Microscopy and Microanalysis

Arthropods are a strikingly diverse phylum of invertebrates with segmented bodies, chitinous exoskeletons, and jointed limbs, many of which are colloquially referred to as insects. Their pigmented and optically dense bodies pose a considerable challenge for microscopy. While early naturalists focused on external features, modern approaches combining tissue clearing and fluorescence microscopy seek to explore internal anatomy in three dimensions. However, both chemical clearing and three-dimensional (3-D) microscopy are specialized techniques and often require considerable adaptation and optimization across species. Here, we introduce a versatile, fast, effective and non-toxic clearing method that renders diverse arthropods transparent within hours to days. Existing upright microscopes or macroscopes can be upgraded with a modular and affordable light-sheet microscope, allowing rapid volumetric imaging of arthropods. Our pipeline is fully compatible with dye staining and immunofluorescence labeling, while endogenous autofluorescence provides valuable anatomical context and facilitates 3-D reconstruction.

Preparing electron-transparent cryo-lamellae is inherently a serial, low-throughput process. During sample handling, milling, and transfer, cryo-fixed cells and their supporting films are subjected to mechanical forces as well as thermal stresses caused by temperature fluctuations. After milling, these extremely thin lamellae remain vulnerable to both mechanical and thermal stress, often leading to cracking or complete disintegration. Consequently, the loss of valuable lamellae is frequently an unavoidable aspect of working with such fragile specimens. In this work, we reconsider the conventional lamella geometry, which is typically a flat, thin cross-sectional slab. During milling, lamellae often become unintentionally bent, complicating the final polishing step required to achieve uniform thinning across their width. To address this limitation, we propose deliberately fabricating lamellae in a pre-bent configuration, i.e. specifically, adopting an arch-shaped profile instead of the traditional flat geometry. The arch shape is intrinsically more mechanically stable than a flat structure, thereby reducing lamella loss due to mechanical failure. Moreover, pre-bent milling patterns facilitate uniform thinning of bent lamellae, which is difficult to achieve using conventional flat milling approaches. In addition to the arch geometry, we investigate corrugated lamellae, characterised by a sinusoidal variation around the plane of a conventional flat lamella. Similarly to the arch shape, the corrugated design offers enhanced mechanical stability compared to traditional flat lamellae. We fabricated a series of test lamellae incorporating both arches and corrugations. High-resolution cryo-TEM imaging was performed to evaluate these structures, demonstrating that non-flat geometries do not compromise cryo-electron tomography performance. Furthermore, finite element method (FEM) simulations were conducted to provide insight into stress distributions within bent and corrugated lamellae.

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.

Cryo-electron ptychography is an emerging technique for studying radiation-sensitive biological specimens, developed from four-dimensional transmission electron microscopy (4D-STEM). Although ptychography has achieved ultrahigh resolution beyond conventional transmission electron microscopy limits for radiation-resistant samples, its application to frozen hydrated biological specimens currently remains at sub-nanometer resolution. Here we overcome this limitation by implementing electron counting with a hybrid-pixel detector, establishing key technical foundations for near-atomic-resolution cryo-ptychography. This counting approach significantly improves weak signal detection in convergent-beam electron diffraction, enabling ptychographic reconstruction at doses below 1 e-/[A]{superscript 2}. Additionally, we found beam-induced motion is effectively eliminated within single scans, suggesting conventional cryoEMs dose-fractionation approach may need reevaluation. Demonstrating high contrast under both low-dose and tilted conditions, along with achieving 3.59 [A] resolution for the [~]700 kDa T20S proteasome, we validate cryo-electron ptychography as a viable general imaging modality that could complement or surpass conventional phase-contrast cryoEM methods.

Single-particle cryo-EM routinely delivers near-atomic structures; however, optimizing data collection requires a delicate balance among magnification, sampling bandwidth, and particle throughput. Although low-magnification super-resolution imaging can recover information beyond the physical Nyquist limit, its benefit varies substantially among specimens. This variability suggests that the effectiveness of super-resolution may depend on whether reconstruction is limited by detector sampling bandwidth or by the recoverable particle signal. However, the conditions that distinguish these two regimes remain poorly defined. Here, we systematically compared super-resolution and physical-pixel workflows at two magnifications using apoferritin (APO) and malate synthase G (MSG) as representative specimens with contrasting molecular size, symmetry, and image contrast. At low magnification, APO exhibited sampling-limited behavior, with super-resolution processing achieving 1.72 Angstrom compared with 2.74 Angstrom for physical-pixel processing. In contrast, MSG exhibited predominantly signal-limited behavior under the same conditions, yielding comparable resolutions of 3.05 Angstrom and 2.98 Angstrom for super-resolution and physical-pixel processing, respectively, despite the increased sampling bandwidth. These contrasting responses were further supported by particle-number saturation, per-particle motion correction, and Q-score analyses, which provided complementary evidence for the underlying sampling-limited and signal-limited regimes. Together, these results provide a practical framework for assessing whether reconstruction quality is predominantly constrained by sampling bandwidth or recoverable particle signal and offer a rational basis for balancing achievable resolution and particle throughput when selecting acquisition strategies.

