Hunger is a complex behavioural state that elicits intense food seeking and consumption. These behaviours are rapidly recapitulated by activation of starvation-sensitive AGRP neurons, which present an entry point for reverse-engineering neural circuits for hunger. Here we mapped synaptic interactions of AGRP neurons with multiple cell populations in mice and probed the contribution of these distinct circuits to feeding behaviour using optogenetic and pharmacogenetic techniques. An inhibitory circuit with paraventricular hypothalamus (PVH) neurons substantially accounted for acute AGRP neuron-evoked eating, whereas two other prominent circuits were insufficient. Within the PVH, we found that AGRP neurons target and inhibit oxytocin neurons, a small population that is selectively lost in Prader-Willi syndrome, a condition involving insatiable hunger. By developing strategies for evaluating molecularly defined circuits, we show that AGRP neuron suppression of oxytocin neurons is critical for evoked feeding. These experiments reveal a new neural circuit that regulates hunger state and pathways associated with overeating disorders.

Serine hydrolases have diverse intracellular substrates, biological functions, and structural plasticity, and are thus important for biocatalyst design. Amongst serine hydrolases, the recently described ybfF enzyme family are promising novel biocatalysts with an unusual bifurcated substrate-binding cleft and the ability to recognize commercially relevant substrates. We characterized in detail the substrate selectivity of a novel ybfF enzyme from Vibrio cholerae (Vc-ybfF) by using a 21-member library of fluorogenic ester substrates. We assigned the roles of the two substrate-binding clefts in controlling the substrate selectivity and folded stability of Vc-ybfF by comprehensive substitution analysis. The overall substrate preference of Vc-ybfF was for short polar chains, but it retained significant activity with a range of cyclic and extended esters. This broad substrate specificity combined with the substitutional analysis demonstrates that the larger binding cleft controls the substrate specificity of Vc-ybfF. Key selectivity residues (Tyr116, Arg120, Tyr209) are also located at the larger binding pocket and control the substrate specificity profile. In the structure of ybfF the narrower binding cleft contains water molecules prepositioned for hydrolysis, but based on substitution this cleft showed only minimal contribution to catalysis. Instead, the residues surrounding the narrow binding cleft and at the entrance to the binding pocket contributed significantly to the folded stability of Vc-ybfF. The relative contributions of each cleft of the binding pocket to the catalytic activity and folded stability of Vc-ybfF provide a valuable map for designing future biocatalysts based on the ybfF scaffold.

To integrate changing environmental cues with high spatial and temporal resolution is critical for animals to orient themselves. Drosophila larvae show an effective motor program to navigate away from light sources. How the larval visual circuit processes light stimuli to control navigational decision remains unknown. The larval visual system is composed of two sensory input channels, Rhodopsin5 (Rh5) and Rhodopsin6 (Rh6) expressing photoreceptors (PRs). We here characterize how spatial and temporal information are used to control navigation. Rh6-PRs are required to perceive temporal changes of light intensity during head casts, while Rh5-PRs are required to control behaviors that allow navigation in response to spatial cues. We characterize how distinct behaviors are modulated and identify parallel acting and converging features of the visual circuit. Functional features of the larval visual circuit highlight the principle of how early in a sensory circuit distinct behaviors may be computed by partly overlapping sensory pathways.

Single-molecule localization microscopy (SMLM) has had remarkable success in imaging cellular structures with nanometer resolution, but the need for activating only single isolated emitters limits imaging speed and labeling density. Here, we overcome this major limitation using deep learning. We developed DECODE, a computational tool that can localize single emitters at high density in 3D with highest accuracy for a large range of imaging modalities and conditions. In a public software benchmark competition, it outperformed all other fitters on 12 out of 12 data-sets when comparing both detection accuracy and localization error, often by a substantial margin. DECODE allowed us to take live-cell SMLM data with reduced light exposure in just 3 seconds and to image microtubules at ultra-high labeling density. Packaged for simple installation and use, DECODE will enable many labs to reduce imaging times and increase localization density in SMLM.Competing Interest StatementThe authors have declared no competing interest.

