Journal of microscopy最新文献

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.

Metallic nanoparticle dimers have been used to enhance the excitation rate of single-quantum emitters. The interparticle distance (d) of the dimers has a crucial influence on the signal enhancement. Therefore, precise control of d is desired for optimal performance. However, statistical analysis of d has been often restricted to a small number of dimers due to the lack of reliable automatic software tools. For this reason, we developed a novel analysis tool for automatic dimer analysis. Our approach combines particle detection by circle Hough transformation (CHT) with custom classification routines optimised for distinct types of particles. We applied our tool to scanning electron microscopy (SEM) images and achieved great agreement in dimer detection, reaching an agreement of around 97% between automatic analysis and manual inspection for more than 3000 metallic nanoparticle dimers on DNA origami controlled by a combination of multiple DNA strands. Our study revealed the effects of the strand length (L) on the distribution of d. Based on geometric consideration, we expected a strong correlation between L and the standard deviation (σ) of d. We could verify this correlation by characterising four dimer designs with different L while analysing more than 1000 dimers per specimen.

Sidestream dark field (SDF) imaging is a tool for assessing microcirculation, commonly used for early diagnosis and monitoring of sepsis. In this study, we used SDF imaging to observe and assess the microcirculation of the intestinal mucosa during bowel surgery. We also compared different performance between normal mucosa and diseased mucosa using SDF imaging. SDF imaging was conducted in 13 patients to evaluate microcirculation parameters. All patients were assessed at distances of 0, 1, 2, 3 and 4 centimeters (cm) from the edge of the mesentery, respectively. Microcirculatory parameters such as microvascular flow index (MFI), proportion of perfused vessels (PPV), vascular density (VD), total vessel density (TVD), perfused vessel density (PVD) and heterogeneity index (HI) were measured in these patients. Compared to normal intestinal mucosa, the diseased intestinal mucosa exhibited higher values for VD (p = 0.044), TVD (p = 0.006) and PVD (p = 0.007). No significant differences in PPV, MFI and HI were observed between the two groups. The microcirculation parameters (MFI, PPV and PVD) of the intestine at the distal distance of 3 cm were significantly lower than those at a distance of 2 cm (MFI 1.5 (0.75) vs. 3 (0.5), PPV 57.6 (9.1) vs. 97.1 (8.6)% and PVD 11.395 (3.082) vs. 20.726 (4.115) mm/mm²). In conclusion, SDF imaging is an advanced technique that provide real-time visualization of intestinal mucosal microcirculation. It has the potential to assess the blood perfusion of the intestine during surgery.

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.

Scalp hair is a key feature of humans and its variability has been the subject of a broad range of studies. A small subset of these studies has focused on geometric quantification of human scalp hair fibres, however the use of race- and ethnicity-based classification systems makes it challenging to draw objective conclusions about fibre variability. Furthermore, sample preparation techniques for micro-imaging studies often alter the original form of hair fibres. This study sought to determine which of the commonly reported descriptors could be resolved using micro-computed tomography (micro-CT) for fibres of varying curl. Images obtained from micro-CT were used to reconstruct three-dimensional images that were then analysed. The study also explored the capabilities and limitations of micro-CT as an imaging modality by comparing and cross-validating findings with those obtained from scanning electron microscopy (SEM) and laser micrometry. The former deals with surface imaging while the latter deals with cross-sectional measurements. Micro-CT was found to be highly effective at resolving cross-sectional ellipsoidal parameters, but performed more poorly than SEM in reconstructing surface level details at a 2 $\mu m$ resolution. The technique was, however, able to reveal the presence of the medulla in type VI (high curl) hair fibres. When compared with high curl fibres, greater intra-fibre variability was observed for the low and medium curl fibres, highlighting the importance more objective classification systems.

