Primary cilia on granule cell neuron progenitors in the developing cerebellum detect sonic hedgehog to facilitate proliferation. Following differentiation, cerebellar granule cells become the most abundant neuronal cell type in the brain. While granule cell cilia are essential during early developmental stages, they become infrequent upon maturation. Here, we provide nanoscopic resolution of cilia in situ using large-scale electron microscopy volumes and immunostaining of mouse cerebella. In many granule cells, we found intracellular cilia, concealed from the external environment. Cilia were disassembled in differentiating granule cell neurons-in a process we call cilia deconstruction-distinct from premitotic cilia resorption in proliferating progenitors. In differentiating granule cells, cilia deconstruction involved unique disassembly intermediates, and, as maturation progressed, mother centriolar docking at the plasma membrane. Unlike ciliated neurons in other brain regions, our results show the deconstruction of concealed cilia in differentiating granule cells, which might prevent mitogenic hedgehog responsiveness. Ciliary deconstruction could be paradigmatic of cilia removal during differentiation in other tissues.

As we move through the world, we see the same visual scenes from different perspectives. Although we experience perspective deformations, our perception of a scene remains stable. This raises the question of which neuronal representations in visual brain areas are perspective-tuned and which are invariant. Focusing on planar rotations, we introduce a mathematical framework based on the principle of equivariance, which asserts that an image rotation results in a corresponding rotation of neuronal representations, to explain how the same representation can range from being fully tuned to fully invariant. We applied this framework to large-scale simultaneous neuronal recordings from four visual cortical areas in mice, where we found that representations are both tuned and invariant but become more invariant across higher-order areas. While common deep convolutional neural networks show similar trends in orientation-invariance across layers, they are not rotation-equivariant. We propose that equivariance is a prevalent computation of populations of biological neurons to gradually achieve invariance through structured tuning.

Deep neural networks can improve the quality of fluorescence microscopy images. Previous methods, based on Convolutional Neural Networks (CNNs), require time-consuming training of individual models for each experiment, impairing their applicability and generalization. In this study, we propose a novel imaging-transformer based model, Convolutional Neural Network Transformer (CNNT), that outperforms CNN based networks for image denoising. We train a general CNNT based backbone model from pairwise high-low Signal-to-Noise Ratio (SNR) image volumes, gathered from a single type of fluorescence microscope, an instant Structured Illumination Microscope. Fast adaptation to new microscopes is achieved by fine-tuning the backbone on only 5-10 image volume pairs per new experiment. Results show that the CNNT backbone and fine-tuning scheme significantly reduces training time and improves image quality, outperforming models trained using only CNNs such as 3D-RCAN and Noise2Fast. We show three examples of efficacy of this approach in wide-field, two-photon, and confocal fluorescence microscopy.

Optical nanoscopy of intact biological specimens has been transformed by recent advancements in hydrogel-based tissue clearing and expansion, enabling the imaging of cellular and subcellular structures with molecular contrast. However, existing high-resolution fluorescence microscopes have limited imaging depth, which prevents the study of whole-mount specimens without physical sectioning. To address this challenge, we developed “photochemical sectioning,” a spatially precise, light-based sample sectioning process. By combining photochemical sectioning with volumetric lattice light-sheet imaging and petabyte-scale computation, we imaged and reconstructed axons and myelination sheaths across entire mouse olfactory bulbs at nanoscale resolution. An olfactory-bulb-wide analysis of myelinated and unmyelinated axons revealed distinctive patterns of axon degeneration and de-/dysmyelination in the neurodegenerative mouse, highlighting the potential for peta- to exabyte-scale super-resolution studies using this approach.

During brain development, synapses are initially formed in excess and are later eliminated in an activity-dependent manner, with weak synapses being preferentially removed. Previous studies identified glia as mediators of synapse removal, but it is unclear how glia specifically target weak synapses. Here we show that, in the developing mouse visual pathway, inhibiting synaptic transmission induces postsynaptic activation of caspase-3. Caspase-3 is essential for synapse elimination driven by both spontaneous and experience-dependent neural activity. Synapse weakening-induced caspase-3 activation determines the specificity of synapse elimination mediated by microglia but not astrocytes. Furthermore, in a mouse model of Alzheimer’s disease, caspase-3 deficiency protects against synapse loss induced by amyloid-β deposition. Our results reveal caspase-3 activation as a key step in activity-dependent synapse elimination during development and synapse loss in neurodegeneration.

Imaging the 4D choreography of subcellular events in living multicellular organisms at high spatiotemporal resolution could reveal life’s fundamental principles. Yet extracting these principles from petabyte-scale image data requires fusing advanced light microscopy and cutting-edge machine learning models with biological insight and expertise.

