The brain is a disproportionately large consumer of fuel, estimated to expend \~20% of the whole-body energy budget, and therefore it is critical to adequately control brain fuel expenditures while satisfying its on-demand needs for continued function. The brain is also metabolically vulnerable as the inability to adequately fuel cellular processes that support information transfer between cells leads to rapid neurological impairment. We show here that a genetic driver of early onset epileptic encephalopathy (EOEE), SLC13A5, a Na+/citrate cotransporter (NaCT), is critical for gating the activation of local presynaptic glycolysis. We show that SLC13A5 is in part localized to a presynaptic pool of membrane-bound organelles and acts to transiently clear axonal citrate during electrical activity, in turn activating phosphofructokinase 1. We show that loss of SLC13A5 or mistargeting to the plasma membrane results in suppressed glycolytic gating, activity dependent presynaptic bioenergetic deficits and synapse dysfunction.

Prolonged wakefulness leads to persistent, deep recovery sleep (RS). However, the neuronal circuits that mediate this process remain elusive. From a circuit screen in mice, we identified a group of thalamic nucleus reuniens (RE) neurons activated during sleep deprivation (SD) and required for sleep homeostasis. Optogenetic activation of RE neurons leads to an unusual phenotype: presleep behaviors (grooming and nest organizing) followed by prolonged, intense sleep that resembles RS. Inhibiting RE activity during SD impairs subsequent RS, which suggests that these neurons signal sleep need. RE neurons act upstream of sleep-promoting zona incerta cells, and SD triggers plasticity of this circuit to strengthen their connectivity. These findings reveal a circuit mechanism by which sleep need transforms the functional coupling of a sleep circuit to promote persistent, deep sleep.

Representation learning in neural networks may be implemented with supervised or unsupervised algorithms, distinguished by the availability of instruction. In the sensory cortex, perceptual learning drives neural plasticity1-13, but it is not known whether this is due to supervised or unsupervised learning. Here we recorded populations of up to 90,000 neurons simultaneously from the primary visual cortex (V1) and higher visual areas (HVAs) while mice learned multiple tasks, as well as during unrewarded exposure to the same stimuli. Similar to previous studies, we found that neural changes in task mice were correlated with their behavioural learning. However, the neural changes were mostly replicated in mice with unrewarded exposure, suggesting that the changes were in fact due to unsupervised learning. The neural plasticity was highest in the medial HVAs and obeyed visual, rather than spatial, learning rules. In task mice only, we found a ramping reward-prediction signal in anterior HVAs, potentially involved in supervised learning. Our neural results predict that unsupervised learning may accelerate subsequent task learning, a prediction that we validated with behavioural experiments.

Preprint: https://www.biorxiv.org/content/early/2024/02/27/2024.02.25.581990

Accumulating evidence indicates that biological aging can be accelerated by environmental exposures, collectively called the 'exposome'. The skin, as the largest and most exposed organ, can be viewed as a 'window' for the deep exploration of the exposome and its effects on systemic aging. The complex interplay across hallmarks of aging in the skin and systemic biological aging suggests that physiological processes associated with skin aging influence, and are influenced by, systemic hallmarks of aging. This bidirectional relationship provides potential avenues for the prevention of accelerated biological aging and the identification of therapeutic targets. We provide a review of the interactions between skin exposure, aging hallmarks in the skin and associated systemic changes, and their implications in treatment and disease. We also discuss key questions that need to be addressed to maintain skin and overall health, highlighting the need for the development of precise biomarkers and advanced skin models.

Understanding biological systems requires observing features and processes across vast spatial and temporal scales, spanning nanometers to centimeters and milliseconds to days, often using multiple imaging modalities within complex native microenvironments. Yet, achieving this comprehensive view is challenging because microscopes optimized for specific tasks typically lack versatility due to inherent optical and sample handling trade-offs, and frequently suffer performance degradation from sample-induced optical aberrations in multicellular contexts. Here, we present MOSAIC, a reconfigurable microscope that integrates multiple advanced imaging techniques including light-sheet, label-free, super-resolution, and multi-photon, all equipped with adaptive optics. MOSAIC enables non-invasive imaging of subcellular dynamics in both cultured cells and live multicellular organisms, nanoscale mapping of molecular architectures across millimeter-scale expanded tissues, and structural/functional neural imaging within live mice. MOSAIC facilitates correlative studies across biological scales within the same specimen, providing an integrated platform for broad biological investigation.

