Classes Of Neurons Research Articles

Brain-Wide Projections and Differential Encoding of Prefrontal Neuronal Classes Underlying Learned and Innate Threat Avoidance.

To understand how the brain produces behavior, we must elucidate the relationships between neuronal connectivity and function. The medial prefrontal cortex (mPFC) is critical for complex functions including decision-making and mood. mPFC projection neurons collateralize extensively, but the relationships between mPFC neuronal activity and brain-wide connectivity are poorly understood. We performed whole-brain connectivity mapping and fiber photometry to better understand the mPFC circuits that control threat avoidance in male and female mice. Using tissue clearing and light sheet fluorescence microscopy (LSFM), we mapped the brain-wide axon collaterals of populations of mPFC neurons that project to nucleus accumbens (NAc), ventral tegmental area (VTA), or contralateral mPFC (cmPFC). We present DeepTraCE (deep learning-based tracing with combined enhancement), for quantifying bulk-labeled axonal projections in images of cleared tissue, and DeepCOUNT (deep-learning based counting of objects via 3D U-net pixel tagging), for quantifying cell bodies. Anatomical maps produced with DeepTraCE aligned with known axonal projection patterns and revealed class-specific topographic projections within regions. Using TRAP2 mice and DeepCOUNT, we analyzed whole-brain functional connectivity underlying threat avoidance. PL was the most highly connected node with functional connections to subsets of PL-cPL, PL-NAc, and PL-VTA target sites. Using fiber photometry, we found that during threat avoidance, cmPFC and NAc-projectors encoded conditioned stimuli, but only when action was required to avoid threats. mPFC-VTA neurons encoded learned but not innate avoidance behaviors. Together our results present new and optimized approaches for quantitative whole-brain analysis and indicate that anatomically defined classes of mPFC neurons have specialized roles in threat avoidance.SIGNIFICANCE STATEMENT Understanding how the brain produces complex behaviors requires detailed knowledge of the relationships between neuronal connectivity and function. The medial prefrontal cortex (mPFC) plays a key role in learning, mood, and decision-making, including evaluating and responding to threats. mPFC dysfunction is strongly linked to fear, anxiety and mood disorders. Although mPFC circuits are clear therapeutic targets, gaps in our understanding of how they produce cognitive and emotional behaviors prevent us from designing effective interventions. To address this, we developed a high-throughput analysis pipeline for quantifying bulk-labeled fluorescent axons [DeepTraCE (deep learning-based tracing with combined enhancement)] or cell bodies [DeepCOUNT (deep-learning based counting of objects via 3D U-net pixel tagging)] in intact cleared brains. Using DeepTraCE, DeepCOUNT, and fiber photometry, we performed detailed anatomic and functional mapping of mPFC neuronal classes, identifying specialized roles in threat avoidance.

Exploiting semantic information in a spiking neural SLAM system.

To navigate in new environments, an animal must be able to keep track of its position while simultaneously creating and updating an internal map of features in the environment, a problem formulated as simultaneous localization and mapping (SLAM) in the field of robotics. This requires integrating information from different domains, including self-motion cues, sensory, and semantic information. Several specialized neuron classes have been identified in the mammalian brain as being involved in solving SLAM. While biology has inspired a whole class of SLAM algorithms, the use of semantic information has not been explored in such work. We present a novel, biologically plausible SLAM model called SSP-SLAM-a spiking neural network designed using tools for large scale cognitive modeling. Our model uses a vector representation of continuous spatial maps, which can be encoded via spiking neural activity and bound with other features (continuous and discrete) to create compressed structures containing semantic information from multiple domains (e.g., spatial, temporal, visual, conceptual). We demonstrate that the dynamics of these representations can be implemented with a hybrid oscillatory-interference and continuous attractor network of head direction cells. The estimated self-position from this network is used to learn an associative memory between semantically encoded landmarks and their positions, i.e., an environment map, which is used for loop closure. Our experiments demonstrate that environment maps can be learned accurately and their use greatly improves self-position estimation. Furthermore, grid cells, place cells, and object vector cells are observed by this model. We also run our path integrator network on the NengoLoihi neuromorphic emulator to demonstrate feasibility for a full neuromorphic implementation for energy efficient SLAM.

