Journal of microscopy最新文献

A successful scanning tunnelling microscopy (STM) experiment relies on both delicate sample preparation and measurement, and careful image filtering and analysis to provide clear and solid results. Processing and analysis of STM images may result in a tricky task, due to the complexity and specificity of the probed systems. In this paper, we introduce our recently developed, simple Python-based methods for filtering and analysing STM images, with the aim of providing a semi-quantitative treatment of the input data. Case studies will be presented using images obtained through electrochemical STM. Additionally, we propose a straightforward yet effective universal drift-correction tool for SPM image sequences.

Laser scanning fluorescence microscopy (LSFM) is a widely used imaging method, but image quality is often degraded by noise. Averaging techniques can enhance the signal-to-noise ratio (SNR), but while this can improve image quality, excessive frame accumulation can introduce photobleaching and may lead to unnecessarily long acquisition times. A classical software method called PerfectlyAverage is presented to determine the optimal number of frames for averaging in LSFM using SNR, photobleaching, and power spectral density (PSD) measurements. By assessing temporal intensity variations across frames in a time series, PerfectlyAverage identifies the point where additional averaging ceases to provide significant noise reduction. Experiments with fluorescently stained tissue paper and fibroblast cells validated the approach, demonstrating that up to a fourfold reduction in averaging time may be possible. PerfectlyAverage is open source, compatible with any LSFM data, and it is aimed at improving imaging workflows while reducing the reliance on subjective criteria for choosing the number of averages.

Propagation-based imaging (one method of X-ray phase contrast imaging) with microcomputed tomography (PBI-µCT) offers the potential to visualise low-density materials, such as soft tissues and hydrogel constructs, which are difficult to be identified by conventional absorption-based contrast µCT. Conventional µCT reconstruction produces edge-enhanced contrast (EEC) images which preserve sharp boundaries but are susceptible to noise and do not provide consistent grey value representation for the same material. Meanwhile, phase retrieval (PR) algorithms can convert edge enhanced contrast to area contrast to improve signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) but usually results to over-smoothing, thus creating inaccuracies in quantitative analysis. To alleviate these problems, this study developed a deep learning-based method called edge view enhanced phase retrieval (EVEPR), by strategically integrating the complementary spatial features of denoised EEC and PR images, and further applied this method to segment the hydrogel constructs in vivo and ex vivo. EVEPR used paired denoised EEC and PR images to train a deep convolutional neural network (CNN) on a dataset-to-dataset basis. The CNN had been trained on important high-frequency details, for example, edges and boundaries from the EEC image and area contrast from PR images. The CNN predicted result showed enhanced area contrast beyond conventional PR algorithms while improving SNR and CNR. The enhanced CNR especially allowed for the image to be segmented with greater efficiency. EVEPR was applied to in vitro and ex vivo PBI-µCT images of low-density hydrogel constructs. The enhanced visibility and consistency of hydrogel constructs was essential for segmenting such material which usually exhibit extremely poor contrast. The EVEPR images allowed for more accurate segmentation with reduced manual adjustments. The efficiency in segmentation allowed for the generation of a sizeable database of segmented hydrogel scaffolds which were used in conventional data-driven segmentation applications. EVEPR was demonstrated to be a robust post-image processing method capable of significantly enhancing image quality by training a CNN on paired denoised EEC and PR images. This method not only addressed the common issues of over-smoothing and noise susceptibility in conventional PBI-µCT image processing but also allowed for efficient and accurate in vitro and ex vivo image processing applications of low-density materials.

Supramolecular self-assembly on surfaces enables tailored interfaces with applications in nanotechnology. While factors like temperature and solute concentration influence self-assembled molecular networks (SAMNs), the role of spatial confinement remains less explored. Here, we investigate the self-assembly of an alkylated quinonoid zwitterion (QZ-C16) at the liquid-solid interface using scanning tunnelling microscopy (STM), both in in situ as well as ex situ nanocorrals. Engineered nanocorrals not only provide a confined environment for molecular assembly, but also serve as platforms for probing the impact of geometric constraints on self-assembly behaviour. Understanding the intricate dynamics of self-assembly at the nanoscale, particularly the mechanisms by which confinement influences structural organisation, can inform strategies for achieving desired molecular architectures.

For sodium (Na)-ion batteries (SIBs), the next generation of sustainable batteries, hard carbon (HC) composite electrodes are the most used anodes. Here, we demonstrate the potential of modulated electrochemical force microscopy (mec-AFM) to investigate electrochemical strain due to ion insertion at the electrolyte/electrode interface. HC composite anodes have a complex, multiphase structure, which include the HC particles, conductive carbon nanoparticles (carbon black) and the binder. To address the effect of the composite material on the sodium-ion transport, we employ mec-AFM. A HC composite anode was embedded in an epoxy-polymer matrix and was polished to expose a micro-sized area that enabled high-frequency modulation of the ion transport. We analyse the influence of the modulation on interfacial forces and its role in generating electrochemical strain in the composite anode. Multichannel mec-AFM imaging at varying electrode potentials revealed that the observed electrochemical strain predominantly occurred in the softer binder matrix rather than in the HC microparticles. Our findings underscore the significance of ionic transport pathways through the binder matrix and establish mec-AFM as a novel AFM-derived technique for visualising ion dynamics at battery interfaces.

