Microscopy and Microanalysis

The visualisation of large biological samples in cryo-electron tomography requires both few-nanometre resolution and a wide, ideally >10 micrometrre, field of view. For a single image, increasing magnification trades increased resolution for a smaller field of view. Montaging, where high-magnification images are stitched together to increase field of view, addresses the trade-off but is dose-inefficient with round beams that are standard in transmission electron microscopy (TEM), since this illumination shape does not tessellate. Our workflow greatly simplifies montaging schemes for dose-sensitive specimens, allowing large montages of any shape - from individual cells or other features of interest up to whole lamellae - to be collected at high resolution without compromising electron dose efficiency. We demonstrate the workflow by acquiring montage tomograms of whole yeast (Saccharomyces cerevisiae) cells in cryo-FIB lamellae and of human neurons.

Serial-sectioning electron microscopy allows the exploration of the 3D ultrastructure of cells, tissues or whole organisms. Sample preparation is a critical step for this technique, which determines the quality of downstream imaging and analysis. Here we optimize a high-pressure freezing sample-preparation workflow for larvae of the marine annelid Platynereis dumerilii. We tested several fixation and staining methods and screened a large number of specimens by synchrotron X-ray imaging to evaluate specimen integrity and contrast. We further evaluated selected blocks by array tomography SEM to select the best performing protocols. Our protocols and robust workflow could be adapted to similar marine planktonic organisms including ciliated invertebrate larvae.

Cryo-Electron Microscopy (CryoEM) Single Particle Analysis (SPA) is pivotal for elucidating protein structures and their conformational dynamics. Several critical steps are required to obtain accurate maps. First, the \textit{ab initio} relative orientations and shifts between images must be determined. Second, the common issue of specimens presenting a preferred orientation should be adequately addressed. Third, all of the above must account for particle flexibility and sample heterogeneity. Additionally, these steps are interrelated, making the search for all these parameters a real challenge when considering convergence robustness. In this context, we present ReconSIREN, a novel deep learning framework that addresses the key steps outlined before in an \textit{ab initio} manner while greatly minimizing one of the most important practical burdens in SPA, namely the errors introduced in the CryoEM maps by the existence of preferred orientations; the latter is achieved by a change of the optimization approach that avoids the so-called ``attraction problem'', and by the introduction of a new type of geometrical priors. It does so fully \textit{ab initio}, using deep learning training/inference steps without resorting to more traditional optimization approaches, and generating highly accurate initial reconstructions. We present maps as both homogeneous (consensus) maps and a full conformational landscape for analyzing flexibility and heterogeneity. Thanks to its Mixture-of-Experts (MoE) architecture, ReconSIREN distributes the estimation workload across independent experts, improving optimization efficiency and simplifying the process.

Preferred particle orientation, which results from the adsorption of proteins to the air-water interface, is a frequently encountered issue in single-particle cryo-electron microscopy (cryo-EM). Particles then present only a limited number of views, which are oversampled during data acquisition at the expense of throughput, while other views may be missing altogether. In severe cases, a high-resolution reconstruction becomes unattainable. We have recently established that ultrasonic excitation of the sample during vitrification improves the angular distribution by detaching particles from the air-water interface and reshuffling their orientations. Here, we describe an implementation of this principle for a plunge freezing apparatus, in which the ultrasonic transducer is integrated into the head of the pair of tweezers holding the sample, and show that this design is able to reduce preferred orientation. We demonstrate this for hemagglutinin, the canonical example of preferred orientation, and show that the fraction of side and tilted views increases with the excitation amplitude. This allows us to obtain a complete reconstruction of the trimer at 2.5 [A].