Generation of advanced cerebellar organoids for neurogenesis and neuronal network development.

Neurons within the cerebellum form temporal-spatial connections through the cerebellum, and the entire brain. Organoid models provide an opportunity to model the early differentiation of the developing human cerebellum, which is difficult to study in vivo, and affords the opportunity to study neurodegenerative and neurodevelopmental diseases of the cerebellum. Previous cerebellar organoid models focused on early neuron generation and single cell activity. Here, we modify previous protocols to generate more mature cerebellar organoids that allow for the establishment of several classes of mature neurons during cerebellar differentiation and development, including the establishment of neural networks during whole-organoid maturation. This will provide a means to study the generation of several more mature cerebellar cell types, including Purkinje cells, granule cells and interneurons expression as well as neuronal communication for biomedical, clinical and pharmaceutical applications.

A single-cell atlas of the sexually dimorphic Drosophila foreleg and its sensory organs during development.

To respond to the world around them, animals rely on the input of a network of sensory organs distributed throughout the body. Distinct classes of sensory organs are specialized for the detection of specific stimuli such as strain, pressure, or taste. The features that underlie this specialization relate both to the neurons that innervate sensory organs and the accessory cells they comprise. To understand the genetic basis of this diversity of cell types, both within and between sensory organs, we performed single-cell RNA sequencing on the first tarsal segment of the male Drosophila melanogaster foreleg during pupal development. This tissue displays a wide variety of functionally and structurally distinct sensory organs, including campaniform sensilla, mechanosensory bristles, and chemosensory taste bristles, as well as the sex comb, a recently evolved male-specific structure. In this study, we characterize the cellular landscape in which the sensory organs reside, identify a novel cell type that contributes to the construction of the neural lamella, and resolve the transcriptomic differences among support cells within and between sensory organs. We identify the genes that distinguish between mechanosensory and chemosensory neurons, resolve a combinatorial transcription factor code that defines 4 distinct classes of gustatory neurons and several types of mechanosensory neurons, and match the expression of sensory receptor genes to specific neuron classes. Collectively, our work identifies core genetic features of a variety of sensory organs and provides a rich, annotated resource for studying their development and function.

Direct and indirect neurogenesis generate a mosaic of distinct glutamatergic projection neuron types in cerebral cortex

Variations in size and complexity of the cerebral cortex result from differences in neuron number and composition, rooted in evolutionary changes in direct and indirect neurogenesis (dNG and iNG) that are mediated by radial glia and intermediate progenitors (IPs), respectively. How dNG and iNG differentially contribute to neuronal number, diversity, and connectivity are unknown. Establishing a genetic fate-mapping method to differentially visualize dNG and iNG in mice, we found that while both dNG and iNG contribute to all cortical structures, iNG contributes the largest relative proportions to the hippocampus and neocortex. Within the neocortex, whereas dNG generates all major glutamatergic projection neuron (PN) classes, iNG differentially amplifies and diversifies PNs within each class; the two pathways generate distinct PN types and assemble fine mosaics of lineage-based cortical subnetworks. Our results establish a ground-level lineage framework for understanding cortical development and evolution by linking foundational progenitor types and neurogenic pathways to PN types.

A fractional-order visual neural model for small target motion detection

Detecting small moving targets consisting of one or few pixels is technically demanding due to their limited visual features. Motivated by nature, some bio-inspired models have been developed that simulate the behavior of small target motion detectors (STMDs), a class of specialized neurons found in insects’ visual neural systems dominating in detecting small moving targets during activities such as predation or courtship. However, the existing models’ high dependence on the sampling frequency of input videos becomes a bottleneck that seriously hinders their real-time applications in the physical world. This is because massive computational power is required to capture and process high-sampling-frequency videos. While model detection performance in low-sampling-frequency videos is plagued by significant spatial errors and weak responses. To address these issues, we propose an STMD-based visual neural model with a fractional-order difference operator for small target motion detection. The fractional-order operator captures instantaneous luminance change and integrates it with memory information, where the instantaneous information dominates the integrated signal. The STMD network further separates the rising and falling luminance components, which are aligned in the time domain and then multiplied to predict the location of moving small targets. Due to the rapid response of instantaneous information and the supplement of memory information, the proposed model locates the small moving targets accurately and robustly in low-sampling-frequencies. Numerical experiments show that the proposed model significantly improves the detection performance for low-sampling-frequency videos.

