Neuropil is a fundamental form of tissue organization within brains 1 . In neuropils, densely packed neurons synaptically interconnect into precise circuit architecture 2 , 3 , yet the structural and developmental principles governing this nanoscale precision remain largely unknown 4 , 5 . Here, we use diffusion condensation, an iterative data coarse-graining algorithm 6 , to identify nested circuit structures within the C. elegans neuropil (called the nerve ring). We show that the nerve ring neuropil is largely organized into four strata composed of related behavioral circuits. The stratified architecture of the neuropil is a geometrical representation of the functional segregation of sensory information and motor outputs, with specific sensory organs and muscle quadrants mapping onto particular neuropil strata. We identify groups of neurons with unique morphologies that integrate information across strata and that create neural structures that cage the strata within the nerve ring. We use high resolution light-sheet microscopy 7 , 8 , coupled with lineage-tracing and cell-tracking algorithms 9 , 10 , to resolve the developmental sequence and reveal principles of cell position, migration and outgrowth that guide stratified neuropil organization. Our results uncover conserved structural design principles underlying nerve ring neuropil architecture and function, and a pioneer-neuron-based, temporal progression of outgrowth that guides the hierarchical development of the layered neuropil. Our findings provide a systematic blueprint for using structural and developmental approaches to understand neuropil organization within brains.

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Cholesterol depletion by methyl-b-cyclodextrin (MbCD) remodels the plasma membrane's mechanics in cells and its interactions with the underlying cytoskeleton, whereas in red blood cells, it is also known to cause lysis. Currently it's unclear if MbCD alters membrane tension or only enhances membrane-cytoskeleton interactions-and how this relates to cell lysis. We map membrane height fluctuations in single cells and observe that MbCD reduces temporal fluctuations robustly but flattens spatial membrane undulations only slightly. Utilizing models explicitly incorporating membrane confinement besides other viscoelastic factors, we estimate membrane mechanical parameters from the fluctuations' frequency spectrum. This helps us conclude that MbCD enhances membrane tension and does so even on ATP-depleted cell membranes where this occurs despite reduction in confinement. Additionally, on cholesterol depletion, cell membranes display higher intracellular heterogeneity in the amplitude of spatial undulations and membrane tension. MbCD also has a strong impact on the cell membrane's tenacity to mechanical stress, making cells strongly prone to rupture on hypo-osmotic shock with larger rupture diametersan effect not hindered by actomyosin perturbations. Our study thus demonstrates that cholesterol depletion increases membrane tension and its variability, making cells prone to rupture independent of the cytoskeletal state of the cell.

We present Richardson–Lucy network (RLN), a fast and lightweight deep learning method for three-dimensional fluorescence microscopy deconvolution. RLN combines the traditional Richardson–Lucy iteration with a fully convolutional network structure, establishing a connection to the image formation process and thereby improving network performance. Containing only roughly 16,000 parameters, RLN enables four- to 50-fold faster processing than purely data-driven networks with many more parameters. By visual and quantitative analysis, we show that RLN provides better deconvolution, better generalizability and fewer artifacts than other networks, especially along the axial dimension. RLN outperforms classic Richardson–Lucy deconvolution on volumes contaminated with severe out of focus fluorescence or noise and provides four- to sixfold faster reconstructions of large, cleared-tissue datasets than classic multi-view pipelines. We demonstrate RLN’s performance on cells, tissues and embryos imaged with widefield-, light-sheet-, confocal- and super-resolution microscopy.

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