Fluorescence microscopy is a powerful tool for studying biomolecules in their native environments, offering high spatio-temporal resolution but requiring fluorescent labels. Current live-cell compatible labeling strategies repurpose natural systems, such as fluorescent proteins or proteins that bind fluorescent ligands. While advances have been made to engineer natural proteins into labels with minimal size, high brightness, as well as enhanced thermo- and photostability, these approaches often require trade-offs among desirable properties due to the inherent limitations of re-engineering natural proteins. In this work, we present rhodamine binder (Rhobin) tags—de novo-designed proteins that bind rhodamine-derived fluorophores. Unlike traditional approaches, Rhobin tags were developed by directly incorporating desirable features during the computational design process, which resulted in compact binders with outstanding thermostability. Their nanomolar substrate affinity, rapid labeling kinetics, and orthogonality to established labeling systems such as HaloTag and SNAP-tag facilitate versatile live and fixed-cell imaging of diverse subcellular targets in mammalian cells. Transient fluorophore binding further enables advanced imaging techniques, including live-cell super-resolution STED microscopy with reduced photobleaching and single-molecule localization microscopy in live and fixed cells. To the stability of Rhobin tags under extreme environmental conditions, we demonstrate showcase protein tagging and timelapse imaging in the extremophile Sulfolobus acidocaldarius living at 75°C, an application previously inaccessible with existing tags. We anticipate that Rhobin tags will become a central component in the toolbox of fluorescent labels and will pave the way for a new generation of modular protein tags and biosensors with tailor-made properties.

Potassium (K+) channels play a vital role in helping fungal pathogens like Candida albicans adapt to hostile environments within the human body, including during infection. Among these, two-pore domain, outwardly rectifying K+ (TOK) channels, unique to fungi, have remained insufficiently characterized, despite evidence that they support processes in fungi, such as ion homeostasis, growth, and virulence, highlighting their potential as antifungal targets. Here, we present the cryo-EM structure of C. albicans TOK1 (CaTOK), the first resolved structure of a TOK channel and of any fungal K+ channel. CaTOK reveals an eight-transmembrane helical fold unlike any other K+ channel structure determined to date, including a domain formed by helices S1-S4 with unexpected structural homology to channel auxiliary subunits critical for human neuronal signaling. Additionally, a unique cytosolic C-terminal domain interfaces with pore-lining helices, suggesting a role in channel regulation. These findings uncover previously unrecognized structural elements that broaden our understanding of K+channel diversity and regulation and provide initial clues into the structural basis for the unique functional attributes of the TOK family.

To form a blood clot, fibrinogen is converted into fibrin through the action of the enzyme thrombin. Fibrin then polymerizes longitudinally and laterally as it matures into a fiber. Polymerization results in a dense, 3-dimensional branched network. Previous research has shown the relevance of these fibrin gel structures in hemostatic conditions; however, the mechanism by which they form has not been fully resolved. Using light sheet microscopy, 3-dimensional volumes of the fibrin polymerization process were captured. Manual annotation of these microscopy videos revealed that fiber branch points occur through the collision and the binding of diffusing fibers rather than through the splitting of growing fiber termini. However, the density of fibers and amount of data greatly slows manual annotation-based analysis and limits the ability to capture important data, such as growth rates and fiber stiffness. To more quickly process these data, a computational approach was utilized. A custom tracking pipeline, suited to the networks formed by cylindrical fibrin fibers, was developed, beginning with an AI-based classifier. This custom pipeline allowed for the tracking of uniquely labeled fibers over time. Automated merge detection between linking phases further improved accuracy. Additionally, network formation was analyzed through skeletonization techniques to measure the number of branches per junction over time. Combining the skeletonization and tracking methods, single fibers were identified by their lack of branch points and tracked. The addition of branch points to previously tracked objects served as a signal for merge detection. This approach yielded measurements of single fibrin fiber diffusion rates, as well as the first volumetric and length growth rates of fibers throughout polymerization. In addition, the gel point was quantified by analyzing the span of connected objects to characterize the network consolidation over time at the level of single fibers.

Local field potentials (LFPs) reflect coordination among neural populations, yet their exact relationship to neural computation remains unknown. One exception is the theta rhythm of the rodent hippocampus, which organizes sequential firing among place cells, enabling spike timing to track the animal's path through its environment. But when the animal stops, the theta rhythm becomes irregular, which is assumed to disrupt its ability to carry spatial information. Here we challenge this assumption by developing an artificial neural network that discovers position-tuned theta rhythms (pThetas) from LFPs even in the absence of strong theta oscillations. Using recordings from male rats, we provide evidence that pTheta is distinct from the dominant theta rhythm, while reflecting rhythmic coordination among place cell populations. Our work suggests that weak and intermittent oscillations, as seen in many brain regions and species, can convey information commensurate with population spike codes when decoded using information-based rather than variance-based principles.