Complex phenotypes, such as an animal’s behavior, generally depend on an overwhelming number of processes that span a vast range of scales. While there is no reason that behavioral dynamics permit simple models, by subsuming inherent nonlinearities and memory into maximally predictive microstates, we find one for Caenorhabditis elegans foraging. The resulting “Markov worm” is effectively indistinguishable from real worm motion across a range of timescales, and we can decompose our model dynamics both to recover and reveal behavioral states. Finally, we connect postures to trajectories, illuminating how worms explore the environment in different behavioral states. How do we capture the breadth of behavior in animal movement, from rapid body twitches to aging? Using high-resolution videos of the nematode worm Caenorhabditis elegans, we show that a single dynamics connects posture-scale fluctuations with trajectory diffusion and longer-lived behavioral states. We take short posture sequences as an instantaneous behavioral measure, fixing the sequence length for maximal prediction. Within the space of posture sequences, we construct a fine-scale, maximum entropy partition so that transitions among microstates define a high-fidelity Markov model, which we also use as a means of principled coarse-graining. We translate these dynamics into movement using resistive force theory, capturing the statistical properties of foraging trajectories. Predictive across scales, we leverage the longest-lived eigenvectors of the inferred Markov chain to perform a top–down subdivision of the worm’s foraging behavior, revealing both “runs-and-pirouettes” as well as previously uncharacterized finer-scale behaviors. We use our model to investigate the relevance of these fine-scale behaviors for foraging success, recovering a trade-off between local and global search strategies.

Microbiota-derived metabolites have emerged as key regulators of longevity. The metabolic activity of the gut microbiota, influenced by dietary components and ingested chemical compounds, profoundly impacts host fitness. While the benefits of dietary prebiotics are well-known, chemically targeting the gut microbiota to enhance host fitness remains largely unexplored. Here, we report a novel chemical approach to induce a pro-longevity bacterial metabolite in the host gut. We discovered that specific Escherichia coli strains overproduce colanic acids (CAs) when exposed to a low dose of cephaloridine, leading to an increased lifespan in host Caenorhabditis elegans. In the mouse gut, oral administration of low-dose cephaloridine induces the transcription of the capsular biosynthesis operon responsible for CA biosynthesis in commensal E. coli, which overcomes the inhibition of CA biosynthesis above 30 degrees C and enables its induction directly from the microbiota. Importantly, low-dose cephaloridine induces CA independently of its antibiotic properties through a previously unknown mechanism mediated by the membrane-bound histidine kinase ZraS. Our work lays the foundation for microbiota-based therapeutics through the chemical modulation of bacterial metabolism and reveals the promising potential of bacteria-targeting drugs in promoting host longevity.

The contrast between the disruption of genome topology after cohesin loss and the lack of downstream gene expression changes instigates intense debates regarding the structure-function relationship between genome and gene regulation. Here, by analyzing transcriptome and chromatin accessibility at the single-cell level, we discover that, instead of dictating population-wide gene expression levels, cohesin supplies a general function to neutralize stochastic coexpression tendencies of cis-linked genes in single cells. Notably, cohesin loss induces widespread gene coactivation and chromatin co-opening tens of million bases apart in cis. Spatial genome and protein imaging reveals that cohesin prevents gene co-bursting along the chromosome and blocks spatial mixing of transcriptional hubs. Single-molecule imaging shows that cohesin confines the exploration of diverse enhancer and core promoter binding transcriptional regulators. Together, these results support that cohesin arranges nuclear topology to control gene coexpression in single cells.

Photoactivatable or "caged" pharmacological agents combine the high spatiotemporal specificity of light application with the molecular specificity of drugs. A key factor in all optopharmacology experiments is the mechanism of uncaging, which dictates the photochemical quantum yield and determines the byproducts produced by the light-driven chemical reaction. In previous work, we demonstrated that coumarin-based photolabile groups could be used to cage tertiary amine drugs as quaternary ammonium salts. Although stable, water-soluble, and useful for experiments in brain tissue, these first-generation compounds exhibit relatively low uncaging quantum yield (Φ < 1%) and release the toxic byproduct formaldehyde upon photolysis. Here, we elucidate the photochemical mechanisms of coumarin-caged tertiary amines and then optimize the major pathway using chemical modification. We discovered that the combination of 3,3-dicarboxyazetidine and bromine substituents shift the mechanism of release to heterolysis, eliminating the formaldehyde byproduct and giving photolabile tertiary amine drugs with Φ > 20%─a 35-fold increase in uncaging efficiency. This new "ABC" cage allows synthesis of improved photoactivatable derivatives of escitalopram and nicotine along with a novel caged agonist of the oxytocin receptor.