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5,448 results for “Neuron”
Raw data for: Dynamic instability of dendrite tips generates the highly branched morphologies of sensory neurons
<p>The highly ramified arbors of neuronal dendrites provide the substrate for the high connectivity and computational power of the brain. Altered dendritic morphology is associated with neuronal diseases. Many molecules have been shown to play crucial roles in shaping and maintaining dendrite morphology. Yet, the underlying principles by which molecular interactions generate branched morphologies are not understood. To elucidate these principles, we visualized the growth of dendrites throughout larval development of Drosophila sensory neurons and discovered that the tips of dendrites undergo dynamic instability, transitioning rapidly and stochastically between growing, shrinking, and paused states. By incorporating these measured dynamics into a novel, agent-based computational model, we showed that the complex and highly variable dendritic morphologies of these cells are a consequence of the stochastic dynamics of their dendrite tips. These principles may generalize to branching of other neuronal cell types, as well as to branching at the subcellular and tissue levels.</p>
Population and single dopamine neuron activity during classical conditioning
<p>We trained naive or trained mice to associate odor cues with outcome (water, air puff or no outcome), and recorded dopamine cell body activity in the vetral tegmental area (VTA), dopamine axon activity in the ventral striatum (VS) or dopamine release in VS with optic fiber fluorometry (photometry). In different set of mice, single dopamine neuron activiy was recorded with 2-photon microscope. In some of these mice, we reversed odor-outcome contingency so that an odor that was associated with no outcome or air puff became associated with water reward. Licking pattern was also recorded.</p>
Parkinson's disease-associated, sex-specific changes in DNA methylation at PARK7 (DJ-1), ATXN1, SLC17A6, NR4A2, and PTPRN2 in cortical neurons
<p>Evidence for epigenetic regulation playing a role in Parkinson's disease (PD) is growing, particularly for DNA methylation. Approximately 90% of PD cases are due to a complex interaction between age, genes, and environmental factors, and epigenetic marks are thought to mediate the relationship between aging, genetics, the environment, and disease risk. To date, there are a small number of published genome-wide studies of DNA methylation in PD, but none accounted for cell-type or sex in their analyses. Given the heterogeneity of bulk brain tissue samples and known sex differences in PD risk, progression, and severity, these are critical variables to account for. In this first genome-wide analysis of DNA methylation in an enriched neuronal population from PD post-mortem parietal cortex, we report sex-specific PD-associated methylation changes in <em>PARK7</em> (DJ-1), <em>SLC17A6</em> (VGLUT2), <em>PTPRN2</em> (IA-2β), <em>NR4A2</em> (NURR1), and other genes involved in developmental pathways, neurotransmitter packaging and release, and axon and neuron projection guidance.</p>
High-density electrode recordings reveal strong and specific connections between retinal ganglion cells and midbrain neurons.
<p>This is the source file to be use for debugging problems on your own set-up. This files is a good example to both the repository provided for visual processing on the Kremow GitHub:</p> <p>https://github.com/KremkowLab/Axon-on-Neuropixels-in-Kilosort</p> <p>https://github.com/KremkowLab/tangential_recording</p>
Source data for "Ca2+ channels couple spiking to mitochondrial metabolism in substantia nigra dopaminergic neurons"
<p><strong>Fig.1Aa-c.tif</strong></p> <p>2PLSM images (Fura-2 filled neuron) used for the reconstruction in Fig.1A</p> <p> </p> <p><strong>Fig1CDJ.xlsx</strong></p> <p>Numerical data for the charts in Fig. 1C, Fig.1D, Fig.1J</p> <p> </p> <p><strong>Fig.1F_GCEPIA1er.tif</strong></p> <p>Confocal image (green channel, G-CEPIA1er) for Fig. 1F</p> <p> </p> <p><strong>Fig.1F_TH.tif</strong></p> <p>Confocal image (blue channel, anti-TH immunostaining) for Fig. 1F</p> <p> </p> <p><strong>Fig.1G_GCEPIA1er.tif</strong></p> <p>Confocal image (green channel, G-CEPIA1er) for Fig. 1G</p> <p> </p> <p><strong>Fig.1G_CRT.tif</strong></p> <p>Confocal image (magenta channel, anti-CRT immunostaining) for Fig. 