The most sophisticated existing methods to generate 3D isotropic super-resolution (SR) from non-isotropic electron microscopy (EM) are based on learned dictionaries. Unfortunately, none of the existing methods generate practically satisfying results. For 2D natural images, recently developed super-resolution methods that use deep learning have been shown to significantly outperform the previous state of the art. We have adapted one of the most successful architectures (FSRCNN) for 3D super-resolution, and compared its performance to a 3D U-Net architecture that has not been used previously to generate super-resolution. We trained both architectures on artificially downscaled isotropic ground truth from focused ion beam milling scanning EM (FIB-SEM) and tested the performance for various hyperparameter settings.

Our results indicate that both architectures can successfully generate 3D isotropic super-resolution from non-isotropic EM, with the U-Net performing consistently better. We propose several promising directions for practical application.

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations and experiments to show that applying the trained ‘de-aberration’ networks outperforms alternative methods, providing restoration on par with adaptive optics techniques; and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in C. elegans embryos.

Limited color channels in fluorescence microscopy have long constrained spatial analysis in biological specimens. Here, we introduce cycle Hybridization Chain Reaction (HCR), a method that integrates multicycle DNA barcoding with HCR to overcome this limitation. cycleHCR enables highly multiplexed imaging of RNA and proteins using a unified barcode system. Whole-embryo transcriptomics imaging achieved precise three-dimensional gene expression and cell fate mapping across a specimen depth of ~310 μm. When combined with expansion microscopy, cycleHCR revealed an intricate network of 10 subcellular structures in mouse embryonic fibroblasts. In mouse hippocampal slices, multiplex RNA and protein imaging uncovered complex gene expression gradients and cell-type-specific nuclear structural variations. cycleHCR provides a quantitative framework for elucidating spatial regulation in deep tissue contexts for research and potentially diagnostic applications.

bioRxiv preprint: 10.1101/2024.05.17.594641

Sample-induced aberrations and optical imperfections limit the resolution of fluorescence microscopy. Phase diversity is a powerful technique that leverages complementary phase information in sequentially acquired images with deliberately introduced aberrations--the phase diversities--to enable phase and object reconstruction and restore diffraction-limited resolution. These phase diversities are typically introduced into the optical path via a deformable mirror. Existing phase-diversity-based methods are limited to Zernike modes, require large numbers of diversity images, or depend on accurate mirror calibration--which are all suboptimal. We present DeepPD, a deep learning-based framework that combines neural representations of the object and wavefront with a learned model of the deformable mirror to jointly estimate both object and phase from only five images. DeepPD improves robustness and reconstruction quality over previous approaches, even under severe aberrations. We demonstrate its performance on calibration targets and biological samples, including immunolabeled myosin in fixed PtK2 cells.

Seizures induced by visual stimulation (photosensitive epilepsy; PSE) represent a common type of epilepsy in humans, but the molecular mechanisms and genetic drivers underlying PSE remain unknown, and no good genetic animal models have been identified as yet. Here, we show an animal model of PSE, in , owing to defective cortex glia. The cortex glial membranes are severely compromised in ceramide phosphoethanolamine synthase ()-null mutants and fail to encapsulate the neuronal cell bodies in the neuronal cortex. Expression of human sphingomyelin synthase 1, which synthesizes the closely related ceramide phosphocholine (sphingomyelin), rescues the cortex glial abnormalities and PSE, underscoring the evolutionarily conserved role of these lipids in glial membranes. Further, we show the compromise in plasma membrane structure that underlies the glial cell membrane collapse in mutants and leads to the PSE phenotype.

Contacts between endosomes and the endoplasmic reticulum (ER) promote endosomal tubule fission, but the mechanisms involved and consequences of tubule fission failure are incompletely understood. We found that interaction between the microtubule-severing enzyme spastin and the ESCRT protein IST1 at ER-endosome contacts drives endosomal tubule fission. Failure of fission caused defective sorting of mannose 6-phosphate receptor, with consequently disrupted lysosomal enzyme trafficking and abnormal lysosomal morphology, including in mouse primary neurons and human stem cell-derived neurons. Consistent with a role for ER-mediated endosomal tubule fission in lysosome function, similar lysosomal abnormalities were seen in cellular models lacking the WASH complex component strumpellin or the ER morphogen REEP1. Mutations in spastin, strumpellin, or REEP1 cause hereditary spastic paraplegia (HSP), a disease characterized by axonal degeneration. Our results implicate failure of the ER-endosome contact process in axonopathy and suggest that coupling of ER-mediated endosomal tubule fission to lysosome function links different classes of HSP proteins, previously considered functionally distinct, into a unifying pathway for axonal degeneration.