Journal of microscopy最新文献

Mitochondria are double-membrane organelles whose architecture enables ATP (Adenosine Triphosphate) production, redox signalling, calcium homeostasis, and apoptosis. Visualisation of mitochondria requires imaging technologies across spatial and temporal scales. Conventional fluorescence microscopy techniques, such as wide-field, confocal, spinning-disk, and light-sheet microscopy, enable the real-time observation of mitochondrial networks and dynamics in live cells. Super-resolution methods, including structured illumination microscopy (SIM), stimulated emission depletion microscopy (STED), photoactivated localisation microscopy (PALM), stochastic optical reconstruction microscopy (STORM), and expansion microscopy, provide access to fine sub-mitochondrial structures, such as cristae, overcoming the diffraction limit. Additionally, proximity-based approaches such as FRET (Förster Resonance Energy Transfer), split-fluorescent proteins, and proximity ligation assays allow researchers to probe sub-compartmental interactions and organelle contact sites with nanometre-level sensitivity. Electron microscopy (EM) complements optical techniques by offering near-molecular resolution of mitochondrial ultrastructure, including membranes, cristae, and inter-organelle interfaces. In this review, we comprehensively examined the principles, capabilities, and limitations of these diverse imaging modalities, with a focus on recent advances. We highlight the development of novel fluorescent probes, integrated correlative techniques, and computational analysis pipelines to expand the utility of mitochondrial imaging. By placing these innovations in historical and theoretical contexts, we aim to clarify how each method works and why it is suited to biological questions. Finally, we explore how mitochondrial imaging has revolutionised our understanding of physiology and pathology.

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.

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.

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.

Multi-slice electron ptychography has attracted significant interest in recent years, thanks to notable experimental successes in ultra-high resolution, depth-resolved imaging of atomic structure. However, the theoretical dependence of depth of field on experimental parameters is not well understood. In this paper we use simulated data to compare the depth of field of through focal annular-dark field and multi-slice electron ptychography over a range of acceleration voltages and convergence angles. We show that at both low convergence angle and at low electron energy, multi-slice ptychography has significantly improved depth of field over through focal ADF imaging.

The scanning helium microscope (SHeM) is a new technology that uses a beam of neutral helium atoms to image surfaces non-destructively and with extreme surface sensitivity. Here, we present the application of the SHeM to image bacterial biofilms. We demonstrate that the SHeM uniquely and natively visualises the surface of the extracellular polymeric substance matrix in the absence of contrast agents and dyes and without inducing radiative damage.

Fluorescence lifetime imaging microscopy (FLIM) translates the duration of excited states of fluorophores into lifetime information as an additional source of contrast in images of biological samples. This offers the possibility to separate fluorophores particularly beneficial in case of similar excitation spectra. Here, we demonstrate the distinction of fluorescent molecules based on FLIM phasor analysis, called lifetime separation, in live-cell imaging using open-source software for analysis. We showcase two applications using Caenorhabditis elegans as a model system. First, we separated the highly spectrally overlapping fluorophores mCherry and mKate2 to distinctively track tagged proteins in six-dimensional datasets to investigate cell division in the developing early embryo. Second, we separated fluorescence of tagged proteins of interest from masking natural autofluorescence in adult hermaphrodites. For FLIM data handling and workflow implementation, we developed the open-source plugin napari-flim-phasor-plotter to implement conversion, visualisation, analysis and reuse of FLIM data of different formats. Our work thus advances technical applications and bioimage data management and analysis in FLIM microscopy for life science research.