Microscopy and Microanalysis

Four-dimensional scanning transmission electron microscopy (4D-STEM) enables the acquisition of diffraction patterns at every probe position in a dense array. For imaging applications this approach offers significant benefits in terms of spatial resolution and contrast enhancement. In this work, we present the development of a synchronous scan generator integrated with SerialEM software to enable automation of complex experimental protocols such as tomography. The proposed hardware functions as an interface between SerialEM, the scan controls of the microscope, a fast annular dark-field detector, and a synchronized trigger for a pixelated detector. Our previous implementation, named SavvyScan, relied on a dedicated computer equipped with a multichannel acquisition and signal-generation cards, as well as a separate microcontroller for synchronization. Here, we report a low-cost implementation based on a Red Pitaya board, utilizing direct programming of its embedded FPGA and Linux server components. We provide detailed instructions for system installation and operation, along with practical guidance for modifying the source code. System performance is validated through oscilloscope measurements and imaging of a replica grating sample. The utility of the approach is further demonstrated by generating a 3D electron tomogram of a cryogenic sample of mitochondria from a tilt series of shadow montage projections.

Birds evolved large, cognitively capable forebrains independently of mammals, yet comparative analyses of avian brain organization have been constrained by the lack of standardized resources capable of resolving internal parcellation and long-range connectivity across species. Here, we present a comparative MRI resource spanning 16 avian species representing major clades and diverse ecological niches. We analyzed high-resolution T2-weighted and diffusion-weighted datasets suitable for direct interspecific comparison. T2-weighted morphometry revealed pronounced region-specific variation in internal brain architecture, including lineage-dependent differences in the relative prominence of major brain divisions and commissural structures, supporting a pattern of mosaic diversification rather than uniform scaling. To validate MRI-derived anatomical boundaries, we compared MRI parcellations with complementary histological analyses in three representative taxa (the large-billed crow, gentoo penguin, and mandarin duck), demonstrating close correspondence between MRI-defined borders and cytoarchitectonic transitions identified by Nissl staining, as well as major myelinated compartments visualized by Luxol Fast Blue staining. Moreover, diffusion MRI tractography and fractional anisotropy (FA) mapping further revealed both conserved and species-specific features of large-scale brain organization. Seed-based tractography of the optic lobe, dorsal cortex, cerebellum, and anterior cortex in chick, gentoo penguin, and large-billed crow revealed conserved within-compartment trajectory patterns alongside marked region-specific interspecific differences, particularly in optic-lobe-associated long-range trajectories. Whole-brain FA maps revealed complementary variation in regional microstructural organization across taxa. Together, this comparative MRI framework provides a cross-validated foundation for linking internal brain anatomy and long-range connectivity to ecological and evolutionary diversification in birds, with broader applications to comparative neuroanatomy across amniotes.

Images in the electron microscope are formed by electron scattering and focusing. The spherical geometry of these processes gives rise to two coherent, conjugate spherical wave fronts, known as Ewald spheres. These spheres are associated with the two halves of the contrast transfer function (CTF), and their widths are determined by the focal gradient through the specimen. To properly correct for the CTF, each half of the CTF must be applied to an image individually and integrated into the reconstruction into the corresponding Ewald sphere. Theory indicates that this dual Ewald sphere reconstruction method should recover the maximal amount of information possible. This method was compared to the other reconstruction methods commonly used: the projection approximation (ignoring the Ewald sphere), the simple insertion and the single sideband methods. In simulated reconstructions the dual Ewald sphere method recovered the most information when the correct half of the CTF is matched to the corresponding Ewald sphere. If the wrong half is matched, the result worse than the projection approximation method. Examining reconstructions from real data indicated that the dual Ewald sphere method performs at least as well as the simple insertion method, but not as good as in simulations. The likely reason is the two-fold ambiguity in the assigned orientations of the particle images, which remains an issue to pursue in further studies. In conclusion, the dual Ewald sphere reconstruction method may offer the best way to calculate very high resolution reconstructions when the micrograph quality warrants it. HighlightsO_LIThe dual Ewald sphere reconstruction corrects for the two halves of the CTF. C_LIO_LIThe signs of the two halves of the CTF must correspond to the focal gradient. C_LIO_LIDetermining the focal gradient for individual particle images remains unresolved. C_LIO_LIComplex reconstructions indicate any real space phases are artifacts. C_LI

