Tracking calcium dynamics from individual neurons in behaving animals

Satiety controls behavior in Hydra through an interplay of pre-enteric and central nervous system-like neuron populations

Hunger and satiety can have an influence on decision-making, sensory processing, and motor behavior by altering the internal state of the brain. This process necessitates the integration of peripheral sensory stimuli into the central nervous system. Here, we show how animals without a central nervous system such as the cnidarian Hydra measure and integrate satiety into neuronal circuits and which specific neuronal populations are involved. We demonstrate that this simple nervous system, previously referred to as diffuse, has an endodermal subpopulation (N4) similar to the enteric nervous system (feeding-associated behavior) and an ectodermal population (N3) that performs central nervous system-like functions (physiology/motor). This view of a supposedly simple nervous system could open an important window into the origin of more complex nervous systems.

Breaking the neural code of a cnidarian: Learning principles of neuroscience from the "vulgar" Hydra

The cnidarian Hydra vulgaris is a small polyp with a nervous system of few hundred neurons belonging to a dozen cell types, organized in two nerve nets without cephalization or ganglia. Using this simple neural "chassis", Hydra can maintain a stable repertoire of behaviors, even performing complex fixed-action patterns, such as somersaulting and feeding. The ability to image the activity of Hydra's entire neural and muscle tissue has revealed that Hydra's nerve nets are divided into coactive ensembles of neurons, associated with specific movements. These ensembles can be activated by neuropeptides and interact using cross-inhibition circuits and implement integrate-to-threshold algorithms. In addition, Hydra's nervous system can self-assemble from dissociated cells in a stepwise modular architecture. Studies of Hydra and other cnidarians could enable the systematic deciphering of the neural basis of its behavior and help provide perspective on basic principles of neuroscience.

Versatile multiple object tracking in sparse 2D/3D videos via deformable image registration

Tracking body parts in behaving animals, extracting fluorescence signals from cells embedded in deforming tissue, and analyzing cell migration patterns during development all require tracking objects with partially correlated motion. As dataset sizes increase, manual tracking of objects becomes prohibitively inefficient and slow, necessitating automated and semi-automated computational tools. Unfortunately, existing methods for multiple object tracking (MOT) are either developed for specific datasets and hence do not generalize well to other datasets, or require large amounts of training data that are not readily available. This is further exacerbated when tracking fluorescent sources in moving and deforming tissues, where the lack of unique features and sparsely populated images create a challenging environment, especially for modern deep learning techniques. By leveraging technology recently developed for spatial transformer networks, we propose ZephIR, an image registration framework for semi-supervised MOT in 2D and 3D videos. ZephIR can generalize to a wide range of biological systems by incorporating adjustable parameters that encode spatial (sparsity, texture, rigidity) and temporal priors of a given data class. We demonstrate the accuracy and versatility of our approach in a variety of applications, including tracking the body parts of a behaving mouse and neurons in the brain of a freely moving C. elegans. We provide an open-source package along with a web-based graphical user interface that allows users to provide small numbers of annotations to interactively improve tracking results.

Automated neuron tracking inside moving and deforming C. elegans using deep learning and targeted augmentation

Reading out neuronal activity from three-dimensional (3D) functional imaging requires segmenting and tracking individual neurons. This is challenging in behaving animals if the brain moves and deforms. The traditional approach is to train a convolutional neural network with ground-truth (GT) annotations of images representing different brain postures. For 3D images, this is very labor intensive. We introduce 'targeted augmentation', a method to automatically synthesize artificial annotations from a few manual annotations. Our method ('Targettrack') learns the internal deformations of the brain to synthesize annotations for new postures by deforming GT annotations. This reduces the need for manual annotation and proofreading. A graphical user interface allows the application of the method end-to-end. We demonstrate Targettrack on recordings where neurons are labeled as key points or 3D volumes. Analyzing freely moving animals exposed to odor pulses, we uncover rich patterns in interneuron dynamics, including switching neuronal entrainment on and off.

