Individual Neurons Research Articles

Research progress on insect visual electrophysiological techniques.

Insect visual electrophysiological techniques are important to study the electrical characteristics of photoreceptor cells and visual neurons in insects, including electroretinography (ERG) and microelectrode intracellular recording (MIR). ERG records the changes of voltage or electric current in the retina of insects in response to different light stimuli, which occurs outside the cell. MIR records the changes in individual photoreceptor cells or visual neurons of an insect exposed to different lights, which occurs inside the cell. Insect visual electrophysiological techniques can explore the mechanism of electrophysiological response of insects' vision to light and reveal their sensitive light spectra and photoreceptor types. This review introduced the basic structure and the principle of ERG and MIR, and summarized their applications in insect researches in the past 20 years, which would provide references for elucidating the mechanism of light perception in insects and the use of insect phototropism to control pests.

NeuronBridge: an intuitive web application for neuronal morphology search across large data sets

BackgroundNeuroscience research in Drosophila is benefiting from large-scale connectomics efforts using electron microscopy (EM) to reveal all the neurons in a brain and their connections. To exploit this knowledge base, researchers relate a connectome’s structure to neuronal function, often by studying individual neuron cell types. Vast libraries of fly driver lines expressing fluorescent reporter genes in sets of neurons have been created and imaged using confocal light microscopy (LM), enabling the targeting of neurons for experimentation. However, creating a fly line for driving gene expression within a single neuron found in an EM connectome remains a challenge, as it typically requires identifying a pair of driver lines where only the neuron of interest is expressed in both. This task and other emerging scientific workflows require finding similar neurons across large data sets imaged using different modalities.ResultsHere, we present NeuronBridge, a web application for easily and rapidly finding putative morphological matches between large data sets of neurons imaged using different modalities. We describe the functionality and construction of the NeuronBridge service, including its user-friendly graphical user interface (GUI), extensible data model, serverless cloud architecture, and massively parallel image search engine.ConclusionsNeuronBridge fills a critical gap in the Drosophila research workflow and is used by hundreds of neuroscience researchers around the world. We offer our software code, open APIs, and processed data sets for integration and reuse, and provide the application as a service at http://neuronbridge.janelia.org.

Multiple objects evoke fluctuating responses in several regions of the visual pathway.

How neural representations preserve information about multiple stimuli is mysterious. Because tuning of individual neurons is coarse (e.g., visual receptive field diameters can exceed perceptual resolution), the populations of neurons potentially responsive to each individual stimulus can overlap, raising the question of how information about each item might be segregated and preserved in the population. We recently reported evidence for a potential solution to this problem: when two stimuli were present, some neurons in the macaque visual cortical areas V1 and V4 exhibited fluctuating firing patterns, as if they responded to only one individual stimulus at a time (Jun et al., 2022). However, whether such an information encoding strategy is ubiquitous in the visual pathway and thus could constitute a general phenomenon remains unknown. Here, we provide new evidence that such fluctuating activity is also evoked by multiple stimuli in visual areas responsible for processing visual motion (middle temporal visual area, MT), and faces (middle fundus and anterolateral face patches in inferotemporal cortex - areas MF and AL), thus extending the scope of circumstances in which fluctuating activity is observed. Furthermore, consistent with our previous results in the early visual area V1, MT exhibits fluctuations between the representations of two stimuli when these form distinguishable objects but not when they fuse into one perceived object, suggesting that fluctuating activity patterns may underlie visual object formation. Taken together, these findings point toward an updated model of how the brain preserves sensory information about multiple stimuli for subsequent processing and behavioral action.

Functional labeling of individualized postsynaptic neurons using optogenetics and trans-Tango in Drosophila (FLIPSOT).

