Antigen Receptor Function in the Context of the Nanoscale Organization of the B Cell Membrane.

The B cell antigen receptor (BCR) plays a central role in the self/nonself selection of B lymphocytes and in their activation by cognate antigen during the clonal selection process. It was long thought that most cell surface receptors, including the BCR, were freely diffusing and randomly distributed. Since the advent of superresolution techniques, it has become clear that the plasma membrane is compartmentalized and highly organized at the nanometer scale. Hence, a complete understanding of the precise conformation and activation mechanism of the BCR must take into account the organization of the B cell plasma membrane. We review here the recent literature on the nanoscale organization of the lymphocyte membrane and discuss how this new information influences our view of the conformational changes that the BCR undergoes during activation.

Eukaryotic Ribosome Assembly.

During the last ten years, developments in cryo-electron microscopy have transformed our understanding of eukaryotic ribosome assembly. As a result, the field has advanced from a list of the vast array of ribosome assembly factors toward an emerging molecular movie in which individual frames are represented by structures of stable ribosome assembly intermediates with complementary biochemical and genetic data. In this review, we discuss the mechanisms driving the assembly of yeast and human small and large ribosomal subunits. A particular emphasis is placed on the most recent findings that illustrate key concepts of ribosome assembly, such as folding of preribosomal RNA, the enforced chronology of assembly, enzyme-mediated irreversible transitions, and proofreading of preribosomal particles.

A Cool Look at Positive-Strand RNA Virus Replication Organelles: New Insights from Cryo-Electron Microscopy.

Positive-strand RNA viruses encompass a variety of established and emerging eukaryotic pathogens. Their genome replication is confined to specialized cytoplasmic membrane compartments known as replication organelles (ROs). These ROs derive from host membranes, transformed into distinct structures such as invaginated spherules or intricate membrane networks including single- and/or double-membrane vesicles. ROs play a vital role in orchestrating viral RNA synthesis and evading detection by innate immune sensors of the host. In recent years, groundbreaking cryo-electron microscopy studies conducted with several prototypic viruses have significantly advanced our understanding of RO structure and function. Notably, these studies unveiled the presence of crown-shaped multimeric viral protein complexes that seem to actively participate in viral RNA synthesis and regulate the release of newly synthesized RNA into the cytosol for translation and packaging. These findings have shed light on novel viral functions and fascinating macromolecular complexes that delineate promising new avenues for future research.

Structural and functional basis of VLDLR usage by Eastern equine encephalitis virus.

The very-low-density lipoprotein receptor (VLDLR) comprises eight LDLR type A (LA) domains and supports entry of distantly related alphaviruses, including Eastern equine encephalitis virus (EEEV) and Semliki Forest virus (SFV). Here, by resolving multiple cryo-electron microscopy structures of EEEV-VLDLR complexes and performing mutagenesis and functional studies, we show that EEEV uses multiple sites (E1/E2 cleft and E2 A domain) to engage more than one LA domain simultaneously. However, no single LA domain is necessary or sufficient to support efficient EEEV infection. Whereas all EEEV strains show conservation of two VLDLR-binding sites, the EEEV PE-6 strain and a few other EEE complex members feature a single amino acid substitution that enables binding of LA domains to an additional site on the E2 B domain. These structural and functional analyses informed the design of a minimal VLDLR decoy receptor that neutralizes EEEV infection and protects mice from lethal challenge.

Time-series reconstruction of the molecular architecture of human centriole assembly.

Centriole biogenesis, as in most organelle assemblies, involves the sequential recruitment of sub-structural elements that will support its function. To uncover this process, we correlated the spatial location of 24 centriolar proteins with structural features using expansion microscopy. A time-series reconstruction of protein distributions throughout human procentriole assembly unveiled the molecular architecture of the centriole biogenesis steps. We found that the process initiates with the formation of a naked cartwheel devoid of microtubules. Next, the bloom phase progresses with microtubule blade assembly, concomitantly with radial separation and rapid cartwheel growth. In the subsequent elongation phase, the tubulin backbone grows linearly with the recruitment of the A-C linker, followed by proteins of the inner scaffold (IS). By following six structural modules, we modeled 4D assembly of the human centriole. Collectively, this work provides a framework to investigate the spatial and temporal assembly of large macromolecules.

