Journal of Structural Biology: X

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.

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.

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.

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.

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.

Cellular cryo-electron tomography (cryo-ET) reveals high-resolution details of macromolecules within their native cellular environment. However, in situ cryo-ET datasets are large and highly heterogeneous, which makes comprehensive identification and extraction of the many different elements of cellular architecture for high-resolution analysis a challenging, time-consuming and often tedious task. Here we present easymode, a library of pretrained general segmentation networks for cryo-ET, trained on over 4,000 tilt series spanning a large and diverse variety of sources. Easymode enables in situ structural determination workflows by rendering tomogram content computationally accessible, without requiring any per-dataset training. Beyond directly facilitating high-resolution subtomogram averaging of a selection of widely prevalent complexes, we show how easymode can be used to leverage cellular context in subtomogram averaging workflows, helping identify, align, or filter particle sets, and enabling automated mapping of the cellular landscape surrounding target proteins. We use easymode to determine the in situ structure of rare inosine monophosphate dehydrogenase (IMPDH) filaments at 4.0 A resolution, and to map and visualize the surrounding cellular environment.

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.

GFAP is a type III intermediate filament primarily found within astrocytes and is known to maintain proper cell structure and mechanical strength. Mutations in GFAP are implicated in the pathology of Alexander disease, a neurodegenerative disease characterized by cytoplasmic inclusions of protein, known as Rosenthal fibers. GFAP has a typical type III intermediate filament domain structure, consisting of a highly conserved alpha-helical rod domain bracketed by an intrinsically disordered N-terminal head and C-terminal tail domains. While the general domain organization of monomeric GFAP and the assembly process for higher order quaternary structures are known, we lack an atomic resolution mechanistic understanding of GFAP assembly into mature filaments. Understanding the structure of GFAP filaments and how mutations disrupt this structure will provide vital information into how mutations produce Alexander disease pathology. As a first step towards a mechanistic description, we characterized GFAP wild type tetrameric and filamentous assemblies using solid state NMR and compared the results to those obtained from an assembly-deficient GFAP mutant. For wild-type GFAP, we observe surprisingly uniform rigid alpha helical structure and can spectroscopically resolve highly mobile intrinsically disordered regions in the filament assemblies. Wild type tetramers show increased mobility, likely arising from the head and tail domains. Mutation of the highly conserved cysteine at position 294 to serine results in an inability to form full-length filament assemblies. We show that the rigid regions of the C294S mutant assemblies largely remain structurally consistent with wild type tetrameric assemblies but differ from wild-type filament assemblies. There is an increase in highly mobile regions for the C294S mutant relative to the wild-type. Our results provide a foundation for developing solid state NMR approaches to characterize intermediate filament assembly mechanisms and the interfering effect of disease mutations.

Accurate determination of state occupancies is essential for interpreting the structural heterogeneity inherent in time-resolved crystallography. However, in cases of high spatial overlap between states, as commonly observed in time-resolved crystallography data, the strong correlation between occupancy and atomic displacement parameters (ADPs) can render single point estimates from standard refinement protocols unreliable. We introduce MEROS (Multi-state Ensemble Refinement for Occupancy Statistics), a pipeline that implements an ensemble refinement approach to assess the post-refinement occupancy-ADP statistics of multiple overlapping states. MEROS utilizes a Monte Carlo sampling of the parameter space, performing independent refinements from randomized starting occupancies and ADP values to empirically characterize the convergence and uncertainty of the solution. The method is implemented as a modular Python pipeline that wraps established refinement programs, ensuring compatibility with existing workflows. We demonstrate its applicability in two case studies: a two-state ligand binding model in T4 lysozyme L99A and a four-state covalent catalysis mechanism in {beta}-lactamase CTX-M-14. MEROS provides occupancy and ADP mean values with standard deviations that directly quantify the informational content of the experimental diffraction data.

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.

Structural heterogeneity in biomolecules, arising from both compositional and conformational variability, limits resolution and interpretability of cryo-electron tomography (cryo-ET). Here, we present OPUS-ET, a deep learning framework that resolves multiscale heterogeneity throughout the cryo-ET workflow. OPUS-ET combines a composition decoder that captures compositional differences with a conformation decoder that models large-scale motions, thereby providing a hierarchical representation of structural heterogeneity. Starting from noisy template-matching candidates with templates of varying similarity or quality, OPUS-ET efficiently enriches target particle populations and delivers sub-nanometer in situ reconstructions in a single round. It leads to improved resolutions by up to 4.5 [A] over expert annotations or existing deep-learning approaches in four benchmark systems, and reveals continuous conformational landscapes capturing F-F flexible coupling in mitochondrial ATP synthase and tRNA-translocation intermediates in eukaryotic and bacterial ribosomes. Together, these results establish OPUS-ET as a powerful computational tool for linking particle purification, high-resolution reconstruction, and analysis of structural heterogeneity in cryo-ET, with demonstrated robustness to template quality, initial pose noise, and clustering parameters.

Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, can use host derived lipids as carbon and energy source for survival. Mammalian cell entry (Mce) associated membrane (Mam) proteins are important for the stability of lipid importing Mce complexes. Mtb has five homologs of Mam proteins referred as orphaned Mam (OmamA-E) proteins. A recent study suggested that OmamC (Rv1363c) is essential for the storage and utilization of lipids under starvation in Mtb. To understand the structure and interactions of OmamC, we generated a truncated soluble variant of OmamC (OmamC129-261). Here, we report on the challenges encountered during the crystallization and structure determination of OmamC129-261 and the strategies applied to overcome them. Despite the AlphaFold2 predicted model proving an initial molecular replacement solution, experimental phasing was necessary to determine the structure of OmamC129-261. Heat treatment of protein prior to crystallization setup removed partially unfolded protein present and played a critical role in enhancing the reproducibility and diffraction quality of OmamC129-261 crystals. Although reported earlier, it is not a widely used method. It is worth to try this method, especially, when faced with poor reproducibility and diffraction of crystals.

Understanding the behavior of - and {beta}-cells within intact human islets is essential for elucidating mechanisms of metabolic control in diabetes. Current cell-type identification strategies rely on destructive labeling or on advanced imaging modalities such as Fluorescence Lifetime Imaging Microscopy (FLIM), which provide rich metabolic information but require specialized instrumentation and acquisition protocols. Here we show that structured intracellular intensity patterns derived from endogenous autofluorescence are sufficient to discriminate and {beta} cells in living human islets. Using rotation-invariant Local Ternary Pattern (LTP) descriptors combined with morphological features, we achieve highly accurate classification (AUC = 0.92), improving upon previously reported benchmarks. The resulting framework is lightweight, interpretable, and compatible with standard imaging configurations, enabling accessible and scalable analysis of label-free microscopy data. Interpretability analyses demonstrate that discrimination is driven predominantly by fine-scale intracellular intensity organization rather than global morphology. In the spectral window employed, cytoplasmic autofluorescence is prominently shaped by lipofuscin-rich granules. Consistent with prior reports of higher lipofuscin accumulation in {beta}-cells, the dominant features identified here likely reflect differences in granule abundance and spatial organization between endocrine cell types. These findings indicate that endogenous intensity patterns encode sufficient structural information for reliable /{beta} discrimination, providing a biologically grounded and fully non-destructive framework for the identification of pancreatic islet cell types.

Accurate particle picking from noisy cryo-EM micrographs is essential for high-resolution reconstruction. Current deep learning methods rely on manually annotated data, which is labor-intensive, subjective, and limits particle recall under low signal-to-noise ratio (SNR). Here we introduce ParSeek, an automated picker trained entirely on synthetic data without human annotation. Synthetic micrographs are generated by projecting known 3D structures into realistic background patches that reproduce experimental noise. On seven public cryo-EM datasets, ParSeek outperformed Topaz and CryoSegNet on four datasets, achieving the highest F1-score (up to 0.82) and reaching 0.63 on a challenging membrane protein dataset. Density maps from ParSeek-picked particles showed cross-correlation coefficients up to 0.995 with the reference and a minimal resolution difference of 0.1 [A]. ParSeek also overcame severe orientation bias on an influenza dataset, yielding a reasonable reconstruction. Applied to three experimental datasets (an antibody-antigen complex and two GPCRs), ParSeek enabled reconstructions at 5.0 [A], 4.0 [A], and 2.8 [A], respectively. The 2.8 [A] map resolved side-chain densities and ligand flexibility. This study establishes a fully synthetic-data-driven strategy that eliminates manual annotation for training cryo-EM deep-learning models, paving the way for automated, unbiased particle picking.

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.

The Tar-DNA Binding Protein-43 C-terminal region, TDP43LC, has been previously shown to form amyloid-like fibrils with distinct folds in ALS and FTD. In both diseases, proteinaceous inclusions contain TDP43 C-terminal protein fragments as well as phosphorylated TDP43. Here, we use solution NMR to show that soluble phosphomimetic TDP43LC, P-TDP43LC, is structurally similar to wild-type TDP43LC. Disperse P-TDP43LC, like wild-type protein, contains a central helical region flanked by long disordered regions. Despite this similarity, our turbidity measurements, imaging, and kinetic assays show that P-TDP43LC has different aggregation behavior than wild-type protein. Using solid state NMR measurements we find that that phosphomimetic mutations alter the wild-type fibril conformation. Electrostatic repulsion from negatively charged sidechains, despite having little effect on the soluble proteins structure, perturbs amyloid-like fibril formation and selects for a different conformation in vitro. These results shed light on the structural role of TDP43LC phosphorylation in fibril formation in disease. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/725298v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@1c63aforg.highwire.dtl.DTLVardef@1d48ed6org.highwire.dtl.DTLVardef@1ed8fd3org.highwire.dtl.DTLVardef@17d67a8_HPS_FORMAT_FIGEXP M_FIG C_FIG SynopsisPhosphomimetic mutations at ALS and FTD neurodegeneration-associated sites in an amyloid forming protein perturbs the aggregated structure compared to wild-type protein.