Maximizing the morphological and molecular fidelity of preserved mammalian brain tissue is essential for basic neuroscience and brain banking, where tissue quality determines the reliability of downstream analyses. Aldehyde fixation and cryogenic storage are the most powerful preservation techniques available. However, how to best combine them is incompletely characterized. Here, we tested twelve different approaches to preserve the murine brain, including standard aldehyde perfusion fixation, variants of aldehyde-stabilized cryopreservation (ASC) using ethylene glycol (EG), aldehyde fixation followed by unprotected freezing, and several cryopreservation protocols without preceding aldehyde fixation, such as interleaved equilibration with vitrification solution. Preservation quality was assessed using light microscopy, transmission electron microscopy, and patch-level feature analysis with the DINOv2 vision foundation model. We found that ASC with sodium dodecyl sulfate (SDS)-mediated blood-brain barrier permeabilization preserved nuclear morphology, myelin periodicity, and neuropil texture comparable to standard aldehyde perfusion fixation, providing the first independent replication of ASC. Omitting SDS caused severe parenchymal dehydration, due to a mismatch between water and cryoprotectant transport. Aldehyde fixation followed by unprotected slow freezing confined ice damage primarily to perivascular zones, while fast freezing led to intranuclear clefts and cavities. Among non-fixation protocols, interleaved equilibration with vitrification solution preserved myelinated axon profiles and chromatin patterns, but induced perivascular edema. High concentration glycerol perfusion caused significant osmotic dehydration. Straight freezing of unfixed tissue without cryoprotectant was followed by membrane disruption, but resulted in the visualization of more structure than expected, likely as a result of structural restitution upon thawing. Taken together, our results provide a framework for matching preservation strategies to the needs of different types of brain banking and neuroscience research.

Cryogenic electron microscopy (cryo-EM) has enabled rapid progress in structural biology throughout the last decade. However, cryo-EM sample preparation still represents a major bottleneck in high-resolution structure determination. Many proteins and macromolecular complexes interact with the air-water interface during sample preparation, which can cause protein denaturation, complex disassembly, and preferred particle orientation. Development of methods to alleviate these issues is an active field of investigation. However, a universally applicable solution to this problem has not been found so far. Here, we describe the simple and cost-effective usage of synthetic peptides with defined amino acid sequence to protect cryo-EM samples from complex disassembly and reduce preferred orientation. We show benefits during cryo-EM grid preparation of a large E3 ubiquitin ligase termed the anaphase-promoting complex (APC/C), and the small CDK11:cyclin L:SAP30BP complex.

Cryo-electron tomography of 100-200 nm lamellae prepared by focused ion beam milling enables structural analysis of macromolecules within cells, tissues, and small organisms, but lamella preparation remains time-consuming and limits throughput. Although automated workflows enable unsupervised fabrication, they commonly produce variable results because fixed, pre-calibrated parameters are applied without accounting for lamella-to-lamella variability. To overcome these limitations, we developed an Adaptive Milling approach that adapts milling parameters based on imaging feedback. We implemented this approach for the critical polishing stage as Adaptive Polishing, which uses machine-learning-based segmentation of scanning electron microscopy images to identify lamella features predictive of imminent collapse. These are used to periodically assess whether to continue or stop thinning during polishing. We performed a systematic comparison of the quality of lamellae prepared with Adaptive Polishing and conventional automation, and demonstrate that Adaptive Polishing produces thinner lamellae with greater consistency in thickness, and performs well for in situ cryo-electron tomography. Adaptive Polishing was tested on three different microscopes with minimal model retraining required, demonstrating transferability of the approach. We implemented Adaptive Polishing as an open-source plugin for fibsemOS together with machine-learning model training software and pre-trained models.

Atomistic simulations often struggle to capture the full conformational landscape of biomolecular complexes on experimentally relevant timescales, leaving gaps in our understanding of functionally relevant thermodynamics and kinetics. Cryo-electron microscopy (cryo-EM) provides high-resolution structural snapshots spanning multiple conformational states, but translating these static images into dynamic mechanisms remains challenging. Existing approaches for integrating cryo-EM data with molecular simulations either perturb the underlying dynamics through artificial biasing potentials or remain confined to conformations already present in the prior ensemble. We introduce cryoWEight, an automated and extensible Python framework that couples iterative weighted ensemble (WE) simulations with ensemble reweighting against cryo-EM particle images to steer simulation trajectories towards conformational states consistent with cryo-EM populations. We have found that reweighting in the image space introduces a directional bias towards the target distribution even when the prior ensemble has no overlap with the target distribution in the collective variable space. The reweighting requires only the state likelihoods that set the ensemble weights, and any observable that defines these likelihoods can serve as the target distribution, therefore generalizing beyond the application of cryo-EM. cryoWEight is validated on three proteins: chignolin, the N-terminal domain of ribosomal protein L9, and adenylate kinase. The framework converges towards the target conformational states across different sampling strategies and noise levels, providing a scalable route to integrate cryo-EM data with enhanced sampling approaches.

