Lipid droplets (LDs) are neutral lipid storage organelles that transfer lipids to various organelles including peroxisomes. Here, we show that the hereditary spastic paraplegia protein M1 Spastin, a membrane-bound AAA ATPase found on LDs, coordinates fatty acid (FA) trafficking from LDs to peroxisomes through two inter-related mechanisms. First, M1 Spastin forms a tethering complex with peroxisomal ABCD1 to promote LD-peroxisome contact formation. Second, M1 Spastin recruits the membrane-shaping ESCRT-III proteins IST1 and CHMP1B to LDs via its MIT domain to facilitate LD-to-peroxisome FA trafficking, possibly through IST1 and CHMP1B modifying LD membrane morphology. Furthermore, M1 Spastin, IST1 and CHMP1B are all required to relieve LDs of lipid peroxidation. The roles of M1 Spastin in tethering LDs to peroxisomes and in recruiting ESCRT-III components to LD-peroxisome contact sites for FA trafficking may help explain the pathogenesis of diseases associated with defective FA metabolism in LDs and peroxisomes.

Metabolic coordination between neurons and astrocytes is critical for the health of the brain. However, neuron-astrocyte coupling of lipid metabolism, particularly in response to neural activity, remains largely uncharacterized. Here, we demonstrate that toxic fatty acids (FAs) produced in hyperactive neurons are transferred to astrocytic lipid droplets by ApoE-positive lipid particles. Astrocytes consume the FAs stored in lipid droplets via mitochondrial β-oxidation in response to neuronal activity and turn on a detoxification gene expression program. Our findings reveal that FA metabolism is coupled in neurons and astrocytes to protect neurons from FA toxicity during periods of enhanced activity. This coordinated mechanism for metabolizing FAs could underlie both homeostasis and a variety of disease states of the brain.

Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) generates 3D datasets optimally suited for segmentation of cell ultrastructure and automated connectome tracing but is limited to small fields of view and is therefore incompatible with the new generation of ultrafast multibeam SEMs. In contrast, section-based techniques are multibeam-compatible but are limited in z-resolution making automatic segmentation of cellular ultrastructure difficult. Here we demonstrate a novel 3D electron microscopy technique, Gas Cluster Ion Beam SEM (GCIB-SEM), in which top-down, wide-area ion milling is performed on a series of thick sections, acquiring < 10 nm isotropic datasets of each which are then stitched together to span the full sectioned volume. Based on our results, incorporating GCIB-SEM into existing single beam and multibeam SEM workflows should be straightforward and should dramatically increase reliability while simultaneously improving z-resolution by a factor of 3 or more.

Optical and electron microscopy have made tremendous inroads toward understanding the complexity of the brain. However, optical microscopy offers insufficient resolution to reveal subcellular details, and electron microscopy lacks the throughput and molecular contrast to visualize specific molecular constituents over millimeter-scale or larger dimensions. We combined expansion microscopy and lattice light-sheet microscopy to image the nanoscale spatial relationships between proteins across the thickness of the mouse cortex or the entire Drosophila brain. These included synaptic proteins at dendritic spines, myelination along axons, and presynaptic densities at dopaminergic neurons in every fly brain region. The technology should enable statistically rich, large-scale studies of neural development, sexual dimorphism, degree of stereotypy, and structural correlations to behavior or neural activity, all with molecular contrast.

Understanding the circuit mechanisms behind motion detection is a long-standing question in visual neuroscience. In , recent synapse-level connectomes in the optic lobe, particularly in ON-pathway (T4) receptive-field circuits, in concert with physiological studies, suggest an increasingly intricate motion model compared with the ubiquitous Hassenstein-Reichardt model, while our knowledge of OFF-pathway (T5) has been incomplete. Here we present a conclusive and comprehensive connectome that for the first time integrates detailed connectivity information for inputs to both T4 and T5 pathways in a single EM dataset covering the entire optic lobe. With novel reconstruction methods using automated synapse prediction suited to such a large connectome, we successfully corroborate previous findings in the T4 pathway and comprehensively identify inputs and receptive fields for T5. While the two pathways are likely evolutionarily linked and indeed exhibit many similarities, we uncover interesting differences and interactions that may underlie their distinct functional properties.