Biology subjects

Righetto, R. D.

Publications and source records attributed to Righetto, R. D..

13 recordsLinked to original sources

bioRxiv2026

Molecular architecture of the ciliary base in mammalian multiciliated cells

Multiciliated epithelial cells (MCCs) generate tens to hundreds of motile cilia to drive fluid flow in diverse physiological contexts. While the axonemal structure of motile cilia has been described extensively in recent years, the molecular architecture of the transition zone, basal body, and surrounding ciliary environment of MCCs remain more elusive. Here, we use cryo-focused ion beam (cryo-FIB) milling and cryo-electron tomography (cryo-ET) to obtain in situ 3D views of the ciliary base within intact MCCs from mammalian trachea, complemented by in situ cross-linking mass spectrometry (XL/MS) and ultrastructure expansion microscopy (U-ExM) for molecular identification. Our data reveal spatially-defined modifications of microtubule architecture from the proximal centriole to the early axoneme, including transition zone-specific features such as an A-B linker bridging microtubule doublets and a helical assembly of microtubule inner proteins (MIPs). We show that the ciliary necklace, a feature observed in many motile cilia, is spatially aligned with the transition zone and quantify its regular organization within the membrane. Our in situ data capture rarely observed events, including intraflagellar transport (IFT) trains connecting to ciliary vesicles tethered to undocked centrioles. The surrounding ciliary environment contains intermediate filaments that encircle the basal bodies and bundled actin filaments that elaborate microvilli structures between the cilia. Integration of XL/MS and U-ExM identified novel microtubule associated proteins (MAPs), MIPs, and membrane-associated proteins localized to these distinct subdomains. This work provides a molecular and structural map of the mammalian MCC ciliary base, revealing architectural principles that underlie its assembly, organization, and function.

cell biology↗

bioRxiv2025

Icecream: High-Fidelity Equivariant Cryo-Electron Tomography

Cryo-electron tomography (cryo-ET) visualizes 3D cellular architecture in near-native states. Recent deep-learning methods (CryoCARE, IsoNet, DeepDeWedge, CryoLithe) improve denoising and artifact correction, but performance remains limited by very low signal-to-noise ratio, a restricted angular range ( missing wedge), and the lack of ground truth. Here, we present Icecream, which follows the broad template of earlier self-supervised approaches, but treats symmetry in a way consistent with the recent equivariant imaging framework (Chen et al., 2021). Coupled with several engineering refinements, including mixed-precision arithmetic, Icecream achieves substantially better denoising and more reliable missing-wedge recovery, while reducing training and inference time relative to comparable baselines. Across diverse experimental datasets, we observe consistent gains in reconstruction quality, both visually and as quantified by Fourier shell correlation (FSC). Our framework extends to any tomography problem that provides two statistically independent reconstructions of the same volume; in cryo-ET these are obtained by dose splitting or angular partitioning of the tilt series.

bioinformatics↗

bioRxiv2024

Towards community-driven visual proteomics with large-scale cryo-electron tomography of Chlamydomonas reinhardtii

In situ cryo-electron tomography (cryo-ET) has emerged as the method of choice to investigate structures of biomolecules in their native context. However, challenges remain in the efficient production of large-scale cryo-ET datasets, as well as the community sharing of this information-rich data. Here, we applied a cryogenic plasma-based focused ion beam (cryo-PFIB) instrument for high-throughput milling of the green alga Chlamydomonas reinhardtii, a useful model organism for in situ visualization of numerous fundamental cellular processes. Combining cryo-PFIB sample preparation with recent advances in cryo-ET data acquisition and processing, we generated a dataset of 1829 reconstructed and annotated tomograms, which we provide as a community resource to drive method development and inspire biological discovery. To assay the quality of this dataset, we performed subtomogram averaging (STA) of both soluble and membrane-bound complexes ranging in size from >3 MDa to [~]200 kDa, including 80S ribosomes, Rubisco, nucleosomes, microtubules, clathrin, photosystem II, and mitochondrial ATP synthase. The majority of these density maps reached sub-nanometer resolution, demonstrating the potential of this C. reinhardtii dataset, as well as the promise of modern cryo-ET workflows and open data sharing towards visual proteomics.

cell biology↗

bioRxiv2024

In-cell architecture of the mitochondrial respiratory chain

Mitochondria produce energy through oxidative phosphorylation, carried out by five membrane-bound complexes collectively known as the respiratory chain. These complexes work in concert to transfer electrons and pump protons, leading to ATP regeneration. The precise organization of these complexes in native cells is debated, notably their assembly into higher-order supercomplexes called respirasomes. Here, we use in situ cryo-electron tomography to visualize the native structures and organization of several major mitochondrial complexes inside Chlamydomonas reinhardtii cells. ATP synthases and respiratory complexes are segregated into curved and flat crista membrane domains, respectively. Respiratory complexes I, III, and IV assemble into a single type of respirasome, from which we determined a native 5 [A]-resolution structure showing the binding of electron carrier cytochrome c. Combined with single-particle cryo-electron microscopy reconstruction at 2.4 [A] resolution, we assemble a detailed model of how the respiratory complexes interact with each other inside native mitochondria.