Expansion microscopy (ExM) physically magnifies preserved biological specimens by using a specimen-permeating hydrogel to pull biomolecules, such as proteins, apart from each other. ExM protocols exhibit a tension between the need to initially fix proteins to each other during sample preservation, to retain proteins and their nanoscale organization, and the need to separate proteins from each other isotropically, to achieve physical magnification. We here propose to address this tension by fixing proteins directly to the hydrogel network itself, which then serves as a common substrate for both initial fixation and final separation. We equip proteins with chemical handles, then form a specimen-permeating hydrogel that binds the handles, in a ~15-minute chemical process conducted entirely at -20oC. This network fixation ExM (nfExM) procedure enables excellent epitope retention and nanoscale organization preservation, in cultured cells. nfExM may help support ultrastructural investigations of protein complexes and interactions.

For more than a century we have pictured the dendritic spine as the postsynaptic point where a single presynaptic bouton delivers its message. That picture is correct but incomplete, since it overlooks other neuronal elements surrounding each spine. Here we characterize that neighborhood for the first time by counting and measuring the proximity of the elements encircling each postsynaptic spine. We call this neighborhood the vicinome: the full set of neuronal and glial elements surrounding a dendritic spine. We used ground-truth three-dimensional meshes from the H01 petavoxel dataset, a reconstruction of the human cerebral cortex. We analyzed 4550 neighboring elements across 322 spines from 17 pyramidal neurons in cortical layers 2 through 5, with each spine classified as apical or basal. Neighbor number followed a depth gradient, falling from 17.2 {+/-} 4.2 elements per spine in layer 2 apical spines to 12.5 {+/-} 3.1 in layer 5 basal spines. A linear mixed model with a random intercept per neuron confirmed a strong effect of layer (p = 4.3 x 10-5) with large effect sizes (Cohen's d up to 1.45) and no effect of polarity. The minimum spine-to-neighbor distance stayed uniform, near 30 nm in every group, and although some depth-related contrasts were significant, all effect sizes were negligible (Cohen's d below 0.21). We conclude that cortical depth, and not pyramidal neuron polarity, was related to the number of neuronal elements neighboring the spine. We suggest that vicinome crowding could offer a measurable observable for comparison across regions, ages, species, and disease.

Tardigrades are minute animals renowned for environmental stress tolerance, and their remarkable biology has motivated extensive molecular studies using biochemical and biophysical approaches. Yet the native structures of endogenous macromolecular assemblies in tardigrades remain largely inaccessible, in part because only limited biological material is available. Here we show that purification-free single-particle cryo-EM, guided by long-read cDNA sequence assignment, can recover multiple endogenous assemblies directly from crude extract of a wild-derived Macrobiotus cf. shonaicus culture. We reconstructed a dimeric vitellogenin (Vg) particle at 3.10 [A] resolution, a previously uncharacterized tardigrade ferritin at 3.09 [A] resolution, and a low-resolution ribosome map. The Vg particle is a glycosylated and metal-containing assembly with structural evidence of proteolytic processing and contains lipid-like densities occupying a broad chamber whose walls include N-linked glycans. The ferritin structure revealed a divergent C-terminal helix that lines a mineral-like density observed at a position corresponding to previously described mineralization sites in ferritin-family cages. These results establish purification-free cryo-EM as a route to structural analysis of endogenous macromolecular assemblies from minute animal samples. In tardigrades, this approach provides a foundation for future state-dependent structural studies, including desiccation, rehydration and reproduction.

Structural model building and refinement need many decisions between different programs. These decisions often depend on an experienced researcher who knows how the programs work together and how to read experimental data. We used Claude Code with Claude Opus 5.5 to supervise this work for 24 cryo-EM and X-ray cases. The work finished in about 36 hours on one workstation. The agent prepared the inputs, ran established programs, read the validation results, compared candidate models and chose the next step. For 15 deposited cryo-EM models with weak starting geometry, the median MolProbity score improved from 2.62 to 1.76. For three crystal structures, the agent models came close to the published Rfree values. Chemical information such as ligands, ions and modifications must be provided by the researcher, in the same way as the sequence. With this information, the agent can place and check these components. These results show that agent supervision is acceptable when every decision can be inspected. The researcher makes the final scientific judgment. We suggest practical requirements for this kind of use. Future AI agents may help researchers obtain high quality and reliable structures of biological macromolecules.