Nucleus Accumbens Shell Neurons Encode the Kinematics of Reward Approach Locomotion

The nucleus accumbens (NAc) is considered an interface between motivation and action, with NAc neurons playing an important role in promoting reward approach. However, the encoding by NAc neurons that contributes to this role remains unknown. We recorded 62 NAc neurons in male Wistar rats (n = 5) running towards rewarded locations in an 8-arm radial maze. Variables related to locomotor approach kinematics were the best predictors of the firing rate for most NAc neurons. Nearly 18% of the recorded neurons were inhibited during the entire approach run (locomotion-off cells), suggesting that reduction in firing of these neurons promotes initiation of locomotor approach. 27% of the neurons presented a peak of activity during acceleration followed by a valley during deceleration (acceleration-on cells). Together, these neurons accounted for most of the speed and acceleration encoding identified in our analysis. In contrast, a further 16% of neurons presented a valley during acceleration followed by a peak just prior to or after reaching reward (deceleration-on cells). These findings suggest that these three classes of NAc neurons influence the time course of speed changes during locomotor approach to reward.

Membrane potential dynamics of excitatory and inhibitory neurons in mouse barrel cortex during active whisker sensing.

Neocortical neurons can increasingly be divided into well-defined classes, but their activity patterns during quantified behavior remain to be fully determined. Here, we obtained membrane potential recordings from various classes of excitatory and inhibitory neurons located across different cortical depths in the primary whisker somatosensory barrel cortex of awake head-restrained mice during quiet wakefulness, free whisking and active touch. Excitatory neurons, especially those located superficially, were hyperpolarized with low action potential firing rates relative to inhibitory neurons. Parvalbumin-expressing inhibitory neurons on average fired at the highest rates, responding strongly and rapidly to whisker touch. Vasoactive intestinal peptide-expressing inhibitory neurons were excited during whisking, but responded to active touch only after a delay. Somatostatin-expressing inhibitory neurons had the smallest membrane potential fluctuations and exhibited hyperpolarising responses at whisking onset for superficial, but not deep, neurons. Interestingly, rapid repetitive whisker touch evoked excitatory responses in somatostatin-expressing inhibitory neurons, but not when the intercontact interval was long. Our analyses suggest that distinct genetically-defined classes of neurons at different subpial depths have differential activity patterns depending upon behavioral state providing a basis for constraining future computational models of neocortical function.

Effects of experimental ulcerative colitis on myenteric neurons in P2X7-knockout mice.

This study aimed to investigate the distal colon myenteric plexus and enteric glial cells (EGCs) in P2X7 receptor-deficient (P2X7-/-) animals after the induction of experimental ulcerative colitis. 2,4,6-Trinitrobenzene sulfonic acid (TNBS) was injected into the distal colon of C57BL/6 (WT) and P2X7 receptor gene-deficient (P2X7-/-, KO) animals. Distal colon tissues in the WT and KO groups were analyzed 24h and 4days after administration. The tissues were analyzed by double immunofluorescence of the P2X7 receptor with neuronal nitric oxide synthase (nNOS)-immunoreactive (ir), choline acetyltransferase (ChAT)-ir, and PGP9.5 (pan neuronal)-ir, and their morphology was assessed by histology. The quantitative analysis revealed 13.9% and 7.1% decreases in the number of P2X7 receptor-immunoreactive (ir) per ganglion in the 24h-WT/colitis and 4day-WT/colitis groups, respectively. No reduction in the number of nNOS-ir, choline ChAT-ir, and PGP9.5-ir neurons per ganglion was observed in the 4day-KO/colitis group. In addition, a reduction of 19.3% in the number of GFAP (glial fibrillary acidic protein)-expressing cells per ganglion was found in the 24h-WT/colitis group, and a 19% increase in the number of these cells was detected in the 4day-WT/colitis group. No profile area changes in neurons were observed in the 24h-WT and 24h-KO groups. The 4day-WT/colitis and 4day-KO/colitis groups showed increases in the profile neuronal areas of nNOS, ChAT, and PGP9.5. The histological analysis showed hyperemia, edema, or cellular infiltration in the 24h-WT/colitis and 4day-WT/colitis groups. Edema was observed in the 4day-KO/colitis group, which showed no histological changes compared with the 24h-KO/colitis group. We concluded that ulcerative colitis differentially affected the neuronal classes in the WT and KO animals, demonstrating the potential participation and neuroprotective effect of the P2X7 receptor in enteric neurons in inflammatory bowel disease.

