Matriglycan is a linear glycan (xylose-β1,3-glucuronate), which binds proteins in the extracellular matrix that contain laminin-globular domains and Lassa Fever Virus. It is indispensable for neuromuscular function. Matriglycan of insufficient length can cause muscular dystrophy with abnormal brain and eye development. LARGE1 (Like-acetylglucosaminyltransferase-1) uniquely synthesizes matriglycan on dystroglycan. The mechanism of matriglycan synthesis is not obvious from cryo-EM reconstructions of LARGE1. However, by reconstituting activity in vitro on recombinant prodystroglycan we show that the presence of the dystroglycan N-terminal domain (DGN), phosphorylated core M3, and a xylose-glucuronate primer are necessary for matriglycan polymerization by LARGE1. By introducing active site mutations, we demonstrate that LARGE1 processively polymerizes matriglycan on prodystroglycan, with its length regulated by the dystroglycan prodomain, DGN. Our enzymatic analysis of LARGE1 uncovers the mechanism of matriglycan synthesis on dystroglycan, which can form the basis for therapeutic strategies to treat matriglycan-deficient neuromuscular disorders and arenaviral infections.

Sex differences in behaviour exist across the animal kingdom, typically under strong genetic regulation. In Drosophila, previous work has shown that fruitless and doublesex transcription factors identify neurons driving sexually dimorphic behaviour. However, the organisation of dimorphic neurons into functional circuits remains unclear.We now present the connectome of the entire Drosophila male central nervous system. This contains 166,691 neurons spanning the brain and ventral nerve cord, fully proofread and comprehensively annotated including fruitless and doublesex expression and 11,691 cell types. By comparison with a previous female brain connectome, we provide the first comprehensive description of the differences between male and female brains to synaptic resolution. Of 7,319 cross-matched cell types in the central brain, 114 are dimorphic with an additional 262 male- and 69 female-specific (totalling 4.8% of neurons in males and 2.4% in females).This resource enables analysis of full sensory-to-motor circuits underlying complex behaviours as well as the impact of dimorphic elements. Sex-specific and dimorphic neurons are concentrated in higher brain centres while the sensory and motor periphery are largely isomorphic. Within higher centres, male-specific connections are organised into hotspots defined by male-specific neurons or the presence of male-specific arbours on neurons that are otherwise similar between sexes. Numerous circuit switches reroute sensory information to form conserved, antagonistic circuits controlling opposing behaviours.

The study of foraging is central to a renewed interest in naturalistic behavior in neuroscience. Applying a foraging framework grounded in behavioral ecology has enabled probing of the mechanisms underlying cognitive processes such as decision-making within a more ecological context. Yet, foraging also involves myriad other aspects, including navigation of complex environments, sensory processing, and social interactions. Here, we first provide a brief overview of the neuroscience of foraging decisions, and then combine insights from behavioral ecology and neuroscience to review the role of these additional dimensions of foraging. We conclude by highlighting four opportunities for the continued development of foraging as an ethological framework for neuroscience: integrating normative and implementation-level models, developing new tools, enabling cross-species comparisons, and fostering interdisciplinary collaboration.

BACKGROUND: Kidney epithelial cells perform complex vectorial fluid and solute transport at high volumes and rapid rates. Their structural organization both reflects and enables these sophisticated physiological functions. However, our understanding of the nanoscale spatial organization and intracellular ultrastructure that underlies these crucial cellular functions remains limited.

METHODS: To address this knowledge gap, we generated and reconstructed an extensive electron microscopic dataset of renal proximal tubule (PT) epithelial cells at isotropic resolutions down to 4nm. We employed artificial intelligence-based segmentation tools to identify, trace, and measure all major subcellular components. We complemented this analysis with immunofluorescence microscopy to connect subcellular architecture to biochemical function.

RESULTS: Our ultrastructural analysis revealed complex organization of membrane-bound compartments in proximal tubule cells. The apical endocytic system featured deep invaginations connected to an anastomosing meshwork of dense apical tubules, rather than discrete structures. The endoplasmic reticulum displayed distinct structural domains: fenestrated sheets in the basolateral region and smaller, disconnected clusters in the subapical region. We identified, quantified, and visualized membrane contact sites between endoplasmic reticulum, plasma membrane, mitochondria, and apical endocytic compartments. Immunofluorescence microscopy demonstrated distinct localization patterns for endoplasmic reticulum resident proteins at mitochondrial and plasma membrane interfaces.

