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.

Cryo-electron tomography is an expanding technology for the study of macromolecules, viruses, and cells. It is often applied to specimens that are too large or heterogeneous for methods based on 2D image averaging such as single particle analysis, e.g., intracellular membranes or organelles. Current practice records a tilt series of projection images in rotation. Reconstruction is normally an ill-posed mathematical problem. Particularly for the under-determined case of sparse data, discrete tilt angles, and a limited tilt range, characteristic artifacts appear in the reconstructed slices. Much of what appears as noise is in fact structural: the projection of contrast from different planes. Various schemes are employed to regularize the reconstruction, including machine-learning frameworks built on neural networks. To the extent that the noise is structural, it might be suppressed by deconvolution with a suitable kernel. This was demonstrated and has been used regularly in cryo-STEM tomography of thick specimens where the under-sampling problem is particularly acute. Here we present 3dcon as an open-source extension of the entropy-regularized deconvolution algorithm that had been adopted from fluorescence microscopy. It takes advantage of modern computing hardware for convenient and fast processing. Deconvolution is entirely algorithmic, meaning that successful processing of the data does not depend on the data itself. As such it should be robust in a wide variety of applications.

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

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

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

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

Frontier Structural Biology methods are transitioning from analysis of reconstituted macromolecular complexes in vitro to imaging of macromolecular assemblies within the physiological confines of the cell. Preparation of samples for in situ cryoEM analysis requires FIB milling or ultramicrotome sectioning, laborious and technically challenging procedures that are low-throughput and require a high degree of technical skills. We have devised a simple approach for cryoEM of nuclear macromolecular complexes that preserves to a high degree their physiological environment while removing the need for thin sectioning of the sample. The method requires only the preparation of nuclear extracts without additional purification or enrichment steps. We applied the method to obtain a 2.3 [A] cryoEM structure of nucleosomes visualised directly in the nuclear lysate of human cells. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/732463v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@15f4785org.highwire.dtl.DTLVardef@506f84org.highwire.dtl.DTLVardef@c95ceaorg.highwire.dtl.DTLVardef@1f326da_HPS_FORMAT_FIGEXP M_FIG C_FIG

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

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.

Multi-organelle segmentation in volumetric electron microscopy (vEM) faces several challenges, including severe class imbalance, the presence of small, rare classes, and inconsistent class coverage across crops. While recent work has focused primarily on architectural design, the impact of sampling, loss functions, and masking strategies on training effectiveness remains comparatively underexplored in vEM organelle segmentation. Here, we systematically evaluate sampling strategies, loss configurations, masking approaches, and model families (CNNs and vision transformers) on the CellMap benchmark. Using 289 annotated 3D FIB-SEM crops, we establish a 32-class segmentation benchmark with stratified train, validation, and test splits, and evaluate all the methods under the same training and inference settings. Across controlled ablations, the proposed combination of repeat-factor sampling, Tversky-BCE loss, and masking achieved the strongest rare-class performance, increasing rare-class mean Dice (mDice) from 0.3244 under uniform sampling to 0.3409. This corresponds to an absolute gain of +0.0165 mDice and a 5.1% relative improvement, while preserving comparable performance on common classes. Overall, we find that sampling, loss design, and masking contribute as much to performance variation as the choice of architecture, highlighting the importance of training-recipe design alongside model architecture in vEM organelle segmentation.

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

The laser phase plate (LPP) enables phase-contrast imaging in cryogenic electron microscopy (cryoEM), enhancing image contrast without compromising high-resolution information. Here we report the implementation of a crossed laser phase plate (xLPP) comprising two optical cavities oriented orthogonally, installed in a ThermoFisher Scientific Krios G4 microscope equipped with a newly designed transfer lens module. We demonstrate the expected, strong contrast enhancement and stable, additive phase shifts of 90{degrees}, with a contrast transfer function (CTF) that closely matches theory. Single-particle analysis (SPA) of apoferritin, a standard benchmark sample, reached a resolution of 1.79 [A], demonstrating the system is capable of acquiring high-resolution cryoEM data. When imaging thick E. coli cells ([~]350 nm), the xLPP enhances contrast and increases low-frequency template-matching signal. Together, these results establish the feasibility of the xLPP and highlight its potential for high-contrast, high-resolution cryoEM imaging of biological systems.