High-resolution volumetric imaging constrains compartmental models to explore synaptic integration and temporal processing by cochlear nucleus globular bushy cells.

Globular bushy cells (GBCs) of the cochlear nucleus play central roles in the temporal processing of sound. Despite investigation over many decades, fundamental questions remain about their dendrite structure, afferent innervation, and integration of synaptic inputs. Here, we use volume electron microscopy (EM) of the mouse cochlear nucleus to construct synaptic maps that precisely specify convergence ratios and synaptic weights for auditory nerve innervation and accurate surface areas of all postsynaptic compartments. Detailed biophysically based compartmental models can help develop hypotheses regarding how GBCs integrate inputs to yield their recorded responses to sound. We established a pipeline to export a precise reconstruction of auditory nerve axons and their endbulb terminals together with high-resolution dendrite, soma, and axon reconstructions into biophysically detailed compartmental models that could be activated by a standard cochlear transduction model. With these constraints, the models predict auditory nerve input profiles whereby all endbulbs onto a GBC are subthreshold (coincidence detection mode), or one or two inputs are suprathreshold (mixed mode). The models also predict the relative importance of dendrite geometry, soma size, and axon initial segment length in setting action potential threshold and generating heterogeneity in sound-evoked responses, and thereby propose mechanisms by which GBCs may homeostatically adjust their excitability. Volume EM also reveals new dendritic structures and dendrites that lack innervation. This framework defines a pathway from subcellular morphology to synaptic connectivity, and facilitates investigation into the roles of specific cellular features in sound encoding. We also clarify the need for new experimental measurements to provide missing cellular parameters, and predict responses to sound for further in vivo studies, thereby serving as a template for investigation of other neuron classes.

Model discovery to link neural activity to behavioral tasks.

Brains are not engineered solutions to a well-defined problem but arose through selective pressure acting on random variation. It is therefore unclear how well a model chosen by an experimenter can relate neural activity to experimental conditions. Here, we developed 'model identification of neural encoding (MINE).' MINE is an accessible framework using convolutional neural networks (CNNs) to discover and characterize a model that relates aspects of tasks to neural activity. Although flexible, CNNs are difficult to interpret. We use Taylor decomposition approaches to understand the discovered model and how it maps task features to activity. We apply MINE to a published cortical dataset as well as experiments designed to probe thermoregulatory circuits in zebrafish. Here, MINE allowed us to characterize neurons according to their receptive field and computational complexity, features that anatomically segregate in the brain. We also identified a new class of neurons that integrate thermosensory and behavioral information that eluded us previously when using traditional clustering and regression-based approaches.

Chronic exposure to odors at naturally occurring concentrations triggers limited plasticity in early stages of Drosophila olfactory processing.

In insects and mammals, olfactory experience in early life alters olfactory behavior and function in later life. In the vinegar fly Drosophila, flies chronically exposed to a high concentration of a monomolecular odor exhibit reduced behavioral aversion to the familiar odor when it is reencountered. This change in olfactory behavior has been attributed to selective decreases in the sensitivity of second-order olfactory projection neurons (PNs) in the antennal lobe that respond to the overrepresented odor. However, since odorant compounds do not occur at similarly high concentrations in natural sources, the role of odor experience-dependent plasticity in natural environments is unclear. Here, we investigated olfactory plasticity in the antennal lobe of flies chronically exposed to odors at concentrations that are typically encountered in natural odor sources. These stimuli were chosen to each strongly and selectively excite a single class of primary olfactory receptor neuron (ORN), thus facilitating a rigorous assessment of the selectivity of olfactory plasticity for PNs directly excited by overrepresented stimuli. Unexpectedly, we found that chronic exposure to three such odors did not result in decreased PN sensitivity but rather mildly increased responses to weak stimuli in most PN types. Odor-evoked PN activity in response to stronger stimuli was mostly unaffected by odor experience. When present, plasticity was observed broadly in multiple PN types and thus was not selective for PNs receiving direct input from the chronically active ORNs. We further investigated the DL5 olfactory coding channel and found that chronic odor-mediated excitation of its input ORNs did not affect PN intrinsic properties, local inhibitory innervation, ORN responses or ORN-PN synaptic strength; however, broad-acting lateral excitation evoked by some odors was increased. These results show that PN odor coding is only mildly affected by strong persistent activation of a single olfactory input, highlighting the stability of early stages of insect olfactory processing to significant perturbations in the sensory environment.

