Microscopy and Microanalysis

Due to recent technological advances, in situ structural cell biology is becoming a high throughput microscopy technique as all the steps of the workflow, from sample preparation to data analysis, are executed faster, more reliable and more reproducible. Sample thinning by cryoFIB-SEM is an essential tool in preparing electron transparent lamellae of biological specimens suitable for further characterization by cryoET. Modern cryoFIB-SEM instruments can be operated remotely and are capable of automated and unsupervised lamellae preparation. To take full advantage of these developments they need a constant supply of LN2 to maintain cryogenic conditions inside the microscope chamber. Here, we introduce a custom automated LN2 refill system that is compatible with gas cooled cryostages, supports long-term cryoFIB-SEM operations and liberates the user from highly repetitive and manual work. We believe this solution can be utilized with other cryoSEM or cryoFIB-SEM devices requiring N2 gas-flow cooling.

We introduce a workflow to identify oligomeric structures that are recorded with single-molecule localization microscopy (SMLM) under cryogenic conditions. Typically, these oligomers are assumed to consist of protomers arranged as equilateral two-dimensional polygons and every protomer is labeled with a dye molecule for visualization. Unlike previous work, we consider scenarios in which the sample plane has an unknown orientation relative to the focal plane. Our contribution is a high-precision plane-fitting algorithm to determine the sample plane, combined with geometrical transformations and two circle-fitting algorithms to identify the oligomeric structures. Our simulations on synthetic data demonstrate that the proposed workflow achieves high accuracy in estimating both the unknown tilted plane and the oligomer size.

Standardized biological specimens are essential for optimizing cryoEM workflows and benchmarking instrument performance. While apoferritin fulfills this role for single-particle analysis, no equivalent exists for cryo-electron tomography. Ribosomes are frequently used but require large datasets due to C1 symmetry and structural heterogeneity, limiting rapid optimization and standardized comparison of workflows. Here, we present PP7 virus-like particles (VLPs) overexpressed in E. coli as a scalable in situ benchmark. VLPs have high orders of symmetry enabling rapid, high-resolution validation of tomographic pipelines from minimal datasets, while their distinct structural features across low to high resolutions provide a practical resolution metric.

Cryo-electron tomography (cryo-ET) is a powerful approach for visualizing macromolecular structures directly within cells, but its broader application is limited by the difficulty of reliably targeting specific structures for imaging. In particular, capturing small or rare objects within FIB-milled lamellae remains a major bottleneck. Here, we establish fluorescence-guided cryo-FIB milling workflows that overcome key sources of targeting error and enable routine capture of structures across a wide size range. For larger objects (>500 nm), we develop a single step registration-based targeting strategy that combines FIB-milled fiducials with physically grounded depth correction to account for focal shifts arising from refractive index mismatch. For smaller targets (150-500 nm), we implement real-time fluorescence-guided milling on a commercially available FIB SEM instrument with an integrated cryo fluorescence microscope allowing dynamic monitoring and precise termination of milling at the onset of target ablation. Using this strategy, we achieve consistent recovery of lamellae containing the targeted structure, including small single-copy organelles such as centrioles and cilia. Together, these workflows expand the accessible target space for cryo-ET and provide practical solutions for studying cellular structures that have previously been difficult to capture.

The emergence of single-particle cryoEM as a powerful method for structure determination has in large part been fueled by its ability to resolve both single static structures and complex conformational landscapes. Indeed, modern approaches to the heterogeneous reconstruction task can resolve 100s-1,000s of different maps from a single cryoEM dataset. How accurate these algorithms are, however, has proven difficult to rigorously assess, due to a lack of suitable benchmark datasets containing both realistic noise features and ground-truth labels. To address this obstacle, we recently developed a series of benchmark datasets that leverage the targeting power of Cas9 and the programmable heterogeneity of DNA to newly offer access to ground-truth per-particle structural labels in real data. Here, we challenged two popular heterogeneous reconstruction algorithms with mixed particle stacks resampled in silico from these datasets, finding that existing approaches resolve the encoded heterogeneity with limited accuracy. In particular, in realistic particle stacks with complex, multi-scale, and multi-axis heterogeneity, we observed that reconstruction of encoded heterogeneity depended strongly on the application of prior information about where heterogeneity was expected, and that individual particle assignments were made with significant error even when the correct structural states were reconstructed. Both molecular breathing motions and data collection features, such as defocus and projection angle, contributed to the observed particle assignment error. These results highlight important shortcomings of existing heterogeneous reconstruction methods and suggest new avenues for method development in both data collection strategies and in heterogeneous classification and reconstruction algorithms.