Cryogenic scanning electron microscopy (cryo-SEM) is a powerful imaging technique used in cellular biology, providing high-resolution micrographs that show the complexity and dynamics of biological systems. The use of high-pressure freezing (HPF) for specimen fixation preserves cellular structures in their native, hydrated state, avoiding the artefacts introduced by conventional chemical fixation, while modern microscopes provide high-resolution imaging at low electron acceleration voltage, giving fine structural details. That makes cryo-SEM a unique tool for understanding cellular complexity. However, operating the SEM at cryogenic conditions requires careful optimisation of working parameters to avoid artefacts. In our work, we explore the potential of cryo-SEM for haematology and general cell studies. We discuss the impact of a combination of different signals and work distance on specimen appearance and present examples of studies on healthy human blood cells under physiological conditions. Our findings illustrate the breadth of information that can be obtained from these data, highlighting the technique's capacity to enhance our understanding of cellular biology.

Atomic force microscopy (AFM) plays a pivotal role in cell biology research. It enables scientists to observe the morphology of cell surfaces at the nanoscale, providing essential data for understanding cellular functions, including cell-cell interactions and responses to the microenvironment. Nevertheless, AFM-captured cell images frequently suffer from artefacts, which significantly hinder detailed analyses of cell structures. In this study, we developed a cross-module resolution enhancement method for post-processing AFM cell images. The method leverages the AFM topological deep learning neural network. We propose an enhanced spatial fusion structure and an optimised back-projection mechanism within an adversarial-based super-resolution network to detect weak signals and complex textures unique to AFM cell images. Furthermore, we designed a crossover-based frequency division module, capitalising on the distinct frequency characteristics of AFM images. This module effectively separates and enhances features pertinent to cell structure. In this paper, experiments were conducted using AFM images of various cells, and the results demonstrated the model's superiority. It substantially enhances image quality compared to existing methods. Specifically, the peak signal-to-noise ratio (PSNR) of the reconstructed image increased by 1.65 decibels, from 28.121 to 29.771, the structural similarity (SSIM) increased by 0.041, from 0.746 to 0.787, the Learned Perceptual Image Patch Similarity (LPIPS) decreased by 0.205, from 0.437 to 0.232, the Fréchet Inception Distance (FID) decreased by 6.996, from 55.442 to 48.446 and the Natural Image Quality Evaluator (NIQE) decreased by 0.847, from 4.296 to 3.449.

Lay abstract: This study proposes a deep learning-based cross-module method for super-resolving AFM cell images, integrating frequency division and adaptive fusion modules. It boosts PSNR by 1.65 dB and SSIM by 0.041, accurately recovering cellular microstructures, thus significantly aiding cell biology research and biomedicine applications.

Single molecule localisation microscopy (SMLM) can provide two-dimensional super-resolved image data from conventional fluorescence microscopes, while three dimensional (3D) SMLM usually involves a modification of the microscope, for example, to engineer a predictable axial variation in the point spread function. Here we demonstrate a 3D SMLM approach (we call ‘easyZloc') utilising a lightweight Convolutional Neural Network that is generally applicable, including with ‘standard’ (unmodified) fluorescence microscopes, and which we consider may be practically useful in a high throughput SMLM workflow. We demonstrate the reconstruction of nuclear pore complexes with comparable performance to previously reported methods but with a significant reduction in computational power and execution time. 3D reconstructions of the nuclear envelope and an actin sample over a larger axial range are also shown.

In the field of atomic force microscopy (AFM), image quality is frequently compromised by distortions that impact measurement precision. These distortions are caused by a combination of factors such as the hysteresis, creep, and drift of the piezoelectric actuators during the scanning process. To address this issue, a spiral scanning path method is proposed in this paper. The block is used as the smallest scanning unit, with overlapping scanning parts between adjacent blocks, allowing for real-time calculation and compensation of distortions. Utilising the spiral scanning path method, compared with the formerly proposed correlation scanning method, a strong correlation between the blocks from the beginning to the end of the scanning process, effectively reducing the accumulation of drift during the scanning process, thereby significantly improving the issue of image distortion. An evaluation method for distortion correction based on scanning images is also introduced in this paper, which can assess the effectiveness of the proposed scanning method. Experimental results confirm that the spiral path scanning method proposed significantly improves the distortion correction compared to traditional methods. When the width of the scanning image is 600 pixels, the distortion is reduced by 94.9%. The proposed spiral correlated scanning method can be applied to long-term precise scanning scenarios in atomic force microscopy.