Frequency-dependent centrifugal modulation of the activity of different classes of mitral and tufted cells in olfactory bulb.

Mitral/tufted cells (M/TCs), the principal output neuron classes form complex circuits with bulbar neurons and long-range centrifugal circuits with higher processing areas such as the horizontal limb of the diagonal band of Broca (HDB). The precise excitability of output neurons is sculpted by local inhibitory circuits. Here, light-gated cation channel channelrhodopsin-2 (ChR2) was expressed in HDB GABAergic neurons to investigate the short-term plasticity of evoked postsynaptic currents/potentials of HDB input to all classes of M/TCs and effects on firing in the acute slice preparation. Activation of the HDB directly inhibited all classes of output neurons exhibiting frequency-dependent short-term depression of evoked inhibitory postsynaptic current (eIPSC)/potential (eIPSP), resulting in decreased inhibition of responses to olfactory nerve input as a function of input frequency. In contrast, activation of an indirect circuit of HDB→interneurons→M/TCs induced frequency-dependent disinhibition, resulting in short-term facilitation of evoked excitatory postsynaptic current (eEPSC) eliciting a burst or cluster of spiking in M/TCs. The facilitatory effects of elevated HDB input frequency were strongest on deeper output neurons (deep tufted and mitral cells) and negligible on peripheral output neurons (external and superficial tufted cells). Taken together, GABAergic HDB activation generates frequency-dependent regulation that differentially affects the excitability and responses across the five classes of M/TCs. This regulation may help maintain the precise balance between inhibition and excitation of neuronal circuits across the populations of output neurons in the face of changes in an animal sniffing rate, putatively to enhance and sharpen the tuning specificity of individual or classes of M/TCs to odors.NEW & NOTEWORTHY Neuronal circuits in the olfactory bulb closely modulate olfactory bulb output activity. Activation of GABAergic circuits from the HDB to the olfactory bulb has both direct and indirect action differentially across the five classes of M/TC bulbar output neurons. The net effect enhances the excitability of deeper output neurons as HDB frequency increases, altering the relative inhibition-excitation balance of output circuits. We hypothesize that this sharpens the tuning specificity of classes of M/TCs to odors during sensory processing.

Activity-regulated gene expression across cell types of the mouse hippocampus.

Activity-regulated gene (ARG) expression patterns in the hippocampus (HPC) regulate synaptic plasticity, learning, and memory, and are linked to both risk and treatment responses for many neuropsychiatric disorders. The HPC contains discrete classes of neurons with specialized functions, but cell type-specific activity-regulated transcriptional programs are not well characterized. Here, we used single-nucleus RNA-sequencing (snRNA-seq) in a mouse model of acute electroconvulsive seizures (ECS) to identify cell type-specific molecular signatures associated with induced activity in HPC neurons. We used unsupervised clustering and a priori marker genes to computationally annotate 15,990 high-quality HPC neuronal nuclei from N = 4 mice across all major HPC subregions and neuron types. Activity-induced transcriptomic responses were divergent across neuron populations, with dentate granule cells being particularly responsive to activity. Differential expression analysis identified both upregulated and downregulated cell type-specific gene sets in neurons following ECS. Within these gene sets, we identified enrichment of pathways associated with varying biological processes such as synapse organization, cellular signaling, and transcriptional regulation. Finally, we used matrix factorization to reveal continuous gene expression patterns differentially associated with cell type, ECS, and biological processes. This work provides a rich resource for interrogating activity-regulated transcriptional responses in HPC neurons at single-nuclei resolution in the context of ECS, which can provide biological insight into the roles of defined neuronal subtypes in HPC function.