Multiple neuronal populations control the eating behavior in Hydra and are responsive to microbial signals

Although recent studies indicate the impact of microbes on the central nervous systems and behavior, it remains unclear how the relationship between the functionality of the nervous system, behavior, and the microbiota evolved. In this work, we analyzed the eating behavior of Hydra, a host that has a simple nervous system and a low-complexity microbiota. To identify the neuronal subpopulations involved, we used a subpopulation-specific cell ablation system and calcium imaging. The role of the microbiota was uncovered by manipulating the diversity of the natural microbiota. We show that different neuronal subpopulations are functioning together to control eating behavior. Animals with a drastically reduced microbiome had severe difficulties in mouth opening due to a significantly increased level of glutamate. This could be reversed by adding a full complement of the microbiota. In summary, we provide a mechanistic explanation of how Hydra's nervous system controls eating behavior and what role microbes play in this.

Effects of Nonthermal Radiofrequency Stimulation on Neuronal Activity and Neural Circuit in Mice

Whether the nonthermal effects of radiofrequency radiation (RFR) exist and how nonthermal RFR acts on the nervous system are unknown. An animal model of spatial memory impairment is established by exposing mice to 2856-MHz RFR in the range of thermal noise (≤1 °C). Glutamate release in the dorsal hippocampus (dHPC) CA1 region is not significantly changed after radiofrequency exposure, whereas dopamine release is reduced. Importantly, RFR enhances glutamatergic CA1 pyramidal neuron calcium activity by nonthermal mechanisms, which recover to the basal level with RFR termination. Furthermore, suppressed dHPC dopamine release induced by radiofrequency exposure is due to decreased density of dopaminergic projections from the locus coeruleus to dHPC, and artificial activation of dopamine axon terminals or D1 receptors in dHPC CA1 improve memory damage in mice exposed to RFR. These findings indicate that nonthermal radiofrequency stimulation modulates ongoing neuronal activity and affects nervous system function at the neural circuit level.

A complete biomechanical model of Hydra contractile behaviors, from neural drive to muscle to movement

How does neural activity drive muscles to produce behavior? The recent development of genetic lines in Hydra that allow complete calcium imaging of both neuronal and muscle activity, as well as systematic machine learning quantification of behaviors, makes this small cnidarian an ideal model system to understand and model the complete transformation from neural firing to body movements. To achieve this, we have built a neuromechanical model of Hydra's fluid-filled hydrostatic skeleton, showing how drive by neuronal activity activates distinct patterns of muscle activity and body column biomechanics. Our model is based on experimental measurements of neuronal and muscle activity and assumes gap junctional coupling among muscle cells and calcium-dependent force generation by muscles. With these assumptions, we can robustly reproduce a basic set of Hydra's behaviors. We can further explain puzzling experimental observations, including the dual timescale kinetics observed in muscle activation and the engagement of ectodermal and endodermal muscles in different behaviors. This work delineates the spatiotemporal control space of Hydra movement and can serve as a template for future efforts to systematically decipher the transformations in the neural basis of behavior.

TACI: An ImageJ Plugin for 3D Calcium Imaging Analysis

Research in neuroscience has evolved to use complex imaging and computational tools to extract comprehensive information from data sets. Calcium imaging is a widely used technique that requires sophisticated software to obtain reliable results, but many laboratories struggle to adopt computational methods when updating protocols to meet modern standards. Difficulties arise due to a lack of programming knowledge and paywalls for software. In addition, cells of interest display movements in all directions during calcium imaging. Many approaches have been developed to correct the motion in the lateral (x/y) direction. This paper describes a workflow using a new ImageJ plugin, TrackMate Analysis of Calcium Imaging (TACI), to examine motion on the z-axis in 3D calcium imaging. This software identifies the maximum fluorescence value from all the z-positions a neuron appears in and uses it to represent the neuron's intensity at the corresponding t-position. Therefore, this tool can separate neurons overlapping in the lateral (x/y) direction but appearing on distinct z-planes. As an ImageJ plugin, TACI is a user-friendly, open-source computational tool for 3D calcium imaging analysis. We validated this workflow using fly larval thermosensitive neurons that displayed movements in all directions during temperature fluctuation and a 3D calcium imaging dataset acquired from the fly brain.