A population of neurons interconnected by synapses constitutes a neural circuit, which performs specific functions upon activation. It is essential to identify both anatomical and functional entities of neural circuits to comprehend the components and processes necessary for healthy brain function and the changes that characterize brain disorders. To date, few methods are available to study these two aspects of a neural circuit simultaneously. In this study, we developed FLIPSOT, or functional labeling of individualized postsynaptic neurons using optogenetics and trans-Tango. FLIPSOT uses (1) trans-Tango to access postsynaptic neurons genetically, (2) optogenetic approaches to activate (FLIPSOTa) or inhibit (FLIPSOTi) postsynaptic neurons in a random and sparse manner, and (3) fluorescence markers tagged with optogenetic genes to visualize these neurons. Therefore, FLIPSOT allows using a presynaptic driver to identify the behavioral function of individual postsynaptic neurons. It is readily applied to identify functions of individual postsynaptic neurons and has the potential to be adapted for use in mammalian circuits.

The Relative Contribution of Glycine-GABA Cotransmission in the Core of the Respiratory Network.

The preBötzinger complex (preBötC) and the Bötzinger complex (BötC) are interconnected neural circuits that are involved in the regulation of breathing in mammals. Fast inhibitory neurotransmission is known to play an important role in the interaction of these two regions. Moreover, the corelease of glycine and GABA has been described in the respiratory network, but the contribution of the individual neurotransmitter in different pathways remains elusive. In sagittal brainstem slices of neonatal mice, we employed a laser point illumination system to activate glycinergic neurons expressing channelrhodopsin-2 (ChR2). This approach allowed us to discern the contribution of glycine and GABA to postsynaptic currents of individual whole-cell clamped neurons in the preBötC and BötC through the application of glycine and GABA receptor-specific antagonists. In more than 90% of the recordings, both transmitters contributed to the evoked IPSCs, with the glycinergic component being larger than the GABAergic component. The GABAergic component appeared to be most prominent when stimulation and recording were both performed within the preBötC. Taken together, our data suggest that GABA-glycine cotransmission is the default mode in the respiratory network of neonatal mice with regional differences that may be important in tuning the network activity.

Sex differences in single neuron function and proteomics profiles examined by patch-clamp and mass spectrometry in the locus coeruleus of the adult mouse.

This study aimed to characterize the properties of locus coeruleus (LC) noradrenergic neurons in male and female mice. We also sought to investigate sex-specific differences in membrane properties, action potential generation, and protein expression profiles to understand the mechanisms underlying neuronal excitability variations. Utilizing a genetic mouse model by crossing Dbhcre knock-in mice with tdTomato Ai14 transgenic mice, LC neurons were identified using fluorescence microscopy. Neuronal functional properties were assessed using patch-clamp recordings. Proteomic analyses of individual LC neuron soma was conducted using mass spectrometry to discern protein expression profiles. Data are available via ProteomeXchange with identifier PXD045844. Female LC noradrenergic neurons displayed greater membrane capacitance than those in male mice. Male LC neurons demonstrated greater spontaneous and evoked action potential generation compared to females. Male LC neurons exhibited a lower rheobase and achieved higher peak frequencies with similar current injections. Proteomic analysis revealed differences in protein expression profiles between sexes, with male mice displaying a notably larger unique protein set compared to females. Notably, pathways pertinent to protein synthesis, degradation, and recycling, such as EIF2 and glucocorticoid receptor signaling, showed reduced expression in females. Male LC noradrenergic neurons exhibit higher intrinsic excitability compared to those from females. The discernible sex-based differences in excitability could be ascribed to varying protein expression profiles, especially within pathways that regulate protein synthesis and degradation. This study lays the groundwork for future studies focusing on the interplay between proteomics and neuronal function examined in individual cells.

Large-scale cranial window for in vivo mouse brain imaging utilizing fluoropolymer nanosheet and light-curable resin

Two-photon microscopy enables in vivo imaging of neuronal activity in mammalian brains at high resolution. However, two-photon imaging tools for stable, long-term, and simultaneous study of multiple brain regions in same mice are lacking. Here, we propose a method to create large cranial windows covering such as the whole parietal cortex and cerebellum in mice using fluoropolymer nanosheets covered with light-curable resin (termed the ‘Nanosheet Incorporated into light-curable REsin’ or NIRE method). NIRE method can produce cranial windows conforming the curved cortical and cerebellar surfaces, without motion artifacts in awake mice, and maintain transparency for >5 months. In addition, we demonstrate that NIRE method can be used for in vivo two-photon imaging of neuronal ensembles, individual neurons and subcellular structures such as dendritic spines. The NIRE method can facilitate in vivo large-scale analysis of heretofore inaccessible neural processes, such as the neuroplastic changes associated with maturation, learning and neural pathogenesis.