Flexible neural control of transition points within the egg-laying behavioral sequence in Drosophila

Innate behaviors are frequently comprised of ordered sequences of component actions that progress to satisfy essential drives. Progression is governed by specialized sensory cues that induce transitions between components within the appropriate context. Here we have characterized the structure of the egg-laying behavioral sequence in Drosophila and found significant variability in the transitions between component actions that affords the organism an adaptive flexibility. We identified distinct classes of interoceptive and exteroceptive sensory neurons that control the timing and direction of transitions between the terminal components of the sequence. We also identified a pair of motor neurons that enact the final transition to egg expulsion. These results provide a logic for the organization of innate behavior in which sensory information processed at critical junctures allows for flexible adjustments in component actions to satisfy drives across varied internal and external environments.

Correlated variability in primate superior colliculus depends on functional class

Correlated variability in neuronal activity (spike count correlations, rSC) can constrain how information is read out from populations of neurons. Traditionally, rSC is reported as a single value summarizing a brain area. However, single values, like summary statistics, stand to obscure underlying features of the constituent elements. We predict that in brain areas containing distinct neuronal subpopulations, different subpopulations will exhibit distinct levels of rSC that are not captured by the population rSC. We tested this idea in macaque superior colliculus (SC), a structure containing several functional classes (i.e., subpopulations) of neurons. We found that during saccade tasks, different functional classes exhibited differing degrees of rSC. “Delay class” neurons displayed the highest rSC, especially during saccades that relied on working memory. Such dependence of rSC on functional class and cognitive demand underscores the importance of taking functional subpopulations into account when attempting to model or infer population coding principles.

Modulation of cerebellar cortical, cerebellar nuclear and vestibular nuclear activity using alternating electric currents.

Cerebellar transcranial alternating current stimulation (ctACS) has shown promise as a therapeutic modality for treating a variety of neurological disorders, and for affecting normal learning processes. Yet, little is known about how electric fields induced by applied currents affect cerebellar activity in the mammalian cerebellum under in vivo conditions. Alternating current (AC) stimulation with frequencies from 0.5 to 20 Hz was applied to the surface of the cerebellum in anesthetized rats. Extracellular recordings were obtained from Purkinje cells (PC), cerebellar and vestibular nuclear neurons, and other cerebellar cortical neurons. AC stimulation modulated the activity of all classes of neurons. Cerebellar and vestibular nuclear neurons most often showed increased spike activity during the negative phase of the AC stimulation. Purkinje cell simple spike activity was also increased during the negative phase at most locations, except for the cortex directly below the stimulus electrode, where activity was most often increased during the positive phase of the AC cycle. Other cortical neurons showed a more mixed, generally weaker pattern of modulation. The patterns of Purkinje cell responses suggest that AC stimulation induces a complex electrical field with changes in amplitude and orientation between local regions that may reflect the folding of the cerebellar cortex. Direct measurements of the induced electric field show that it deviates significantly from the theoretically predicted radial field for an isotropic, homogeneous medium, in both its orientation and magnitude. These results have relevance for models of the electric field induced in the cerebellum by AC stimulation.

Fast-Spiking Interneurons of the Premotor Cortex Contribute to Initiation and Execution of Spontaneous Actions.

Planning and execution of voluntary movement depend on the contribution of distinct classes of neurons in primary motor and premotor areas. However, timing and pattern of activation of GABAergic cells during specific motor behaviors remain only partly understood. Here, we directly compared the response properties of putative pyramidal neurons (PNs) and GABAergic fast-spiking neurons (FSNs) during spontaneous licking and forelimb movements in male mice. Recordings centered on the face/mouth motor field of the anterolateral motor cortex (ALM) revealed that FSNs fire longer than PNs and earlier for licking, but not for forelimb movements. Computational analysis revealed that FSNs carry vastly more information than PNs about the onset of movement. While PNs differently modulate their discharge during distinct motor acts, most FSNs respond with a stereotyped increase in firing rate. Accordingly, the informational redundancy was greater among FSNs than PNs. Finally, optogenetic silencing of a subset of FSNs reduced spontaneous licking movement. These data suggest that a global rise of inhibition contributes to the initiation and execution of spontaneous motor actions.SIGNIFICANCE STATEMENT Our study contributes to clarifying the causal role of fast-spiking neurons (FSNs) in driving initiation and execution of specific, spontaneous movements. Within the face/mouth motor field of mice premotor cortex, FSNs fire before pyramidal neurons (PNs) with a specific activation pattern: they reach their peak of activity earlier than PNs during the initiation of licking, but not of forelimb, movements; duration of FSNs activity is also greater and exhibits less selectivity for the movement type, as compared with that of PNs. Accordingly, FSNs appear to carry more redundant information than PNs. Optogenetic silencing of FSNs reduced spontaneous licking movement, suggesting that FSNs contribute to the initiation and execution of specific spontaneous movements, possibly by sculpting response selectivity of nearby PNs.