molecular biology↗

bioRxiv2024

Surforama: interactive exploration of volumetric data by leveraging 3D surfaces

MotivationVisualization and annotation of segmented surfaces is of paramount importance for studying membrane proteins in their native cellular environment by cryogenic electron tomography (cryo-ET). Yet, analyzing membrane proteins and their organization is challenging due to their small sizes and the need to consider local context constrained to the membrane surface. ResultsTo interactively visualize, annotate, and analyze proteins in cellular context from cryo-ET data, we have developed Surforama, a Python package and napari plugin. For interactive visualization of membrane proteins in tomograms, Surforama renders the local densities projected on the surface of the segmentations. Suforama additionally provides tools to annotate and analyze particles on the membrane surfaces. Finally, for compatibility with other tools in the cryo-ET analysis ecosystem, results can be exported as RELION-formatted STAR files. As a demonstration, we performed subtomogram averaging and neighborhood analysis of photosystem II proteins in thylakoid membranes from the green alga Chlamydomonas reinhardtii. Availability and implementationPython package, code and examples are available at: https://github.com/cellcanvas/surforama

bioinformatics↗

bioRxiv2024

Mean Shift Clustering as a Loss Function for Accurate and Segmentation-aware Localization of Macromolecules in Cryo-electron Tomography

Cryo-electron tomography allows us to visualize and analyze the native cellular environment on a molecular level in 3D. To reliably study structures and interactions of proteins, they need to be accurately localized. Recent detection methods train a segmentation network and use post-processing to determine protein locations, often leading to inaccurate and inconsistent locations. We present an end-to-end learning approach for more accurate protein center identification by introducing a differentiable, scoremap-guided Mean Shift clustering implementation. To make training computationally feasible, we sample random cluster points instead of processing the entire image. We show that our Mean Shift loss leads to more accurate cluster center positions compared to the classical Dice loss. When combining these loss functions, we can enhance 3D protein shape preservation and improve clustering with more accurate, localization-focused score maps. In addition to improved protein localization, our method provides more efficient training with sparse ground truth annotations, due to our point sampling strategy.

bioinformatics↗

bioRxiv2024

MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography

Cryo-electron tomography (cryo-ET) provides unique insights into macromolecular complexes in their native environments, yet membrane analysis remains a major bottleneck due to low signal-to-noise ratios, missing wedge artifacts, and the complexity of membrane-associated proteins. Existing tools often require extensive manual annotation, struggle with generalization across datasets, and lack integrated solutions for segmentation, protein localization, and quantitative analysis. We introduce MemBrain v2, a deep learning-enabled framework that unifies these tasks into a streamlined pipeline. MemBrain-seg leverages a diverse, collaboratively generated training dataset and specialized model training strategies to achieve generalizable membrane segmentation across variable tomographic conditions. MemBrain-pick enables data-efficient localization of membrane-bound proteins by integrating geometric constraints with deep learning, reducing the need for extensive manual annotation. MemBrain-stats provides quantitative insights into protein distributions, computing spatial metrics to analyze intra-membrane particle organization. MemBrain v2 integrates seamlessly into cryo-ET workflows, providing an accessible and structured approach to membrane analysis. The full package is available at https://github.com/CellArchLab/MemBrain-v2.

bioinformatics↗

bioRxiv2023

Diatom pyrenoids are encased in a protein shell that enables efficient CO2 fixation

Pyrenoids are subcompartments of algal chloroplasts that concentrate Rubisco enzymes and their CO2 substrate, thereby increasing the efficiency of carbon fixation. Diatoms perform up to 20% of global CO2 fixation, but their pyrenoids remain poorly characterized at a molecular level. Here, we used in vivo photo-crosslinking to catalogue components of diatom pyrenoids and identified a pyrenoid shell (PyShell) protein, which we localized to the pyrenoid periphery of both the pennate diatom, Pheaodactylum tricornutum, and the centric diatom, Thalassiosira pseudonana. In situ cryo-electron tomography (cryo-ET) revealed that the pyrenoids of both diatom species are encased in a lattice-like protein sheath. Disruption of PyShell expression in T. pseudonana resulted in the absence of this protein sheath, altered pyrenoid morphology, and a high-CO2 requiring phenotype, with impaired growth and reduced carbon fixation efficiency under standard atmospheric conditions. Pyrenoids in mutant cells were fragmented and lacked the thylakoid membranes that normally traverse the Rubisco matrix, demonstrating how the PyShell plays a guiding role in establishing pyrenoid architecture. Recombinant PyShell proteins self-assembled into helical tubes, enabling us to determine a 3.0 [A]-resolution PyShell structure. We then fit this in vitro structure into an in situ subtomogram average of the pyrenoids protein sheath, yielding a putative atomic model of the PyShell within diatom cells. The structure and function of the diatom PyShell provides a new molecular view of how CO2 is assimilated in the ocean, a crucial biome that is on the front lines of climate change.

cell biology↗

bioRxiv2022

MemBrain: A Deep Learning-aided Pipeline for Automated Detection of Membrane Proteins in Cryo-electron Tomograms

