Electron microscopy (EM) is an indispensable technique to visualize biological ultrastructure in health and disease. High-throughput EM further enables larger scales and volumes to be recorded within feasible timeframes. Multibeam optical scanning transmission EM (OSTEM) utilizes multiple beamlets and optical separation of the transmitted electrons to increase imaging throughput with transmission-based imaging. However, the compatibility of multibeam OSTEM with routine sample preparation protocols and the effect of machine settings on image quality remain largely unknown. Here, we show multibeam OSTEM to be an order of magnitude faster than (scanning) transmission EM while yielding comparable high-quality images of tissue processed with standard high-contrast staining protocols. Multibeam OSTEM benefits from embedding approaches that introduce high contrast but is flexible in the type of stain used. Optimal results are obtained using an acceleration voltage of 5 kV, where section thickness and pixel dwell time require a balance between throughput and image quality. Our results show high-throughput EM with imaging quality comparable with commonly used transmission-based modalities, enabling biological ultrastructure analysis across larger scales and volumes.

Electron microscopy (EM) combined with energy dispersive x-ray (EDX) imaging (or ‘ColorEM’) of cells and tissues provides ultrastructural insight complemented with elemental context. The resulting hyperspectral datasets can be used to map the relative abundance of specific elements or subjected to more data-driven approaches such as spectral mixture analysis or clustering to highlight the ultrastructural components of interest. Despite the benefits of automatic segmentation over manual annotation, EDX imaging is two orders of magnitude slower than EM imaging precluding its routine use for segmentation. Large-scale ColorEM, however, does generate sufficient annotated labels, which we use as ground truth to train U-Net models, and thus enables the transfer of these labels to conventional EM data. Here, we present ColorEM-Net, a label-free segmentation technique based on features obtained from unsupervised clustering of ColorEM data. ColorEM-Net achieves label-free identification with over 95% accuracy for nuclei, lysosomes and exocrine granules. However, with an accuracy of 79%, the recognition of endocrine granules needs further effort in training for reliable segmentation. By reusing open-access ColorEM datasets, this approach facilitates automated segmentation of EM data, while eliminating the need for manual annotation and achieving scalability for tissue-scale segmentation.

Recent advances in electron microscopy techniques have led to a significant scale up in volumetric imaging of biological tissue. The throughput of electron microscopes, however, remains a limiting factor for the volume that can be imaged in high resolution within reasonable time. Faster detection methods will improve throughput. Here, we have characterized and benchmarked a novel detection technique for scanning electron microscopy: optical scanning transmission electron microscopy (OSTEM). A qualitative and quantitative comparison was performed between OSTEM, secondary and backscattered electron detection and annular dark field detection in scanning transmission electron microscopy. Our analysis shows that OSTEM produces images similar to backscattered electron detection in terms of contrast, resolution and signal-to-noise ratio. OSTEM can complement large scale imaging with (scanning) transmission electron microscopy and has the potential to speed up imaging in single-beam scanning electron microscope.

Elucidating the 3D nanoscale structure of tissues and cells is essential for understanding the complexity of biological processes. Electron microscopy (EM) offers the resolution needed for reliable interpretation, but the limited throughput of electron microscopes has hindered its ability to effectively image large volumes. We report a workflow for volume EM with FAST-EM, a novel multibeam scanning transmission electron microscope that speeds up acquisition by scanning the sample in parallel with 64 electron beams. FAST-EM makes use of optical detection to separate the signals of the individual beams. The acquisition and 3D reconstruction of ultrastructural data from multiple biological samples is demonstrated. The results show that the workflow is capable of producing large reconstructed volumes with high resolution and contrast to address biological research questions within feasible acquisition time frames.

Microscopic analysis of molecules and physiology in living cells and systems is a powerful tool in life sciences. While in vivo subcellular microscopic analysis of healthy and diseased human organs remains impossible, zebrafish larvae allow studying pathophysiology of many organs using in vivo microscopy. Here, we review the potential of the larval zebrafish pancreas in the context of islets of Langerhans and Type 1 diabetes. We highlight the match of zebrafish larvae with the expanding toolbox of fluorescent probes that monitor cell identity, fate and/or physiology in real time. Moreover, fast and efficient modulation and localization of fluorescence at a subcellular level, through fluorescence microscopy, including confocal and light sheet (single plane illumination) microscopes tailored to in vivo larval research, is addressed. These developments make the zebrafish larvae an extremely powerful research tool for translational research. We foresee that living larval zebrafish models will replace many cell line-based studies in understanding the contribution of molecules, organelles and cells to organ pathophysiology in whole organisms.