cell biology. After processing the images, the molecular localisations are typically stored in a table as xy (or xyz) coordinates, with additional information, such as number of photons, etc. This set of coordinates can be used to generate an image to visualise the molecular distribution, for example, a 2D or 3D histogram of localisations. Many different methods have been devised to analyse SMLM data, among which cluster analysis of the localisations is popular. However, it can be useful to first segment the data, to extract the localisations in a specific region of a cell or in individual cells, prior to downstream analysis. Here we describe a pipeline for annotating localisations in an SMLM dataset in which we compared membrane segmentation approaches, including Otsu thresholding and machine learning models, and subsequent cell segmentation. We used an SMLM dataset derived from dSTORM images of sectioned cell pellets, stained for the membrane proteins EGFR (epidermal growth factor receptor) and EREG (epiregulin) as a test dataset. We found that a Cellpose model retrained on our data performed the best in the membrane segmentation task, allowing us to perform downstream cluster analysis of membrane versus cell interior localisations. We anticipate this will be generally useful for SMLM analysis.

Super-resolution structured-illumination microscopy (SIM) is a powerful technique that allows one to surpass the diffraction limit by up to a factor two. Yet, its practical use is hampered by its sensitivity to imaging conditions which makes it prone to reconstruction artefacts. In this work, we present FlexSIM, a flexible SIM reconstruction method capable to handle highly challenging data. Specifically, we demonstrate the ability of FlexSIM to deal with the distortion of patterns, the high level of noise encountered in live imaging, as well as out-of-focus fluorescence. Moreover, we show that FlexSIM achieves state-of-the-art performance over a variety of open SIM datasets.

Micropatterning is reliable method for quantifying pluripotency of human-induced pluripotent stem cells (hiPSCs) that differentiate to form a spatial pattern of sorted, ordered and nonoverlapped three germ layers on the micropattern. In this study, we propose a deep learning method to quantify spatial patterning of the germ layers in the early differentiation stage of hiPSCs using micropattern images. We propose decoding and encoding U-net structures learning labelled Hoechst (DNA-stained) hiPSC regions with corresponding Hoechst and bright-field micropattern images to segment hiPSCs on Hoechst or bright-field images. We also propose a U-net structure to extract extraembryonic regions on a micropattern, and an algorithm to compares intensities of the fluorescence images staining respective germ-layer cells and extract their regions. The proposed method thus can quantify the pluripotency of a hiPSC line with spatial patterning including cell numbers, areas and distributions of germ-layer and extraembryonic cells on a micropattern, and reveal the formation process of hiPSCs and germ layers in the early differentiation stage by segmenting live-cell bright-field images. In our assay, the cell-number accuracy achieved 86% and 85%, and the cell region accuracy 89% and 81% for segmenting Hoechst and bright-field micropattern images, respectively. Applications to micropattern images of multiple hiPSC lines, micropattern sizes, groups of markers, living and fixed cells show the proposed method can be expected to be a useful protocol and tool to quantify pluripotency of a new hiPSC line before providing it to the scientific community.

The wavenumber nonlinearity leads to blurred reconstructed images in spectral-domain optical coherence tomography (SDOCT). In this work, a wavenumber-linearisation method without calibration devices is presented, based on the fact that the difference between the phases of adjacent peak and valley points is equal to $�\pi$. The theoretical model is derived, and the efficacy of the method was proven by acquiring SDOCT data from TiO₂ phantom and zebrafish. The results exhibit the superior performance of our method. Compared with the linear phase-based method, the resolution could be improved at least a factor of 2. Compared with the polynomial fitting method, the resolution could also be improved by nearly half.

In single-molecule microscopy, a big question is how precisely we can estimate the location of a single molecule. Our research shows that by using iterative localisation microscopy and factoring in the prior information, we can boost precision and reduce the number of photons needed. Leveraging the Van Trees inequality aids in determining the optimal precision achievable. Our approach holds promise for wider application in discerning the optimal precision across diverse imaging scenarios, encompassing various illumination strategies, point spread functions and overarching control methodologies.

Flow or collective movement is a frequently observed phenomenon for many cellular components including the cytoskeletal proteins actin and myosin. To study protein flow in living cells, we and others have previously used spatiotemporal image correlation spectroscopy (STICS) analysis on fluorescence microscopy image time series. Yet, in cells, multiple protein flows often occur simultaneously on different scales resulting in superimposed fluorescence intensity fluctuations that are challenging to separate using STICS. Here, we exploited the characteristic that distinct protein flows often occur at different spatial scales present in the image series to disentangle superimposed protein flow dynamics. We employed a newly developed and an established spatial filtering algorithm to alternatively accentuate or attenuate local image intensity heterogeneity across different spatial scales. Subsequently, we analysed the spatially filtered time series with STICS, allowing the quantification of two distinct superimposed flows within the image time series. As a proof of principle of our analysis approach, we used simulated fluorescence intensity fluctuations as well as time series of nonmuscle myosin II in endothelial cells and actin-based podosomes in dendritic cells and revealed simultaneously occurring contiguous and noncontiguous flow dynamics in each of these systems. Altogether, this work extends the application of STICS for the quantification of multiple protein flow dynamics in complex biological systems including the actomyosin cytoskeleton.