Emergence of complex oscillatory dynamics in the neuronal networks with long activity time of inhibitory synapses

The brain displays complex dynamics, including collective oscillations, and extensive research has been conducted to understand their generation. However, our understanding of how biological constraints influence these oscillations is incomplete. This study investigates the essential properties of neuronal networks needed to generate oscillations resembling those in the brain. A simple discrete-time model of interconnected excitable elements is developed, capable of closely resembling the complex oscillations observed in biological neural networks. In the model, synaptic connections remain active for a duration exceeding individual neuron activity. We show that the inhibitory synapses must exhibit longer activity than excitatory synapses to produce a diverse range of the dynamical states, including biologically plausible oscillations. Upon meeting this condition, the transition between different dynamical states can be controlled by external stochastic input to the neurons. The study provides a comprehensive explanation for the emergence of distinct dynamical states in neural networks based on specific parameters.

Automatic monitoring of neural activity with single-cell resolution in behaving Hydra

The ability to record every spike from every neuron in a behaving animal is one of the holy grails of neuroscience. Here, we report coming one step closer towards this goal with the development of an end-to-end pipeline that automatically tracks and extracts calcium signals from individual neurons in the cnidarian Hydra vulgaris. We imaged dually labeled (nuclear tdTomato and cytoplasmic GCaMP7s) transgenic Hydra and developed an open-source Python platform (TraSE-IN) for the Tracking and Spike Estimation of Individual Neurons in the animal during behavior. The TraSE-IN platform comprises a series of modules that segments and tracks each nucleus over time and extracts the corresponding calcium activity in the GCaMP channel. Another series of signal processing modules allows robust prediction of individual spikes from each neuron’s calcium signal. This complete pipeline will facilitate the automatic generation and analysis of large-scale datasets of single-cell resolution neural activity in Hydra, and potentially other model organisms, paving the way towards deciphering the neural code of an entire animal.

Synchrony in Networks of Type 2 Interneurons Is More Robust to Noise with Hyperpolarizing Inhibition Compared to Shunting Inhibition in Both the Stochastic Population Oscillator and the Coupled Oscillator Regimes.

Synchronization in the gamma band (25-150 Hz) is mediated by PV+ inhibitory interneurons, and evidence is accumulating for the essential role of gamma oscillations in cognition. Oscillations can arise in inhibitory networks via synaptic interactions between individual oscillatory neurons (mean-driven) or via strong recurrent inhibition that destabilizes the stationary background firing rate in the fluctuation-driven balanced state, causing an oscillation in the population firing rate. Previous theoretical work focused on model neurons with Hodgkin's Type 1 excitability (integrators) connected by current-based synapses. Here we show that networks comprised of simple Type 2 oscillators (resonators) exhibit a supercritical Hopf bifurcation between synchrony and asynchrony and a gradual transition via cycle skipping from coupled oscillators to stochastic population oscillator (SPO), as previously shown for Type 1. We extended our analysis to homogeneous networks with conductance rather than current based synapses and found that networks with hyperpolarizing inhibitory synapses were more robust to noise than those with shunting synapses, both in the coupled oscillator and SPO regime. Assuming that reversal potentials are uniformly distributed between shunting and hyperpolarized values, as observed in one experimental study, converting synapses to purely hyperpolarizing favored synchrony in all cases, whereas conversion to purely shunting synapses made synchrony less robust except at very high conductance strengths. In mature neurons the synaptic reversal potential is controlled by chloride cotransporters that control the intracellular concentrations of chloride and bicarbonate ions, suggesting these transporters as a potential therapeutic target to enhance gamma synchrony and cognition.

D1 and D2 medium spiny neurons in the nucleus accumbens core have distinct and valence-independent roles in learning.

