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2451 Janelia Publications
Showing 81-90 of 2451 resultsThe reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. We present a computational pipeline for aligning ssEM images with several key elements. Self-supervised convolutional nets are trained via metric learning to encode and align image pairs, and they are used to initialize iterative fine-tuning of alignment. A procedure called vector voting increases robustness to image artifacts or missing image data. For speedup the series is divided into blocks that are distributed to computational workers for alignment. The blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time-consuming global optimization. We apply our pipeline to a whole fly brain dataset, and show improved accuracy relative to prior state of the art. We also demonstrate that our pipeline scales to a cubic millimeter of mouse visual cortex. Our pipeline is publicly available through two open source Python packages.
Light sheet microscopy is a powerful technique for visualizing dynamic biological processes in 3D. Studying large specimens or recording time series with high spatial and temporal resolution generates large datasets, often exceeding terabytes and potentially reaching petabytes in size. Handling these massive datasets is challenging for conventional data processing tools with their memory and performance limitations. To overcome these issues, we developed LLSM5DTools, a software solution specifically designed for the efficient management of petabyte-scale light sheet microscopy data. This toolkit, optimized for memory and per-formance, features fast image readers and writers, efficient geometric transformations, high-performance Richardson-Lucy deconvolution, and scalable Zarr-based stitching. These advancements enable LLSM5DTools to perform over ten times faster than current state-of-the-art methods, facilitating real-time processing of large datasets and opening new avenues for biological discoveries in large-scale imaging experiments.
Chemigenetic tags are versatile labels for fluorescence microscopy that combine some of the advantages of genetically encoded tags with small molecule fluorophores. The Fluorescence Activating and absorbance Shifting Tags (FASTs) bind a series of highly fluorogenic and cell-permeable chromophores. Furthermore, FASTs can be used in complementation-based systems for detecting or inducing protein-protein interactions, depending on the exact FAST protein variant chosen. In this study, we systematically explore substitution patterns on FAST fluorogens and generate a series of fluorogens that bind to FAST variants, thereby activating their fluorescence. This effort led to the discovery of a novel fluorogen with superior properties, as well as a fluorogen that transforms splitFAST systems into a fluorogenic dimerizer, eliminating the need for additional protein engineering.
Mood-altering compounds hold promise for the treatment of many psychiatric disorders, such as depression, but connecting their molecular, circuit, and behavioral effects has been challenging. Here we find that, analogous to effects in rodent learned helplessness models, ketamine pre-exposure persistently suppresses futility-induced passivity in larval zebrafish. While antidepressants are thought to primarily act on neurons, brain-wide imaging in behaving zebrafish showed that ketamine elevates intracellular calcium in astroglia for many minutes, followed by persistent calcium downregulation post-washout. Calcium elevation depends on astroglial α1-adrenergic receptors and is required for suppression of passivity. Chemo-/optogenetic perturbations of noradrenergic neurons and astroglia demonstrate that the aftereffects of glial calcium elevation are sufficient to suppress passivity by inhibiting neuronal-astroglial integration of behavioral futility. Imaging in mouse cortex reveals that ketamine elevates astroglial calcium through conserved pathways, suggesting that ketamine exerts its behavioral effects by persistently modulating evolutionarily ancient neuromodulatory systems spanning neurons and astroglia.
Fluorescence microscopy is an invaluable tool in biology, yet its performance is compromised when the wavefront of light is distorted due to optical imperfections or the refractile nature of the sample. Such optical aberrations can dramatically lower the information content of images by degrading image contrast, resolution, and signal. Adaptive optics (AO) methods can sense and subsequently cancel the aberrated wavefront, but are too complex, inefficient, slow, or expensive for routine adoption by most labs. Here we introduce a rapid, sensitive, and robust wavefront sensing scheme based on phase diversity, a method successfully deployed in astronomy but underused in microscopy. Our method enables accurate wavefront sensing to less than λ/35 root mean square (RMS) error with few measurements, and AO with no additional hardware besides a corrective element. After validating the method with simulations, we demonstrate calibration of a deformable mirror > 100-fold faster than comparable methods (corresponding to wavefront sensing on the ~100 ms scale), and sensing and subsequent correction of severe aberrations (RMS wavefront distortion exceeding λ/2), restoring diffraction-limited imaging on extended biological samples.