Sarcomeres, the basic repeating unit of striated muscle, are joined together by crosslinked actin filaments found at the boundaries of muscle sarcomeres, termed Z-discs. Z-discs play a key role in cardiac signalling and disease, however, the arrangement and function of many of the proteins present in the Z-disc remain to be understood. Here, we determined the organisation of 3 key proteins, ZASP, [a]-Actinin-2 and the Z1Z2 epitope of titin, located within the Z-disc. We fluorescently labelled these proteins in cardiac myofibrils using Adhirons specific to each protein and used interferometric photoactivated localization microscopy (iPALM) to obtain the 3D position of these proteins to a high precision (<10nm in x,y,z). We then used PERPL (Pattern Extraction from Relative Positions of Localisations) to analyse patterns in the relative positions of the proteins and reveal their underlying organisation. This analysis revealed that ZASP and [a]-Actinin-2 have a similar repeating organisation, but that the organisation of Z1Z2 is different.

Structure elucidation of biological macromolecules by single particle cryogenic electron microscopy (SPA cryo-EM) or cryogenic electron tomography (cryo-ET) relies on low-dose imaging on cryogenic transmission electron microscopes (cryo-TEMs). Routine microscope setup remains technically demanding and can be time-consuming, particularly for inexperienced or infrequent users. We present LowDoseWizard, a guided workflow implemented in SerialEM that enables rapid and standardised setup of cryo-TEM imaging conditions. From minimal user input, the workflow configures microscope optics, camera parameters and image shift settings for all low-dose imaging states, and guides the user through key daily alignment procedures including beam shift offset calibration, objective lens astigmatism correction and coma-free alignment. The workflow is organised into modular routines that can be executed sequentially or independently, while microscope-specific acquisition parameters are defined in editable configuration files, allowing flexible adaptation to different instruments without modification of the core scripts. Across user sessions on three microscopes at EMBL Heidelberg, the complete setup required on average less than 15 minutes. To assess whether predefined imaging conditions generated by the workflow are compatible with high-resolution data collection, we acquired apoferritin data on a 200 kV Glacios and a 300 kV Titan Krios. These datasets yielded reconstructions at 1.62 [A] and 1.09 [A] resolution, respectively, demonstrating that rapid, guided setup can support near-atomic and atomic-resolution single particle cryo-EM. LowDoseWizard lowers the barrier to robust cryo-TEM setup, reduces the time spent on routine parameter selection and alignment, and helps users focus on sample-specific aspects of data acquisition such as target selection. The workflow should be particularly valuable in shared instrumentation environments, where accessibility, reproducibility and efficient microscope use are critical.

Cell-free expression using Leishmania tarentolae lysates allows rapid expression of eukaryotic proteins directly from DNA templates. We develop a pipeline that combines cell-free expression system with cryogenic fluorescence microscopy that we term CC-FLEXCET (Correlative Cell-Free Leishmania EXpression and Cryo-Electron Tomography), to target and visualize expressed protein complexes by cryo-electron tomography at high resolution. We demonstrate the utility of this method by structurally characterising the filaments of the full-length apoptosis-associated speck like protein containing CARD (ASC) protein. Cell-free expression of ASC results in a polymeric structure characteristic of its cellular speck assembly. Sub-tomogram averaging allows us to resolve both the pyrin domain (PYD) to medium resolution, and show, for the first time, the arrangement of the flexibly linked caspase recruitment domain (CARD). Finally, we observed an interaction between the ASC filament and the L. tarentolae ribosome. Using template matching and quantitative approaches, we characterise this interaction and determine that there is a random structural association between the filament and the ribosome, with 57% of ribosomes oriented with the LSU toward the ASC polymer. CC-FLEXCET facilitates structural analysis of macromolecules and protein-lipid assemblies without need of purification, providing a pipeline from DNA template to protein expression to cryo-tilt series acquisition, within a single day.