Cryogenic electron microscopy (cryoEM) is now routinely used for high-resolution structure determination of biological macromolecules. However, many biological specimens exhibit varying degrees of preferred orientation on cryoEM grids, resulting in uneven sampling of three-dimensional Fourier space. This orientation bias produces anisotropic reconstruction artifacts and, in severe cases, can exacerbate particle misalignment during iterative refinement, thereby limiting the success rate of near-atomic resolution cryoEM structure determination. This protocol provides a practical guide for applying spIsoNet, a self-supervised deep-learning method, to mitigate preferred-orientation issues in cryoEM reconstructions. We describe two complementary workflows: (1) map Anisotropy Correction to correct anisotropic artifacts of cryoEM maps and (2) particle Misalignment Correction, which integrates spIsoNet with RELION external reconstruction to improve particle-pose estimation. We demonstrate these workflows using two influenza hemagglutinin (HA) trimer datasets representing moderate and severe degrees of preferred-orientation bias. The protocol includes installation instructions, parameter-selection guidance, quality-control checkpoints and troubleshooting advice, and can typically be completed in ~7 hours on a workstation equipped with four NVIDIA A100 GPUs. Together, these workflows provide step-by-step guidance for using the open-source spIsoNet software to mitigate the preferred-orientation problem directly from experimental data.

Cryo-correlative light and electron microscopy methods enable targeted structural analysis of fluorescently labelled features in vitrified specimens. However, correlative workflows on high-pressure frozen samples often remain challenging due to the lack of persistent landmarks for reliable sample tracking and image registration between different microscopes. Standard high-pressure freezing carriers provide little intrinsic reference information, as the exposed sample surface is often smooth and rotationally ambiguous, complicating localisation of regions of interest across imaging platforms. Here, we introduce PinCorr, a 3-mm high-pressure freezing carrier with an integrated coordinate system formed by four asymmetrically arranged pillars with distinct geometries. These built-in landmarks remain visible after freezing and provide a stable, sample-independent reference frame for orientation and correlation between cryo-fluorescence microscopy and electron microscopy. We show that PinCorr supports fluorescence-guided cryo-volume imaging, serial lift-out for cryo-electron tomography and freeze-substitution workflows followed by room-temperature on-section correlation. PinCorr thus provides a hardware-based approach to establishing a persistent spatial reference frame in HPF-based correlative imaging workflows for thick and multicellular specimens.

The preparation of multi panel figures remains a labor intensive step in scientific publication. Albeit there are specific tools available to solve this problem, they are often highly specialized, difficult to install, or time consuming to learn. Griphus is a standalone graphical application designed for rapid composition and experimentation with multi panel figures, developed by and for zoological taxonomists. Functions specifically designed for multi panel composition include automatic figure numbering and placement, aspect ratio operations, spacers, layout rotation, layout suggestions, and automatic generation of figure legends, including scale bar descriptions. The software can perform both spatial interpretation of images on the canvas and work with a simple, editable layout formula. It also enables instant multi panel composition, with numbered images and automatic contrast selection for the numbers, obtained simply by loading images. User defined parameters such as target printable dimensions, resolution, spacing, and color mode are preserved throughout the work. The program produces coordinated outputs consisting of the final composite figure, a readable file describing the layout structure, and a .gri file storing images, transformations, and parameters for exact regeneration. Griphus is intended as a complementary tool to professional image software, providing a simple and efficient environment for constructing high quality multi panel figures.

Cryo-volume electron microscopy (CVEM) enables three-dimensional imaging of biological ultrastructure in a near-native state but has been limited by low image contrast and charging artifacts that hinder data interpretation and complicate automation of data acquisition. Here we present an experimental and computational workflow that combines orthogonal cryo-SEM imaging, spot-geometry optimized O+ plasma-FIB milling, dedicated acquisition-control routines, and dedicated image alignment procedure. The workflow enables autonomous acquisition of volumetric datasets from vitrified cells and tissues at [~]15-20 nm isotropic resolution. In addition, sub-volume averaging of 113 nuclear pore complexes extracted from CVEM dataset of Cos-7 cell yielded its reconstruction at 9.4 nm resolution. Together, these results establish CVEM as a robust platform for autonomous high-resolution volumetric imaging and structural analysis of vitrified biological specimens.

Cryo-electron microscopy (cryo-EM) of biological specimens is limited by radiation damage and a low signal-to-noise ratio (SNR). Here, we show that reducing the illuminated area substantially slows the observed diffraction decay in protein microcrystals. We further show that narrow parallel-beam electron diffraction from thin non-crystalline biological specimens provides substantially higher reciprocal-space SNR than conventional cryo-EM imaging. We developed a multimodal scanning workflow, 4D-para-STEM, that records narrow-beam diffraction patterns together with corresponding images. Using viruses, peptide assemblies, and microtubules, we demonstrate interpretable diffraction signals from both crystalline and non-crystalline biological specimens. Together, these results show that narrow parallel-beam scanning reduces observed radiation damage and improves the SNR in cryo-EM.