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.

Damage to biological specimens by the electron beam is the fundamental resolution-limiting factor in cryoelectron microscopy (cryo-EM) single particle analysis. There is, however, currently no method to accurately infer fluence-dependent changes to the specimen structure during electron irradiation. We develop a Bayesian framework to fit a sequence of atomic models to a series of cryo-EM reconstructions produced at increasing fluence. In particular, our algorithm is able to infer the ensemble average position and atomic displacement parameter of every atom in the macromolecule as a function of fluence. Application of the algorithm to cryo-EM datasets shows that the molecule expands during imaging and identifies environment-dependent variations in beam-induced damage. We use our results to propose a stochastic process model of this phenomenon. We envisage that our method will lead to a better mechanistic understanding of radiation damage to biological specimens and may contribute to efforts to mitigate its effects.

Microtubules are cytoskeletal filaments that are involved in intracellular transport, cell division, and motility. Despite their biological importance, determining their high-resolution structures via cryo-electron microscopy remains a significant technical challenge due to their polymorphisms and pseudo-helical assembly. Current processing workflows are complex, often requiring the integration of multiple software packages and custom scripts, which creates a steep learning curve for many research groups. To address these limitations, we introduce CsMT, a streamlined workflow implemented entirely within the CryoSPARC environment and using synthetic references. CsMT simplifies microtubule reconstruction by utilizing a novel protofilament-pair classification approach, which effectively handles the inherent pseudo-symmetry and structural heterogeneity of microtubules with minimal manual intervention. Our workflow is versatile, capable of processing both undecorated and decorated microtubules while accurately determining seams and performing high-resolution refinement. We demonstrate the efficacy of this workflow by achieving a 2.3 and 2.7 [A] resolution reconstruction of homotypic and heterotypic maps of undecorated microtubules, matching the best-resolved microtubule structures in the field. By unifying the pipeline into a single and portable workflow, CsMT enhances reproducibility and accessibility, empowering more laboratories to explore the structural biology of microtubules and associated proteins, yielding new insights into their function.

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.

Quantitative measurements of insect flight behaviour are essential for understanding the biomechanics, control, and ecology of flight, yet obtaining such measurements under free-flight conditions remains challenging. Small body size, rapid wing motion, visual symmetry, and frequent occlusions complicate three-dimensional pose estimation, often requiring restrictive experimental setups or substantial manual annotation. We present FLiTrak3D, an open-source Python package for estimating insect flight kinematics from multi-view videography. It combines machine-learning-based markerless tracking with biomechanical modelling to reconstruct and parametrise insect body and wing motion. The workflow integrates image preprocessing, including dynamic image cropping and enhancement, two-dimensional bodypart localisation, three-dimensional reconstruction, and optimisation-based skeletal fitting. A key innovation is the use of spatio-temporal encoding across synchronised camera views and adjacent frames to improve neural-network awareness of spatial and temporal context during bodypart localisation. Multi-view image stitching allows the network to use cross-view spatial relationships, reducing left-right bodypart misidentifications, while temporal encoding stacks consecutive greyscale frames into RGB images provide short-term motion information. A species-specific skeleton is then fitted to reconstructed keypoints to enforce kinematic constraints, and estimate body and wing orientations. We demonstrate the approach using free-flying Aedes aegypti mosquitoes, achieving bodypart localisation errors close to human labelling, and realistic flight kinematics.

The quality of single particle cryo-EM reconstructions can be severely degraded when an insufficient variety of 3D particle orientations is present in the image data, limiting downstream model building and interpretation. However, it is often difficult to ascertain whether or not a particular dataset suffers from such preferred orientation since the required orientation coverage depends on target geometry, alignment accuracy, and particle quality. To simplify diagnosis of preferred orientation, we present two complementary methods. First, the conical Fourier Shell Correlation Area Ratio (cFAR) compares the worst- and best-correlating conical regions of 3D Fourier space to quantify half-map anisotropy into a single, easily interpretable score ranging from zero to one. Second, Relative Signal, a companion to cFAR, directly relates signal content to viewing direction so that under-sampled views can be identified. We characterize our methods and compare them to existing anisotropy detection approaches on synthetic data and on 14 real datasets that span sundry molecular weights and structure types. Implementations of both cFAR and Relative Signal are included in CryoSPARC v4.5 and later versions.

