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 essential during early developmental stages, the fate of granule cell cilia is unknown. Here, we provide nanoscopic resolution of ciliary dynamics by studying developmental changes in granule cell cilia using large-scale electron microscopy volumes and immunostaining of mouse cerebella. We found that many granule cell primary cilia were intracellular and concealed from the external environment. Cilia were disassembed in differentiating granule cell neurons in a process we call cilia deconstruction that was distinct from pre-mitotic cilia resorption in proliferating progenitors. In differentiating granule cells, ciliary loss involved unique disassembly intermediates, and, as maturation progressed, mother centriolar docking at the plasma membrane. Cilia did not reform from the docked centrioles, rather, in adult mice granule cell neurons remained unciliated. Many neurons in other brain regions require cilia to regulate function and connectivity. In contrast, our results show that granule cell progenitors had concealed cilia that underwent deconstruction potentially to prevent mitogenic hedgehog responsiveness. The ciliary deconstruction mechanism we describe could be paradigmatic of cilia removal during differentiation in other tissues.

Fluorescence microscopy is an invaluable tool in biology, yet its performance is compromised when the wavefront of light is distorted due to optical imperfections or the refractile nature of the sample. Such optical aberrations can dramatically lower the information content of images by degrading image contrast, resolution, and signal. Adaptive optics (AO) methods can sense and subsequently cancel the aberrated wavefront, but are too complex, inefficient, slow, or expensive for routine adoption by most labs. Here we introduce a rapid, sensitive, and robust wavefront sensing scheme based on phase diversity, a method successfully deployed in astronomy but underused in microscopy. Our method enables accurate wavefront sensing to less than λ/35 root mean square (RMS) error with few measurements, and AO with no additional hardware besides a corrective element. After validating the method with simulations, we demonstrate calibration of a deformable mirror > 100-fold faster than comparable methods (corresponding to wavefront sensing on the ~100 ms scale), and sensing and subsequent correction of severe aberrations (RMS wavefront distortion exceeding λ/2), restoring diffraction-limited imaging on extended biological samples.

The Hippo pathway was originally discovered to control tissue growth in Drosophila and includes the Hippo kinase (Hpo; MST1/2 in mammals), scaffold protein Salvador (Sav; SAV1 in mammals) and the Warts kinase (Wts; LATS1/2 in mammals). The Hpo kinase is activated by binding to Crumbs-Expanded (Crb-Ex) and/or Merlin-Kibra (Mer-Kib) proteins at the apical domain of epithelial cells. Here we show that activation of Hpo also involves the formation of supramolecular complexes with properties of a biomolecular condensate, including concentration dependence and sensitivity to starvation, macromolecular crowding, or 1,6-hexanediol treatment. Overexpressing Ex or Kib induces formation of micron-scale Hpo condensates in the cytoplasm, rather than at the apical membrane. Several Hippo pathway components contain unstructured low-complexity domains and purified Hpo-Sav complexes undergo phase separation in vitro. Formation of Hpo condensates is conserved in human cells. We propose that apical Hpo kinase activation occurs in phase separated "signalosomes" induced by clustering of upstream pathway components.

Targeting deep brain structures during electrophysiology and injections requires intensive training and expertise. Even with experience, researchers often can't be certain that a probe is placed precisely in a target location and this complexity scales with the number of simultaneous probes used in an experiment. Here, we present Pinpoint, open-source software that allows for interactive exploration of stereotaxic insertion plans. Once an insertion plan is created, Pinpoint allows users to save these online and share them with collaborators. 3D modeling tools allow users to explore their insertions alongside rig and implant hardware and ensure plans are physically possible. Probes in Pinpoint can be linked to electronic micro-manipulators allowing real-time visualization of current brain region targets alongside neural data. In addition, Pinpoint can control manipulators to automate and parallelize the insertion process. Compared to previously available software, Pinpoint's easy access through web browsers, extensive features, and real-time experiment integration enable more efficient and reproducible recordings.

A prominent trend in single-cell transcriptomics is providing spatial context alongside a characterization of each cell's molecular state. This typically requires targeting an a priori selection of genes, often covering less than 1% of the genome, and a key question is how to optimally determine the small gene panel. We address this challenge by introducing a flexible deep learning framework, PERSIST, to identify informative gene targets for spatial transcriptomics studies by leveraging reference scRNA-seq data. Using datasets spanning different brain regions, species, and scRNA-seq technologies, we show that PERSIST reliably identifies panels that provide more accurate prediction of the genome-wide expression profile, thereby capturing more information with fewer genes. PERSIST can be adapted to specific biological goals, and we demonstrate that PERSIST's binarization of gene expression levels enables models trained on scRNA-seq data to generalize with to spatial transcriptomics data, despite the complex shift between these technologies.