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

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

Volume electron microscopy (vEM) is the most advanced and scalable technique for reconstructing synaptic-level wiring diagrams of the nervous system. Following image acquisition, the first critical step is reconstruction of a digitized volume, which assembles millions of electron microscope images into a coherent 3D volume that will underpin all downstream analyses. Existing methods work best with artifact-free datasets or rely on computationally intensive deep learning approaches or time-consuming human editing, restricting the broader applicability of vEM. To circumvent these challenges, we have developed FEABAS, a scalable, cross-platform, open-source software package designed to elastically montage and align electron microscope image datasets with high efficiency and precision. It leverages adaptive mesh modeling and finite element methods, enabling robust handling of datasets containing common artifacts such as wrinkles, folds, tears, and broken sections, while maintaining a lightweight, accessible implementation suitable for diverse computational environments.

In situ structural analysis allows direct visualization of protein structures in their native cellular environments, but near-atomic resolution in cellular lamellae has been significantly limited to exceptionally large complexes such as ribosomes. A key factor underlying this limitation is the degradation of data quality of thin lamellae caused by substantial subsurface damage from cryo-focused ion beam (cryo-FIB). Here, we developed a cryogenic low-energy polishing in FIB approach, which reliably produces thin lamellae with low damage across different cell types. This advance, combined with in situ single particles analysis, has pushed down the molecular weight lower limit for in situ reconstruction at near-atomic resolution to [~]400 kDa. We demonstrate this by resolving photosynthetic complexes (3.4 [A] and 3.3 [A]), metabolic enzymes (3.3 [A]), chloroplast ribosome (4.0 [A]) and respiratory chain complexes (3.7 [A]) from Chlamydomonas reinhardtii. Furthermore, the efficient workflow enables rapid structural feedback upon changes in cellular states, offering a practical way to perform multi-condition in situ structural analysis.

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.

All-gold electron microscopy (EM) grids reduce beam-induced motion relative to conventional holey carbon supports and provide biocompatible substrates for cellular cryo-EM. However, placing customizable all-gold grid fabrication in the hands of researchers requires accessible processes based on standard microfabrication tools. We report a wafer-scale process using microfabrication techniques available in most academic cleanrooms such as lift-off metallization, electroplating, and sacrificial layer release to fabricate 594 all-gold grids per 4-inch wafer without individual grid handling. A numerical electroplating model provides a quantitative framework to relate gold deposition, grid-bar thickness, and tilt-compatible grid geometry. We show that oval 2 {micro}m x 6 {micro}m foil holes bias on-grid actin organization by substrate geometry alone, without chemical micropatterning. The EM grids supported a 2.15 [A] apoferritin single-particle reconstruction on a 200 kV cryo-TEM and are compatible with protein micropatterning and cell culture. This platform establishes an accessible route to programmable, application-specific all-gold cryo-EM supports that couple high-resolution structural imaging with engineered control of cellular organization.

Deep learning neural networks provide a powerful approach for segmenting low-contrast cryogenic electron microscopy (cryoEM) images. However, model performance can vary significantly across imaging conditions and may hinder downstream quantitative analyses. Here, we present a structured evaluation workflow to systematically screen segmentation models based on performance, inference speed, robustness across imaging conditions, and reliability of downstream quantitative measurements. Using the Bacterial Cell Envelope Thickness Tool (BCET) as a test case, we evaluate multiple architectures (YOLOv11, YOLO26, U-Net, Detectron2, and SAM3) under low-dose and ultralow-dose cryoEM conditions. While several models achieve high metrics, model choice strongly influences downstream measurements of envelope thickness. Models optimized for high F1-scores may produce unreliable segmentation masks from object crowding, interpolation artifacts or imaging conditions. Our results reveal distinct trade-offs between performance, speed, and robustness amongst models. YOLOv11 provides the highest fidelity membrane segmentation for quantitative measurements and the Meta-based model SAM3 offers improved robustness under ultralow-dose conditions with competitive inference performance. This work provides practical guidance for model selection in cryoEM workflows, emphasizing that optimal choice depends on experimental priorities and downstream analysis requirements rather than metrics alone. These findings are broadly relevant to cryoEM workflows as AI-based analysis expands beyond the biological sciences. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/730486v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@f29df4org.highwire.dtl.DTLVardef@601d6eorg.highwire.dtl.DTLVardef@2c5023org.highwire.dtl.DTLVardef@1413f76_HPS_FORMAT_FIGEXP M_FIG C_FIG

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.