Microscopy and Microanalysis

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.

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.

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.

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.

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.

Researchers using electron microscopy must often balance a trade-off between obtaining high-resolution structural information and preserving sufficient cellular context. At one end of this spectrum, single particle cryo-electron microscopy and cryo-electron tomography provide near-molecular detail but are typically limited to relatively small fields of view. At the other, volume electron microscopy approaches, such as scanning electron microscopy of resin-embedded specimens, capture large cellular volumes but generally at lower resolution. Consequently, linking nanoscale structural information to larger cellular architecture remains a significant challenge. To address this gap, we optimised a transmission electron tomography workflow for resin-embedded malaria parasites that allows us to visualise targeted regions of interest at nanometre-scale resolution while retaining several micrometres of surrounding cellular context. Here, we present our current best-practice pipeline for sample preparation, tomogram acquisition, and reconstruction. In addition, we introduce VolWeaver, a data-processing framework, that integrates high-resolution tomographic datasets into serial section volume reconstructions, enabling the visualisation and interpretation of ultrastructural features within their broader cellular environment.

Single-particle cryo-electron microscopy (cryo-EM) pose estimation is traditionally solved anew for each dataset, where iterative refinement is done from scratch while the estimator learns to store the molecule in its weights. In this work, we show that pose inference is a generalizable, specimen-agnostic operation when conditioned explicitly on a reference volume. We introduce ARCHER, an amortized contrastive classifier that models the pose posterior over a discrete rotation grid. Trained across a variety of protein structures, it operates zero-shot without retraining per structure. This transferability is grounded in Fourier-space information mechanics, where all specimen dependence is captured by the reference structure's power spectrum and spatial extent. ARCHER achieves a median angular error of 5.0{degrees} on 100 held-out test structures and 2.5{degrees} on experimental particles, matching dedicated estimators within 0.16[A] in 3D reconstruction. Crucially, downstream conformational signal is preserved. The leading conformational coordinate correlates at 0.97 with deposited benchmarks, faithfully reconstructing free-energy basins and mobile domains. These results overall demonstrate that cryo-EM pose estimation can be generalized across different structures.

Solution X-ray scattering provides unique structural information on biomolecules under biological conditions, resolving conformational heterogeneity and time-resolved structural changes. The scattering profiles contain limited information, and interpretation largely relies on fitting candidate structures guided by priors. Direct reconstruction of electron density maps is desirable, but so far has been prevented by the difficulty of incorporating such prior knowledge. Here we propose XSSDense, a framework that couples a variational autoencoder trained on electron densities from predicted or simulated protein ensembles with a genetic algorithm to refine densities against scattering data. We validate XSSDense on synthetic data for crambin, recover the conformational heterogeneity of the unfolded state of Avena sativa light-oxygen-voltage sensing domain 2, resolve a de-novo density for the pre-unfolding state of the same protein, and provide a new structural description of the signalling-state ensemble of photoactive yellow protein. XSSDense enables structurally grounded electron density reconstructions that intrinsically capture conformational heterogeneity.

The convergent lady beetle Hippodamia convergens is widespread in the Americas and considered an important beneficial insect due to its use in pest control. Early work on this species characterized the beetles as freeze-avoidant (freeze-intolerant), suggesting they survive low winter temperatures by physiologically preventing ice formation to temperatures as low as -15{degrees}C. Here, we show that H. convergens can be freeze-tolerant if ice formation occurs at relatively high temperatures. There was 100% survival following inoculative freezing at -0.5{degrees}C and exposure to -3{degrees}C for 20 hours, as well as freezing that spontaneously occurred in fed beetles exposed to -4{degrees}C for 4 hours. Males exposed to 0{degrees}C or -4{degrees}C for 4 hours had similar mating behaviours (latency to first mating, copulation duration) as control beetles exposed to 4{degrees}C, although sample sizes were too small to determine whether freezing itself had an effect on these behaviours. Several putative cryoprotectants were detected in fat body tissue of H. convergens: glycerol, proline, trehalose, and myo-inositol - in order of abundance. Exposure to -3{degrees}C for 20 hours, whether frozen or unfrozen, did not statistically affect cryoprotectant accumulation, although there was a trend towards increased glycerol following freezing. This study is the first to describe inoculative freeze tolerance in a lady beetle and establishes a baseline for future studies that examine the mechanisms underlying this freeze tolerance and the effects of freezing on reproductive behaviour. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=69 SRC="FIGDIR/small/744689v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@1c0382org.highwire.dtl.DTLVardef@12a5932org.highwire.dtl.DTLVardef@145f4adorg.highwire.dtl.DTLVardef@1c2cb22_HPS_FORMAT_FIGEXP M_FIG C_FIG O_LIHippodamia convergens can tolerate freezing at high subzero temperatures C_LIO_LIMating behaviour of males is normal after chilling or freezing C_LIO_LICryoprotectant accumulation may support overwintering survival C_LI

