Medial frontal cortical areas are thought to play a critical role in the brain's ability to flexibly deploy strategies that are effective in complex settings. Still, the specific circuit computations that underpin this foundational aspect of intelligence remain unclear. Here, by examining neural ensemble activity in rats that sample different strategies in a self-guided search for latent task structure, we demonstrate a robust tracking of individual strategy prevalence in the anterior cingulate cortex (ACC), especially in an area homologous to primate area 32D. Prevalence encoding in the ACC is wide-scale, independent of reward delivery, and persists through a substantial ensemble reorganization that tags ACC representations with contextual content. Our findings argue that ACC ensemble dynamics is structured by a summary statistic of recent behavioral choices, raising the possibility that ACC plays a role in estimating - through statistical learning - which actions promote the occurrence of events in the environment.

Ionic driving forces provide the net electromotive force for ion movement across membranes and are therefore a fundamental property of all cells. In the nervous system, chloride driving force (DFCl) determines inhibitory signaling, as fast synaptic inhibition is mediated by chloride-permeable GABAA and glycine receptors. Here we present a new tool for all-Optical Reporting of CHloride Ion Driving force (ORCHID). We demonstrate ORCHID’s ability to provide accurate, high-throughput measurements of resting and dynamic DFCl from genetically targeted cell types over a range of timescales. ORCHID confirms theoretical predictions about the biophysical mechanisms that establish DFCl, reveals novel differences in DFCl between neurons and astrocytes under different network conditions, and affords the first in vivo measurements of intact DFCl in mouse cortical neurons. This work extends our understanding of chloride homeostasis and inhibitory synaptic transmission and establishes a precedent for utilizing all-optical methods to assess ionic driving force.

Ionic driving forces provide the net electromotive force for ion movement across receptors, channels, and transporters, and are a fundamental property of all cells. In the brain for example, fast synaptic inhibition is mediated by chloride permeable GABAA receptors, and single-cell intracellular recordings have been the only method for estimating driving forces across these receptors (DFGABAA). Here we present a new tool for quantifying inhibitory receptor driving force named ORCHID: all-Optical Reporting of CHloride Ion Driving force. We demonstrate ORCHID’s ability to provide accurate, high-throughput measurements of resting and dynamic DFGABAA from genetically targeted cell types over multiple timescales. ORCHID confirms theoretical predictions about the biophysical mechanisms that establish DFGABAA, reveals novel differences in DFGABAA between neurons and astrocytes, and affords the first in vivo measurements of intact DFGABAA. This work extends our understanding of inhibitory synaptic transmission and establishes a precedent for all-optical methods to assess ionic driving forces.

Artificial activation of anatomically localized, genetically defined hypothalamic neuron populations is known to trigger distinct innate behaviors, suggesting a hypothalamic nucleus-centered organization of behavior control. To assess whether the encoding of behavior is similarly anatomically confined, we performed simultaneous neuron recordings across twenty hypothalamic regions in freely moving animals. Here we show that distinct but anatomically distributed neuron ensembles encode the social and fear behavior classes, primarily through mixed selectivity. While behavior class-encoding ensembles were spatially distributed, individual ensembles exhibited strong localization bias. Encoding models identified that behavior actions, but not motion-related variables, explained a large fraction of hypothalamic neuron activity variance. These results identify unexpected complexity in the hypothalamic encoding of instincts and provide a foundation for understanding the role of distributed neural representations in the expression of behaviors driven by hardwired circuits.

The endoplasmic reticulum (ER) forms a dynamic network that contacts other cellular membranes to regulate stress responses, calcium signalling and lipid transfer. Here, using high-resolution volume electron microscopy, we find that the ER forms a previously unknown association with keratin intermediate filaments and desmosomal cell-cell junctions. Peripheral ER assembles into mirror image-like arrangements at desmosomes and exhibits nanometre proximity to keratin filaments and the desmosome cytoplasmic plaque. ER tubules exhibit stable associations with desmosomes, and perturbation of desmosomes or keratin filaments alters ER organization, mobility and expression of ER stress transcripts. These findings indicate that desmosomes and the keratin cytoskeleton regulate the distribution, function and dynamics of the ER network. Overall, this study reveals a previously unknown subcellular architecture defined by the structural integration of ER tubules with an epithelial intercellular junction.