CONCLUSIONS: This study provides novel insights into proximal tubule cell organization, revealing specialized compartmentalization and unexpected connections between membrane-bound organelles. We identified previously uncharacterized structures, including mitochondria-plasma membrane bridges and an interconnected endocytic meshwork, suggesting mechanisms for efficient energy distribution, cargo processing and structural support. Morphological differences between 4nm and 8nm datasets indicate subsegment-specific specializations within the proximal tubule. This comprehensive open-source dataset provides a foundation for understanding how subcellular architecture supports specialized epithelial function in health and disease.

Whether recovering after a gust of wind, or rapidly saccading away from an oncoming predator, fruit flies show remarkable aerial dexterity about their body roll axis. Here, we investigated the detailed wing kinematic changes during free-flight roll motion and probed the neuromuscular basis for such changes. Consistent with previous work, we observed that flies manipulated the stroke amplitude difference between their wings to control their roll angle. Here, we show that flies are capable of achieving such changes by altering the stroke amplitude of either or both of their wings. Further we found that during corrections flies can also take advantage of an aerodynamically significant change in the angle of attack of their uppermost wing. Curiously, these corrective wing changes cannot be eliminated when motor neurons hypothesized to be used during roll maneuvers (i1, i2, b1, b2, and b3) are individually inhibited. However, free-flight optogenetic manipulations and quasi-steady aerodynamic calculations show that each of these motor neurons individually can effect kinematic changes consistent with a roll correction. Combining this evidence with an analysis of haltere inputs found in the BANC connectome, we propose that the observed robustness could be the result of two sets of muscular redundancies that receive shared inputs from haltere sensory afferents: one set, containing b1 and b2, is able to increase the stroke amplitude of the lower wing; while the other set, containing i1, i2, and b3, is able to decrease the stroke amplitude and wing pitch angle of the upper wing. Because of the redundancy in the input sensory information and output wing motion in the muscles in each cluster, the fly is able to perform roll stability maneuvers even when one of the constituent motor neurons is inhibited. This framework proposes new ways fast aerial maneuverability can be implemented when dealing with the fly’s most unstable rotational degree of freedom.

High-resolution tissue imaging is often compromised by sample-induced optical aberrations that degrade resolution and contrast. Although wavefront sensor-based adaptive optics (AO) can measure these aberrations, such hardware solutions are typically complex, expensive to implement and slow when serially mapping spatially varying aberrations across large fields of view. Here we introduce AOVIFT (adaptive optical vision Fourier transformer)-a machine learning-based aberration sensing framework built around a three-dimensional multistage vision transformer that operates on Fourier domain embeddings. AOVIFT infers aberrations and restores diffraction-limited performance in puncta-labeled specimens with substantially reduced computational cost, training time and memory footprint compared to conventional architectures or real-space networks. We validated AOVIFT on live gene-edited zebrafish embryos, demonstrating its ability to correct spatially varying aberrations using either a deformable mirror or postacquisition deconvolution. By eliminating the need for the guide star and wavefront sensing hardware and simplifying the experimental workflow, AOVIFT lowers technical barriers for high-resolution volumetric microscopy across diverse biological samples.

