Journal of microscopy最新文献

Cellulose materials are suitable to replace plastic in food packaging. They are hydrophilic and may have poor barrier properties that affect the shelf life of the food due to the migration of contaminants. The wet lamination of microfibrillated cellulose films on cellulose materials appears as a promising way to improve their barrier properties by forming a bilayer material. These barrier properties depend on the microstructural properties (porosity, pores connectivity, specific surface area or contact surface area between the two layers) of both layers, notably the film, which are poorly known. Therefore, a multiscale approach is proposed to estimate such microstructural parameters by combining three 3D imaging methods: synchrotron X-ray micro-/nanotomography and FIB-SEM tomography. The 3D microstructure of two different bilayer materials obtained with two Microfibrillated Cellulose (MFC) grades is investigated. For the first time, a full 3D representation of such material is presented. Regardless of the scale under consideration, our results showed that both films present a dense structure with very low porosity and no pore connectivity along the thickness. The MFC film produced with the smallest MFC fibrils led to a more homogeneous and less porous layer with a larger contact surface to the paper.

We compare three different methods of X-ray analysis in a scanning electron microscope (SEM): energy-dispersive X-ray spectroscopy (EDX), wavelength-dispersive X-ray spectroscopy (WDX) and micro X-ray fluorescence (μXRF). These methods are all applied to the same gallium arsenide (GaAs) wafer with a 0.8 nm layer of indium arsenide (InAs) on top. All methods allow detection and quantification of the indium L-line intensity from the thin InAs layer. EDX is the easiest to perform, WDX is the most sensitive and μXRF a novel technique where a poly-capillary optics is used to focus an X-ray beam from a high-voltage X-ray tube onto a small spot several micrometres wide and the characteristic X-rays produced are detected by a solid-state silicon detector similar to that used in EDX. It is to our knowledge the first time a sub-nanometre layer is reliably detected and analysed using μXRF in an SEM.

Expansion microscopy (ExM) is a powerful high-resolution imaging technique that enhances the spatial resolution of conventional light microscopy by physically enlarging biological specimens by embedding and cross-linking them in a swellable polymer network. This review explores the combination of ExM with commonly used advanced fluorescence imaging modalities, including light sheet fluorescence microscopy (LSFM), stimulated emission depletion (STED), structured illumination microscopy (SIM), single-molecule localisation microscopy (SMLM), and computational super-resolution radial fluctuations (SRRF) to push the boundaries of achievable resolution in biological imaging. By integrating ExM with these optical and analytical approaches, researchers can visualise subcellular structures and molecular complexes with unprecedented clarity, enabling the study of intricate biological processes that are otherwise inaccessible with conventional light microscopy methods. The review covers the theoretical resolutions attainable with each combined technique, example biological questions they can address, and key considerations for optimising their use. Together, these advancements offer novel insights into nanoscale cellular and subcellular structures, opening new avenues for exploration in fields such as neuroscience, cancer research, and developmental biology.

Some biological systems exhibit nanoscale constructions to produce optical effects. This study utilises Atomic Force Microscopy (AFM) and Tip-Enhanced Raman Spectroscopy (TERS) to study the complex bionanometric structure of cicada wings. Topographical irregularities of the wings due to $�\alpha$-chitin nanopillars hinder the probe's approach to the sample, a crucial step in overcoming the light diffraction limit in TERS measurements. To mitigate this issue, graphene was deposited, promoting surface smoothing and ensuring a reliable TERS measurement. Combined analyses of AFM and TERS mapping revealed a significant enhancement of the graphene 2D band, particularly in the regions surrounding the nanometric pillars, while the characteristic $�\alpha$-chitin Raman peaks are evident on top of the pillars, clarifying details of how light passed through the material.

Localisation microscopy often relies on detailed models of point-spread functions. For applications such as deconvolution or PSF engineering, accurate models for light propagation in imaging systems with a high numerical aperture are required. Different models have been proposed based on 2D Fourier transforms or 1D Bessel integrals. The most precise ones combine a vectorial description of the electric field and accurate aberration models. However, it may be unclear which model to choose as there is no comprehensive comparison between the Fourier and Bessel approaches yet. Moreover, many existing libraries are written in Java (e.g., our previous PSF generator software) or MATLAB, which hinders their integration into deep learning algorithms. In this work, we start from the original Richards-Wolf integral and revisit both approaches in a systematic way. We present a unifying framework in which we prove the equivalence between the Fourier and Bessel strategies and detail a variety of correction factors applicable to both of them. Then, we provide a high-performance implementation of our theoretical framework in the form of an open-source library that is built on top of PyTorch, a popular library for deep learning. It enables us to benchmark the accuracy and computational speed of different models and allows for an in-depth comparison of the existing models for the first time. We show that the Bessel strategy is optimal for axisymmetric beams, while the Fourier approach can be applied to more general scenarios. Our work enables the efficient computation of a point-spread function on CPU or GPU, which can then be included in simulation and optimisation pipelines.

Expansion microscopy (ExM) enables superresolution imaging by embedding biological specimens in a swellable hydrogel, followed by optical clearing and physical expansion. Here we present a detailed protocol for applying ExM to embryonic stages of Paracentrotus lividus, a widely used model in developmental biology. Embryos at cleavage and gastrula stages, as well as pluteus larvae, were successfully expanded fourfold after proteinase digestion. Preexpansion Airyscan imaging provided only limited subcellular information due to autofluorescence, whereas post-expansion samples displayed markedly reduced background and resolved fine structures. To address distortions caused by the calcified skeleton in pluteus larvae, we incorporated a decalcification step with EDTA, which preserved morphology and enabled isotropic expansion. Distinct NHS ester dyes further allowed differential labelling of the fertilisation envelope and blastomeres, illustrating the versatility of this approach. Together, these adaptations establish a reproducible workflow for ExM in marine invertebrates, offering a valuable methodological resource and a foundation for future applications in developmental and environmental research.

Accurate detection of mitosis is crucial in automated cell analysis, yet many existing methods depend heavily on deep learning models or complex detection techniques, which can be computationally intensive and error-prone, particularly when segmentation is incomplete. This study presents a novel unsupervised method for mitosis detection, leveraging the geometric properties of the Cassini oval to reduce computational costs and enhance robustness. Our approach integrates a newly developed deep learning model, MaxSigNet, for initial cell segmentation. We subsequently employ the Cassini oval in its single-ring mode to detect mother cells in the initial frame and switch to double-ring mode in subsequent frames to identify daughter cells and confirm mitosis events. The success of this method hinges on the presence of equal non-zero foci values in the mother cell and distinct non-zero foci values in the daughter cells, which indicate accurate mitosis detection. The method was evaluated across six datasets from four different cell lines, achieving perfect F1, Recall and Precision scores on four datasets, with scores of 96% and 85% on the remaining two. Comparative analysis demonstrated that our method outperformed similar approaches in F1 and Recall metrics. Additionally, the method showed substantial robustness to incomplete segmentation, with only a 20% average drop in F1 scores when tested with older segmentation methods like K-means, Felzenszwalb and Watershed. The proposed method offers a significant advancement in mitosis detection by leveraging the Cassini oval's properties, providing a reliable and efficient solution for automated cell analysis systems. This approach promises to enhance the accuracy and efficiency of cellular behaviour studies, with potential applications in various biomedical research fields.