At the core of value-based learning is the nucleus accumbens (NAc). D1- and D2-receptor-containing medium spiny neurons (MSNs) in the NAc core are hypothesized to have opposing valence-based roles in behavior. Using optical imaging and manipulation approaches in mice, we show that neither D1 nor D2 MSNs signal valence. D1 MSN responses were evoked by stimuli regardless of valence or contingency. D2 MSNs were evoked by both cues and outcomes, were dynamically changed with learning, and tracked valence-free prediction error at the population and individual neuron level. Finally, D2 MSN responses to cues were necessary for associative learning. Thus, D1 and D2 MSNs work in tandem, rather than in opposition, by signaling specific properties of stimuli to control learning.

Mechanical stimulation and electrophysiological monitoring at subcellular resolution reveals differential mechanosensation of neurons within networks

A growing consensus that the brain is a mechanosensitive organ is driving the need for tools that mechanically stimulate and simultaneously record the electrophysiological response of neurons within neuronal networks. Here we introduce a synchronized combination of atomic force microscopy, high-density microelectrode array and fluorescence microscopy to monitor neuronal networks and to mechanically characterize and stimulate individual neurons at piconewton force sensitivity and nanometre precision while monitoring their electrophysiological activity at subcellular spatial and millisecond temporal resolution. No correlation is found between mechanical stiffness and electrophysiological activity of neuronal compartments. Furthermore, spontaneously active neurons show exceptional functional resilience to static mechanical compression of their soma. However, application of fast transient (∼500 ms) mechanical stimuli to the neuronal soma can evoke action potentials, which depend on the anchoring of neuronal membrane and actin cytoskeleton. Neurons show higher responsivity, including bursts of action potentials, to slower transient mechanical stimuli (∼60 s). Moreover, transient and repetitive application of the same compression modulates the neuronal firing rate. Seemingly, neuronal networks can differentiate and respond to specific characteristics of mechanical stimulation. Ultimately, the developed multiparametric tool opens the door to explore manifold nanomechanobiological responses of neuronal systems and new ways of mechanical control.

Hippocampal replay sequence governed by spontaneous brain-wide dynamics.

Neurons in the hippocampus exhibit spontaneous spiking activity during rest that appears to recapitulate previously experienced events. While this replay activity is frequently linked to memory consolidation and learning, the underlying mechanisms are not well understood. Recent large-scale neural recordings in mice have demonstrated that resting-state spontaneous activity is expressed as quasi-periodic cascades of spiking activity that pervade the forebrain, with each cascade engaging a high proportion of recorded neurons. Hippocampal ripples are known to be coordinated with cortical dynamics; however, less is known about the occurrence of replay activity relative to other brain-wide spontaneous events. Here we analyzed responses across the mouse brain to multiple viewings of natural movies, as well as subsequent patterns of neural activity during rest. We found that hippocampal neurons showed time-selectivity, with individual neurons responding consistently during particular moments of the movie. During rest, the population of time-selective hippocampal neurons showed both forward and time-reversed replay activity that matched the sequence observed in the movie. Importantly, these replay events were strongly time-locked to brain-wide spiking cascades, with forward and time-reversed replay activity associated with distinct cascade types. Thus, intrinsic hippocampal replay activity is temporally structured according to large-scale spontaneous physiology affecting areas throughout the forebrain. These findings shed light on the coordination between hippocampal and cortical circuits thought to be critical for memory consolidation.

Advancing the path to in-vivo imaging in freely moving mice via multimode-multicore fiber based holographic endoscopy.