Background and ObjectiveCryo-electron tomography (cryo-ET) is an imaging technique that enables 3D visualization of the native cellular environment at sub-nanometer resolution, providing unpreceded insights into the molecular organization of cells. However, cryo-electron tomograms suffer from low signal-to-noise ratios and anisotropic resolution, which makes subsequent image analysis challenging. In particular, the automated detection of membrane-embedded proteins is a problem still lacking satisfactory solutions. MethodsWe present MemBrain - a new deep learning-based pipeline that automatically detects membrane-bound protein complexes in cryo-electron tomograms. After subvolumes are sampled along a segmented membrane, each subvolume is assigned a score using a convolutional neural network (CNN), and protein positions are extracted by a clustering algorithm. Incorporating rotational subvolume normalization and using a tiny receptive field simplify the task of protein detection and thus facilitate the network training. ResultsMemBrain requires only a small quantity of training labels and achieves excellent performance with only a single annotated membrane (F1 score: 0.88). A detailed evaluation shows that our fully trained pipeline outperforms existing classical computer vision-based and CNN-based approaches by a large margin (F1 score: 0.92 vs. max. 0.63). Furthermore, in addition to protein center positions, MemBrain can determine protein orientations, which has not been implemented by any existing CNN-based method to date. We also show that a pre-trained MemBrain program generalizes to tomograms acquired using different cryo-ET methods and depicting different types of cells. ConclusionsMemBrain is a powerful and label-efficient tool for the detection of membrane protein complexes in cryo-ET data, with the potential to be used in a wide range of biological studies. It is generalizable to various kinds of tomograms, making it possible to use pretrained models for different tasks. Its efficiency in terms of required annotations also allows rapid training and fine-tuning of models. The corresponding code, pretrained models, and instructions for operating the MemBrain program can be found at: https://github.com/CellArchLab/MemBrain

bioinformatics↗

bioRxiv2021

Cryo-EM structure of native human thyroglobulin

The thyroglobulin (Tg) protein is essential to thyroid hormone synthesis, playing a vital role in the regulation of metabolism, development and growth. Its structure is conserved among vertebrates. Tg is delivered through the secretory pathway of the thyroid follicular unit to the central colloid depository, where it is iodinated at specific tyrosine sites to form mono- or diiodotyrosine, which combine to produce triiodothyronine (T3) and thyroxine (T4), respectively. Synthesis of these hormones depends on the precise 3D structure of Tg, which has remained unknown despite decades of research. Here, we present the cryo-electron microscopy structure of human thyroglobulin (hTg) to a global resolution of 3.2 [A]. The structure provides detailed information on the location of the hTg hormonogenic sites and reveals the position as well as the role of many of its glycosylation sites. Our results offer structural insight into thyroid hormonogenesis and provide a fundamental understanding of clinically relevant hTg mutations, which can improve treatment of thyroid diseases.

biophysics↗

bioRxiv2020

High-resolution cryo-EM structure of urease from the pathogen Yersinia enterocolitica

Urease converts urea into ammonia and carbon dioxide and makes urea available as a nitrogen source for all forms of life except animals. In human bacterial pathogens, ureases also aid in the invasion of acidic environments such as the stomach by raising the surrounding pH. Here, we report the structure of urease from the pathogen Yersinia enterocolitica at better than 2 [A] resolution from cryo-electron microscopy. Y. enterocolitica urease is a dodecameric assembly of a trimer of three protein chains, ureA, ureB and ureC. The high data quality enables detailed visualization of the urease bimetal active site and of the impact of radiation damage. Our data are of sufficient quality to support drug development efforts.

molecular biology↗

bioRxiv2017

Image processing techniques for high-resolution structure determination from badly ordered 2D crystals

2D electron crystallography can be used to study small membrane proteins in their native environment. Obtaining highly ordered 2D crystals is difficult and time-consuming. However, 2D crystals diffracting to only 10-12 [A] can be prepared relatively conveniently in most cases. We have developed image-processing algorithms allowing to generate a high resolution 3D structure from cryo-electron crystallography images of badly ordered crystals. These include movie-mode unbending, refinement over sub-tiles of the images in order to locally refine the sample tilt geometry; implementation of different CTF correction schemes; and an iterative method to apply known constraints in the real and reciprocal space to approximate amplitudes and phases in the so-called missing cone regions. These algorithms applied to a dataset of the potassium channel MloK1 show significant resolution improvements to approximately 5[A].\n\nAbbreviations

biophysics↗

bioRxiv2017

Focus: The interface between data collection and data processing in cryo-EM

We present a new software package called Focus that interfaces cryo-transmission electron microscopy (cryo-EM) data collection with computer image processing. Focus creates a user-friendly environment to import and manage data recorded by direct electron detectors and perform elemental image processing tasks in a high-throughput manner while new data is being acquired at the microscope. It provides the functionality required to remotely monitor the progress of data collection and data processing, which is essential now that automation in cryo-EM allows a steady flow of images of single particles, two-dimensional crystals, or electron tomography data to be recorded in overnight sessions. The rapid detection of any errors that may occur greatly increases the productivity of recording sessions at the electron microscope.

biophysics↗