In this paper, we introduce Nagini3D, a 3D biological image segmentation method that hybridizes two high-performing yet highly complementary segmentation paradigms: deep learning-based approaches and snake-based methods. Our approach leverages both the high efficiency of data-driven techniques and the convenient shape representation provided by active surfaces, which proves highly practical for incorporating a priori knowledge as well as for the post-analysis of segmented objects. By considering a complete 3D volume, our method ensures segmentation consistency across all three dimensions, unlike many approaches that handle volumes in a slice-by-slice manner. Additionally, it theoretically enables the representation of any object with spherical topology, overcoming the limitations of common 3D methods that rely on object convexity assumptions. Furthermore, the continuous representation of objects allows for a resolution-independent description, facilitating the extraction of various geometric features of interest (e.g., local curvature, principal deformations). We demonstrate both the quality of the segmentation produced by our method and the richness of the accessible geometric features through an extensive set of experiments conducted on multiple real and simulated datasets, including objects with challenging geometries.

Tissue microenvironments comprise cellular and acellular components whose three-dimensional (3D) architecture guides disease fate. Direct imaging of intact specimens by light-sheet and multiphoton microscopy, and computational reconstruction from serial sections, have established that 3D spatial context reveals cell and tissue organization inaccessible at single planes. Computational reconstruction in particular can leverage archived human tissue, benefiting from the cost-effectiveness, robustness, scalable storage, workflow compatibility, and century-long pathobiology knowledge of histology, and can integrate multiple spatial modalities. However, sectioning can introduce tears and folds, and computational alignment can further distort tissue integrity. Here we introduce SpaReg, a sparsity-based 3D reconstruction method spanning histology, spatial proteomics and spatial transcriptomics. Across multiple organs, SpaReg robustly reconstructs large tissue volumes with preserved subcellular morphology despite sectioning artifacts. On a standardized histology benchmark, SpaReg achieves the best balance between 3D reconstruction accuracy and tissue integrity, and on spatial transcriptomics benchmarks it ranks among the leading methods while scaling to hundreds of sections and millions of cells in a dataset that several existing methods fail to process. Preservation of subcellular morphology by SpaReg also enables training of a Hematoxylin and Eosin (H&E)-based epithelial, T and B cell classifier, generating single-cell-resolved 3D maps directly from H&E. Applied to pancreatic tissue containing pancreatic ductal adenocarcinoma arising from an intraductal papillary mucinous neoplasm, these maps reveal that 2D sections overestimate immune exclusion, and resolve lymphoid aggregates in 3D. SpaReg, therefore, provides a scalable foundation for morphologically faithful, multimodal 3D atlases and spatially informed disease modeling

Continuous protein conformations are essential for understanding fundamental biological processes and supporting drug discovery. Although cryo-EM can resolve individual states at high resolution, recovering continuous heterogeneity from 2D particle images remains challenging. High noise, motion blur, and limited structural priors make it difficult to accurately generate and validate high-resolution continuous conformations using raw particle data. Here, we introduce cryoMV, a framework that integrates structure-prior-guided modeling with real-particle validation for continuous conformational transitions. CryoMV uses reference density maps to establish structural anchors and motion priors, models candidate transition paths between selected conformations, and transfers the learned representation to raw 2D cryo-EM particle images. Each candidate conformation is subsequently evaluated using the estimated particle poses and contrast transfer functions. Supported conformations are reconstructed through raw particle back-projection and assessed using canonical half-maps and Fourier shell correlation. On EMPIAR-10516 and EMPIAR-10345, cryoMV achieves excellent performance in terms of robustness, verifiability, and reconstruction resolution. By incorporating structure-prior modeling and evidence from the raw particles, cryoMV offers an explicit mechanism for assessing whether generated conformations are supported by experimental data and provides a practical approach to reducing model-induced artifacts in continuous cryo-EM heterogeneity analysis.

Volumetric imaging is increasingly important in fields such as neuroscience, where quantifying structure and mapping features are inherently three-dimensional problems. Block-face serial two-photon tomography (STPT) has emerged as a powerful approach for high-resolution, whole-organ volumetric imaging. STPT avoids complex sample preparation and balances image quality, speed, and scalability. However, STPT remains niche, in part because of the absence of flexible, general-purpose control software. Here we present BakingTray, an open-source software platform for STPT that extends ScanImage. BakingTrays modular framework accommodates diverse hardware configurations and allows users to build systems customised to their needs. Twelve such systems, which we call BrainSaws, have been constructed worldwide since 2016, with more under construction, and have so far contributed to 35 research papers. The three systems at the Sainsbury Wellcome Centre alone have acquired over 7,000 samples in a busy multi-user facility. We demonstrate the platform across its core neuroscience applications: electrode track localisation, brain-wide cell counting, and bulk projection mapping. Beyond these, we map the cell-type-specific synaptic output of inhibitory neurons in the ventral lateral geniculate nucleus and, with whole-mount staining, amyloid plaques brain-wide in a mouse model of Alzheimers disease. BakingTray is a mature platform for groups or facilities needing a customised volumetric imaging solution, whether built new or converted cheaply from an existing two-photon microscope.