Preprint: https://doi.org/10.48550/arXiv.2503.12593

Covalent inhibitors are an emerging class of therapeutics, but methods to comprehensively profile their binding kinetics and selectivity across the proteome have been limited. Here we introduce COOKIE-Pro (COvalent Occupancy KInetic Enrichment via Proteomics), an unbiased method for quantifying irreversible covalent inhibitor binding kinetics on a proteome-wide scale. COOKIE-Pro uses a two-step incubation process with mass spectrometry-based proteomics to determine k and K values for covalent inhibitors against both on-target and off-target proteins. We validated COOKIE-Pro using BTK inhibitors spebrutinib and ibrutinib, accurately reproducing known kinetic parameters and identifying both expected and unreported off-targets. The method revealed that spebrutinib has over 10-fold higher potency for TEC kinase compared to its intended target BTK. To demonstrate the method's utility for high-throughput screening, we applied a streamlined two-point strategy to a library of 16 covalent fragments. This approach successfully generated thousands of kinetic profiles, enabling the quantitative decoupling of intrinsic chemical reactivity from binding affinity at scale and validating the method's broad applicability. By providing a comprehensive view of covalent inhibitor binding across the proteome, COOKIE-Pro represents a powerful tool for optimizing the potency and selectivity of covalent drugs during preclinical development.

bioRxiv preprint: https://doi.org/10.1101/2025.06.19.660637

Complete synaptic wiring diagrams, or connectomes, of whole brains open new opportunities for studying the structure-function relationship of neural circuits. However, the large number of nodes and edges in the graphs makes the analysis challenging. Here, we present the Connectome Interpreter (https://github.com/YijieYin/connectome_interpreter), an open-source software toolkit for efficient graph traversal to find polysynaptic pathways, compute the effective connectivity and receptive fields for arbitrarily deep neurons, slice out subcircuits, and non-linear but differentiable circuit modeling, implemented using efficient approaches tailored to the high density and size of connectomes such as that of the fruit fly Drosophila melanogaster. Our approach delivers results orders of magnitude faster than conventional methods in consumer computer hardware. We demonstrate the capabilities of our toolkit with select applications, including quantifying the density of polysynaptic connections in the whole adult fruit fly brain, exploring the necessity for non-linearities in circuit modeling, and combining known function of neurons with the connectome to aid in formulating hypotheses of circuit function.

Endoplasmic reticulum exit sites (ERES) are specialized, ribosome-free ER subdomains that serve as dynamic portals for COPII-mediated export of proteins from the ER. Beyond their role in the secretory pathway, ERES are implicated in diverse processes, including autophagy and the maturation of lipid droplets, highlighting their functional plasticity. ERES integrate cargo load, membrane tension and spatial cues to remodel their architecture and function in real time. This Roadmap synthesizes our current knowledge on the biogenesis, structural diversity and regulatory logic of ERES. We highlight key unanswered questions in the field, particularly concerning how ERES integrate signals to coordinate protein trafficking under varying cellular states. Finally, we propose a multidisciplinary framework - leveraging advances in high-resolution imaging, synthetic reconstitution and computational modelling - to delineate the principles governing the function and plasticity of ERES. Understanding these mechanisms holds significant potential for developing targeted therapeutic strategies in diseases linked to trafficking dysfunction.

Mapping nanoscale neuronal morphology with molecular annotations is critical for understanding healthy and dysfunctional brain circuits. Current methods are constrained by image segmentation errors and by sample defects (e.g., signal gaps, section loss). Genetic strategies promise to overcome these challenges by using easily distinguishable cell identity labels. However, multicolor approaches are spectrally limited in diversity, whereas nucleic acid barcoding lacks a cell-filling morphology signal for segmentation. Here, we introduce PRISM (Protein-barcode Reconstruction via Iterative Staining with Molecular annotations), a platform that integrates combinatorial delivery of antigenically distinct, cell-filling proteins with tissue expansion, multi-cycle imaging, barcode-augmented reconstruction, and molecular annotation. Protein barcodes increase label diversity by >750-fold over multicolor labeling and enable morphology reconstruction with intrinsic error correction. We acquired a \~10 million μm3 volume of mouse hippocampal area CA2/3, multiplexed across 23 barcode antigen and synaptic marker channels. By combining barcodes with shape information we achieve an 8x increase in automatic tracing accuracy of genetically labelled neurons. We demonstrate PRISM supports automatic proofreading across micron-scale spatial gaps and reconnects neurites across discontinuities spanning hundreds of microns. Using PRISM’s molecular annotation capability, we map the distribution of synapses onto traced neural morphology, characterizing challenging synaptic structures such as thorny excrescences (TEs), and discovering a size correlation among spatially proximal TEs on the same dendrite. PRISM thus supports self-correcting neuron reconstruction with molecular context.