Neuronal physiology depends on a neuron’s ion channel composition and unique morphology. Variable ion channel compositions can produce similar neuronal physiologies across animals. Less is known regarding the morphological precision required to produce reliable neuronal physiology. Theoretical studies suggest that moraphology is tightly tuned to minimize wiring and conduction delay of synaptic events. We utilize high-resolution confocal microscopy and custom computational tools to characterize the morphologies of four neuron types in the stomatogastric ganglion (STG) of the crab \textitCancer borealis. Macroscopic branching patterns and fine cable properties are variable within and across neuron types. We compare these neuronal structures to synthetic minimal spanning neurite trees constrained by a wiring cost equation and find that STG neurons do not adhere to prevailing hypotheses regarding wiring optimization principles. In this highly modulated and oscillating circuit, neuronal structures appear to be governed by a space-filling mechanism that outweighs the cost of inefficient wiring.

The conventional view of neurons is that synaptic inputs are integrated on a timescale of milliseconds to seconds in the dendrites, with action potential initiation occurring in the axon initial segment. We found a much slower form of integration that leads to action potential initiation in the distal axon, well beyond the initial segment. In a subset of rodent hippocampal and neocortical interneurons, hundreds of spikes, evoked over minutes, resulted in persistent firing that lasted for a similar duration. Although axonal action potential firing was required to trigger persistent firing, somatic depolarization was not. In paired recordings, persistent firing was not restricted to the stimulated neuron; it could also be produced in the unstimulated cell. Thus, these interneurons can slowly integrate spiking, share the output across a coupled network of axons and respond with persistent firing even in the absence of input to the soma or dendrites.

Glycans are one of the fundamental biopolymers encountered in living systems. Compared to polynucleotide and polypeptide biosynthesis, polysaccharide biosynthesis is a uniquely combinatorial process to which interdependent enzymes with seemingly broad specificities contribute. The resulting intracellular cell surface, and secreted glycans play key roles in health and disease, from embryogenesis to cancer progression. The study and modulation of glycans in cell and organismal biology is aided by small molecule inhibitors of the enzymes involved in glycan biosynthesis. In this review, we survey the arsenal of currently available inhibitors, focusing on agents which have been independently validated in diverse systems. We highlight the utility of these inhibitors and drawbacks to their use, emphasizing the need for innovation for basic research as well as for therapeutic applications.

Pixel and superpixel classifiers have become essential tools for EM segmentation algorithms. Training these classifiers remains a major bottleneck primarily due to the requirement of completely annotating the dataset which is tedious, error-prone and costly. In this paper, we propose an interactive learning scheme for the superpixel classifier for EM segmentation. Our algorithm is "active semi-supervised" because it requests the labels of a small number of examples from user and applies label propagation technique to generate these queries. Using only a small set (<20%) of all datapoints, the proposed algorithm consistently generates a classifier almost as accurate as that estimated from a complete groundtruth. We provide segmentation results on multiple datasets to show the strength of these classifiers.

Pixel and superpixel classifiers have become essential tools for EM segmentation algorithms. Training these classifiers remains a major bottleneck primarily due to the requirement of completely annotating the dataset which is tedious, error-prone and costly. In this paper, we propose an interactive learning scheme for the superpixel classifier for EM segmentation. Our algorithm is 'active semi-supervised' because it requests the labels of a small number of examples from user and applies label propagation technique to generate these queries. Using only a small set (< 20%) of all datapoints, the proposed algorithm consistently generates a classifier almost as accurate as that estimated from a complete groundtruth. We provide segmentation results on multiple datasets to show the strength of these classifiers.