Hair-thin multimode optical fiber-based holographic endoscopes have gained considerable interest in modern neuroscience for their ability to achieve cellular and even subcellular resolution during in-vivo deep brain imaging. However, the application of multimode fibers in freely moving animals presents a persistent challenge as it is difficult to maintain optimal imaging performance while the fiber undergoes deformations. We propose a fiber solution for challenging in-vivo applications with the capability of deep brain high spatial resolution imaging and neuronal activity monitoring in anesthetized as well as awake behaving mice. We used our previously developed multimode-multicore fiber to record fluorescently labeled neurons in anesthetized mice. Our exhibits a cascaded refractive index structure, enabling two distinct regimes of light transport that imitate either a multimode or a multicore fiber. The has been specifically designed for use in the initial phase of an in-vivo experiment, allowing for the navigation of the endoscope's distal end toward the targeted brain structure. The multicore regime enables the transfer of light to and from each individual neuron within the field of view. For chronic experiments in awake behaving mice, it is crucial to allow for disconnecting the fiber and the animal between experiments. Therefore, we provide here an effective solution and establish a protocol for reconnection of two segments of with hexagonally arranged corelets. We successfully utilized the to image neurons in anaesthetized transgenic mice expressing enhanced green fluorescent protein. Additionally, we compared imaging results obtained with the with larger numerical aperture (NA) fibers in fixed whole-brain tissue. This study focuses on addressing challenges and providing insights into the use of multimode-multicore fibers as imaging solutions for in-vivo applications. We suggest that the upcoming version of the increases the overall NA between the two cladding layers to allow for access to high resolution spatial imaging. As the NA increases in the multimode regime, the fiber diameter and ring structure must be reduced to minimize the computational burden and invasiveness.

A novel deep learning graph attention network for Alzheimer’s disease image segmentation

Neuronal cell segmentation identifies and separates individual neurons in an image, typically to study their properties or analyze their organization in the nervous system. This is significant because neurological problems and diseases can only be treated effectively when the structure and function of neurons are understood. The proposed method is based on convolutional neural networks (CNNs) and graph attention networks (GATs) for segmenting biomedical images. A contracting path built upon a couple of convolution layers and max pooling is included in the architecture to capture context. After that, the GATs are applied to the captured context. In GATs, each node in the graph is associated with a vector of hidden features, and the model calculates attention coefficients between pairs of nodes. These attention coefficients are learned during training and can be used to weigh the contribution of each node’s features to the representation of the graph. An expanding path that utilizes the outputs generated by GATs paves the way for exact segmentation. The dataset comprises 606 microscopic images, mainly categorized into different cell types (astrocytes, cortex, and SHSY5Y). By implementing our proposed U-GAT algorithm, we obtained the highest accuracy of 86.5% and an F1 score of 0.719 compared to the CNN, U-Net, SegResNet, SegNet VGG16, and GAT benchmarking algorithms. This proposed method could help researchers in the biotech industry develop novel drugs since a more accurate deep-learning method is essential for segmenting complex images like neuronal images.

Learning what matters: Synaptic plasticity with invariance to second-order input correlations.

Cortical populations of neurons develop sparse representations adapted to the statistics of the environment. To learn efficient population codes, synaptic plasticity mechanisms must differentiate relevant latent features from spurious input correlations, which are omnipresent in cortical networks. Here, we develop a theory for sparse coding and synaptic plasticity that is invariant to second-order correlations in the input. Going beyond classical Hebbian learning, our learning objective explains the functional form of observed excitatory plasticity mechanisms, showing how Hebbian long-term depression (LTD) cancels the sensitivity to second-order correlations so that receptive fields become aligned with features hidden in higher-order statistics. Invariance to second-order correlations enhances the versatility of biologically realistic learning models, supporting optimal decoding from noisy inputs and sparse population coding from spatially correlated stimuli. In a spiking model with triplet spike-timing-dependent plasticity (STDP), we show that individual neurons can learn localized oriented receptive fields, circumventing the need for input preprocessing, such as whitening, or population-level lateral inhibition. The theory advances our understanding of local unsupervised learning in cortical circuits, offers new interpretations of the Bienenstock-Cooper-Munro and triplet STDP models, and assigns a specific functional role to synaptic LTD mechanisms in pyramidal neurons.

O-GlcNAc signaling increases neuron regeneration through one-carbon metabolism in Caenorhabditis elegans.

