Pupal behavior emerges from unstructured muscle activity in response to neuromodulation in Drosophila.

Identifying neural substrates of behavior requires defining actions in terms that map onto brain activity. Brain and muscle activity naturally correlate via the output of motor neurons, but apart from simple movements it has been difficult to define behavior in terms of muscle contractions. By mapping the musculature of the pupal fruit fly and comprehensively imaging muscle activation at single-cell resolution, we here describe a multiphasic behavioral sequence in Drosophila. Our characterization identifies a previously undescribed behavioral phase and permits extraction of major movements by a convolutional neural network. We deconstruct movements into a syllabary of co-active muscles and identify specific syllables that are sensitive to neuromodulatory manipulations. We find that muscle activity shows considerable variability, with sequential increases in stereotypy dependent upon neuromodulation. Our work provides a platform for studying whole-animal behavior, quantifying its variability across multiple spatiotemporal scales, and analyzing its neuromodulatory regulation at cellular resolution.

How do we find out how the brain works? One way is to use imaging techniques to visualise an animal's brain in action as it performs simple behaviours: as the animal moves, parts of its brain light up under the microscope. For laboratory animals like fruit flies, which have relatively small brains, this lets us observe their brain activity right down to the level of individual brain cells. The brain directs movements via collective activity of the body's muscles. Our ability to track the activity of individual muscles is, however, more limited than our ability to observe single brain cells: even modern imaging technology still cannot monitor the activity of all the muscle cells in an animal's body as it moves about. Yet this is precisely the information that scientists need to fully understand how the brain generates behaviour. Fruit flies perform specific behaviours at certain stages of their life cycle. When the fly pupa begins to metamorphose into an adult insect, it performs a fixed sequence of movements involving a set number of muscles, which is called the pupal ecdysis sequence. This initial movement sequence and the rest of metamorphosis both occur within the confines of the pupal case, which is a small, hardened shell surrounding the whole animal. Elliott et al. set out to determine if the fruit fly pupa's ecdysis sequence could be used as a kind of model, to describe a simple behaviour at the level of individual muscles. Imaging experiments used fly pupae that were genetically engineered to produce an activity-dependent fluorescent protein in their muscle cells. Pupal cases were treated with a chemical to make them transparent, allowing easy observation of their visually 'labelled' muscles. This yielded a near-complete record of muscle activity during metamorphosis. Initially, individual muscles became active in small groups. The groups then synchronised with each other over the different regions of the pupa's body to form distinct movements, much as syllables join to form words. This synchronisation was key to progression through metamorphosis and was co-ordinated at each step by specialised nerve cells that produce or respond to specific hormones. These results reveal how the brain might direct muscle activity to produce movement patterns. In the future, Elliott et al. hope to compare data on muscle activity with comprehensive records of brain cell activity, to shed new light on how the brain, muscles, and other factors work together to control behaviour.

Confocal Microscopy: Principles and Modern Practices.

In light microscopy, illuminating light is passed through the sample as uniformly as possible over the field of view. For thicker samples, where the objective lens does not have sufficient depth of focus, light from sample planes above and below the focal plane will also be detected. The out-of-focus light will add blur to the image, reducing the resolution. In fluorescence microscopy, any dye molecules in the field of view will be stimulated, including those in out-of-focus planes. Confocal microscopy provides a means of rejecting the out-of-focus light from the detector such that it does not contribute blur to the images being collected. This technique allows for high-resolution imaging in thick tissues. In a confocal microscope, the illumination and detection optics are focused on the same diffraction-limited spot in the sample, which is the only spot imaged by the detector during a confocal scan. To generate a complete image, the spot must be moved over the sample and data collected point by point. A significant advantage of the confocal microscope is the optical sectioning provided, which allows for 3D reconstruction of a sample from high-resolution stacks of images. Several types of confocal microscopes have been developed for this purpose, and each has different advantages and disadvantages. This article provides a concise introduction to confocal microscopy. © 2019 by John Wiley & Sons, Inc.

Hyperspectral imaging for simultaneous measurements of two FRET biosensors in pancreatic ß-cells.

Fluorescent protein (FP) biosensors based on Förster resonance energy transfer (FRET) are commonly used to study molecular processes in living cells. There are FP-FRET biosensors for many cellular molecules, but it remains difficult to perform simultaneous measurements of multiple biosensors. The overlapping emission spectra of the commonly used FPs, including CFP/YFP and GFP/RFP make dual FRET measurements challenging. In addition, a snapshot imaging modality is required for simultaneous imaging. The Image Mapping Spectrometer (IMS) is a snapshot hyperspectral imaging system that collects high resolution spectral data and can be used to overcome these challenges. We have previously demonstrated the IMS's capabilities for simultaneously imaging GFP and CFP/YFP-based biosensors in pancreatic ß-cells. Here, we demonstrate a further capability of the IMS to image simultaneously two FRET biosensors with a single excitation band, one for cAMP and the other for Caspase-3. We use these measurements to measure simultaneously cAMP signaling and Caspase-3 activation in pancreatic ß-cells during oxidative stress and hyperglycemia, which are essential components in the pathology of diabetes.

Neuromodulatory connectivity defines the structure of a behavioral neural network.