The single-cell and spatial transcriptional landscape of human gastrulation and early brain development

The emergence of the three germ layers and the lineage-specific precursor cells orchestrating organogenesis represent fundamental milestones during early embryonic development. We analyzed the transcriptional profiles of over 400,000 cells from 14 human samples collected from post-conceptional weeks (PCW) 3 to 12 to delineate the dynamic molecular and cellular landscape of early gastrulation and nervous system development. We described the diversification of cell types, the spatial patterning of neural tube cells, and the signaling pathways likely involved in transforming epiblast cells into neuroepithelial cells and then into radial glia. We resolved 24 clusters of radial glial cells along the neural tube and outlined differentiation trajectories for the main classes of neurons. Lastly, we identified conserved and distinctive features across species by comparing early embryonic single-cell transcriptomic profiles between humans and mice. This comprehensive atlas sheds light on the molecular mechanisms underlying gastrulation and early human brain development.

Scaling down synaptic strength results in changes to the pattern of fictive breathing in a model of the brainstem respiratory network: medullary and pontine sources

Opioids remain a primary vehicle for pain management prescribed by clinicians. However, opioid-induced respiratory depression (OIRD) can cause apneas and be fatal at elevated doses. Two sites of action for these opioids have been suggested in the brainstem, one in the pons and the other in the preBötzinger Complex of the medulla. Additionally, there is electrophysiological evidence suggesting that an inward-rectifying K + current is opened upon μ-opioid receptor (μOR) activation causing neuronal hyperpolarizations. Activation of μORs may cause depletion of glutamate release from excitatory neurons. Our previous simulation efforts have demonstrated the plausibility of presynaptic actions of opioids among inspiratory neurons of the medulla and pons in depressing inspiratory drive or eliciting apnea within specific and singular synaptic connections. Here, we tested the effects of diminishing the strength of excitatory synaptic currents among several different classes of respiratory neurons in a large-scale model of the brainstem respiratory network and whether it would replicate OIRD. We simulated the simultaneous actions of μOR on multiple presynaptic sites among the medullary inspiratory network. We began by scaling down the synaptic conductances of target populations of medullary inspiratory neurons. The core of the model is made up of neurons that produce the fundamental inspiratory rhythm (“I-Driver” neurons), as well as neurons that normally have an inspiratory decrementing (“I-Dec”) or inspiratory augmenting (“I-Aug”) firing pattern and various expiratory phase firing patterns (“E-Dec,” “E-Aug,” “E-Tonic”). I-Driver neurons are analogous to medullary preBötzinger Complex neurons that putatively express μORs. In our model, these neurons project back onto other I-Driver neurons to promote recurrent excitation but also project onto I-Aug and I-Dec populations. Decreasing the synaptic strength from I-Driver neurons to these populations collectively slows fictive breathing until extended apnea occurs. To further understand how this apnea develops we systematically scaled down the synaptic strengths of I-Driver to each of the other specific populations and found that the excitatory connection from I-Driver to I-Dec populations is most influential in producing apnea. However, we also found that excitatory drive from tonic, non-respiratory modulated (NRM) neurons in the pons to the medulla could influence this pattern by grading down synaptic strength. In addition to any direct hyperpolarizing effects of opioids on μOR-expressing neurons, these data support the plausibility that OIRD could arise from changes due to glutamate release. This work was supported by the National Institutes of Health grant 1R01HL155721-01, 1R01HL163008, and T32HL134621. This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.