Nitrate (NO3-) uptake and distribution are critical to plant life. Although the upstream regulation of nitrate uptake and downstream responses to nitrate in a variety of cells have been well-studied, it is still not possible to directly visualize the spatial and temporal distribution of nitrate with high resolution at the cellular level. Here, we report a nuclear-localized, genetically encoded biosensor, nlsNitraMeter3.0, for the quantitative visualization of nitrate distribution in Arabidopsis thaliana. The biosensor tracked the spatiotemporal distribution of nitrate along the primary root axis and disruptions by genetic mutation of transport (low nitrate uptake) and assimilation (high nitrate accumulation). The developed biosensor effectively monitors nitrate concentrations at cellular level in real time and spatiotemporal changes during the plant life cycle.

Advances in microscopy hold great promise for allowing quantitative and precise measurement of morphological and molecular phenomena at the single-cell level in bacteria; however, the potential of this approach is ultimately limited by the availability of methods to faithfully segment cells independent of their morphological or optical characteristics. Here, we present Omnipose, a deep neural network image-segmentation algorithm. Unique network outputs such as the gradient of the distance field allow Omnipose to accurately segment cells on which current algorithms, including its predecessor, Cellpose, produce errors. We show that Omnipose achieves unprecedented segmentation performance on mixed bacterial cultures, antibiotic-treated cells and cells of elongated or branched morphology. Furthermore, the benefits of Omnipose extend to non-bacterial subjects, varied imaging modalities and three-dimensional objects. Finally, we demonstrate the utility of Omnipose in the characterization of extreme morphological phenotypes that arise during interbacterial antagonism. Our results distinguish Omnipose as a powerful tool for characterizing diverse and arbitrarily shaped cell types from imaging data.

RNA-guided systems, such as CRISPR-Cas, combine programmable substrate recognition with enzymatic function, a combination that has been used advantageously to develop powerful molecular technologies. Structural studies of these systems have illuminated how the RNA and protein jointly recognize and cleave their substrates, guiding rational engineering for further technology development. Recent work identified a new class of RNA-guided systems, termed OMEGA, which include IscB, the likely ancestor of Cas9, and the nickase IsrB, a homologue of IscB lacking the HNH nuclease domain. IsrB consists of only around 350 amino acids, but its small size is counterbalanced by a relatively large RNA guide (roughly 300-nt ωRNA). Here, we report the cryogenic-electron microscopy structure of Desulfovirgula thermocuniculi IsrB (DtIsrB) in complex with its cognate ωRNA and a target DNA. We find the overall structure of the IsrB protein shares a common scaffold with Cas9. In contrast to Cas9, however, which uses a recognition (REC) lobe to facilitate target selection, IsrB relies on its ωRNA, part of which forms an intricate ternary structure positioned analogously to REC. Structural analyses of IsrB and its ωRNA as well as comparisons to other RNA-guided systems highlight the functional interplay between protein and RNA, advancing our understanding of the biology and evolution of these diverse systems.

Brains must represent the outside world so that animals survive and thrive. In early sensory systems, neural populations have diverse receptive fields structured to detect important features in inputs, yet significant variability has been ignored in classical models of sensory neurons. We model neuronal receptive fields as random, variable samples from parameterized distributions and demonstrate this model in two sensory modalities using data from insect mechanosensors and mammalian primary visual cortex. Our approach leads to a significant theoretical connection between the foundational concepts of receptive fields and random features, a leading theory for understanding artificial neural networks. The modeled neurons perform a randomized wavelet transform on inputs, which removes high frequency noise and boosts the signal. Further, these random feature neurons enable learning from fewer training samples and with smaller networks in artificial tasks. This structured random model of receptive fields provides a unifying, mathematically tractable framework to understand sensory encodings across both spatial and temporal domains.

The goal when imaging bioprocesses with optical microscopy is to acquire the most spatiotemporal information with the least invasiveness. Deep neural networks have substantially improved optical microscopy, including image super-resolution and restoration, but still have substantial potential for artifacts. In this study, we developed rationalized deep learning (rDL) for structured illumination microscopy and lattice light sheet microscopy (LLSM) by incorporating prior knowledge of illumination patterns and, thereby, rationally guiding the network to denoise raw images. Here we demonstrate that rDL structured illumination microscopy eliminates spectral bias-induced resolution degradation and reduces model uncertainty by five-fold, improving the super-resolution information by more than ten-fold over other computational approaches. Moreover, rDL applied to LLSM enables self-supervised training by using the spatial or temporal continuity of noisy data itself, yielding results similar to those of supervised methods. We demonstrate the utility of rDL by imaging the rapid kinetics of motile cilia, nucleolar protein condensation during light-sensitive mitosis and long-term interactions between membranous and membrane-less organelles.