Neural networks are typically defined by their synaptic connectivity, yet synaptic wiring diagrams often provide limited insight into network function. This is due partly to the importance of non-synaptic communication by neuromodulators, which can dynamically reconfigure circuit activity to alter its output. Here, we systematically map the patterns of neuromodulatory connectivity in a network that governs a developmentally critical behavioral sequence in Drosophila. This sequence, which mediates pupal ecdysis, is governed by the serial release of several key factors, which act both somatically as hormones and within the brain as neuromodulators. By identifying and characterizing the functions of the neuronal targets of these factors, we find that they define hierarchically organized layers of the network controlling the pupal ecdysis sequence: a modular input layer, an intermediate central pattern generating layer, and a motor output layer. Mapping neuromodulatory connections in this system thus defines the functional architecture of the network.

Somatostatin and insulin mediate glucose-inhibited glucagon secretion in the pancreatic α-cell by lowering cAMP.

The dysregulation of glucose-inhibited glucagon secretion from the pancreatic islet α-cell is a critical component of diabetes pathology and metabolic disease. We show a previously uncharacterized [Ca(2+)]i-independent mechanism of glucagon suppression in human and murine pancreatic islets whereby cAMP and PKA signaling are decreased. This decrease is driven by the combination of somatostatin, which inhibits adenylyl cyclase production of cAMP via the Gαi subunit of the SSTR2, and insulin, which acts via its receptor to activate phosphodiesterase 3B and degrade cytosolic cAMP. Our data indicate that both somatostatin and insulin signaling are required to suppress cAMP/PKA and glucagon secretion from both human and murine α-cells, and the combination of these two signaling mechanisms is sufficient to reduce glucagon secretion from isolated α-cells as well as islets. Thus, we conclude that somatostatin and insulin together are critical paracrine mediators of glucose-inhibited glucagon secretion and function by lowering cAMP/PKA signaling with increasing glucose.

Interleukin-6 amplifies glucagon secretion: coordinated control via the brain and pancreas.

Inappropriate glucagon secretion contributes to hyperglycemia in inflammatory disease. Previous work implicates the proinflammatory cytokine interleukin-6 (IL-6) in glucagon secretion. IL-6-KO mice have a blunted glucagon response to lipopolysaccharide (LPS) that is restored by intravenous replacement of IL-6. Given that IL-6 has previously been demonstrated to have a transcriptional (i.e., slow) effect on glucagon secretion from islets, we hypothesized that the rapid increase in glucagon following LPS occurred by a faster mechanism, such as by action within the brain. Using chronically catheterized conscious mice, we have demonstrated that central IL-6 stimulates glucagon secretion uniquely in the presence of an accompanying stressor (hypoglycemia or LPS). Contrary to our hypothesis, however, we found that IL-6 amplifies glucagon secretion in two ways; IL-6 not only stimulates glucagon secretion via the brain but also by direct action on islets. Interestingly, IL-6 augments glucagon secretion from both sites only in the presence of an accompanying stressor (such as epinephrine). Given that both adrenergic tone and plasma IL-6 are elevated in multiple inflammatory diseases, the interactions of the IL-6 and catecholaminergic signaling pathways in regulating GCG secretion may contribute to our present understanding of these diseases.

NFI transcription factors interact with FOXA1 to regulate prostate-specific gene expression.

Androgen receptor (AR) action throughout prostate development and in maintenance of the prostatic epithelium is partly controlled by interactions between AR and forkhead box (FOX) transcription factors, particularly FOXA1. We sought to identity additional FOXA1 binding partners that may mediate prostate-specific gene expression. Here we identify the nuclear factor I (NFI) family of transcription factors as novel FOXA1 binding proteins. All four family members (NFIA, NFIB, NFIC, and NFIX) can interact with FOXA1, and knockdown studies in androgen-dependent LNCaP cells determined that modulating expression of NFI family members results in changes in AR target gene expression. This effect is probably mediated by binding of NFI family members to AR target gene promoters, because chromatin immunoprecipitation (ChIP) studies found that NFIB bound to the prostate-specific antigen enhancer. Förster resonance energy transfer studies revealed that FOXA1 is capable of bringing AR and NFIX into proximity, indicating that FOXA1 facilitates the AR and NFI interaction by bridging the complex. To determine the extent to which NFI family members regulate AR/FOXA1 target genes, motif analysis of publicly available data for ChIP followed by sequencing was undertaken. This analysis revealed that 34.4% of peaks bound by AR and FOXA1 contain NFI binding sites. Validation of 8 of these peaks by ChIP revealed that NFI family members can bind 6 of these predicted genomic elements, and 4 of the 8 associated genes undergo gene expression changes as a result of individual NFI knockdown. These observations suggest that NFI regulation of FOXA1/AR action is a frequent event, with individual family members playing distinct roles in AR target gene expression.

Real-time hyperspectral fluorescence imaging of pancreatic ß-cell dynamics with the image mapping spectrometer.

The development of multi-colored fluorescent proteins, nanocrystals and organic fluorophores, along with the resulting engineered biosensors, has revolutionized the study of protein localization and dynamics in living cells. Hyperspectral imaging has proven to be a useful approach for such studies, but this technique is often limited by low signal and insufficient temporal resolution. Here, we present an implementation of a snapshot hyperspectral imaging device, the image mapping spectrometer (IMS), which acquires full spectral information simultaneously from each pixel in the field without scanning. The IMS is capable of real-time signal capture from multiple fluorophores with high collection efficiency (∼65%) and image acquisition rate (up to 7.2 fps). To demonstrate the capabilities of the IMS in cellular applications, we have combined fluorescent protein (FP)-FRET and [Ca(2+)](i) biosensors to measure simultaneously intracellular cAMP and [Ca(2+)](i) signaling in pancreatic ß-cells. Additionally, we have compared quantitatively the IMS detection efficiency with a laser-scanning confocal microscope.