Here, we present MC-Bayes, a Python-based script for processing cryo-electron microscopy EER movies on one or more GPUs using MotionCor3 in a user-friendly manner. Further, it generates the .star files necessary for RELION to perform Bayesian polishing (a.k.a.: reference-based motion correction) with EER movies. Until now, Bayesian polishing of EER data was only possible if the CPU-based "RELIONCor" implementation of MotionCor2 was used, which is sub-optimal on GPU-heavy cryo-EM processing systems. This wrapper was created for those facilities and/or users who may have (many) powerful GPUs, but for whatever reason have few CPU cores or less system RAM. Leveraging MotionCor3, MC-Bayes allows motion correction of EER data 2 or more times faster (depending on system) than the RELION CPU implementation, except in circumstances where dozens or hundreds of CPU cores with high quantities of system RAM can be utilised.

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].

The accurate segmentation of lizard claws is important as they are materially heterogeneous, comprising both bone and keratinous tissue. This study presents a deep learning framework for the automated segmentation of lizard claw tissues, specifically bone and keratin, from CT imaging data. A dataset comprising 14 lizard claws was used in this work, with annotations generated through a superpixel based labeling approach to provide ground truth reference segmentations. To evaluate the effect of spatial context on segmentation performance, both 2D and 2.5D CNN architectures using DeepLabV3 with ResNet-50, ResNet-101, and Inception-ResNet-v2 backbones were investigated, with predictions subsequently reconstructed into three-dimensional volumes for analysis. Performance was assessed using a leave one out cross validation (LOOCV) strategy and evaluated with 3D Dice Similarity Coefficient (DSC), Intersection over Union (IoU), Sensitivity (Recall), 95th Percentile Hausdorff Distance (HD95), and Relative Volume Error (RVE). Experimental results demonstrate that 2.5D CNN architectures consistently outperform their 2D counterparts across all evaluation metrics, highlighting the importance of incorporating inter-slice contextual information for volumetric tissue segmentation. From amongst the models, the 2.5D Inception-ResNet-v2 achieved the best overall performance, reaching a validation accuracy of 97.5% and producing segmentation results that closely align with ground-truth tissue structures. Our findings demonstrate the effectiveness of 2.5D deep learning approaches for the high accuracy segmentation of heterogeneous lizard claw tissues from CT data, whilst providing a robust framework for automated morphological analysis in comparative anatomical studies.

X-ray fiber diffraction patterns exhibit four-fold symmetry that can be exploited, through folding and averaging, to improve signal-to-noise ratio. Accurate folding requires a precise sub-pixel estimate of the symmetry center and precise orientation of the meridional pattern axis to the fiber axis: small center or angular errors blur diffraction features, reduce layer-line sharpness, and introduce errors in spacing measurements. A pixel-level estimate is often too imprecise for this purpose, and detector gaps further complicate the alignment objective. We formulate the masked quadrant-folding problem, define a four-quadrant symmetry loss that consistently excludes invalid pixels, and evaluate several refinement strategies: hierarchical coarse-to-fine grid search; ECC-based rigid registration with global center/orientation correction fitting; ECC registration followed by local gradient refinement; and a hybrid that appends a local grid search on a cropped pattern. Direct gradient optimization from the rough QF alignment was found to be unreliable. Grid search provides a robust, interpretable baseline that directly minimizes the folding objective but is substantially slower than registration; ECC gives a fast near-correct alignment, and the hybrid closes the accuracy gap to brute-force search at a fraction of its runtime. On real datasets with calibration data, applying a calibration center with optimized rotation is effectively optimal. The hybrid center-refinement method has been integrated into the MuscleX package.