Super-resolution (SR) microscopy is a cutting-edge method that can provide detailed structural information with high resolution. However, the thickness of the specimen has been a major limitation for SR methods, and large biological structures have posed a challenge. To overcome this, the key step is to optimise sample preparation to ensure optical homogeneity and clarity, which can enhance the capabilities of SR methods for the acquisition of thicker structures. Oocytes are the largest cells in the mammalian body and are crucial objects in reproductive biology. They are especially useful for studying membrane proteins. However, oocytes are extremely fragile and sensitive to mechanical manipulation and osmotic shocks, making sample preparation a critical and challenging step. We present an innovative, simple and sensitive approach to oocyte sample preparation for 3D STED acquisition. This involves alcohol dehydration and mounting into a high refractive index medium. This extended preparation procedure allowed us to successfully obtain a unique two-channel 3D STED SR image of an entire mouse oocyte. By optimising sample preparation, it is possible to overcome current limitations of SR methods and obtain high-resolution images of large biological structures, such as oocytes, in order to study fundamental biological processes. Lay Abstract: Super-resolution (SR) microscopy is a cutting-edge tool that allows scientists to view incredibly fine details in biological samples. However, it struggles with larger, thicker specimens, as they need to be optically clear and uniform for the best imaging results. In this study, we refined the sample preparation process to make it more suitable for SR microscopy. Our method includes carefully dehydrating biological samples with alcohol and then transferring them into a mounting medium that enhances optical clarity. This improved protocol enables high-resolution imaging of thick biological structures, which was previously challenging. By optimizing this preparation method, we hope to expand the use of SR microscopy for studying large biological samples, helping scientists better understand complex biological structures.

Ex vivo x-ray angiography provides high-resolution, three-dimensional information on vascular phenotypes down to the level of capillaries. Sample preparation for ex vivo angiography starts with the removal of blood from the vascular system, followed by perfusion with an x-ray dense contrast agent mixed with a carrier such as gelatine or a polymer. Subsequently, the vascular micro-architecture of harvested organs is imaged in the intact fixed organ. In the present study, we present novel microscopic dual-energy CT (microDECT) imaging protocols that allow to visualise and analyse microvasculature in situ with reference to the morphology of hard and soft tissue. We show that the spectral contrast of µAngiofil and Micropaque barium sulphate in perfused specimens allows for the effective separation of vasculature from mineralised skeletal tissues. Furthermore, we demonstrate the counterstaining of perfused specimens using established x-ray dense contrast agents to depict blood vessels together with the morphology of soft tissue. Phosphotungstic acid (PTA) is used as a counterstain that shows excellent spectral contrast in both µAngiofil and Micropaque barium sulphate-perfused specimens. A novel Sorensen-buffered PTA protocol is introduced as a counterstain for µAngiofil specimens, as the polyurethane polymer is susceptible to artefacts when using conventional staining solutions. Finally, we demonstrate that counterstained samples can be automatically processed into three separate image channels (skeletal tissue, vasculature and stained soft tissue), which offers multiple new options for data analysis. The presented microDECT workflows are suited as tools to screen and quantify microvasculature and can be implemented in various correlative imaging pipelines to target regions of interest for downstream light microscopic investigation.

The evaluation of large experimental datasets is a fundamental aspect of research in every scientific field. Streamlining this process can improve the reliability of results while making data analysis more efficient and faster to execute. In biomedical research it is often very important to determine the type of cell death after various treatments. Thus, differentiating between viable, apoptotic, and necrotic cells provide critical insights into the treatment efficacy, a key aspect in the field of drug development. Fluorescent microscopy is perceived as a widely used technique for cell metabolism assessment and can therefore be used to investigate treatment outcomes after staining samples with cell death detection kit. However, accurate evaluation of therapeutic results requires quantitative analysis, often necessitating extensive postprocessing of imaging data. In this study, we introduce a complementary tool designed as a macro for the Fiji platform, enabling the automated postprocessing of fluorescent microscopy images to accurately distinguish and quantify viable, apoptotic, and necrotic cells.