Connectomic reconstruction from large image volumes produces segmentation and synaptic-assignment errors that must be resolved to support downstream analyses. As datasets have grown larger and teams more distributed, proofreading has become a critical operational bottleneck. Workflows for proofreading and error correction have not scaled commensurately with connectomic data production and may not accommodate heterogeneous proofreader expertise and machine-generated candidate edits. New tools are therefore needed to organize, prioritize, and coordinate proofreading at volume scale. Here we present NeuVue, a task-management and prioritization framework that operationalizes proofreading through atomic, auditable tasks for individual and team review, multistage routing across proofreader cohorts, performance and volume-state tracking, and integration with community annotation, visualization, and analysis services. We report the use of NeuVue across two volumetric datasets, supporting scalable proofreading by over forty proofreaders and producing over fifty thousand edits. NeuVue provides a reproducible human-in-the-loop framework for generating, validating, and maintaining large connectomic datasets.

Altair-dvOPM is an open-access direct-view oblique plane microscope designed for large-field, three-dimensional imaging of cleared and expanded tissue sections. By combining photographic-lens-based detection, externally launched oblique illumination and precision-registered modular baseplates, the system achieves micrometer-scale lateral resolution over a ~5.4 mm field of view without custom objectives or highly specialized alignment procedures. We demonstrate imaging across scales, from subcellular structures in expanded cells to centimeter-scale expanded tissue sections, and provide documentation, CAD files, Zemax models and open-source control software to support replication and extension.

The ichneumonid subfamily Eucerotinae has been thought to be almost absent from the tropics, with the only known Afrotropical species found in Madagascar. We report the subfamily to be present in the mainland Afrotropics, and describe a new species, Euceros species 1 from Uganda and Cameroon (name not yet shown in preprint). The subfamily had likely not been observed in the mainland Afrotropics before due to low abundances and insufficient sampling. More Eucerotinae likely remain to be discovered in tropical Africa and Asia, although tropical America may genuinely have few eucerotine species. Much more extensive sampling will be needed before it is possible to make confident estimates of how eucerotine diversity is distributed globally.

Atomic force microscopy (AFM) enables nanometre-scale, label-free imaging of biomolecules and surfaces under near-native conditions, yet quantitative analysis of AFM data remains limited compared to other bioimaging modalities. This limitation largely arises from the absence of open, automated tools capable of addressing AFM-specific artefacts, data formats, and topographical outputs. Here, we present the latest version of TopoStats, an open-source Python package for automated and quantitative AFM image analysis, developed as a deep-learning enabled advancement of our original TopoStats software to support more complex samples and richer molecular characterisation. The pipeline integrates all key processing stages, including image flattening and noise correction, object detection and segmentation, morphometric feature extraction, and strand tracing with topological classification. Designed for accessibility and reproducibility, TopoStats adheres to the FAIR for Research Software (FAIR4RS) principles and provides configurable workflows adaptable to diverse biological samples. Combining high-resolution AFM and our analysis pipeline allows the quantification of subtle structural changes within a heterogeneous sample set, revealing properties not accessible with other structural biology techniques. We demonstrate the effectiveness of our pipeline to differentiate between plasmids with both different topology and sequence, by extracting meaningful quantitative descriptors that distinguish the samples with statistical significance. Collectively, these developments establish TopoStats as a versatile framework for high-throughput, quantitative AFM analysis, advancing AFM from a fundamentally qualitative visualisation technique toward a quantitative analytical tool.

The spatial resolution of optical imaging systems is fundamentally restricted by the diffraction limit. However, in widefield live-cell microscopy, the achievable resolution is further constrained by the specimen motion, which indicates the existence of a fundamental spatio-temporal resolution trade-off between signal accumulation during the full frame integration and the resulting motion blur. To improve the fidelity with which moving objects can be imaged, a quantitative understanding of this spatio-temporal trade-off is necessary. Here, we present a systematic analysis of motion-induced resolution dynamics measured with spectral signal-to-noise ratio (SSNR). We developed a simulation framework which models the image formation of objects undergoing arbitrary motion, to evaluate the degradation of the spatial resolution under translational and rotational dynamics. Our results demonstrate that for translating objects, the spatial resolution is anisotropically reduced as a function of the orientation of the object relative to the motion vector, leading to the spectral signal-to-noise ratio degrading by up to 50% and the resolution by up to 40% for a 90{degrees} change in the motion direction. Furthermore, we show that for rotational motion, conventional radially averaged metrics such as the Fourier Ring Correlation are not able to quantify the effects of angular blur. On the other hand, the SSNR is able to accurately quantify this degradation. These findings underscore the necessity of an object-oriented imaging approach, in which acquisition parameters such as exposure time are tuned to specific biological spatio-temporal characteristics to optimize the trade-off between motion blur and spatial fidelity.