Analysis of 3D pathology samples using weakly supervised AI.

Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features. Recurrence risk-stratification models were trained on prostate cancer specimens imaged with open-top light-sheet microscopy or microcomputed tomography. By comprehensively capturing 3D morphologies, 3D volume-based prognostication achieves superior performance to traditional 2D slice-based approaches, including clinical/histopathological baselines from six certified genitourinary pathologists. Incorporating greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, further emphasizing the value of capturing larger extents of heterogeneous morphology.

Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster.

High-resolution electron microscopy of nervous systems has enabled the reconstruction of synaptic connectomes. However, we do not know the synaptic sign for each connection (i.e., whether a connection is excitatory or inhibitory), which is implied by the released transmitter. We demonstrate that artificial neural networks can predict transmitter types for presynapses from electron micrographs: a network trained to predict six transmitters (acetylcholine, glutamate, GABA, serotonin, dopamine, octopamine) achieves an accuracy of 87% for individual synapses, 94% for neurons, and 91% for known cell types across a D. melanogaster whole brain. We visualize the ultrastructural features used for prediction, discovering subtle but significant differences between transmitter phenotypes. We also analyze transmitter distributions across the brain and find that neurons that develop together largely express only one fast-acting transmitter (acetylcholine, glutamate, or GABA). We hope that our publicly available predictions act as an accelerant for neuroscientific hypothesis generation for the fly.

Machine learning interpretable models of cell mechanics from protein images.

Cellular form and function emerge from complex mechanochemical systems within the cytoplasm. Currently, no systematic strategy exists to infer large-scale physical properties of a cell from its molecular components. This is an obstacle to understanding processes such as cell adhesion and migration. Here, we develop a data-driven modeling pipeline to learn the mechanical behavior of adherent cells. We first train neural networks to predict cellular forces from images of cytoskeletal proteins. Strikingly, experimental images of a single focal adhesion (FA) protein, such as zyxin, are sufficient to predict forces and can generalize to unseen biological regimes. Using this observation, we develop two approaches-one constrained by physics and the other agnostic-to construct data-driven continuum models of cellular forces. Both reveal how cellular forces are encoded by two distinct length scales. Beyond adherent cell mechanics, our work serves as a case study for integrating neural networks into predictive models for cell biology.

Molecular mechanism of distinct chemokine engagement and functional divergence of the human Duffy antigen receptor.

The Duffy antigen receptor is a seven-transmembrane (7TM) protein expressed primarily at the surface of red blood cells and displays strikingly promiscuous binding to multiple inflammatory and homeostatic chemokines. It serves as the basis of the Duffy blood group system in humans and also acts as the primary attachment site for malarial parasite Plasmodium vivax and pore-forming toxins secreted by Staphylococcus aureus. Here, we comprehensively profile transducer coupling of this receptor, discover potential non-canonical signaling pathways, and determine the cryoelectron microscopy (cryo-EM) structure in complex with the chemokine CCL7. The structure reveals a distinct binding mode of chemokines, as reflected by relatively superficial binding and a partially formed orthosteric binding pocket. We also observe a dramatic shortening of TM5 and 6 on the intracellular side, which precludes the formation of the docking site for canonical signal transducers, thereby providing a possible explanation for the distinct pharmacological and functional phenotype of this receptor.

Spatial proteomics in neurons at single-protein resolution.

To understand biological processes, it is necessary to reveal the molecular heterogeneity of cells by gaining access to the location and interaction of all biomolecules. Significant advances were achieved by super-resolution microscopy, but such methods are still far from reaching the multiplexing capacity of proteomics. Here, we introduce secondary label-based unlimited multiplexed DNA-PAINT (SUM-PAINT), a high-throughput imaging method that is capable of achieving virtually unlimited multiplexing at better than 15 nm resolution. Using SUM-PAINT, we generated 30-plex single-molecule resolved datasets in neurons and adapted omics-inspired analysis for data exploration. This allowed us to reveal the complexity of synaptic heterogeneity, leading to the discovery of a distinct synapse type. We not only provide a resource for researchers, but also an integrated acquisition and analysis workflow for comprehensive spatial proteomics at single-protein resolution.