The ultrastructure of endothelial cells (ECs) "in situ" is of great interest due to their involvement in many physiological processes. In some organs, these cells form transcellular pores or fenestrae, allowing for the rapid exchange of molecules between blood and interstitium. Despite their importance, no optical images of these dynamic morphological structures have yet been acquired in situ. Major obstacles to their in-situ imaging are the lack of specifical labels for fenestrae and their size well below the optical diffraction limit. Here, we report how we have overcome these challenges and managed to visualize the EC ultrastructure in situ in 25 {micro}m thick liver sections. To enable this, a lipophilic, fluorescent membrane dye was infused into the portal vein of murine livers to stain the sinusoidal ECs before the organ was harvested. Tissue sections were subsequently imaged using a novel, super-resolution optical-sectioning structured illumination microscope (OS-SIM), providing approx. 170 nm spatial resolution with significantly faster image acquisition compared to confocal microscopy.

Hydrozoa is a group of relatively simple animals with a well-developed nervous system. The nervous system in all hydrozoan medusae is highly conserved and includes outer and inner nerve rings at the bell margin, a neural network in the manubrium, and radial neural pathways that connect them. However, one element of the nervous system shows substantial variability among species: the subumbrella neural network. We examined the structure of the nervous and muscular systems in the subumbrella of 14 species of hydrozoan medusae. The main conclusion of this study is that the distribution of neural networks in the subumbrella strongly correlates with the distribution of smooth radial muscles. This correlation suggests that smooth radial muscles are the primary target of the subumbrella nervous system. Most species in the order Anthoathecata show a trend toward secondary loss of the neural networks and radial smooth muscle fibers in the subumbrella region, concentrating neural elements and smooth muscles only in the radial pathways along the radial canals. By contrast, all studied species in the order Leptothecata have neural networks in the subumbrella area, as well as radial smooth muscle fibers spread throughout the entire subumbrella region. The correlation between radial smooth muscles and the nervous system is also observed in the radial pathways along the radial canals. All species with thick bundles of smooth radial muscles along the radial canals have clearly defined, dense neural pathways running along or even embedded within the smooth muscle bundles.

Tessellated calcified cartilage (TCC) is a hallmark of the chondrichthyan skeleton, yet its development early in ontogeny across the four major groups (batoids, galeomorphs, squalomorphs, and holocephalans) remains poorly understood. Specialised traits of TCC, such as multi-layered TCC and internal mineralised trabeculae, typically develop in response to feeding mechanics. In this study, we evaluated TCC morphology in the jaws of 12 representative taxa to observe its structure at an early ontogenetic stage to determine whether these specialised features had yet developed. Batoids consistently exhibited well-developed, homogeneous, polygonal tesserae early in ontogeny regardless of jaw morphology or feeding habit. In contrast, galeomorphs displayed high morphological heterogeneity. Notably, we document the first report of an extensive internal trabecular network in a non-batoid elasmobranch, observed in Ginglymostoma cirratum, which may serve to resist the mechanical pressures of specialised suction feeding. Furthermore, we identified voussoir tesserae in galeomorphs for the first time, extending their documented presence across all elasmobranch groups, where they display an inverted aspect ratio (wider than tall) compared to mature forms. The durophagous Mustelus mustelus exhibited surprisingly poor TCC development despite being a durophagous feeder, pointing to a pronounced ontogenetic lag. In Squatina oculata, TCC was characterised by large and thick tesserae and extensive fused tesseral regions which may relate to its explosive ambush predation mode, whereas the holocephalan Chimaera exhibited a poorly mineralized, mesh-like structure without resolvable discrete tesserae or trabeculae-matching findings from previous studies. Across all specimens, multi-layered TCC was absent, confirming that multi-layering develops later in ontogeny. These results demonstrate that generalised models of TCC development based on one group or a few taxa fail to capture the broader diversity of TCC morphology. It also opens up many exciting avenues for further study, and forms the basis for comparisons with fossil chondrichthyans, to investigate the evolution of TCC.

Dinoflagellate chromosomes adopt a highly condensed and organized morphology, with periodic bands and arches observed by traditional Transmission Electron Microscopy (TEM). However, the limited two-dimensional field of view of TEM has prevented a precise characterization of the inherently three-dimensional organization of dinoflagellate chromosomes. Moreover, given the vast diversity among dinoflagellate species and the lack of a systematic comparison of their chromosomes, it remains unclear whether dinoflagellate chromosomes share common organizational features or instead exhibit significant cell- or species-specific differences. Here, we acquire three whole-nucleus 3D Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) datasets at 4 nm voxel size for each of four dinoflagellate species: Symbiodinium microadriaticum, Breviolum minutum, Fugacium kawagutii, and Crypthecodinium cohnii. We compile these data with previously published image volumes from four additional species and present an analysis of the largest collection of dinoflagellate FIB-SEM images to date. Common features observed across all eight species include the absence of physical confinement or spatial clustering of chromosomes in the nucleus. In addition, by decomposing each chromosome into a weighted sum of orthogonal shapes using Spherical Harmonics Expansion, we find a principal component encapsulating 88% of the total shape variance that is common to all species. However, our analysis also reveals differences in chromosome morphology across species. First, while many chromosomes exhibit surface ridges with left-handed helical twist, the proportion of chromosomes with such ridges varies extensively across species. Second, while chromosomes in most species are discrete and well-separated, chromosomes in F. kawagutii are interconnected in a single contiguous network. Lastly, to our knowledge, we report the first observation in eukaryotic cells of toroid-shaped DNA objects, whose numbers vary dramatically across cells and species. Overall, our results show that dinoflagellate chromosomes exhibit both shared organizational features and pronounced species-specific deviations.