For decades, the photon counting histogram (PCH) was used as the sole method to quantify fluorophore numbers in a diffraction-limited focal volume. This technique combines spatial excitation profiles, and the distribution of photon counts to register the photon emission statistics of individual fluorophores. However, this approach has not yet been transferred to widefield fluorescent imaging due to the lack of fast and single photon sensitive camera sensors which can capture the photon emission statistics of a single fluorophore. Here, we explore avenues towards quantitative analysis of the active fluorophore number by leveraging recent advancements in single photon avalanche diode (SPAD) array technology. Binary exposures of a SPAD array can be synchronized with picosecond laser pulses to measure the PCH in a widefield setting. Then, by modeling the statistical relationship between the active fluorophore number and the PCH in a region of interest following a laser pulse, we can perform Bayesian inference of this number. The model is demonstrated experimentally by counting quantum dots and various numbers of fluorescent dye molecules bound to DNA origamis. We find that this method has several important applications in widefield microscopy, including enhanced localization microscopy and constrained fitting of multiple unresolvable fluorescent emitters.

Intraspecific morphological variability presents a complex challenge for biological systematics and biomonitoring, particularly for organisms with high phenotypic plasticity, such as zooplankton. Morphological differences between individuals of the water flea species Bosmina longirostris (Crustacea: Cladocera) are difficult to distinguish visually, parthenogenetic females look morphologically uniform within the species; nevertheless, they demonstrate differences attributable to their geographic origin and developmental stage. A reference dataset of microscopic images was created for the study, including populations from two geographically separated regions (seven ones from European Russia and seven ones from Sakhalin Island in the Pacific Ocean (Far East of Russia) and two age groups, demonstrating the ability of a neural network classify to successfully the intraspecific morphological variation. This study demonstrates that deep learning methods are prospective for the detection and understanding of fine morphological intraspecific differences in the cladocerans.

The evolution of eye coloration in mammals and its potential ecological significance remain understudied. Evidence from anthropoid primates suggests that photoprotective demands are crucial determinants of pigmentation in the peri-iridal tissues, which encompass the conjunctiva and portions of the sclera peripheral to the iris. However, it is unclear to what extent these findings can be generalized. Here, we quantify peri-iridal brightness in a photographic sample of 62 terrestrial non-primate mammal species (n = 930). Phylogenetically-controlled analyses revealed significant effects of eye size as well as ecology on ocular pigmentation. Peri-iridal brightness exhibits a notable phylogenetic signal, correlates negatively with eye size and hence exposure to UV light, and is more pronounced in nocturnal species. Significant interspecific effects of latitude on peri-iridal brightness were not recovered, but tentative evidence for non-negligible impacts of this variable at the intraspecific level were found. Overall, these results align with and help to contextualize findings on primates and suggest that photoprotective demands importantly shape ocular appearance across the mammalian radiation. Furthermore, they have implications for hypotheses tying eye pigmentation chiefly to gaze signaling and provide a broad evolutionary framework for the emergence of human eye appearance.

BackgroundAdult vestimentiferan tubeworms inhabiting hydrothermal vents and cold seeps lack a mouth and anus and rely entirely on organic matter produced by sulfur -oxidizing autotrophic bacterial symbionts in their trophosomes. These symbionts, which predominantly belong to the genus Proteobacteria, are acquired horizontally from the environment. However, the effects of rearing conditions that differ from natural habitats on the microbiome composition or abundance of these bacteria remain unclear. MethodsWe conducted a metagenomic analysis of Lamellibrachia satsuma reared in an aquarium under sulfide-supplemented and sulfide-free conditions. ResultsImmediately after collection, the microbiome was dominated by known symbionts within {gamma}-Proteobacteria, exhibiting low species diversity. After 6 months of rearing, the abundance of these symbionts significantly decreased under both conditions, whereas overall bacterial diversity increased. In particular, -Proteobacteria became more abundant under sulfide-supplemented conditions, while {delta}-Proteobacteria predominated in the absence of sulfide. Despite these changes, symbionts were not entirely lost, and the hosts survived for 6 months, likely due to their low metabolic rate. These findings suggest that the microbiome of L. satsuma can respond flexibly to changes in the rearing environment. They also indicate that the hosts metabolism can be maintained even with a smaller quantity of symbiotic bacteria.