1G</p> <p> </p> <p><strong>Fig.1H_GCEPIA1er.tif</strong></p> <p>Confocal image (green channel, G-CEPIA1er) for Fig. 1H</p> <p> </p> <p><strong>Fig.1H_TH.tif</strong></p> <p>Confocal image (blue channel, anti-TH immunostaining) for Fig. 1H</p> <p> </p> <p><strong>Fig. 2B_mitoGCaMP6.tif</strong></p> <p>Confocal image (green channel, mito-GCaMP6) for Fig. 2B</p> <p> </p> <p><strong>Fig.2B_TH.tif</strong></p> <p>Confocal image (blue channel, anti-TH immunostaining) for Fig. 2B</p> <p> </p> <p><strong>Fig. 2C_mitoGCaMP6.tif</strong></p> <p>Confocal image (green channel, mito-GCaMP6) for Fig. 2C</p> <p> </p> <p><strong>Fig.2C_COXIV.tif</strong></p> <p>Confocal image (magenta channel, anti-COXIV immunostaining) for Fig. 2C</p> <p> </p> <p><strong>Fig. 2D_mitoGCaMP6.tif</strong></p> <p>Confocal image (green channel, mito-GCaMP6) for Fig. 2D</p> <p> </p> <p><strong>Fig.2D_TH.tif</strong></p> <p>Confocal image (blue channel, anti-TH immunostaining) for Fig. 2D</p> <p> </p> <p><strong>Fig.2FGJL.xlsx</strong></p> <p>Numerical data for the charts in Fig. 2F, Fig.2G, Fig.2J, Fig.2L</p> <p> </p> <p><strong>Fig.3BDE.xlsx</strong></p> <p>Numerical data for the charts in Fig. 3B, Fig.3D, Fig.3E</p> <p> </p> <p><strong>Fig.3C_Alexa.tif</strong></p> <p>MAX Projection of z-stack of 2PLSM images (magenta channel, Alexa 594 dye) used to generate Fig.3C top panel</p> <p> </p> <p><strong>Fig.3C_mitoGCaMP6.tif</strong></p> <p>MAX Projection of z-stack of 2PLSM images (green channel, mito-GCaMP6) used to generate Fig.3C top panel</p> <p> </p> <p><strong>Fig.3C_inset_dendritic_mitoGCaMP6.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.3C bottom left panel</p> <p> </p> <p><strong>Fig.3C_inset_soma_mitoGCaMP6.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.3C bottom right panel</p> <p> </p> <p><strong>Fig. 4A_PercevalHR.tif</strong></p> <p>Confocal image (green channel, PercevalHR) for Fig.4A</p> <p> </p> <p><strong>Fig.4A_TH.tif</strong></p> <p>Confocal image (blue channel, anti-TH immunostaining) for Fig.4A</p> <p> </p> <p><strong>Fig. 4B_PercevalHR.tif</strong></p> <p>Confocal image (green channel, PercevalHR) for Fig.4B</p> <p> </p> <p><strong>Fig.4B_TH.tif</strong></p> <p>Confocal image (blue channel, anti-TH immunostaining) for Fig.4B</p> <p> </p> <p><strong>Fig.4G.xlsx</strong></p> <p>Numerical data for the charts in Fig.4G</p> <p> </p> <p><strong>Fig.5BDFHI.xlsx</strong></p> <p>Numerical data for the charts in Fig.5B, Fig.5D, Fig.5F, Fig.5H, Fig.5I</p> <p> </p> <p><strong>Fig.6ADEFJKLN.xlsx</strong></p> <p>Numerical data for the charts in Fig.6A, Fig.6D, Fig.6E, Fig.6F, Fig.6J, Fig.6K, Fig.6L, Fig.6N</p> <p> </p> <p><strong>Fig.6H_mitoroGFP.tif</strong></p> <p>2PLSM image (green channel, mito-roGFP) for Fig.6H</p> <p> </p> <p><strong>Fig.6M_MCU-KO.tif</strong></p> <p>Combined EM micrographs used to generate Fig. 6M right side</p> <p> </p> <p><strong>Fig.6M_wildtype.tif</strong></p> <p>Combined EM micrographs used to generate Fig. 6M left side</p> <p> </p> <p><strong>Fig.7CEFG.xlsx</strong></p> <p>Numerical data for the charts in Fig.7C, Fig.7E, Fig.7F, Fig.7G</p> <p> </p> <p><strong>Fig.8CEHJK.xlsx</strong></p> <p>Numerical data for the charts in Fig.8C, Fig.8E, Fig.8H, Fig.8J, Fig.8K</p> <p> </p> <p><strong>Fig.S1A_bottom.tif</strong></p> <p>2PLSM image (green channel, G-CEPIA1er) for Fig.S1A bottom panel (low Ca2+)</p> <p> </p> <p><strong>Fig.S1A_top.tif</strong></p> <p>2PLSM image (green channel, G-CEPIA1er) for Fig.S1A top panel (high Ca2+)</p> <p> </p> <p><strong>Fig.S1B.xlsx</strong></p> <p>Numerical data for the chart in Fig.S1B</p> <p> </p> <p><strong>Fig.S2A_baseline.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2A middle panel (baseline)</p> <p> </p> <p><strong>Fig.S2A_MAX.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2A top panel (high Ca2+)</p> <p> </p> <p><strong>Fig.S2A_min.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2A bottom panel (low Ca2+)</p> <p> </p> <p><strong>Fig.S2C_baseline.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2C middle panel (baseline)</p> <p> </p> <p><strong>Fig.S2C_MAX.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2C top panel (high Ca2+)</p> <p> </p> <p><strong>Fig.S2C_min.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2C bottom panel (low Ca2+)</p> <p> </p> <p><strong>Fig.S2E_baseline.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2E bottom left panel (baseline)</p> <p> </p> <p><strong>Fig.S2E_end.