Multiplexed protein imaging enables spatially resolved analysis of molecular organization in tissues, but existing spatial proteomics platforms remain constrained in scalability, throughput, and integration with RNA measurements and interpretable computational analysis. Here, we present an integrated spatial omics framework that combines highly multiplexed protein and RNA imaging with explainable machine learning to map cell-type-specific molecular and structural architectures at tissue scale. Using this platform, we simultaneously profiled up to 46 proteins and 79 RNA species across \~370,000 cells in intact mouse brain tissue at diffraction-limited subcellular resolution (\~260 nm). We developed a scalable, open-source computational pipeline for large-scale image processing and analysis, and show that nuclear protein and chromatin features alone are sufficient to accurately classify brain cell types and their spatial organization. Incorporation of explainable deep learning further enabled identification of human-interpretable, cell-type-specific subnuclear structural features directly from imaging data, with independent quantitative validation. Together, this integrated experimental and computational framework enables tissue-scale spatial proteomics-based cell-type classification and structural feature discovery, providing a broadly applicable platform for mechanistic studies, high-content screening, and translational applications.

Coordinated lateralized movements are critical for natural orienting behaviors, but their neural bases remain poorly understood. The deep superior colliculus (dSC) integrates a wide range of inputs to select targets for orienting movements and coordinates downstream activity to initiate and execute movement. The substantia nigra pars reticulata (SNr) is thought to disinhibit dSC to facilitate movement, but much remains unknown about the relationship between SNr activity, dSC activity, and movement. We recorded from both regions using high-density probes in head-fixed mice performing directional orienting tasks. We found that dSC and SNr activity reflected task variables preceding and throughout movement. However, the direction-dependence of dSC activity was weaker than in other orienting behaviors, and the relationship between movement-related dSC and SNr activity was inconsistent with disinhibition of dSC determining the initiation or direction of movement. Analyses of similar data curated by the International Brain Laboratory yielded consistent results. These findings suggest diverse roles for modulatory input from SNr to dSC in shaping motor behavior.

Many animals possess mechanosensory neurons that fire when a limb nears the limit of its physical range, but the function of these proprioceptive limit detectors remains poorly understood. Here, we investigate a class of proprioceptors on the Drosophila leg called hair plates. Using calcium imaging in behaving flies, we find that a hair plate on the fly coxa (CxHP8) detects the limits of anterior leg movement. By reconstructing CxHP8 axons in an electron microscopy dataset, we found that they are wired to excite posterior leg movement and inhibit anterior leg movement. Consistent with this connectivity, optogenetic activation of CxHP8 neurons elicited posterior postural reflexes, while silencing altered the swing-to-stance transition during walking. Finally, we use comprehensive reconstruction of peripheral morphology and downstream connectivity to predict the function of other hair plates distributed across the fly leg. Our results suggest that each hair plate is specialized to control specific sensorimotor reflexes that are matched to the joint limit it detects. They also illustrate the feasibility of predicting sensorimotor reflexes from a connectome with identified proprioceptive inputs and motor outputs.

Graph neural networks trained to predict observable dynamics can be used to decompose the temporal activity of complex heterogeneous systems into simple, interpretable representations. Here we apply this framework to simulated neural assemblies with thousands of neurons and demonstrate that it can jointly reveal the connectivity matrix, the neuron types, the signaling functions, and in some cases hidden external stimuli. In contrast to existing machine learning approaches such as recurrent neural networks and transformers, which emphasize predictive accuracy but offer limited interpretability, our method provides both reliable forecasts of neural activity and interpretable decomposition of the mechanisms governing large neural assemblies.

From insects to mammals, essential brain functions, such as forming long-term memories (LTMs), increase metabolic activity in stimulated neurons to meet the energetic demand associated with brain activation. However, while impairing neuronal metabolism limits brain performance, whether expanding the metabolic capacity of neurons boosts brain function remains poorly understood. Here, we show that LTM formation of flies and mice can be enhanced by increasing mitochondrial metabolism in central memory circuits. By knocking down the mitochondrial Ca exporter Letm1, we favour Ca retention in the mitochondrial matrix of neurons due to reduction of mitochondrial H/Ca exchange. The resulting increase in mitochondrial Ca over-activates mitochondrial metabolism in neurons of central memory circuits, leading to improved LTM storage in training paradigms in which wild-type counterparts of both species fail to remember. Our findings unveil an evolutionarily conserved mechanism that controls mitochondrial metabolism in neurons and indicate its involvement in shaping higher brain functions, such as LTM.

The brain’s capabilities rely on both the molecular properties of individual cells and their interactions across brain-wide networks. However, relating gene expression to activity in individual neurons across the entire brain remains elusive. Here we developed an experimental-computational platform, WARP, for whole-brain imaging of neuronal activity during behavior, expansion-assisted spatial transcriptomics, and cellular-level registration of these two modalities. Through joint analysis of whole-brain neuronal activity during multiple behaviors, cellular gene expression, and anatomy, we identified functions of molecularly defined populations — including luminance coding in a cckb-pou4f2 midbrain population and task-structured activity in pvalb7-eomesa hippocampal-like neurons — and defined over 2,000 other function-gene-anatomy subpopulations. Analysis of this unprecedented multimodal dataset also revealed that most gene-matched neurons showed stronger activity correlations, highlighting a brain-wide role for gene expression in functional organization. WARP establishes a foundational platform and open-access dataset for cross-experiment discovery, high-throughput function-to-gene mapping, unification of cell biology and systems neuroscience, and scalable circuit modeling at the whole-brain scale.