The translocase of the outer mitochondrial membrane (TOM) complex is the main entry gate for mitochondrial proteins. Approximately 99 % of mitochondrial proteins are synthesized as precursor proteins (preproteins) in the cytosol and subsequently translocated into mitochondria through the TOM complex. The TOM complex exists in a dynamic equilibrium among multiple assembly states through spatial rearrangements of its subunits. The recent cryo-electron microscopy (cryo-EM) studies revealed near-atomic structures of the TOM core dimer, whereas previous biochemical studies indicated the TOM complex functions as a trimer in intact mitochondria. However, the relationship between the core dimer and the functional trimer remains unclear. In the present study, we analyzed the dynamics of the TOM complex using high-speed atomic force microscopy (HS-AFM) to investigate the assembly states and conformation transitions of the TOM complexes. We demonstrated that purified yeast TOM complexes predominantly adopt a trimeric organization but dynamically dissociate into dimeric and monomeric states during HS-AFM observation. The trimeric particles observed by HS-AFM exhibited spherical molecular shapes consistent with a trimeric structural model proposed from previous crosslinking analyses. In contrast, the dissociated dimeric particles closely resembled the dimensions of the TOM core-dimer structures determined by cryo-EM. Furthermore, HS-AFM analyses provided insight into the spatial arrangement of the Tom20 receptor, consistent with previous models of the trimeric TOM complex. These observations enabled characterization of the trimeric TOM complex in vitro and provide a foundation for future structural and functional analyses of TOM complex assembly.

In single particle cryo-EM, analysis of continuous conformational heterogeneity has always been challenging. Both linear and deep learning-based methods treat conformational heterogeneity as perturbations to the consensus average conformation, limiting their capability in analyzing large protein motions. While classic conformational classifications are capable of handling large domain motion, they bin continuous protein dynamics into discrete static substates. Here, we present cryoROLE, a computational tool that extracts the continuous conformational dynamics embedded in the static composite map constructed from multi-body refinement into a landscape of relative orientation between the moving domains. Depicted in real space, the landscape allows intuitive interpretations of domain motion and the population of poses in the conformational space. Applying it to various biological systems reveals hidden conformational dynamics that are relevant to protein functions.

Cryogenic-electron microscopy (cryo-EM) has revolutionized the field of protein structural biology. The structures of large membrane proteins are now routinely determined by cryo-EM to near atomic resolution. However, in the medium resolution range of cryo-EM maps (>[~]2 [A]), negatively charged sidechains of acidic residues are not well-resolved due to the negative electrostatic potential of the region. This may lead to incorrect sidechain models for residues like glutamic acid or aspartic acid that are central for proton transfer activity in various respiratory and photosynthetic enzymes. We previously proposed that the acidic residues with weak or non-existent cryo-EM density can be modeled to represent their low proton affinity conformations. Here, we tested this hypothesis on a larger data set of acidic amino acid residues in two high-resolution respiratory complex I structures. By using faster sidechain modeling and proton affinity prediction tools, we created a workflow that generates sidechain conformations of selected amino acid residues. We validated the sidechain conformation predictions by Q-score analysis and atomistic molecular dynamics simulations in different charged states. The proposed workflow provides a way to rapidly obtain sidechain conformations of acidic residues with weak cryo-EM densities and can be integrated into the existing cryo-EM modeling pipelines to speed up sidechain rotamer prediction.

Genomic DNA is wrapped around core histones to form nucleosomes, which are organized in cells from euchromatin to heterochromatin with distinct genome functions. Although transcription is known to shape chromatin behavior in live cells, it remains unclear how different transcription systems shape chromatin classes and nuclear subcompartments. We developed machine learning-assisted Repli-Histo labeling to classify euchromatin and heterochromatin classes (Classes IA, IB, II, and III) and combined it with single-nucleosome imaging in live cells. Nucleosome motion was progressively constrained from euchromatin to heterochromatin. RNA polymerase II inhibition by THZ1, DRB, or -amanitin increased nucleosome motion in euchromatic Classes IA and IB and in heterochromatin around nucleoli, but not at the nuclear periphery. In contrast, RNA polymerase I inhibition by CX-5461 selectively increased nucleosome motion in Class III heterochromatin around nucleoli. Our study reveals that Pol II and Pol I transcription shape chromatin behavior in distinct chromatin classes and nuclear subcompartments. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=143 SRC="FIGDIR/small/736477v1_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@127137dorg.highwire.dtl.DTLVardef@709a16org.highwire.dtl.DTLVardef@94550corg.highwire.dtl.DTLVardef@5ba6ec_HPS_FORMAT_FIGEXP M_FIG C_FIG