Rapid detection and recognition of whole brain activity in a freely behaving Caenorhabditis elegans

Advanced volumetric imaging methods and genetically encoded activity indicators have permitted a comprehensive characterization of whole brain activity at single neuron resolution in Caenorhabditis elegans. The constant motion and deformation of the nematode nervous system, however, impose a great challenge for consistent identification of densely packed neurons in a behaving animal. Here, we propose a cascade solution for long-term and rapid recognition of head ganglion neurons in a freely moving C. elegans. First, potential neuronal regions from a stack of fluorescence images are detected by a deep learning algorithm. Second, 2-dimensional neuronal regions are fused into 3-dimensional neuron entities. Third, by exploiting the neuronal density distribution surrounding a neuron and relative positional information between neurons, a multi-class artificial neural network transforms engineered neuronal feature vectors into digital neuronal identities. With a small number of training samples, our bottom-up approach is able to process each volume—1024 × 1024 × 18 in voxels—in less than 1 second and achieves an accuracy of 91% in neuronal detection and above 80% in neuronal tracking over a long video recording. Our work represents a step towards rapid and fully automated algorithms for decoding whole brain activity underlying naturalistic behaviors.

An important question in neuroscience is to understand the relationship between brain dynamics and naturalistic behaviors when an animal is freely exploring its environment. In the last decade, it has become possible to genetically engineer animals whose neurons produce fluorescence reporters that change their brightness in response to brain activity. In small animals such as the nematode C. elegans, we can now record the fluorescence changes in and thereby infer neural activity from most neurons in the head of a worm, when the animal is freely moving. These neurons are densely packed in a small volume. Since the brain and body are moving and its shape undergoes significant deformation, a human expert, even after long hours of inspection, may still have difficulty to tell where the neurons are and who they are.

We sought to develop an automatic method for rapidly detecting and tracking most of these neurons in a moving animal. To do this, we asked a human expert to annotate all head neurons—their locations and digital identities—across a small number of volumes. Then, we trained a computer to learn the locations and digital identities of these neurons across different imaging volumes. Our machine inference method is fast and accurate. While it takes a human expert several hours to complete a sequence of volumes, a machine can finish the task in a few minutes. We hope our method provides a better and more efficient engine for extracting knowledge from whole brain imaging datasets and animal behaviors.

Two-photon calcium imaging of neuronal activity

In vivo two-photon calcium imaging (2PCI) is a technique used for recording neuronal activity in the intact brain. It is based on the principle that, when neurons fire action potentials, intracellular calcium levels rise, which can be detected using fluorescent molecules that bind to calcium. This Primer is designed for scientists who are considering embarking on experiments with 2PCI. We provide the reader with a background on the basic concepts behind calcium imaging and on the reasons why 2PCI is an increasingly powerful and versatile technique in neuroscience. The Primer explains the different steps involved in experiments with 2PCI, provides examples of what ideal preparations should look like and explains how data are analysed. We also discuss some of the current limitations of the technique, and the types of solutions to circumvent them. Finally, we conclude by anticipating what the future of 2PCI might look like, emphasizing some of the analysis pipelines that are being developed and international efforts for data sharing.

Translational neuronal ensembles: Neuronal microcircuits in psychology, physiology, pharmacology and pathology

Multi-recording techniques show evidence that neurons coordinate their firing forming ensembles and that brain networks are made by connections between ensembles. While “canonical” microcircuits are composed of interconnected principal neurons and interneurons, it is not clear how they participate in recorded neuronal ensembles: “groups of neurons that show spatiotemporal co-activation”. Understanding synapses and their plasticity has become complex, making hard to consider all details to fill the gap between cellular-synaptic and circuit levels. Therefore, two assumptions became necessary: First, whatever the nature of the synapses these may be simplified by “functional connections”. Second, whatever the mechanisms to achieve synaptic potentiation or depression, the resultant synaptic weights are relatively stable. Both assumptions have experimental basis cited in this review, and tools to analyze neuronal populations are being developed based on them. Microcircuitry processing followed with multi-recording techniques show temporal sequences of neuronal ensembles resembling computational routines. These sequences can be aligned with the steps of behavioral tasks and behavior can be modified upon their manipulation, supporting the hypothesis that they are memory traces. In vitro, recordings show that these temporal sequences can be contained in isolated tissue of histological scale. Sequences found in control conditions differ from those recorded in pathological tissue obtained from animal disease models and those recorded after the actions of clinically useful drugs to treat disease states, setting the basis for new bioassays to test drugs with potential clinical use. These findings make the neuronal ensembles theoretical framework a dynamic neuroscience paradigm.