Cell-surface proteins (CSPs) mediate intercellular communication throughout the lives of multicellular organisms. However, there are no generalizable methods for quantitative CSP profiling in specific cell types in vertebrate tissues. Here, we present in situ cell-surface proteome extraction by extracellular labeling (iPEEL), a proximity labeling method in mice that enables spatiotemporally precise labeling of cell-surface proteomes in a cell-type-specific environment in native tissues for discovery proteomics. Applying iPEEL to developing and mature cerebellar Purkinje cells revealed differential enrichment in CSPs with post-translational protein processing and synaptic functions in the developing and mature cell-surface proteomes, respectively. A proteome-instructed in vivo loss-of-function screen identified a critical, multifaceted role for Armh4 in Purkinje cell dendrite morphogenesis. Armh4 overexpression also disrupts dendrite morphogenesis; this effect requires its conserved cytoplasmic domain and is augmented by disrupting its endocytosis. Our results highlight the utility of CSP profiling in native mammalian tissues for identifying regulators of cell-surface signaling.

Sensory areas are spontaneously active in the absence of sensory stimuli. This spontaneous activity has long been studied; however, its functional role remains largely unknown. Recent advances in technology, allowing large-scale neural recordings in the awake and behaving animal, have transformed our understanding of spontaneous activity. Studies using these recordings have discovered high-dimensional spontaneous activity patterns, correlation between spontaneous activity and behavior, and dissimilarity between spontaneous and sensory-driven activity patterns. These findings are supported by evidence from developing animals, where a transition toward these characteristics is observed as the circuit matures, as well as by evidence from mature animals across species. These newly revealed characteristics call for the formulation of a new role for spontaneous activity in neural sensory computation.

The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience. We describe design and implementation principles for a language for neurophysiology data. Our open-source software (Neurodata Without Borders, NWB) defines and modularizes the interdependent, yet separable, components of a data language. We demonstrate NWB's impact through unified description of neurophysiology data across diverse modalities and species. NWB exists in an ecosystem, which includes data management, analysis, visualization, and archive tools. Thus, the NWB data language enables reproduction, interchange, and reuse of diverse neurophysiology data. More broadly, the design principles of NWB are generally applicable to enhance discovery across biology through data FAIRness.

Understanding molecular mechanisms of cellular pathways requires knowledge of the identities of participating proteins, their cellular localization and their 3D structures. Contemporary workflows typically require multiple techniques to identify target proteins, track their localization using fluorescence microscopy, followed by in vitro structure determination. To identify mammal-specific sperm proteins and understand their functions, we developed a visual proteomics workflow to directly address these challenges. Our in situ cryo-electron tomography and subtomogram averaging provided 6.0 Å resolution reconstructions of axonemal microtubules and their associated proteins. The well-resolved secondary and tertiary structures allowed us to computationally match, in an unbiased manner, novel densities in our 3D reconstruction maps with 21,615 AlphaFold2-predicted protein models of the mouse proteome. We identified Tektin 5, CCDC105 and SPACA9 as novel microtubule inner proteins that form an extensive network crosslinking the lumen of microtubule and existing proteins. Additional biochemical and mass spectrometry analyses helped validate potential candidates. The novel axonemal sperm structures identified by this approach form an extensive interaction network within the lumen of microtubules, suggesting they have a role in the mechanical and elastic properties of the microtubule filaments required for the vigorous beating motions of flagella.

Training spiking recurrent neural networks on neuronal recordings or behavioral tasks has become a prominent tool to study computations in the brain. With an increasing size and complexity of neural recordings, there is a need for fast algorithms that can scale to large datasets. We present optimized CPU and GPU implementations of the recursive least-squares algorithm in spiking neural networks. The GPU implementation allows training networks to reproduce neural activity of an order of millions neurons at order of magnitude times faster than the CPU implementation. We demonstrate this by applying our algorithm to reproduce the activity of > 66, 000 recorded neurons of a mouse performing a decision-making task. The fast implementation enables efficient training of large-scale spiking models, thus allowing for in-silico study of the dynamics and connectivity underlying multi-area computations.