Preprint: https://www.biorxiv.org/content/early/2025/06/13/2025.06.02.657494

We present a correlated light and electron microscopy (CLEM) dataset from a 7-day-old larval zebrafish, integrating confocal imaging of genetically labeled excitatory (vglut2a) and inhibitory (gad1b) neurons with nanometer-resolution serial section EM. The dataset spans the brain and anterior spinal cord, capturing >180,000 segmented soma, >40,000 molecularly annotated neurons, and 30 million synapses, most of which were classified as excitatory, inhibitory, or modulatory. To characterize the directional flow of activity across the brain, we leverage the synaptic and cell body annotations to compute region-wise input and output drive indices at single cell resolution. We illustrate the dataset’s utility by dissecting and validating circuits in three distinct systems: water flow direction encoding in the lateral line, recurrent excitation and contralateral inhibition in a hindbrain motion integrator, and functionally relevant targeted long-range projections from a tegmental excitatory nucleus, demonstrating that this resource enables rigorous hypothesis testing as well as exploratory-driven circuit analysis. The dataset is integrated into an open-access platform optimized to facilitate community reconstruction and discovery efforts throughout the larval zebrafish brain.

Preprint: https://www.biorxiv.org/content/early/2025/06/15/2025.06.10.658982

Optogenetic activators with red-shifted excitation spectra, such as Chrimson, have significantly advanced Drosophila neuroscience. However, until recently, available optogenetic inhibitors required shorter activation wavelengths, which don’t penetrate tissue as effectively and are stronger visual stimuli to the animal, potentially confounding behavioral results. Here, we assess the efficacy of two newly identified anion-conducting channelrhodopsins with spectral sensitivities similar to Chrimson: A1ACR and HfACR (RubyACRs). Electrophysiology and functional imaging confirmed that RubyACRs effectively hyperpolarize neurons, with stronger and faster effects than the widely used inhibitor GtACR1. Activation of RubyACRs led to circuit-specific behavioral changes in three different neuronal groups. In glutamatergic motor neurons, activating RubyACRs suppressed adult locomotor activity. In PPL1-γ1pedc dopaminergic neurons, pairing odors with RubyACR activation during learning produced odor responses consistent with synaptic silencing. Finally, activation of RubyACRs in the pIP10 neuron suppressed pulse song during courtship. Together, these results demonstrate that RubyACRs are effective and reliable tools for neuronal inhibition in Drosophila, expanding the optogenetic toolkit for circuit dissection in freely behaving animals.

Preprint: https://www.biorxiv.org/content/early/2025/06/15/2025.06.13.659144

Metazoans detect and differentiate between innocuous (non-painful) and/or noxious (harmful) environmental cues using primary sensory neurons, which serve as the first node in a neural network that computes stimulus-specific behaviors to either navigate away from injury-causing conditions or to perform protective behaviors that mitigate extensive injury. The ability of an animal to detect and respond to various sensory stimuli depends upon molecular diversity in the primary sensors and the underlying neural circuitry responsible for the relevant behavioral action selection. Recent studies in Drosophila larvae have revealed that somatosensory class III multidendritic (CIII md) neurons function as multimodal sensors regulating distinct behavioral responses to innocuous mechanical and nociceptive thermal stimuli. Recent advances in circuit bases of behavior have identified and functionally validated \Drosophila larval somatosensory circuitry involved in innocuous (mechanical) and noxious (heat and mechanical) cues. However, central processing of cold nociceptive cues remained unexplored. We implicate multisensory integrators (Basins), premotor (Down-and-Back), and projection (A09e and TePns) neurons as neural substrates required for cold-evoked behavioral and calcium responses. Neural silencing of cell types downstream of CIII md neurons led to significant reductions in cold-evoked behaviors, and neural co-activation of CIII md neurons plus additional cell types facilitated larval contraction (CT) responses. Further, we demonstrate that optogenetic activation of CIII md neurons evokes calcium increases in these neurons. Finally, we characterize the premotor to motor neuron network underlying cold-evoked CT and delineate the muscular basis of CT response. Collectively, we demonstrate how Drosophila larvae process cold stimuli through functionally diverse somatosensory circuitry responsible for generating stimulus-specific behaviors.

Animal behavioural diversity ultimately stems from variation in neural circuitry, yet how central neural circuits evolve remains poorly understood. Studies of neural circuit evolution often focus on a few elements within a network. However, addressing fundamental questions in evolutionary neuroscience, such as whether some elements are more evolvable than others, requires a more global and unbiased approach. Here, we used synapse-level comparative connectomics to examine how an entire olfactory circuit evolves. We compared the full antennal lobe connectome of the larvae of two closely related Drosophila species, D. melanogaster and D. erecta, which differ in their ecological niches and odour-driven behaviours. We found that evolutionary change is unevenly distributed across the network. Some features, including neuron types, neuron numbers and interneuron-to-interneuron connectivity, are highly conserved. These conserved elements delineate a core circuit blueprint presumably required for fundamental olfactory processing. Superimposed on this scaffold, we find rewiring changes that mirror each species ecologies, including a systematic shift in the excitation-to-inhibition balance in the feedforward pathways. We further show that some neurons have changed more than others, and that even within individual neurons some synaptic elements remain conserved while others display major species-specific changes, suggesting evolutionary hot-spots within the circuit. Our findings reveal constrained and adaptable elements within olfactory networks, and establish a framework for identifying general principles in the evolution of neural circuits underlying behaviour.