Journal of microscopy最新文献

Single-molecule localization microscopy (SMLM), which has revolutionized nanoscale imaging, faces challenges in densely labelled samples due to fluorophore clustering, leading to compromised localization accuracy. In this paper, we propose a novel convolutional neural network (CNN)-assisted approach to address the issue of locating the clustered fluorophores. Our CNN is trained on a diverse data set of simulated SMLM images where it learns to predict point spread function (PSF) locations by generating Gaussian blobs as output. Through rigorous evaluation, we demonstrate significant improvements in PSF localization accuracy, especially in densely labelled samples where traditional methods struggle. In addition, we employ blob detection as a post-processing technique to refine the predicted PSF locations and enhance localization precision. Our study underscores the efficacy of CNN in addressing clustering challenges in SMLM, thereby advancing spatial resolution and enabling deeper insights into complex biological structures.

Breast cancer (BC) is characterised by a high level of heterogeneity, which is influenced by the interaction of neoplastic cells with the tumour microenvironment. The diagnostic and prognostic role of the tumour stroma in BC remains to be defined. Differential interference contrast (DIC) microscopy is a label-free imaging technique well suited to visualise weak optical phase objects such as cells and tissue. This study aims to compare stromal collagen fibre characteristics between in situ and invasive breast tumours using DIC microscopy and investigate the prognostic value of collagen parameters in BC. A tissue microarray was generated from 200 cases, comprising ductal carcinoma in situ (DCIS; n = 100) and invasive tumours (n = 100) with an extra 50 (25 invasive BC and 25 DCIS) cases for validation was utilised. Two sections per case were used: one stained with haematoxylin and eosin (H&E) stain for histological review and one unstained for examination using DIC microscopy. Collagen fibre parameters including orientation angle, fibre alignment, fibre density, fibre width, fibre length and fibre straightness were measured. Collagen fibre density was higher in the stroma of invasive BC (161.68 ± 11.2 fibre/µm²) compared to DCIS (p < 0.0001). The collagen fibres were thinner (13.78 ± 1.08 µm), straighter (0.96 ± 0.006, on a scale of 0-1), more disorganised (95.07° ± 11.39°) and less aligned (0.20 ± 0.09, on a 0-1 scale) in the invasive BC compared to DCIS (all p < 0.0001). A model considering these features was developed that could distinguish between DCIS and invasive tumours with 94% accuracy. There were strong correlations between fibre characteristics and clinicopathological parameters in both groups. A statistically significant association between fibre characteristics and patients' outcomes (breast cancer specific survival, and recurrence free survival) was observed in the invasive group but not in DCIS. Although invasive BC and DCIS were both associated with stromal reaction, the structural features of collagen fibres were significantly different in the two disease stages. Analysis of the stroma fibre characteristics in the preoperative core biopsy specimen may help to differentiate pure DCIS from those associated with invasion.

Light-sheet fluorescence microscopy (LSFM), a prominent fluorescence microscopy technique, offers enhanced temporal resolution for imaging biological samples in four dimensions (4D; x, y, z, time). Some of the most recent implementations, including inverted selective plane illumination microscopy (iSPIM) and lattice light-sheet microscopy (LLSM), move the sample substrate at an oblique angle relative to the detection objective's optical axis. Data from such tilted-sample-scan LSFMs require subsequent deskewing and rotation for proper visualisation and analysis. Such data preprocessing operations currently demand substantial memory allocation and pose significant computational challenges for large 4D dataset. The consequence is prolonged data preprocessing time compared to data acquisition time, which limits the ability for live-viewing the data as it is being captured by the microscope. To enable the fast preprocessing of large light-sheet microscopy datasets without significant hardware demand, we have developed WH-Transform, a memory-efficient transformation algorithm for deskewing and rotating the raw dataset, significantly reducing memory usage and the run time by more than 10-fold for large image stacks. Benchmarked against the conventional method and existing software, our approach demonstrates linear runtime compared to the cubic and quadratic runtime of the other approaches. Preprocessing a raw 3D volume of 2 GB (512 × 1536 × 600 pixels) can be accomplished in 3 s using a GPU with 24 GB of memory on a single workstation. Applied to 4D LLSM datasets of human hepatocytes, lung organoid tissue and brain organoid tissue, our method provided rapid and accurate preprocessing within seconds. Importantly, such preprocessing speeds now allow visualisation of the raw microscope data stream in real time, significantly improving the usability of LLSM in biology. In summary, this advancement holds transformative potential for light-sheet microscopy, enabling real-time, on-the-fly data preprocessing, visualisation, and analysis on standard workstations, thereby revolutionising biological imaging applications for LLSM and similar microscopes.

Tissue slices can undergo distortions during processing into resin for light and electron microscopy as a result of differential shrinkage of the various tissue components, and this may necessitate removal of a considerable amount of material from the final resin-embedded tissue block to ensure production of complete sections of the sample. To mitigate this problem, a number of techniques have been devised that ensure the sample is held flat during the final curing/polymerisation of the resin. For embedding in acrylic resins, oxygen must be excluded as it inhibits polymerisation, and methods devised for epoxy resin embedding are generally unsuitable. The method describes the preparation and use of air-tight flat-embedding chambers prepared from Melinex film and provides an inexpensive, technically simpler, and versatile alternative to chambers formed from either Thermanox coverslips or Aclar films that have previously been advocated for such purposes.