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2E bottom right panel</p> <p> </p> <p><strong>Fig.S2E_peak.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2E bottom center panel</p> <p> </p> <p><strong>Fig.S2F.xlsx</strong></p> <p>Numerical data for the chart in Fig.S2F</p> <p> </p> <p><strong>Fig.S3ABC.xlsx</strong></p> <p>Numerical data for the charts in Fig.S3A, Fig.S3B, Fig.S3C</p> <p> </p> <p><strong>Fig.S4CD.xlsx</strong></p> <p>Numerical data for the charts in Fig.S4C, Fig.S4D</p> <p> </p> <p><strong>Fig.S5ABC.xlsx</strong></p> <p>Numerical data for the charts in Fig.S5A, Fig.S5B, Fig.S5C</p> <p> </p> <p><strong>Fig.S7A.tif</strong></p> <p>2PLSM image (green channel, GCaMP6) for Fig.S7A</p> <p> </p> <p><strong>Fig.S7CEFGH.xlsx</strong></p> <p>Numerical data for the charts in Fig.S7C, Fig.S7E, Fig.S7F, Fig.S7G, Fig.S7H</p> <p> </p> <p><strong>Fig.S8ABCDEF.xlsx</strong></p> <p>Numerical data for the charts in Fig.S8A, Fig.S8B, Fig.S8C, Fig.S8D, Fig.S8E, Fig.S8F</p> <p> </p> <p><strong>Fig.S9AHIJK.xlsx</strong></p> <p>Numerical data for the charts in Fig.S9A, Fig.S9H, Fig.S9I, Fig.S9J, Fig.S9K</p> <p> </p> <p><strong>Fig.S9B_wildtype_DLStr.tif</strong></p> <p>Confocal image of dorso-laterateral striatum in wildtype mouse (red channel, anti-TH immunostaining) for Fig.S9B</p> <p> </p> <p><strong>Fig.S9C_MCU-KO_DLStr.tif</strong></p> <p>Confocal image of dorso-laterateral striatum in MCU-KO mouse (red channel, anti-TH immunostaining) for Fig.S9C</p> <p> </p> <p><strong>Fig.S9D_wildtype_SN.tif</strong></p> <p>Confocal image of midbrain in wildtype mouse (red channel, anti-TH immunostaining) for Fig.S9D</p> <p> </p> <p><strong>Fig.S9E_MCU-KO_SN.tif</strong></p> <p>Confocal image of midbrain in MCU-KO mouse (red channel, anti-TH immunostaining) for Fig.S9E</p> <p> </p> <p><strong>Fig.S9F_wildtype_openfield.png</strong></p> <p>Open field path tracked for wildtype mouse for Fig.S9F</p> <p> </p> <p><strong>Fig.S9G_MCU-KO_openfield.png</strong></p> <p>Open field path tracked for MCU-KO mouse for Fig.S9G</p> <p> </p> <p><strong>Fig.S10A.xlsx</strong></p> <p>Numerical data for the charts in Fig.S10A</p>
Sequence tracks of Ago2 Neural stem cells and differentiated neurons from single-cells- Related to Fig. 5
<p>Single neural stem cells were isolated from the Hippocampus of newborn mice generated from a hybrid cross. Some of these cells were differentiated In vitro and either the NSC or differentiated neurons were lysed and underwent a reverse transcription. The newly formed cDNA was used as a template to amplify expressed Ago2 transcript which was then sent off for Sanger sequencing. A SNP located within the exon was used to determine whether the transcript from that cell was generated from the maternal or paternal allele.</p>
Proximity labeling of tau interactions in primary neurons and mouse brain
<p><span>Microtubule-associated protein tau is a central factor in Alzheimer's disease and other tauopathies. However, physiological functions of tau are unclear. Here, we used proximity labelling proteomics to chart functional tau interactomes in primary neurons and mouse brai<span>n <span>in vivo</span></span><span>. Here, we use proximity labelling with the biotin ligase BioID2 to map interactomes of tau in neurons. Data sets relate to mass spectrometry and protein identification of biotinylated proteins in primary neurons and in mouse brain after delivery of BioID2-tau fusion protein or BioID2 control protein by adeno-associated virus (AAV). Mouse brain samples are either from P35 wild-type mice with intracranial AAV delivery at P0 or from tau knockout mice at P60 after hippocampal delivery of AAV. Details on BioID2 fusion protein expression, biotin supplementation and sample extraction can be obtained in the associated publication.<br></span></span></p>
Neocortical Localization and Thalamocortical Modulation of Neuronal Hyperexcitability contribute to Fragile X Syndrome: EEG Data
<p>Cleaned 128-channel source electrode data analyzed in manuscript</p>
Pigment-Dispersing Factor expressing neurons provide an infrastructure for conveying circadian information in the honey bee brain
<p>A movie of a 360° view of the 3D-reconstructed PDF (Pigment Dispersing Factor) network in the honey bee brain. (avi file to be opened with for example Fiji ImageJ)</p>
Data for: Multimodal convergence in the pedunculopontine tegmental nucleus: motor, sensory, and theta-frequency inputs influence the activity of single neurons