Screening Biophysical Sensors and Neurite Outgrowth Actuators in Human Induced-Pluripotent-Stem-Cell-Derived Neurons

All living cells maintain a charge distribution across their cell membrane (membrane potential) by carefully controlled ion fluxes. These bioelectric signals regulate cell behavior (such as migration, proliferation, differentiation) as well as higher-level tissue and organ patterning. Thus, voltage gradients represent an important parameter for diagnostics as well as a promising target for therapeutic interventions in birth defects, injury, and cancer. However, despite much progress in cell and molecular biology, little is known about bioelectric states in human stem cells. Here, we present simple methods to simultaneously track ion dynamics, membrane voltage, cell morphology, and cell activity (pH and ROS), using fluorescent reporter dyes in living human neurons derived from induced neural stem cells (hiNSC). We developed and tested functional protocols for manipulating ion fluxes, membrane potential, and cell activity, and tracking neural responses to injury and reinnervation in vitro. Finally, using morphology sensor, we tested and quantified the ability of physiological actuators (neurotransmitters and pH) to manipulate nerve repair and reinnervation. These methods are not specific to a particular cell type and should be broadly applicable to the study of bioelectrical controls across a wide range of combinations of models and endpoints.

Tracking receptor motions at the plasma membrane reveals distinct effects of ligands on CCR5 dynamics depending on its dimerization status

G-protein-coupled receptors (GPCR) are present at the cell surface in different conformational and oligomeric states. However, how these states impact GPCRs biological function and therapeutic targeting remains incompletely known. Here, we investigated this issue in living cells for the CC chemokine receptor 5 (CCR5), a major receptor in inflammation and the principal entry co-receptor for Human Immunodeficiency Viruses type 1 (HIV-1). We used TIRF microscopy and a statistical method to track and classify the motion of different receptor subpopulations. We showed a diversity of ligand-free forms of CCR5 at the cell surface constituted of various oligomeric states and exhibiting transient Brownian and restricted motions. These forms were stabilized differently by distinct ligands. In particular, agonist stimulation restricted the mobility of CCR5 and led to its clustering, a feature depending on β-arrestin, while inverse agonist stimulation exhibited the opposite effect. These results suggest a link between receptor activation and immobilization. Applied to HIV-1 envelope glycoproteins gp120, our quantitative analysis revealed agonist-like properties of gp120s. Distinct gp120s influenced CCR5 dynamics differently, suggesting that they stabilize different CCR5 conformations. Then, using a dimerization-compromized mutant, we showed that dimerization (i) impacts CCR5 precoupling to G proteins, (ii) is a pre-requisite for the immobilization and clustering of receptors upon activation, and (iii) regulates receptor endocytosis, thereby impacting the fate of activated receptors. This study demonstrates that tracking the dynamic behavior of a GPCR is an efficient way to link GPCR conformations to their functions, therefore improving the development of drugs targeting specific receptor conformations.

High-Power Electromagnetic Pulse Exposure of Healthy Mice: Assessment of Effects on Mice Cognitions, Neuronal Activities, and Hippocampal Structures

Electromagnetic pulse (EMP) is a high-energy pulse with an extremely rapid rise time and a broad bandwidth. The brain is a target organ sensitive to electromagnetic radiation (EMR), the biological effects and related mechanisms of EMPs on the brain remain unclear. The objectives of the study were to assess the effects of EMP exposure on mouse cognitions, and the neuronal calcium activities in vivo under different cases of real-time exposure and post exposure. EMP-treated animal model was established by exposing male adult C57BL/6N mice to 300 kV/m EMPs. First, the effects of EMPs on the cognitions, including the spatial learning and memory, avoidance learning and memory, novelty-seeking behavior, and anxiety, were assessed by multiple behavioral experiments. Then, the changes in the neuronal activities of the hippocampal CA1 area in vivo were detected by fiber photometry in both cases of during real-time EMP radiation and post-exposure. Finally, the structures of neurons in hippocampi were observed by optical microscope and transmission electron microscope. We found that EMPs under this condition caused a decline in the spatial learning and memory ability in mice, but no effects on the avoidance learning and memory, novelty-seeking behavior, and anxiety. The neuron activities of hippocampal CA1 were disturbed by EMP exposure, which were inhibited during EMP exposure, but activated immediately after exposure end. Additionally, the CA1 neuron activities, when mice entered the central area in an Open field (OF) test or explored the novelty in a Novel object exploration (NOE) test, were inhibited on day 1 and day 7 after radiation. Besides, damaged structures in hippocampal neurons were observed after EMP radiation. In conclusion, EMP radiation impaired the spatial learning and memory ability and disturbed the neuronal activities in hippocampal CA1 in mice.