Fourier light field microscopy (FLFM) enables high-speed volumetric imaging by encoding multiple angular perspectives of a three-dimensional sample onto a single image. For this reason, FLFM is well-suited to sparse and rapidly evolving biological systems. To aid in the adoption of FLFM, we present OpenFLR, an open-source software framework for real-time volumetric reconstruction, three-dimensional particle tracking, and calcium image processing using FLFM. OpenFLR reconstruction is distributed as four interchangeable interfaces: a Python library, a command-line script, an interactive web application, and an ImageJ/micromanager plugin, so that the pipeline is accessible to both developers and bench biologists. Building on established Richardson-Lucy deconvolution, we use a hybrid experimental-computational PSF calibration strategy and a triangulation approach to tracking to extract particle positions in 3D directly from raw light field frames, bypassing reconstruction. We validate the complete pipeline on GCaMP6s recordings of freely behaving Hydra vulgaris, tracking sparse populations of neurons as they undergo large three-dimensional displacements.

The ability of cryo-electron microscopy (cryo-EM) to interrogate the atomic structure and motion of biomolecules has motivated the development of a wide range of algorithms for heterogeneity analysis. However, evaluating and comparing these methods remains challenging because ground-truth structures are generally unknown for experimental samples. Here, we introduce the 2026 Community-Wide Assessment of Heterogeneous Reconstruction Algorithms (CAHRA), a community-wide challenge centered on three benchmark datasets that probe distinct tasks in heterogeneity analysis. These include (1) compositional heterogeneity arising from mixtures of distinct protein complexes, (2) continuous conformational variability and atomic modeling, and (3) entanglement between molecular conformation and particle pose. We describe the design, construction, and validation of these datasets, as well as their use in the recently completed CAHRA Challenge.

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.

Cryo-electron tomography (cryo-ET) enables near-native visualization of subcellular architectures, yet applying it to moderately thick, multilayered tissues such as the retina is hampered by inadequate vitrification and inaccurate depth-targeting. Here, we developed PLCT, an integrated approach combining modified high-pressure freezing, cryo-ultramicrotome trimming, and plasma-based cryo-FIB milling to overcome these barriers. PLCT reliably vitrified <100 m retinal strips with minimal ice artifacts, navigates precisely to the outer plexiform layer using morphological landmarks, and produces high-quality lamellae suitable for high-resolution cryo-ET. Subtomogram averaging (STA) analysis identified microtubules at 16.33 [A] within retinal horizontal cell processes. Importantly, STA also resolved a 10-nm-diameter filamentous structure at 24.81 [A] in the same processes, featuring six peripheral strands surrounding an elongated central density with continuous intervening cavities, an architecture consistent with intermediate filaments. Together with its native localization and immunoreactivity, these features collectively identify the filaments as neurofilaments. Separately, 3D reconstruction of synaptic ribbons uncovered a previously unrecognized "mahjong tile"-like fine ultrastructure. These results demonstrate that PLCT-produced lamellae are of sufficient quality to support structural analysis in native tissue. Although demonstrated on retinal photoreceptor synapses as a proof-of-principle, PLCT is inherently generalizable, with its depth-navigation and vitrification strategies directly applicable to any multilayered tissues. This work establishes PLCT as a robust, reproducible platform for depth-resolved in situ cryo-ET of multilayered tissues.

Complex biomechanical innovations are often treated as discrete evolutionary breakthroughs, yet their diversification within large radiations remains poorly understood. Beetle leg joints provide a striking example: some weevils possess screw-like coxa-trochanteral articulations in which rotation and axial displacement are mechanically coupled, resembling engineered screw-and-nut mechanisms. Whether these joints represent isolated mechanical extremes, discrete adaptive types or part of a broader continuum of phenotypic variation has remained unknown. Here we combine synchrotron X-ray microtomography, landmark-free atlas-based morphometrics, quantitative functional morphology and phylogenetic comparative analyses to examine the mesocoxa-trochanteral joint in 68 specimens representing seven sampled family-level groups across early-diverging and derived weevil lineages. We show that screw joint evolution combines continuous variation in trochanteral shape with a restricted set of mechanically plausible joint-character combinations, rather than forming sharply separated morphological classes. True screw-and-nut joints are not confined to a distinct region of morphospace, indicating that overall form and mechanical configuration are not necessarily coupled. The occurrence of this configuration in the early diverging Caridae shows that it is not restricted to more derived families. Three-dimensional helix fitting revealed a mosaic geometry, with winding angle showing the clearest relationship with overall shape and joint architecture, whereas axial pitch varied largely independently of shape, size and lineage. Together, these patterns show that screw joint components diversified with different degrees of evolutionary integration. These results recast the weevil screw joint from a singular biomechanical curiosity into a diversified evolutionary system. They suggest that complex functional structures can evolve through the gradual recombination and differential persistence of structurally constrained and evolutionary flexible components, rather than through a single shift from simple to fully specialized designs.