Speckle artifacts and isolated foreground pixels are common in fluorescence microscopy and can interfere with segmentation and subsequent quantitative image analysis. Conventional denoising methods often modify image intensities through filtering or smoothing, potentially altering biologically relevant fluorescence signals. We introduce Sparse Pixel Cluster Cleaning (SPC-Clean), a topology-aware method that removes poorly supported foreground pixels through iterative neighborhood analysis of a thresholded mask. SPC-Clean is deterministic, training-free, preserves original fluorescence intensities for practical microscopy workflows.

Cardiac blood flow is a regulator of several important developmental and remodelling processes in the heart, including through fluid shear forces sensed by the endothelial cells lining the heart. However, optically mapping these flow fields in the complex 3D geometry of the heart is challenging even in transparent animal models such as the zebrafish. One of the main challenges is the difficulty in measuring the out-of-plane (axial) velocity component, preventing accurate mapping of the complete 3-component-3-dimension (3C-3D) blood flow velocity field; image-based techniques such as microscopic particle image velocimetry ({micro}PIV) traditionally only provide the in-plane flow components. Here we present a computational approach to achieve full time-varying 3C-3D blood flow vector mapping using a standard selective plane illumination microscope (SPIM), based on robust cardiac phase assignment, precise measurement-driven registration of sequentially acquired z-stacks, and PIV data fusion from multiple sample orientations. Our approach holds the key to understanding the complex dynamic flow fields within the developing heart, and their role in shaping cardiac development.

Meso2EM is a correlative light and electron microscopy workflow that transfers neurons selected from mesoscale functional images to targeted electron microscopy. We recorded Ca{superscript 2} signals from layer 2/3 neurons across a contiguous 3 x 3 mm cortical field in awake mice and reidentified a selected neuron after fixation and tangential sectioning. Lectin-labeled vascular architecture served as a shared landmark across in vivo two-photon imaging, confocal microscopy, laboratory micro-CT of resin-embedded tissue, and block-surface scanning electron microscopy, guiding focused-ion-beam scanning electron microscopy to the target cell body. The same progressive-targeting principle also supported serial ATUM-SEM reconstruction of an in vivo-tracked dendrite and serial transmission electron microscopy of optically selected dendrites from a patch-clamp-recorded Martinotti cell. Meso2EM therefore provides a practical route for preserving target identity across large changes in scale and specimen state while restricting electron-microscopy acquisition to a selected region.

Quantitative analysis of single-molecule localisation microscopy (SMLM) data remains challenging because biologically diverse, well-annotated datasets are limited, whilst nanoscale protein organisation is heterogeneous and difficult to describe with hand-tuned metrics. We present SynthMLM, a framework that infers interpretable structural descriptors from experimental SMLM data and uses these descriptors to generate synthetic localisation datasets. We demonstrate SynthMLM by generating descriptor-matched synthetic datasets corresponding to diverse experimental SMLM datasets and evaluating their agreement with real data using descriptor-level and embedding-based measures. By enabling controlled generation of synthetic localisation data, SynthMLM provides a practical resource for benchmarking SMLM analysis methods, testing algorithm failure modes, and developing machine-learning workflows where large, labelled datasets are required.

Recent technological advances allow for the large-scale acquisition of genetic and morphological data: high-throughput sequencing has transformed the field of genomics while synchrotron X-ray microtomography enables rapid, noninvasive 3D imaging. However, integrating these approaches for the same specimens is challenging because X-rays can fragment DNA, and DNA extraction damages internal morphology, particularly relevant for small bodied organisms, such as insects. We systematically tested multiple extraction protocols and irradiation conditions across three model insect species. We irradiated more than 1,000 specimens under varying conditions and tested DNA quality through DNA barcoding and UCE sequencing. Our results demonstrate that high-quality DNA and high-resolution tomograms can be obtained from the same individuals, provided that the parameters are carefully optimized and rapid SR-CT scanning precedes DNA extraction. In this respect, our findings establish practical guidelines for combining genomics and phenomics, paving the way for comprehensive integrative digitization of biodiversity.