Lay description: Tissue slices can undergo distortions during processing into resin for light and electron microscopy as a result of differential shrinkage of the various tissue components. Such distortions may necessitate removal of a considerable amount of material to ensure production of complete sections of the sample. For embedding in acrylic resins, oxygen must be excluded as it inhibits polymerisation, and methods devised for epoxy resin flat-embedding are generally unsuitable. Air-tight flat-embedding chambers prepared from either Thermanox coverslips, or a combination of PTFE-coated glass slides, polycarbonate film gaskets, and Aclar film have been advocated for such purposes. Thermanox coverslips are expensive and limited in size to 22 mm × 60 mm, and the alternative method is technically complicated. Melinex film is commercially available as 210 mm × 297 mm sheets and is approximately 1/20th the price of Thermanox and less than half the price of Aclar film. The method describes the preparation and use of embedding chambers made from Melinex film, glass slides and double-sided adhesive tape as a technically simpler, inexpensive and versatile alternative to both Thermanox coverslips and the Aclar film method.

Modern bioimaging core facilities at research institutions are essential for managing and maintaining high-end instruments, providing training and support for researchers in experimental design, image acquisition and data analysis. An important task for these facilities is the professional management of complex multidimensional bioimaging data, which are often produced in large quantity and very different file formats. This article details the process that led to successfully implementing the OME Remote Objects system (OMERO) for bioimage-specific research data management (RDM) at the Core Facility Cellular Imaging (CFCI) at the Technische Universität Dresden (TU Dresden). Ensuring compliance with the FAIR (findable, accessible, interoperable, reusable) principles, we outline here the challenges that we faced in adapting data handling and storage to a new RDM system. These challenges included the introduction of a standardised group-specific naming convention, metadata curation with tagging and Key–Value pairs, and integration of existing image processing workflows. By sharing our experiences, this article aims to provide insights and recommendations for both individual researchers and educational institutions intending to implement OMERO as a management system for bioimaging data. We showcase how tailored decisions and structured approaches lead to successful outcomes in RDM practices.

Lay description: Modern bioimaging facilities at research institutions are crucial for managing advanced equipment and supporting scientists in their research. These facilities help with designing experiments, capturing images, and analyzing data. One of their key tasks is organizing and managing large amounts of complex image data, which often comes in various file formats and are difficult to handle.

This article explains how the Core Facility Cellular Imaging (CFCI) at Technische Universität Dresden successfully implemented a specialized system called OMERO. With this system it is possible to manage and organize bioimaging data sustainably in a way that they are findable, accessible, interoperable and reusable according the FAIR principles. We describe the practical implementation process on exemplary projects within scientific research and medical education. We discuss the challenges we faced, such as creating a standard way to name files, organizing important information about the images (known as metadata), and ensuring that existing image processing methods could work with the new system.

By sharing our experience, we aim to offer practical advice and recommendations for other researchers and institutions interested in using OMERO for managing their bioimaging data. We highlight how careful planning and structured approaches can lead to successful data management practices, making it easier for researchers to store, access, and reuse their valuable data.

The endoplasmic reticulum (ER) is the largest organelle in terms of membrane content, occupying the entire cytoplasmic volume. It is tethered to the cell cortex through ER-plasma membrane contact sites (EPCS). Previous studies have shown that EPCSs labelled by VAP27 align with cortical microtubules, and that ER tubules elongate along microtubules. Here, we addressed the question whether this relationship is bidirectional, with EPCSs influencing microtubule organisation. Using TIRF microscopy to track EPCSs and microtubule dynamics simultaneously, we demonstrate that while EPCSs remain stable, microtubules are highly dynamic and can adjust their positioning based on nearby EPCS in Arabidopsis cotyledon epidermis. In lobes of epidermal cells enclosed by two indentations, where microtubules bundle together, EPCSs flank the bundles and exhibit a distinctive arrangement, forming symmetric arcs in relation to the lobe axis. In guard cells, transversely oriented ER tubules co-align with microtubules. Disrupting microtubules with the drug oryzalin leads to transient guard cells-ER remodelling, followed by its reorganisation into transverse tubules before microtubule recovery. Taken together our observations suggest, that the positioning of EPCSs and cortical microtubules, can affect each other and the organisation of cortical ER.

Roldan, D., Redenbach, C., Schladitz, K., Kübel, C., & Schlabach, S. (2024). Image quality evaluation for FIB-SEM images. Journal of Microscopy, 293(2), 98-117. https://onlinelibrary.wiley.com/doi/10.1111/jmi.13254

Diego Roldan's affiliation appears as “National University, Bogotá, Colombia”

The correct affiliation is “Departamento de Matemáticas, Universidad Nacional de Colombia, Bogotá, Colombia”

We apologise for this error.

Interferometric scattering (iSCAT) microscopy enables high-speed and label-free detection of individual molecules and small nanoparticles. Here we apply point spread function engineering to provide adaptive control of iSCAT images using spatial light modulation. With this approach, we demonstrate improved dynamic spatial filtering, real-time background subtraction, focus control, and signal modulation based on sample orientation.

Despite the widespread use of Scanning Transmission Electron Microscopy (STEM) for observing the structure of materials at the atomic scale, a detailed understanding of some relevant electron beam damage mechanisms is limited. Recent reports suggest that certain types of damage can be modelled as a diffusion process and that the accumulation effects of this process must be kept low in order to reduce damage. We therefore develop an explicit mathematical formulation of spatiotemporal diffusion processes in STEM that take into account both instrument and sample parameters. Furthermore, our framework can aid the design of Diffusion Controlled Sampling (DCS) strategies using optimally selected probe positions in STEM, that constrain the cumulative diffusion distribution. Numerical simulations highlight the variability of the cumulative diffusion distribution for different experimental STEM configurations. These analytical and numerical frameworks can subsequently be used for careful design of 2- and 4-dimensional STEM experiments where beam damage is minimised.