Microtubules are dynamic filaments of tubulin heterodimers that comprise an essential part of the eukaryotic cytoskeleton1. The nucleotide state of tubulin controls microtubule dynamics: stable GTP-microtubules favor polymerization, whereas unstable GDP-microtubules drive depolymerization2. Anticancer compounds such as Taxol (paclitaxel) target microtubule dynamicity by preventing microtubule depolymerization3,4. Despite decades of work, the molecular basis of microtubule dynamics remains poorly defined. Using cryo-EM, we determined [~]2.2 [A] structures of human microtubules in GTP-like (GMPCPP) and GDP states. Comparison of these two states revealed switch-like structural changes as tubulins transition from the pre-hydrolysis (GMPCPP) to the post-hydrolysis (GDP) state. Additional structure determination of Taxol-bound microtubules at [~]2.2 [A] showed that Taxol binding converts the microtubule lattice into a pre-hydrolysis state by reversing the structural switches flipped during GTP hydrolysis. Focusing our analysis on the microtubule seam shows that the pre-hydrolysis conformation of GMPCPP or Taxol-GDP exhibits favorable lateral interactions at the seam, with lattice deformations clearly visible at the GDP seam. Together, our data show the existence of structural switches in tubulin that are coupled to the nucleotide state and are exploited by Taxol to stabilize microtubules into a pre-hydrolysis-like state. (191 words)

Bpa (Bacterial proteasome activator) is a regulatory particle within the Mycobacterium tuberculosis (Mtb) proteasome system that that facilitates ATP-independent substrate engagement and delivery to the 20S core particle (CP) for degradation. The best characterized Bpa substrate is HspR, a transcriptional repressor of Mtb stress-response genes whose Bpa-dependent degradation is required for pathogen virulence. However, the stoichiometry of the Bpa:HspR complex, the molecular mechanism of substrate engagement, and the heterogeneity of the resulting assemblies remain unclear. Here, we combine charge detection mass spectrometry (CDMS) and single-particle electron cryomicroscopy (cryo-EM) as complementary approaches to characterize both apo and HspR-bound Bpa. CDMS revealed a previously unreported undecameric apo species and defines a Bpa12:HspR2 complex stoichiometry with minimal heterogeneity. Cryo-EM, performed without the employment of cross-linking reagents, independently confirmed the presence of undecameric Bpa in solution and localized substrate-associated density to the C-terminal H4 helix of Bpa. Together, these complementary single-particle approaches inform future efforts to target the Mtb proteasome system and provide new molecular insight into proteasomal substrate recognition in prokaryotes. Table of Content Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/722288v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@be4bb6org.highwire.dtl.DTLVardef@15ccf73org.highwire.dtl.DTLVardef@379a2eorg.highwire.dtl.DTLVardef@6b7b90_HPS_FORMAT_FIGEXP M_FIG C_FIG

Aceruloplasminemia (ACP) is a rare neurodegenerative disorder characterized by extreme cerebral iron overload and a shift towards larger iron aggregates, providing a unique possibility to study how iron aggregation shapes MRI contrast in vivo. We introduce a clinically feasible, multi-parametric quantitative MRI (qMRI) framework that combines quantitative susceptibility mapping (QSM), [Formula], and R2 to disentangle changes in total iron concentration from alterations in iron aggregation and its spatial organization at the cellular scale. Our biophysical model links the microstructure sensitive [Formula] ratio and the slope of the susceptibility-relaxation relationship (iron) to iron aggregation size and distribution. In a 3T qMRI study of three patients with ACP and three matched controls, we observe a marked increase in [Formula] and a pronounced increase of the [Formula]-QSM slope (iron: controls 154.09 {+/-} 52.89 s-1ppm-1; patients 296.68 {+/-} 57.18 s-1ppm-1; p = 0.016), consistent with enhanced iron aggregation and altered spatial organization. Model-based decomposition of transverse relaxation indicates that up to approximately 40% of the observed R2* elevation in ACP is attributable to changes in iron distribution beyond increased iron concentration alone. These findings establish a robust, translational qMRI approach for quantitative in vivo assessment of iron aggregation, revealing microstructural drivers of iron-related neurodegeneration that extend beyond bulk iron load.

Building and refining cryo-EM atomic models often requires long, project-specific workflows that combine map inspection, prior structural knowledge, restraints, refinement, validation and expert review. Existing programs perform many individual operations, but coordinating them across iterative model-building sessions remains manual and difficult to audit. We present StructAgent, a user-guided multi-agent resource for cryo-EM model building and refinement. StructAgent couples a domain agent for literature-grounded structural reasoning with an execution agent that runs local software, tracks state, recovers from failures and records provenance. Expert approval gates control major model-changing actions. In three case studies, StructAgent refitted a 64-chain proteasome from an earlier template, audited 530 ribosomal metal-ion sites and guided a chemically ambiguous ligand fit in a folate-metabolism enzyme from ongoing work. These demonstrations show that agentic orchestration can convert modeling intent into auditable, reviewable software workflows while preserving expert control and final scientific judgment.