Animals rely on visual motion for navigating the world, and research in flies has clarified how neural circuits extract information from moving visual scenes. However, the major pathways connecting these patterns of optic flow to behavior remain poorly understood. Using a high-throughput quantitative assay of visually guided behaviors and genetic neuronal silencing, we discovered a region in Drosophila’s protocerebrum critical for visual motion following. We used neuronal silencing, calcium imaging, and optogenetics to identify a single cell type, LPC1, that innervates this region, detects translational optic flow, and plays a key role in regulating forward walking. Moreover, the population of LPC1s can estimate the travelling direction, such as when gaze direction diverges from body heading. By linking specific cell types and their visual computations to specific behaviors, our findings establish a foundation for understanding how the nervous system uses vision to guide navigation.

Recurrent outbreaks of novel zoonotic coronavirus (CoV) diseases since 2000 have high-lighted the importance of developing therapeutics with broad-spectrum activity against CoVs. Because all CoVs use −1 programmed ribosomal frameshifting (−1 PRF) to control expression of key viral proteins, the frameshift signal in viral mRNA that stimulates −1 PRF provides a promising potential target for such therapeutics. To test the viability of this strategy, we explored a group of 6 small-molecule ligands, evaluating their activity against the frameshift signals from a panel of representative bat CoVs—the most likely source of future zoonoses—as well as SARS-CoV-2 and MERS-CoV. We found that whereas some ligands had notable activity against only a few of the frameshift signals, the serine protease inhibitor nafamostat suppressed −1 PRF significantly in several of them, while having limited to no effect on −1 PRF caused by frameshift signals from other viruses used as negative controls. These results suggest it is possible to find small-molecule ligands that inhibit −1 PRF specifically in a broad spectrum of CoVs, establishing the frameshift signal as a viable target for developing pan-coronaviral therapeutics.

Light sheet microscopes enable rapid, high-resolution imaging of biological specimens; however, biological processes span a variety of spatiotemporal scales. Moreover, long-term phenotypes are often instigated by rare or fleeting biological events that are difficult to capture with a single imaging modality and constant imaging parameters. To overcome this limitation, we present smartLLSM, a microscope that incorporates AI-based instrument control to autonomously switch between epifluorescent inverted imaging and lattice light sheet microscopy. We apply this technology to two major scenarios. First, we demonstrate that the instrument provides population-level statistics of cell cycle states across thousands of cells on a coverslip. Second, we show that by using real-time image feedback to switch between imaging modes, the instrument autonomously captures multicolor 3D datasets or 4D time-lapse movies of dividing cells at rates that dramatically exceed human capabilities. Quantitative image analysis on high-content + high-throughput datasets reveal kinetochore and chromosome dynamics in dividing cells and determine the effects of drug perturbation on cells in specific mitotic stages. This new methodology enables efficient detection of rare events within a heterogeneous cell population and records these processes with high spatiotemporal 4D imaging over statistically significant replicates.

The vitamin-C-synthesizing enzyme senescent marker protein 30 (SMP30) is a cold resistance gene in Drosophila, and vitamin C concentration increases in brown adipose tissue post-cold exposure. However, the roles of SMP30 in thermogenesis are unknown. Here, we tested the molecular mechanism of thermogenesis using wild-type (WT) and vitamin C-deficient SMP30-knockout (KO) mice. SMP30-KO mice gained more weight than WT mice without a change in food intake in response to short-term high-fat diet feeding. Indirect calorimetry and cold-challenge experiments indicated that energy expenditure is lower in SMP30-KO mice, which is associated with decreased thermogenesis in adipose tissues. Therefore, SMP30-KO mice do not lose weight during cold exposure, whereas WT mice lose weight markedly. Mechanistically, the levels of serum FGF21 were notably lower in SMP30-KO mice, and vitamin C supplementation in SMP30-KO mice recovered FGF21 expression and thermogenesis, with a marked reduction in body weight during cold exposure. Further experiments revealed that vitamin C activates PPARα to upregulate FGF21. Our findings demonstrate that SMP30-mediated synthesis of vitamin C activates the PPARα/FGF21 axis, contributing to the maintenance of thermogenesis in mice.