Knowledge of one’s own behavioral state—whether one is walking, grooming, or resting—is critical for contextualizing sensory cues including interpreting visual motion and tracking odor sources. Additionally, awareness of one’s own posture is important to avoid initiating destabilizing or physically impossible actions. Ascending neurons (ANs), interneurons in the vertebrate spinal cord or insect ventral nerve cord (VNC) that project to the brain, may provide such high-fidelity behavioral state signals. However, little is known about what ANs encode and where they convey signals in any brain. To address this gap, we performed a large-scale functional screen of AN movement encoding, brain targeting, and motor system patterning in the adult fly, Drosophila melanogaster. Using a new library of AN sparse driver lines, we measured the functional properties of 247 genetically-identifiable ANs by performing two-photon microscopy recordings of neural activity in tethered, behaving flies. Quantitative, deep network-based neural and behavioral analyses revealed that ANs nearly exclusively encode high-level behaviors—primarily walking as well as resting and grooming—rather than low-level joint or limb movements. ANs that convey self-motion—resting, walking, and responses to gust-like puff stimuli—project to the brain’s anterior ventrolateral protocerebrum (AVLP), a multimodal, integrative sensory hub, while those that encode discrete actions—eye grooming, turning, and proboscis extension—project to the brain’s gnathal ganglion (GNG), a locus for action selection. The structure and polarity of AN projections within the VNC are predictive of their functional encoding and imply that ANs participate in motor computations while also relaying state signals to the brain. Illustrative of this are ANs that temporally integrate proboscis extensions over tens-of-seconds, likely through recurrent interconnectivity. Thus, in line with long-held theoretical predictions, ascending populations convey high-level behavioral state signals almost exclusively to brain regions implicated in sensory feature contextualization and action selection.

We present a method to automatically identify and track nuclei in time-lapse microscopy recordings of entire developing embryos. The method combines deep learning and global optimization. On a mouse dataset, it reconstructs 75.8% of cell lineages spanning 1 h, as compared to 31.8% for the competing method. Our approach improves understanding of where and when cell fate decisions are made in developing embryos, tissues, and organs.

NDP52 is an autophagy receptor involved in the recognition and degradation of invading pathogens and damaged organelles. Although NDP52 was first identified in the nucleus and is expressed throughout the cell, to date, there is no clear nuclear functions for NDP52. Here, we use a multidisciplinary approach to characterise the biochemical properties and nuclear roles of NDP52. We find that NDP52 clusters with RNA Polymerase II (RNAPII) at transcription initiation sites and that its overexpression promotes the formation of additional transcriptional clusters. We also show that depletion of NDP52 impacts overall gene expression levels in two model mammalian cells, and that transcription inhibition affects the spatial organisation and molecular dynamics of NDP52 in the nucleus. This directly links NDP52 to a role in RNAPII-dependent transcription. Furthermore, we also show that NDP52 binds specifically and with high affinity to double-stranded DNA (dsDNA) and that this interaction leads to changes in DNA structure in vitro. This, together with our proteomics data indicating enrichment for interactions with nucleosome remodelling proteins and DNA structure regulators, suggests a possible function for NDP52 in chromatin regulation. Overall, here we uncover nuclear roles for NDP52 in gene expression and DNA structure regulation.

Insulin signaling plays a pivotal role in metabolic control and aging, and insulin accordingly is a key factor in several human diseases. Despite this importance, the in vivo activity dynamics of insulin-producing cells (IPCs) are poorly understood. Here, we characterized the effects of locomotion on the activity of IPCs in Drosophila. Using in vivo electrophysiology and calcium imaging, we found that IPCs were strongly inhibited during walking and flight and that their activity rebounded and overshot after cessation of locomotion. Moreover, IPC activity changed rapidly during behavioral transitions, revealing that IPCs are modulated on fast timescales in behaving animals. Optogenetic activation of locomotor networks ex vivo, in the absence of actual locomotion or changes in hemolymph sugar levels, was sufficient to inhibit IPCs. This demonstrates that the behavioral state-dependent inhibition of IPCs is actively controlled by neuronal pathways and is independent of changes in glucose concentration. By contrast, the overshoot in IPC activity after locomotion was absent ex vivo and after starvation, indicating that it was not purely driven by feedforward signals but additionally required feedback derived from changes in hemolymph sugar concentration. We hypothesize that IPC inhibition during locomotion supports mobilization of fuel stores during metabolically demanding behaviors, while the rebound in IPC activity after locomotion contributes to replenishing muscle glycogen stores. In addition, the rapid dynamics of IPC modulation support a potential role of insulin in the state-dependent modulation of sensorimotor processing.

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.