<p>The pedunculopontine tegmental nucleus of the brainstem (PPTg) has extensive interconnections and neuronal-behavioural correlates. It is implicated in movement control and sensorimotor integration. We investigated whether single neuron activity in freely moving rats is correlated with components of skilled forelimb movement and whether individual neurons respond to both motor and sensory events. We found that individual PPTg neurons showed changes in firing rate at different times during the reach. This type of temporally specific modulation is like activity seen elsewhere in voluntary movement control circuits, such as the motor cortex, and suggests that PPTg neural activity is related to different specific events occurring during the reach. In particular, many neuronal modulations were time-locked to the end of the extension phase of the reach, when fine distal movements related to food grasping occur, indicating strong engagement of PPTg in this phase of skilled individual forelimb movements. In addition, some neurons showed brief periods of apparent oscillatory firing in the theta range at specific phases of the reach-to-grasp movement. When movement-related neurons were tested with tone stimuli, many also responded to this auditory input, allowing for sensorimotor integration at the cellular level. Together, these data extend the concept of the PPTg as an integrative structure in the generation of complex movements, by showing that this function extends to the highly coordinated control of the forelimb during skilled reach to grasp movement and that sensory and motor-related information converges on a single neuron, allowing for direct integration at the cellular level.</p>
Data for: Revealing intact neuronal circuitry in centimeter-sized formalin-fixed paraffin-embedded brain
<p>Tissue clearing and labeling techniques have revolutionized brain-wide imaging and analysis, yet their application to clinical formalin-fixed paraffin-embedded (FFPE) blocks remains challenging. We introduce HIF-Clear, a novel method for efficiently clearing and labeling centimeter-thick FFPE specimens using elevated temperature and concentrated detergents. HIF-Clear with multi-round immunolabeling reveals neuron circuitry regulating multiple neurotransmitter systems in a whole FFPE mouse brain, and is able to be used as the evaluation of disease treatment efficiency. HIF-Clear also supports expansion microscopy and can be performed on a non-sectioned 15-year-old FFPE specimen, as well as a 3-month formalin-fixed mouse brain. Thus, HIF-Clear represents a feasible approach for researching archived FFPE specimens for future neuroscientific and 3D neuropathological analyses.</p>
The non-mitotic role of HMMR in regulating the localization of TPX2 and the dynamics of microtubules in neurons
<p>A functional nervous system is built upon the proper morphogenesis of neurons to establish the intricate connection between them. The microtubule cytoskeleton is known to play various essential roles in this morphogenetic process. While many microtubule-associated proteins (MAPs) have been demonstrated to participate in neuronal morphogenesis, the function of many more remains to be determined. This study focuses on a MAP called HMMR in mice, which was originally identified as a hyaluronan binding protein and later found to possess microtubule and centrosome binding capacity. HMMR exhibits high abundance on neuronal microtubules and altering the level of HMMR significantly affects the morphology of neurons. Instead of confining to the centrosome(s) like cells in mitosis, HMMR localizes to microtubules along axons and dendrites. Furthermore, transiently expressing HMMR enhances the stability of neuronal microtubules and increases the formation frequency of growing microtubules along the neurites. HMMR regulates the microtubule localization of a non-centrosomal microtubule nucleator TPX2 along the neurite, offering an explanation for how HMMR contributes to the promotion of growing microtubules. This study sheds light on how progenitor cells utilize proteins involved in mitosis for non-mitotic functions.</p>
Monkey V1/V2 neuronal population response to drifting gratings with varying orientation and contrast