Cellular metabolism plays an essential role in the regrowth and regeneration of a neuron following physical injury. Yet, our knowledge of the specific metabolic pathways that are beneficial to neuron regeneration remains sparse. Previously, we have shown that modulation of O-linked β-N-acetylglucosamine (O-GlcNAc) signaling, a ubiquitous post-translational modification that acts as a cellular nutrient sensor, can significantly enhance in vivo neuron regeneration. Here, we define the specific metabolic pathway by which O-GlcNAc transferase (ogt-1) loss of function mediates increased regenerative outgrowth. Performing in vivo laser axotomy and measuring subsequent regeneration of individual neurons in C. elegans, we find that glycolysis, serine synthesis pathway (SSP), one-carbon metabolism (OCM), and the downstream transsulfuration metabolic pathway (TSP) are all essential in this process. The regenerative effects of ogt-1 mutation are abrogated by genetic and/or pharmacological disruption of OCM and the SSP linking OCM to glycolysis. Testing downstream branches of this pathway, we find that enhanced regeneration is dependent only on the vitamin B12 independent shunt pathway. These results are further supported by RNA sequencing that reveals dramatic transcriptional changes by the ogt-1 mutation, in the genes involved in glycolysis, OCM, TSP, and ATP metabolism. Strikingly, the beneficial effects of the ogt-1 mutation can be recapitulated by simple metabolic supplementation of the OCM metabolite methionine in wild-type animals. Taken together, these data unearth the metabolic pathways involved in the increased regenerative capacity of a damaged neuron in ogt-1 animals and highlight the therapeutic possibilities of OCM and its related pathways in the treatment of neuronal injury.

Driving valence-specific behavior through single-cell resolution control in the amygdala

In this issue of Neuron, Piantadosi etal.1 demonstrate that by precisely controlling the activity of individual negative-valence neurons and positive-valence neurons in the basolateral amygdala, one can alter animals' appetitive or aversive responses, respectively, establishing a causal role of these neurons in valence-specific behavior.

Neuroadaptive Tracking Control of Affine Nonlinear Systems Using Echo State Networks Embedded With Multiclustered Structure and Intrinsic Plasticity.

In this article, we present an echo state network (ESN)-based tracking control approach for a class of affine nonlinear systems. Different from the most existing neural-network (NN)-based control methods that are focused on the feedforward NN, the proposed method adopts a bioinspired recurrent NN fusing with multiple cluster and intrinsic plasticity (IP) to deal with modeling uncertainties and coupling nonlinearities in the systems. The key features of this work can be summarized as follows: 1) the proposed control is built upon the ESN embedded with multiclustered reservoir inspired from the hierarchically clustered organizations of cortical connections in mammalian brains; 2) the developed neuroadaptive control scheme utilizes unsupervised learning rules inspired from the neural plasticity mechanism of the individual neuron in nervous systems, called IP; 3) a multiclustered reservoir with IP is integrated into the algorithm to enhance the approximation performance of NN; and 4) the multiclustered reservoir is constructed offline and is task-independent, rendering the proposed method less expensive in computation. The effectiveness of the method is also confirmed by comparison with the existing neuroadaptive methods via numerical simulations, demonstrating that better tracking precision is achieved by the proposed method.

Fractured columnar small-world functional network organization in volumes of L2/3 of mouse auditory cortex.

The sensory cortices of the brain exhibit large-scale functional topographic organization, such as the tonotopic organization of the primary auditory cortex (A1) according to sound frequency. However, at the level of individual neurons, layer 2/3 (L2/3) A1 appears functionally heterogeneous. To identify if there exists a higher-order functional organization of meso-scale neuronal networks within L2/3 that bridges order and disorder, we used in vivo two-photon calcium imaging of pyramidal neurons to identify networks in three-dimensional volumes of L2/3 A1 in awake mice. Using tonal stimuli, we found diverse receptive fields with measurable colocalization of similarly tuned neurons across depth but less so across L2/3 sublayers. These results indicate a fractured microcolumnar organization with a column radius of ∼50 µm, with a more random organization of the receptive field over larger radii. We further characterized the functional networks formed within L2/3 by analyzing the spatial distribution of signal correlations (SCs). Networks show evidence of Rentian scaling in physical space, suggesting effective spatial embedding of subnetworks. Indeed, functional networks have characteristics of small-world topology, implying that there are clusters of functionally similar neurons with sparse connections between differently tuned neurons. These results indicate that underlying the regularity of the tonotopic map on large scales in L2/3 is significant tuning diversity arranged in a hybrid organization with microcolumnar structures and efficient network topologies.