Animals of the same species exhibit similar behaviours that are advantageously adapted to their body and environment. These behaviours are shaped at the species level by selection pressures over evolutionary timescales. Yet, it remains unclear how these common behavioural adaptations emerge from the idiosyncratic neural circuitry of each individual. The overall organization of neural circuits is preserved across individuals because of their common evolutionarily specified developmental programme. Such organization at the circuit level may constrain neural activity, leading to low-dimensional latent dynamics across the neural population. Accordingly, here we suggested that the shared circuit-level constraints within a species would lead to suitably preserved latent dynamics across individuals. We analysed recordings of neural populations from monkey and mouse motor cortex to demonstrate that neural dynamics in individuals from the same species are surprisingly preserved when they perform similar behaviour. Neural population dynamics were also preserved when animals consciously planned future movements without overt behaviour and enabled the decoding of planned and ongoing movement across different individuals. Furthermore, we found that preserved neural dynamics extend beyond cortical regions to the dorsal striatum, an evolutionarily older structure. Finally, we used neural network models to demonstrate that behavioural similarity is necessary but not sufficient for this preservation. We posit that these emergent dynamics result from evolutionary constraints on brain development and thus reflect fundamental properties of the neural basis of behaviour.

Lysosomes play crucial roles in maintaining cellular homeostasis and promoting organism fitness. The pH of lysosomes is a crucial parameter for their proper function, and it is dynamically influenced by both intracellular and environmental factors. Here, we present a method based on fluorescence lifetime imaging microscopy (FLIM) for quantitatively analyzing lysosomal pH profiles in diverse types of primary mammalian cells and in different tissues of the live organism Caenorhabditis elegans. This FLIM-based method exhibits high sensitivity in resolving subtle pH differences, thereby revealing the heterogeneity of the lysosomal population within a cell and between cell types. The method enables rapid measurement of lysosomal pH changes in response to various environmental stimuli. Furthermore, the FLIM measurement of pH-sensitive dyes circumvents the need for transgenic reporters and mitigates potential confounding factors associated with varying dye concentrations or excitation light intensity. This FLIM approach offers absolute quantification of lysosomal pH and highlights the significance of lysosomal pH heterogeneity and dynamics, providing a valuable tool for studying lysosomal functions and their regulation in various physiological and pathological contexts.

Neurophysiology has long progressed through exploratory experiments and chance discoveries. Anecdotes abound of researchers setting up experiments while listening to spikes in real time and observing a pattern of consistent firing when certain stimuli or behaviors happened. With the advent of large-scale recordings, such close observation of data has become harder because high-dimensional spaces are impenetrable to our pattern-finding intuitions. To help ourselves find patterns in neural data, our lab has been openly developing a visualization framework known as “Rastermap” over the past five years. Rastermap takes advantage of a new global optimization algorithm for sorting neural responses along a one-dimensional manifold. Displayed as a raster plot, the sorted neurons show a variety of activity patterns, which can be more easily identified and interpreted. We first benchmark Rastermap on realistic simulations with multiplexed cognitive variables. Then we demonstrate it on recordings of tens of thousands of neurons from mouse visual and sensorimotor cortex during spontaneous, stimulus-evoked and task-evoked epochs, as well as on whole-brain zebrafish recordings, widefield calcium imaging data, population recordings from rat hippocampus and artificial neural networks. Finally, we illustrate high-dimensional scenarios where Rastermap and similar algorithms cannot be used effectively.

Genetically encoded pH sensors based on fluorescent proteins are valuable tools for the imaging of cellular events that are associated with pH changes, such as exocytosis and endocytosis. Superecliptic pHluorin (SEP) is a pH-sensitive green fluorescent protein (GFP) variant widely used for such applications. Here, we report the rational design, development, structure, and applications of Lime, an improved SEP variant with higher fluorescence brightness and greater pH sensitivity. The X-ray crystal structure of Lime supports the mechanistic rationale that guided the introduction of beneficial mutations. Lime provides substantial improvements relative to SEP for imaging of endocytosis and exocytosis. Furthermore, Lime and its variants are advantageous for a broader range of applications including the detection of synaptic release and neuronal voltage changes.

The goal when imaging bioprocesses with optical microscopy is to acquire the most spatiotemporal information with the least invasiveness. Deep neural networks have substantially improved optical microscopy, including image super-resolution and restoration, but still have substantial potential for artifacts. In this study, we developed rationalized deep learning (rDL) for structured illumination microscopy and lattice light sheet microscopy (LLSM) by incorporating prior knowledge of illumination patterns and, thereby, rationally guiding the network to denoise raw images. Here we demonstrate that rDL structured illumination microscopy eliminates spectral bias-induced resolution degradation and reduces model uncertainty by five-fold, improving the super-resolution information by more than ten-fold over other computational approaches. Moreover, rDL applied to LLSM enables self-supervised training by using the spatial or temporal continuity of noisy data itself, yielding results similar to those of supervised methods. We demonstrate the utility of rDL by imaging the rapid kinetics of motile cilia, nucleolar protein condensation during light-sensitive mitosis and long-term interactions between membranous and membrane-less organelles.