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Cryo-electron tomography (cryo-ET) is a powerful method to elucidate subcellular architecture and to structurally analyse biomolecules in situ by subtomogram averaging (STA). Specimen thickness is a key factor affecting cryo-ET data quality. Cells that are too thick for transmission imaging can be thinned by cryo-focused-ion-beam (cryo-FIB) milling. However, optimal specimen thickness for cryo-ET on lamellae has not been systematically investigated. Furthermore, the ions used to ablate material can cause damage in the lamellae, thereby reducing STA resolution. Here, we systematically benchmark the resolution depending on lamella thickness and the depth of the particles within the sample. Up to ca. 180 nm, lamella thickness does not negatively impact resolution. This shows that there is no need to generate very thin lamellae and thickness can be chosen such that it captures major cellular features. Furthermore, we show that gallium-ion-induced damage extends to depths of up to 30 nm from either lamella surface.
Classical molecular dynamics (MD) simulations provide unmatched spatial and time resolution of protein structure and function. However, accuracy of MD simulations often depends on the quality of force field parameters and the time scale of sampling. Another limitation of conventional MD simulations is that the protonation states of titratable amino acid residues remain fixed during simulations, even though protonation state changes coupled to conformational dynamics are central to protein function. Due to the uncertainty in selecting protonation states, classical MD simulations are sometimes performed with all amino acids modeled in their standard charged states at pH 7. Here we performed and analyzed classical MD simulations on high-resolution cryo-EM structures of two membrane proteins that transfer protons by catalyzing protonation/deprotonation reactions. In simulations performed with amino acids modeled in their standard protonation state the structure diverges far from its starting conformation. In comparison, MD simulations performed with pre-determined protonation states of amino acid residues reproduce the structural conformation, protein hydration, and protein-water and protein-protein interactions of the structure much better. The results suggest it is crucial to perform basic protonation state calculations, especially on structures where protonation changes play an important functional role, prior to launching any MD simulations. Furthermore, the combined approach of protonation state prediction and MD simulations can provide valuable information on the charge states of amino acids in the cryo-EM sample. Even though accurate prediction of protonation states currently remains a challenge, we introduce an approach of combining pKa prediction with cryo-EM density map analysis that helps in improving not only the protonation state predictions, but also the atomic modeling of density data.
Light-driven sodium pumps (NaRs) are unique ion-transporting microbial rhodopsins. The major group of NaRs is characterized by an NDQ motif and has two aspartic acid residues in the central region essential for sodium transport. Here we identified a new subgroup of the NDQ rhodopsins bearing an additional glutamic acid residue in the close vicinity to the retinal Schiff base. We thoroughly characterized a member of this subgroup, namely the protein ErNaR from Erythrobacter sp. HL-111 and showed that the additional glutamic acid results in almost complete loss of pH sensitivity for sodium-pumping activity, which is in contrast to previously studied NaRs. ErNaR is capable of transporting sodium efficiently even at acidic pH levels. X-ray crystallography and single particle cryo-electron microscopy reveal that the additional glutamic acid residue mediates the connection between the other two Schiff base counterions and strongly interacts with the aspartic acid of the characteristic NDQ motif. Hence, it reduces its pKa. Our findings shed light on a new subgroup of NaRs and might serve as a basis for their rational optimization for optogenetics.
Vertebrate life depends on renal function to filter excess fluid and remove low-molecular-weight waste products. An essential component of the kidney filtration barrier is the slit diaphragm (SD), a specialized cell-cell junction between podocytes. Although the constituents of the SD are largely known, its molecular organization remains elusive. Here, we use super-resolution correlative light and electron microscopy to quantify a linear rate of reduction in albumin concentration across the filtration barrier under no-flow conditions. Next, we use cryo-electron tomography of vitreous lamellae from high-pressure frozen native glomeruli to analyze the molecular architecture of the SD. The resulting densities resemble a fishnet pattern. Fitting of Nephrin and Neph1, the main constituents of the SD, results in a complex interaction pattern with multiple contact sites between the molecules. Using molecular dynamics simulations, we construct a blueprint of the SD that explains its molecular architecture. Our architectural understanding of the SD reconciles previous findings and provides a mechanistic framework for the development of novel therapies to treat kidney dysfunction.