Cryo-volume electron microscopy (cryo-vEM) enables near-native visualization of cellular ultrastructure, but its broad use is limited by low image contrast and the high cost of dense voxel-level annotation. Existing automated segmentation methods often generalize poorly across cell types, organelles, and imaging conditions. Here, we introduce SparseSeg, a target-conditioned, sparsity-driven segmentation framework that treats organelle segmentation as a discovery process rather than a closed-set classification task. SparseSeg uses a small number of context-specific exemplars to iteratively propagate reliable supervision through the volume. It combines sparse patch-based sampling, a multi-kernel U-Net, and geometry-consistent refinement to expand accurate segmentation while suppressing context-dependent false positives. Across serial cryo-FIB-SEM and conventional vEM datasets, SparseSeg achieves robust segmentation under extreme sparse annotation, including settings with less than 1% labeled slices. This framework reduces annotation burden while preserving morphological fidelity for quantitative cryo-vEM analysis.

Soft X-ray tomography (SXT) is an emerging modality for whole-cell 3D imaging in near-native states. However, the effective spatial resolution is limited by optical artifacts characterized by the point spread function (PSF). To achieve optimal resolution via PSF inversion, we propose a measurement-supervised deep learning framework. Bypassing purely data-driven neural networks that are prone to hallucinations, we employ a measurement-supervised, instance-specific optimization strategy strictly constrained by a differentiable SXT formation forward model. The structural fidelity was validated using split-tilt Fourier ring correlation (FRC), ensuring the recovered high-frequency features reflect genuine specimen features rather than random artifacts. Our results demonstrate that this optimization consistently increases FRC resolution and enhances visual ultrastructural details across diverse biological structures. Furthermore, by recovering high-frequency features from sparse-angular projections, we show that spatial resolution can be maintained using only half the radiation exposure. This approach effectively compensates for the degradations caused by angular sparsity, providing a hardware-free computational solution to minimize radiation damage, maximize imaging speed, and overcome the optical and dosimetric limits of SXT.

Aquatic animals deploy adhesives, in numerous essential functions, and reversibility is a key adaptation. The molecular mechanisms of reversible wet adhesion remain poorly understood. Using a model organism, the freshwater cnidarian Hydra vulgaris, we dissect the mechanism of molecular assembly in a secreted adhesive and uncover a glycan and protein-based architecture organized by a lectin-like protein, Hydra vulgaris adhesive protein 1 (HvAb1). We identify HvAb1 as a nonredundant organizer of the adhesive matrix, being basal-disc specific and secreted. Knockdown of HvAb1 severely impaired attachment and disrupted footprint architecture in a mosaic pattern, with only HvAb1-positive regions of the adhesive footprint retaining their normal structure. The adhesive is wheat germ agglutinin (WGA)-reactive and contains a fibrillar chitin-based sub-network, synthesized by a basal-disc-specific chitin synthase. Applying exogeneous chitinase abolished both WGA staining and Hydra attachment, indicating that WGA-positive components perform essential roles in adhesion. Our results therefore describe a glycan-dominated matrix, organized via a lectin-like protein (HvAb1), which is reinforced by chitin and enables reversible adhesion underwater. This establishes Hydra as a tractable model to better understand the principles of reversible adhesion underwater and, potentially, inform future bioinspired, sustainable adhesives.

Summary: Single-molecule localization microscopy techniques, such as (direct) stochastic optical reconstruction microscopy ((d)STORM) and photo-activated localization microscopy (PALM) enable the visualization of subcellular molecular organization beyond the diffraction limit of conventional light microscopy. Not only is data acquisition rather slow, but the downstream analysis of localization datasets often remains computationally challenging and time-consuming. Consequently, the complexity and duration of data processing often limit experiments to the acquisition and analysis of only small numbers of cells or regions of interest, thereby restricting the statistical power and biological reliability of SMLM studies. To address this limitation, we developed an open-source Python-based package for automated, high-throughput post-processing and quantitative analysis of SMLM localization data, enabling efficient and straightforward handling of extensive datasets with minimal manual intervention. Availability and implementation: dSTORMQuant (source code and documentation) are freely available on GitHub at https://github.com/BCMM-Bielefeld-University/dSTORMQuant under GPL v3 license.