High-speed atomic force microscopy (HS-AFM) experiments provide unique insights into nanoscale biological processes by directly visualizing the surface topography dynamics of biomolecules, such as proteins, over extended periods of time and generating large imaging data sets. However, the lack of computational methods for automated data analysis has thus far limited the full utilization of these data for the quantitative understanding of single-molecule dynamic. Here, we present the AFM Analysis Arena (A3) within BioAFMviewer as a software platform for automated, high-throughput quantitative analysis of AFM topographic imaging data within a user-friendly interactive graphical interface, implementing a convenient workflow from raw HS-AFM (AFM) imaging to statistical analysis. In several applications to HS-AFM imaging data, we demonstrate methods of automatized molecule detection and dynamic tracking to highlight the available capacities, aiming to transform HS-AFM measurements from observations towards an analytical tool in nanoscale biology.

Time-resolved crystallography can be inaccessible for membrane proteins and other difficult-to-crystallize targets for which suitable crystals are scarce. Here we present an acquisition strategy that records consecutive diffraction images during a small crystal rotation synchronized to a single activation pulse. Matching image segments are merged across crystals to reconstruct successive time windows, enabling a single crystal ensemble to capture an extended structural trajectory. Using the Mesh&Collect workflow at a synchrotron, we obtained ten consecutive 7.5-ms time windows at 2 [A] resolution from 1,255 microcrystals of a bacteriorhodopsin triple mutant at room temperature. An additional 790 crystals extended the observation window to 500 ms. Rotation-based processing, retrospective time binning and dose control maximize information extracted from each crystal, while compatible measurements can be pooled across acquisitions. This approach makes millisecond time-resolved crystallography practical for crystal-limited targets at synchrotrons, with further development potentially extending the temporal resolution down to 1 ms.

Ground-nesting wild bees require suitable soil conditions for their below-ground development and overwintering life stages. Understanding how soil translocation and disturbance affect individuals and the following generations of bees is important for identifying conservation measures for ground-nesting wild bees. We investigated the effects of soil translocation and disturbance on the ground-nesting solitary spring mining bee Colletes cunicularius. In a replicated controlled design, we compared the effects of soil translocation with different degrees of disturbance on the number of surviving bees and the emergence time. We found that soil translocation and disturbance reduced bee survival but did not result in complete mortality, with some individuals surviving even under severe soil disturbance. There was no evidence that soil disturbance affected the timing of spring emergence, suggesting that disturbance is unlikely to alter the timing of nest construction and, consequently, the production of the next generation. Our results indicate that translocating soil containing overwintering bees could be a promising conservation measure to compensate for habitat loss caused by land conversion.

Single-particle cryo-electron microscopy (cryo-EM) plays an increasingly important role in structure-based drug discovery (SBDD) by revealing target protein-ligand interactions at the molecular level, but its application has largely focused on large- and medium-sized protein complexes. Many therapeutically important targets are smaller than 50 kDa and remain highly challenging for cryo-EM analysis because of low signal-to-noise ratios and difficulties in particle alignment. Here, we describe cryoFAST, an integrated workflow that couples ligand-compatible Fab discovery and structure-guided computational maturation with biochemical triage, fiducial-complex assembly, and single-particle cryo-EM. Through phage-display screening and/or AI-guided design, we developed high-affinity Fab binders of approximately 50 kDa that stabilize the target without disrupting ligand-target interactions and then assembled Fab-target complexes for cryo-EM analysis. Using the therapeutically relevant extracellular domain (~20 kDa) of interleukin-4 receptor (IL-4R) as a proof of concept, we determined its cryo-EM structure at 2.74 Angstrom resolution and established a framework for elucidating ligand-binding modes. This workflow provides a practical, efficient, and potentially broadly applicable route to the structural characterization of small protein targets that are otherwise challenging to study. By extending the applicability of cryo-EM to high-value but technically challenging small targets, cryoFAST may open new opportunities for structure-based drug discovery.