Environmental influences on immune phenotypes are well-documented, but our understanding of which elements of the environment affect immune systems, and how, remains vague. Behaviors, including socializing with others, are central to an individual's interaction with its environment. We therefore tracked behavior of rewilded laboratory mice of three inbred strains in outdoor enclosures and examined contributions of behavior, including associations measured from spatiotemporal co-occurrences, to immune phenotypes. We found extensive variation in individual and social behavior among and within mouse strains upon rewilding. In addition, we found that the more associated two individuals were, the more similar their immune phenotypes were. Spatiotemporal association was particularly predictive of similar memory T and B cell profiles and was more influential than sibling relationships or shared infection status. These results highlight the importance of shared spatiotemporal activity patterns and/or social networks for immune phenotype and suggest potential immunological correlates of social life.
Understanding learning through synaptic plasticity rules in the brain is a grand challenge for neuroscience. Here we introduce a novel computational framework for inferring plasticity rules from experimental data on neural activity trajectories and behavioral learning dynamics. Our methodology parameterizes the plasticity function to provide theoretical interpretability and facilitate gradient-based optimization. For instance, we use Taylor series expansions or multilayer perceptrons to approximate plasticity rules, and we adjust their parameters via gradient descent over entire trajectories to closely match observed neural activity and behavioral data. Notably, our approach can learn intricate rules that induce long nonlinear time-dependencies, such as those incorporating postsynaptic activity and current synaptic weights. We validate our method through simulations, accurately recovering established rules, like Oja’s, as well as more complex hypothetical rules incorporating reward-modulated terms. We assess the resilience of our technique to noise and, as a tangible application, apply it to behavioral data from Drosophila during a probabilistic reward-learning experiment. Remarkably, we identify an active forgetting component of reward learning in flies that enhances the predictive accuracy of previous models. Overall, our modeling framework provides an exciting new avenue to elucidate the computational principles governing synaptic plasticity and learning in the brain.
Primary cilia on granule cell neuron progenitors in the developing cerebellum detect sonic hedgehog to facilitate proliferation. Following differentiation, cerebellar granule cells become the most abundant neuronal cell type in the brain. While essential during early developmental stages, the fate of granule cell cilia is unknown. Here, we provide nanoscopic resolution of ciliary dynamics by studying developmental changes in granule cell cilia using large-scale electron microscopy volumes and immunostaining of mouse cerebella. We found that many granule cell primary cilia were intracellular and concealed from the external environment. Cilia were disassembed in differentiating granule cell neurons in a process we call cilia deconstruction that was distinct from pre-mitotic cilia resorption in proliferating progenitors. In differentiating granule cells, ciliary loss involved unique disassembly intermediates, and, as maturation progressed, mother centriolar docking at the plasma membrane. Cilia did not reform from the docked centrioles, rather, in adult mice granule cell neurons remained unciliated. Many neurons in other brain regions require cilia to regulate function and connectivity. In contrast, our results show that granule cell progenitors had concealed cilia that underwent deconstruction potentially to prevent mitogenic hedgehog responsiveness. The ciliary deconstruction mechanism we describe could be paradigmatic of cilia removal during differentiation in other tissues.
Artificial activation of anatomically localized, genetically defined hypothalamic neuron populations is known to trigger distinct innate behaviors, suggesting a hypothalamic nucleus-centered organization of behavior control. To assess whether the encoding of behavior is similarly anatomically confined, we performed simultaneous neuron recordings across twenty hypothalamic regions in freely moving animals. Here we show that distinct but anatomically distributed neuron ensembles encode the social and fear behavior classes, primarily through mixed selectivity. While behavior class-encoding ensembles were spatially distributed, individual ensembles exhibited strong localization bias. Encoding models identified that behavior actions, but not motion-related variables, explained a large fraction of hypothalamic neuron activity variance. These results identify unexpected complexity in the hypothalamic encoding of instincts and provide a foundation for understanding the role of distributed neural representations in the expression of behaviors driven by hardwired circuits.