Single-particle cryoEM is increasingly used to resolve conformational and compositional ensembles, yet objective evaluation of heterogeneous reconstruction methods remains limited by the scarcity of experimental benchmarks with per-particle ground-truth labels. Indeed, many widely used experimental"benchmark" datasets necessarily validate observed states retrospectively while purely synthetic datasets provide ground-truth labels but typically fail to capture experimentally realistic complexities including confounding structural heterogeneity, imaging noise, contaminants, and orientation biases, which dominate real-world analyses. Here we develop an experimentally grounded benchmark dataset for heterogeneous reconstruction using catalytically inactive Streptococcus pyogenes Cas9 bound to a constant sgRNA and to target DNA duplexes engineered to carry extensions of defined length. We assembled, purified, vitrified, and imaged thirteen complexes independently, such that the dataset-of-origin provides an unambiguous label for each particles encoded state while preserving the full experimental complexity of cryoEM data. Independent refinements of the pure datasets recover the engineered DNA-extension signal and define a simple quantitative readout, DNA-extension occupancy, that increases monotonically with designed extension length. The same reconstructions also reveal substantial non-encoded conformational variability within the Cas9 core, showing that this benchmark embeds a known structural signal within broader structural heterogeneity that methods must confront in practice. To separate these axes of variation, we used systematic deep classification to generate curated particle subsets depleted of selected domain motions while retaining the encoded labels. We further provide pooled particle stacks with standardized per-particle poses in a common reference frame and a lightweight framework for in silico particle pooling to generate challenge datasets with user-defined ground-truth distributions of encoded and non-encoded structural heterogeneity. Together, this resource supports robust benchmarking of heterogeneous reconstruction algorithms and provides a biochemically tractable model system for evaluating entire cryoEM pipelines, including alternative data-collection and preprocessing approaches, under experimentally realistic conditions.

Fast extraction of physically relevant information from images using deep neural networks has led to significant advances in fluorescence microscopy and its application to the study of biological systems. For example, the application of deep networks for kernel density (KD) estimation in single-molecule localization microscopy (SMLM) has accelerated super-resolution imaging of densely labeled structures in the cell. However, localization of fluorescent molecules in dense images is a difficult inverse problem with potentially multiple solutions. To model a probability distribution of solutions to this problem, we propose a generative modeling framework for KD estimation in SMLM based on a conditional variational diffusion model (CVDM). In this framework, CVDM is trained to perform localization tasks on low-resolution measurements by modeling a distribution of high-resolution KD estimates. This approach allows us to probe the structure of the distribution on KD estimates and express uncertainty, which is not currently offered by existing deep models for localization microscopy. We demonstrate that this model permits high-fidelity super-resolution, enables the uncertainty estimation of regressed KD estimates, and has important implications for image restoration in single-molecule and super resolution microscopy.

Ionizing radiation induces molecular responses that may be used to estimate exposure when physical dosimeters are unavailable. Here we present two large-scale proteomics datasets generated from mouse dorsal skin punch samples collected following controlled X-ray exposures spanning multiple doses, dose rates, and post-exposure time points. Experiment 1 comprised 96 samples (including 16 reference samples) collected 6 days after exposure to 0-75 cGy delivered at either 30 or 300 cGy/min. Experiment 2 comprised 936 samples (including 236 reference samples) exposed to 0-100 cGy at either 3 or 28 cGy/min dose rates and harvested between 7 and 150 days post-exposure. All samples were processed using a standardized workflow involving automated bead-based digestion and data-independent acquisition mass spectrometry. The datasets include multiple pooled reference sample types, process controls, and system suitability standards ensuring high quality data. All data presented are available via ProteomeXchange at several levels of processing, from raw files through normalized peptide- and protein-level abundance matrices suitable for biomarker discovery and machine learning applications. This dataset will facilitate generation of new insights into the biological changes and molecular signatures resulting from X-ray exposure in mice and may also help inform future studies in humans.