<p>Correlated variability in the visual cortex is modulated by stimulus properties. The stimulus dependence of correlated variability impacts stimulus coding and is indicative of circuit structure. An affine model combining a multiplicative factor and an additive offset has been proposed to explain how correlated variability in primary visual cortex (V1) depends on stimulus orientations. However, whether the affine model could be extended to explain modulations by other stimulus variables or variability shared between two brain areas is unknown. Motivated by a simple neural circuit mechanism, we modified the affine model to better explain the contrast-dependence of neural variability shared within either primary or secondary visual cortex (V1 or V2) as well as the orientation-dependence of neural variability shared between V1 and V2. Our results bridge neural circuit mechanisms and statistical models, and provide a parsimonious explanation for the stimulus-dependence of correlated variability within and between visual areas.</p>
Neuronal Jamming cyberattack over invasive BCIs affecting the resolution of tasks requiring visual capabilities
<p>Dataset associated to the paper "Neuronal Jamming cyberattack over invasive BCIs affecting the resolution of tasks requiring visual capabilities"</p>
Moore_Schultz_Sox14_expressing_neurons
<p><em>Sox14</em>-expressing cell count data from ~50 brain regions in two groups of mice: <em>Sox14/GFP</em> heterozygotes and <em>Sox14</em> null.</p>
Gene Trees for Octopus bocki ANC neuron markers
<p>The evolutionary history was inferred by using the Maximum Likelihood method and Whelan And Goldman model [1]. The trees with the highest log likelihood are shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the JTT model, and then selecting the topology with superior log likelihood value. The trees are drawn to scale, with branch lengths measured in the number of substitutions per site. Evolutionary analyses were conducted in MEGA11 [2]</p> <p>1. Whelan, S. and Goldman, N. (2001). A general empirical model of protein evolution derived from multiple protein families using a maximum-likelihood approach. Molecular Biology and Evolution 18:691-699.<br>2. Tamura K., Stecher G., and Kumar S. (2021). MEGA 11: Molecular Evolutionary Genetics Analysis Version 11. Molecular Biology and Evolution https://doi.org/10.1093/molbev/msab120.</p>
Zebrafish behavioral profiling identifies ligands, targets, and neurons related to sedation and paradoxical excitation
<p>Anesthetics are generally associated with sedation, but some anesthetics can also increase brain and motor activity — a phenomenon known as paradoxical excitation. Previous studies have identified GABAA receptors as the primary targets of most anesthetic drugs, but how these compounds produce paradoxical excitation is poorly understood. To identify and understand such compounds, we applied a behavior-based drug profiling approach. Here, we show that a subset of central nervous system depressants cause paradoxical excitation in zebrafish. Using this behavior as a readout, we screened thousands of compounds and identified dozens of hits that caused paradoxical excitation. Many hit compounds modulated human GABAA receptors, while others appeared to modulate different neuronal targets, including the human serotonin-6 receptor. Ligands at these receptors generally decreased neuronal activity, but paradoxically increased activity in the caudal hindbrain. Together, these studies identify ligands, targets, and neurons affecting sedation and paradoxical excitation in vivo in zebrafish.</p>
Local synaptic inputs support opposing, network-specific odor representations in a widely projecting modulatory neuron
<p>This is the data set for Zhang et al. 2019 "Local synaptic inputs support opposing, network-specific odor representations in a widely projecting modulatory neuron" published at eLife.</p>
Gaze-Stabilizing Central Vestibular Neurons Project Asymmetrically to Extraocular Motoneuron Pools
