Precise, repeatable genetic access to specific neurons via GAL4/UAS and related methods is a key advantage of Drosophila neuroscience. Neuronal targeting is typically documented using light microscopy of full GAL4 expression patterns, which generally lack the single-cell resolution required for reliable cell type identification. Here we use stochastic GAL4 labeling with the MultiColor FlpOut approach to generate cellular resolution confocal images at large scale. We are releasing aligned images of 74,000 such adult central nervous systems. An anticipated use of this resource is to bridge the gap between neurons identified by electron or light microscopy. Identifying individual neurons that make up each GAL4 expression pattern improves the prediction of split-GAL4 combinations targeting particular neurons. To this end we have made the images searchable on the NeuronBridge website. We demonstrate the potential of NeuronBridge to rapidly and effectively identify neuron matches based on morphology across imaging modalities and datasets.

We review the principles of development and deployment of genetically encoded calcium indicators (GECIs) for the detection of neural activity. Our focus is on the popular GCaMP family of green GECIs, culminating in the recent release of the jGCaMP8 sensors, with dramatically improved kinetics relative to previous generations. We summarize the properties of GECIs in multiple color channels (blue, cyan, green, yellow, red, far-red) and highlight areas for further improvement. With their low-millisecond rise-times, the jGCaMP8 indicators allow new classes of experiments following neural activity in timeframes approaching the underlying computations. Abstract legend: GCaMP calcium sensors are widely used to report neuronal activity via fluorescence readout. This article is protected by copyright. All rights reserved.

BRCA2 is an essential tumor suppressor protein involved in promoting faithful repair of DNA lesions. The activity of BRCA2 needs to be tuned precisely to be active when and where it is needed. Here, we quantified the spatio-temporal dynamics of BRCA2 in living cells using aberration-corrected multifocal microscopy (acMFM). Using multicolor imaging to identify DNA damage sites, we were able to quantify its dynamic motion patterns in the nucleus and at DNA damage sites. While a large fraction of BRCA2 molecules localized near DNA damage sites appear immobile, an additional fraction of molecules exhibit restricted motion, providing a potential mechanism to retain an increased number of molecules at DNA lesions. Super-resolution microscopy revealed inhomogeneous localization of BRCA2 relative to other DNA repair factors at sites of DNA damage. This suggests the presence of multiple subcompartments at the chromatin surrounding the DNA lesion, which may play an important role in the contribution of BRCA2 to the regulation of the repair process.

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.

A new method allows researchers to automatically assign cells into different cell types and tissues, a step which is critical for understanding complex organisms.

Focal adhesions (FAs) connect inner workings of the cell to the extracellular matrix to control cell adhesion, migration, and mechanosensing1,2. Previous studies demonstrated that FAs contain three vertical layers, which connect extracellular matrix to the cytoskeleton3,4,5. However, cellular processes rely on precisely-regulated FA turnover, but the molecular machineries that control FA assembly and disassembly have remained elusive. By using super-resolution iPALM microscopy, we identified two unprecedented nanoscale layers within FAs, specified by actin filaments bound to tropomyosin isoforms Tpm1.6 and Tpm3.2. The Tpm1.6-actin filaments beneath the previously identified ‘actin-regulatory layer’ are critical for adhesion maturation and controlled cell motility, whereas the Tpm3.2-actin filament layer towards the bottom of FA facilitates adhesion disassembly. Mechanistically, Tpm3.2 stabilizes KANK-family proteins at adhesions, and hence targets microtubule plus-ends to FAs to catalyse their disassembly. Loss of Tpm3.2 leads to disorganized microtubule network, abnormally stable FAs, and defects in tail retraction during cell migration. Thus, FAs are composed of at least three distinct actin filament layers, each having specific roles in coupling of adhesion to the cytoskeleton, or in controlling adhesion dynamics. In a broader context, these findings demonstrate how distinct actin filament populations can co-exist and perform specific functions within a defined cellular compartment.

Microscopy core facilities are increasingly utilized research resources, but they are generally only available to users within the host institution. Such localized access misses an opportunity to facilitate research across a broader user base. Here, we present the model of an open-access microscopy facility, using the Advanced Imaging Center (AIC) at Howard Hughes Medical Institute Janelia Research Campus as an example. The AIC has pioneered a model whereby advanced microscopy technologies and expertise are made accessible to researchers on a global scale. We detail our experiences in addressing the considerable challenges associated with this model for those who may be interested in launching an open-access imaging facility. Importantly, we focus on how this model can empower researchers, particularly those from resource-constrained settings. This article is protected by copyright. All rights reserved.

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.

High-density, integrated silicon electrodes have begun to transform systems neuroscience, by enabling large-scale neural population recordings with single cell resolution. Existing technologies, however, have provided limited functionality in nonhuman primate species such as macaques, which offer close models of human cognition and behavior. Here, we report the design, fabrication, and performance of Neuropixels 1.0-NHP, a high channel count linear electrode array designed to enable large-scale simultaneous recording in superficial and deep structures within the macaque or other large animal brain. These devices were fabricated in two versions: 4416 electrodes along a 45 mm shank, and 2496 along a 25 mm shank. For both versions, users can programmably select 384 channels, enabling simultaneous multi-area recording with a single probe. We demonstrate recording from over 3000 single neurons within a session, and simultaneous recordings from over 1000 neurons using multiple probes. This technology represents a significant increase in recording access and scalability relative to existing technologies, and enables new classes of experiments involving fine-grained electrophysiological characterization of brain areas, functional connectivity between cells, and simultaneous brain-wide recording at scale.

We present an auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of local shape descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a study comparing several existing methods across various specimen, imaging techniques, and resolutions, auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (flood-filling networks), while being two orders of magnitudes more efficient-a critical requirement for the processing of future petabyte-sized datasets.