Chemical imaging is a powerful branch of modern microscopy encumbered by a lack of flexible, high-throughput analysis tools. Bespoke analytical pipelines typically perform ratiometric analysis on two layers in a multispectral image to describe the relative composition of molecules in a sample. This strategy has been implemented across fields, spanning histopathology, cell biology, environmental science, and materials science. The commercialization of chemical imaging microscopes has facilitated the collection of large multispectral datasets, necessitating accessible ways to process them. This paper describes Multispectral Analysis Graphical User Interface (msaGUI), a desktop graphical user interface to analyze individual and batch datasets of multispectral images. Data is loaded as CSV, TSV, or TIFFs and processed through a user-defined sequence of modular image operations that can be flexibly combined, e.g. to reduce spectral crosstalk or background noise. After analysis, data is visualized as exportable images, histograms, and statistics. To yield publication-quality figures, outputted images are fully customizable. Written in Python with open-source libraries, the msaGUI program is packaged into an executable for Windows and Mac for a fully no-code application. Other operating systems are supported via the Python source code. In summary, msaGUI provides a rapid and user-friendly solution for analyzing and visualizing multispectral data.

Volumetric electron microscopy (EM) has become a critical approach to generating high-resolution reconstructions of brain tissue. As the size of EM volumes increase, use of automated image segmentation within the reconstruction pipeline has become essential, although it generates errors that need correction. The proofreading and correcting of these errors has since become the dominant cost driver in the pipeline, but precisely estimating the sufficient number of proofreading edits to enable meaningful scientific analyses of the reconstructed neuronal networks remains a challenge. We present a fast, computationally inexpensive way to estimate the progress of a connectomic proofreading effort without requiring a priori knowledge of ground truth. We show that simple global graph invariants converge predictably to asymptotic limits with increasing numbers of proofreading edits, informing a quantitative "pencils down" criterion for proofreading completeness. We illustrate our method on two datasets in different stages of proofreading progress, a zebrafish spinal cord and the hemibrain Drosophila melanogaster dataset. Our method reduces the uncertainty associated with the planning and prioritization of proofreading activities and enables data owners to accurately predict and budget the amount of proofreading necessary for their scientific questions.

Microscopes are essential tools for discoveries on a scale invisible to the unaided human eye. The development of immuno-fluorescence followed by molecular biology techniques and fluorescent fusion proteins have revolutionised the use of optical microscopy in bioscience. The quality of the data produced is dependent upon the sample, its preparation and the instrument used. However, instruments can degrade over time without easily visible changes to the produced images and, in turn, negatively impacts results. By testing instruments and doing comparisons between results over time and between different instruments, problems can be highlighted and corrective action can be taken. Using small fluorescent beads the point spread function (PSF) of the microscope can be recorded and the image resolution measured. Beads were prepared in a concentration matched to the field of view size and dried onto coverslips and mounted on slides. The beads were then imaged as 3D Z-stacks of sufficient size to fully enclose the PSF of the system. This data was uploaded to OMERO and processed using OMERO-metrics, an OMERO plugin developed for this purpose. This paper summarizes the development of workflows and protocols to enable this process, presents the results obtained and demonstrates the detection of significant instrument issues.

Volume electron microscopy is essential for understanding cells, tissues, and neural circuits in their native 3D context, but many biological specimens are too large to image exhaustively at nanometer resolution. Researchers therefore must choose between broad anatomical context and ultrastructural detail. We introduce random-access electron microscopy (RAEM), a framework for studying fixed tissue repeatedly across scales rather than imaging it once at a single resolution. RAEM first builds a lower resolution 3D survey of the specimen, then uses accumulated human or AI-derived knowledge of that volume to guide the microscope back to selected physical sites for high resolution imaging. By linking reconstructed 3D coordinates to precise electron-beam positions on the original sections, RAEM enables targeted imaging of membranes, vesicles, and other nanoscale structures within specimens that would be impractical to image exhaustively. We demonstrate RAEM with vesicle-resolved imaging of synaptic boutons in human cortex, targeted imaging of more than one million human cortical mitochondria, hierarchical imaging of a nematode nervous system, and retrospective targeting of a previously published petabyte-scale human cortical volume. RAEM turns serial-section EM into a query-driven, multi-resolution approach for scalable biomedical discovery.