<p><strong>ABSTRACT </strong>Within reflex circuits, specific anatomical projections allow central neurons to relay sensations to effectors that generate movements. A major challenge is to relate anatomical features of central neural populations, such as asymmetric connectivity, to the computations the populations perform. To address this problem, we mapped the anatomy, modeled the function, and discovered a new behavioral role for a genetically defined population of central vestibular neurons in rhombomeres 5–7 of larval zebrafish. First, we found that neurons within this central population project preferentially to motoneurons that move the eyes downward. Concordantly, when the entire population of asymmetrically projecting neurons was stimulated collectively, only downward eye rotations were observed, demonstrating a functional correlate of the anatomical bias. When these neurons are ablated, fish failed to rotate their eyes following either nose-up or nose-down body tilts. This asymmetrically projecting central population thus participates in both upward and downward gaze stabilization. In addition to projecting to motoneurons, central vestibular neurons also receive direct sensory input from peripheral afferents. To infer whether asymmetric projections can facilitate sensory encoding or motor output, we modeled differentially projecting sets of central vestibular neurons. Whereas motor command strength was independent of projection allocation, asymmetric projections enabled more accurate representation of nose-up stimuli. The model shows how asymmetric connectivity could enhance the representation of imbalance during nose-up postures while preserving gaze stabilization performance. Finally, we found that central vestibular neurons were necessary for a vital behavior requiring maintenance of a nose-up posture: swim bladder inflation. These observations suggest that asymmetric connectivity in the vestibular system facilitates representation of ethologically relevant stimuli without compromising reflexive behavior.</p> <p><strong>SIGNIFICANCE STATEMENT</strong> Interneuron populations use specific anatomical projections to transform sensations into reflexive actions. Here we examined how the anatomical composition of a genetically defined population of balance interneurons in the larval zebrafish relates to the computations it performs. First, we found that the population of interneurons that stabilize gaze preferentially project to motoneurons that move the eyes downward. Next, we discovered through modeling that such projection patterns can enhance the encoding of nose-up sensations without compromising gaze stabilization. Finally, we found that loss of these interneurons impairs a vital behavior, swim bladder inflation, that relies on maintaining a nose-up posture. These observations suggest that anatomical specialization permits neural circuits to represent relevant features of the environment without compromising behavior.</p>
Piezo2 in bladder-innervating sensory neurons contributes to regulation of contractile activity in cystitis
<p>Overactivity of the urinary bladder constitutes one of the main symptoms in various lower urinary tract disorders and it significantly deteriorates quality of life. The crucial role of mechanotransduction in bladder-innervating unmyelinated C-type primary sensory nerve fibres in initiating the voiding reflex in the overactive bladder has been well established. However, the molecular identity of the mechanotransducer(s) in bladder-innervating C-type sensory neurons is still elusive. The mechanosensitive ion channel Piezo2 in primary sensory neurons has been implicated in various physiological and pathological functions including proprioception, the detection of low and high threshold mechanical stimuli in the skin, and the development of mechanical hypersensitivity in peripheral pathologies. Here, we investigated whether or not Piezo2 expressed in primary sensory neurons could contribute to bladder overactivity in cystitis. Characterisation of Piezo2 expression revealed that about 1/5 of primary sensory neurons express this ion channel and at least half of the Piezo2-expressing cells express various markers for unmyelinated C-type sensory neurons. Further, we found that Piezo2 is expressed predominantly in lamina I and II of the spinal cord, where C-type sensory neurons terminate. We disclosed that Piezo2 is expressed in sensory neuron terminals innervating peripheral tissues including the bladder wall and noxious but not innocuous pressure in the urinary bladder activates Piezo2-expressing primary sensory neurons. Finally, we show that instillation of GsMTx that blocks Piezo2 significantly reduces overactivity in the inflamed but not in the naive urinary bladder. Together these findings indicate that Piezo2 in bladder-innervating unmyelinated C-type neurons contributes to the detection of noxious pressure and regulation of contractile activity in cystitis.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.