Vertebrate life depends on renal function to filter excess fluid and remove low-molecular-weight waste products. An essential component of the kidney filtration barrier is the slit diaphragm (SD), a specialized cell-cell junction between podocytes. Although the constituents of the SD are largely known, its molecular organization remains elusive. Here, we use super-resolution correlative light and electron microscopy to quantify a linear rate of reduction in albumin concentration across the filtration barrier. Next, we use cryo-electron tomography of vitreous lamellae from high-pressure frozen native glomeruli to analyze the molecular architecture of the SD. The resulting densities resemble a fishnet pattern. Fitting of Nephrin and Neph1, the main constituents of the SD, results in a complex interaction pattern with multiple contact sites between the molecules. Using molecular dynamics flexible fitting, we construct a blueprint of the SD, where we describe all interactions. Our architectural understanding of the SD reconciles previous findings and provides a mechanistic framework for the development of novel therapies to treat kidney dysfunction.
The ubiquitin (Ub) code denotes the complex Ub architectures, including Ub chains of different length, linkage-type and linkage combinations, which enable ubiquitination to control a wide range of protein fates. Although many linkage-specific interactors have been described, how interactors are able to decode more complex architectures is not fully understood. We conducted a Ub interactor screen, in humans and yeast, using Ub chains of varying length, as well as, homotypic and heterotypic branched chains of the two most abundant linkage types – K48- and K63-linked Ub. We identified some of the first K48/K63 branch-specific Ub interactors, including histone ADP-ribosyltransferase PARP10/ARTD10, E3 ligase UBR4 and huntingtin-interacting protein HIP1. Furthermore, we revealed the importance of chain length by identifying interactors with a preference for Ub3 over Ub2 chains, including Ub-directed endoprotease DDI2, autophagy receptor CCDC50 and p97-adaptor FAF1. Crucially, we compared datasets collected using two common DUB inhibitors – Chloroacetamide and N-ethylmaleimide. This revealed inhibitor-dependent interactors, highlighting the importance of inhibitor consideration during pulldown studies. This dataset is a key resource for understanding how the Ub code is read.
Microbial rhodopsins are omnipresent on Earth, however the vast majority of them remain uncharacterized. Here we describe a new rhodopsin clade from cold-adapted organisms and cold environments, such as glaciers, denoted as CryoRhodopsins (CryoRs). Our data suggest that CryoRs have photosensory activity. A distinguishing feature of the clade is the presence of a buried arginine residue close to the cytoplasmic face of its members. Combining single-particle cryo-electron microscopy and X-ray crystallography with the rhodopsin activation by light, we demonstrate that the arginine stabilizes a strongly blue-shifted intermediate of an extremely slow CryoRhodopsin photocycle. Together with extensive spectroscopic characterization, our investigations on CryoR1 and CryoR2 proteins reveal mechanisms of photoswitching in the newly identified clade and demonstrate principles of the adaptation of these rhodopsins to low temperatures.
Nuclear pore complexes (NPCs) constitute giant channels within the nuclear envelope that mediate nucleocytoplasmic exchange. NPC diameter is thought to be regulated by nuclear envelope tension, but how such diameter changes are physiologically linked to cell differentiation, where mechanical properties of nuclei are remodeled and nuclear mechanosensing occurs, remains unstudied. Here we used cryo-electron tomography to show that NPCs dilate during differentiation of mouse embryonic stem cells into neural progenitors. In Nup133-deficient cells, which are known to display impaired neural differentiation, NPCs however fail to dilate. By analyzing the architectures of individual NPCs with template matching, we revealed that the Nup133-deficient NPCs are structurally heterogeneous and frequently disintegrate, resulting in the formation of large nuclear envelope openings. We propose that the elasticity of the NPC scaffold mechanically safeguards the nuclear envelope. Our studies provide a molecular explanation for how genetic perturbation of scaffolding components of macromolecular complexes causes tissue-specific phenotypes.
Microbial rhodopsins are omnipresent on Earth, however the vast majority of them remain uncharacterized. Here we describe a new rhodopsin group from cold-adapted organisms and cold environments, such as glaciers, denoted as CryoRhodopsins (CryoRs). Our data suggest that CryoRs have dual functionality switching between inward transmembrane proton translocation and photosensory activity, both of which can be modulated with UV light. CryoR1 exhibits two subpopulations in the ground state, which upon light activation lead to transient photocurrents of opposing polarities. A distinguishing feature of the group is the presence of a buried arginine residue close to the cytoplasmic face of its members. Combining single-particle cryo-electron microscopy and X-ray crystallography with the rhodopsin activation by lit, we demonstrate that the arginine stabilizes a UV-absorbing intermediate of an extremely slow CryoRhodopsin photocycle. Together with extensive spectroscopic characterization, our investigations on CryoR1 and CryoR2 proteins reveal mechanisms of photoswitching in the newly identified group and demonstrate principles of the adaptation of these rhodopsins to low temperatures.Microbial rhodopsins are omnipresent on Earth, however the vast majority of them remain uncharacterized. Here we describe a new rhodopsin group from cold-adapted organisms and cold environments, such as glaciers, denoted as CryoRhodopsins (CryoRs). Our data suggest that CryoRs have dual functionality switching between inward transmembrane proton translocation and photosensory activity, both of which can be modulated with UV light. CryoR1 exhibits two subpopulations in the ground state, which upon light activation lead to transient photocurrents of opposing polarities. A distinguishing feature of the group is the presence of a buried arginine residue close to the cytoplasmic face of its members. Combining single-particle cryo-electron microscopy and X-ray crystallography with the rhodopsin activation by light, we demonstrate that the arginine stabilizes a UV-absorbing intermediate of an extremely slow CryoRhodopsin photocycle. Together with extensive spectroscopic characterization, our investigations on CryoR1 and CryoR2 proteins reveal mechanisms of photoswitching in the newly identified group and demonstrate principles of the adaptation of these rhodopsins to low temperatures.
Dynamic imaging of landmark organelles, such as nuclei, cell membrane, nuclear envelope, and lipid droplets enables image-based phenotyping of functional states of cells. Multispectral fluorescent imaging of landmark organelles requires labor-intensive labeling, limits throughput, and compromises cell health. Virtual staining of label-free images with deep neural networks is an emerging solution for this problem. Multiplexed imaging of cellular landmarks from scattered light and subsequent demultiplexing with virtual staining saves the light spectrum for imaging additional molecular reporters, photomanipulation, or other tasks. Published approaches for virtual staining of landmark organelles are fragile in the presence of nuisance variations in imaging, culture conditions, and cell types. This paper reports model training protocols for virtual staining of nuclei and membranes robust to cell types, cell states, and imaging parameters. We developed a flexible and scalable convolutional architecture, named UNeXt2, for supervised training and self-supervised pre-training. The strategies we report here enable robust virtual staining of nuclei and cell membranes in multiple cell types, including neuromasts of zebrafish, across a range of imaging conditions. We assess the models by comparing the intensity, segmentations, and application-specific measurements obtained from virtually stained and experimentally stained nuclei and membranes. The models rescue the missing label, non-uniform expression of labels, and photobleaching. We share three pre-trained models, named VSCyto3D, VSCyto2D, and VSNeuromast, as well as VisCy, a PyTorch-based pipeline for training, inference, and deployment that leverages the modern OME-Zarr format.
Upon infection, human immunodeficiency virus (HIV-1) releases its cone-shaped capsid into the cytoplasm of infected T-cells and macrophages. As its largest known cargo, the capsid enters the nuclear pore complex (NPC), driven by interactions with numerous FG-repeat nucleoporins (FG-Nups). Whether NPCs structurally adapt to capsid passage and whether capsids are modified during passage remains unknown, however. Here, we combined super-resolution and correlative microscopy with cryo electron tomography and molecular simulations to study nuclear entry of HIV-1 capsids in primary human macrophages. We found that cytosolically bound cyclophilin A is stripped off capsids entering the NPC, and the capsid hexagonal lattice remains largely intact inside and beyond the central channel. Strikingly, the NPC scaffold rings frequently crack during capsid passage, consistent with computer simulations indicating the need for NPC widening. The unique cone shape of the HIV-1 capsid facilitates its entry into NPCs and helps to crack their rings.
Dynamic imaging of landmark organelles, such as nuclei, cell membrane, nuclear envelope, and lipid droplets enables image-based phenotyping of functional states of cells. Multispectral fluorescent imaging of landmark organelles requires labor-intensive labeling, limits throughput, and compromises cell health. Virtual staining of label-free images with deep neural networks is an emerging solution for this problem. Multiplexed imaging of cellular landmarks from scattered light and subsequent demultiplexing with virtual staining saves the light spectrum for imaging additional molecular reporters, photomanipulation, or other tasks. Published approaches for virtual staining of landmark organelles are fragile in the presence of nuisance variations in imaging, culture conditions, and cell types. This paper reports model training protocols for virtual staining of nuclei and membranes robust to label-free imaging parameters, cell states, and cell types. We developed a flexible and scalable convolutional architecture, named UNeXt2, for supervised training and self-supervised pre-training. The strategies we report here enable robust virtual staining of nuclei and cell membranes in multiple cell types, including neuromasts of zebrafish, across a range of imaging conditions. We assess the models by comparing the intensity, segmentations, and application-specific measurements obtained from virtually stained and experimentally stained nuclei and membranes. The models rescue the missing label, non-uniform expression of labels, and photobleaching. We share three pre-trained models, named VSCyto3D, VSCyto2D, and VSNeuromast, as well as VisCy, a PyTorch-based pipeline for training, inference, and deployment that leverages the modern OME-Zarr format.