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5,448 results for “Neuron”
Data for Chromatin compaction precedes apoptosis in developing neurons by Rose et al.
<p>Data for Chromatin compaction precedes apoptosis in developing neurons by Rose et al.</p>
Protein aggregation and calcium dysregulation are the earliest hallmarks of familial Parkinson's disease in human midbrain dopaminergic neurons
<p>Mutations in the <em>SNCA</em> gene cause autosomal dominant Parkinson’s disease (PD), with loss of dopaminergic neurons in the substantia nigra, and aggregation of α-synuclein. The sequence of molecular events that proceed from an <em>SNCA</em> mutation during development, to end stage pathology is unknown. Utilising human induced pluripotent stem cells (hiPSCs), we resolved the temporal sequence of SNCA induced pathophysiological events in order to discover early, and likely causative, events. Our small molecule-based protocol generates highly enriched midbrain dopaminergic (mDA) neurons: molecular identity was confirmed using single-cell RNA sequencing and proteomics, and functional identity through dopamine synthesis, and measures of electrophysiological activity. At the earliest stage of differentiation, prior to maturation to mDA neurons, we demonstrate the initial formation of small β-sheet rich oligomeric aggregates, in <em>SNCA</em>-mutant cultures. Aggregation persists and progresses, ultimately resulting in the accumulation of phosphorylated aggregates. Impaired intracellular calcium signalling, increased basal calcium, and impairments in mitochondrial calcium handling occurred early at day 34-41 post differentiation. Once midbrain identity fully developed, at day 48-62 post differentiation, <em>SNCA</em>-mutant neurons exhibited mitochondrial dysfunction, oxidative stress, lysosomal swelling and increased autophagy. Ultimately these multiple cellular stresses lead to abnormal excitability, altered neuronal activity, and cell death. Our differentiation paradigm generates an efficient model for studying disease mechanisms in PD, and highlights that protein misfolding to generate intraneuronal oligomers is one of the earliest critical events driving disease in human neurons, rather than a late-stage hallmark of the disease.</p>
Linguistic Models for Requirement Quality Analysis: NeuroNER model
<p>Juyeon Kang and Jungyeul Park (in preparation). Linguistic Models for Requirement Quality Analysis. Will be submitted to Requirements Engineering (Springer). November 2017. </p> <p>It requires NeuroNER, available at http://neuroner.com</p> <p>See also Kang J, Park J. Generating a Linguistic Model for Requirement Quality Analysis. In: <em>Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters (PACLIC 30)</em>. Seoul, Korea; 2016:439-447. http://aclweb.org/anthology/Y/Y16/Y16-3016.pdf</p> <p> </p> <p> </p>
Data for Evaluation of altered cell-cell communication between glia and neurons in the hippocampus of 3xTg-AD mice at two time points
<p>processed_data.tar.gz contains all files from the data directory associated with the 230418_TS_AgingCCC GitHub project and includes the following:</p> <ul> <li> <p>CellRangerCounts/</p> </li> <ul> <li> <p>post_soupX/ : contains 12 directories for 12 samples, which each contain 3 files obtained from ambient RNA removal with soupX. Below is a representative example, but the post_soupX directory contains one directory for each of the 12 samples:</p> </li> <ul> <li> <p>S01_6m_AD/</p> </li> <ul> <li> <p>barcodes.tsv</p> </li> <li> <p>genes.tsv</p> </li> <li> <p>matrix.mtx</p> </li> </ul> </ul> <li> <p>pre_soupX/ : contains 12 directories for 12 samples, which each contain 2 files obtained from Cell Ranger after aligning fastq files to the reference genome. Below is a representative example, but this directory contains 1 directory for each individual sample:</p> </li> <ul> <li> <p>S01_6m_AD/outs/</p> </li> <ul> <li> <p>filtered_feature_ bc_matrix.h5</p> </li> <li> <p>raw_feature_bc_matrix.h5 </p> </li> </ul> </ul> </ul> <li> <p>PANDA_inputs/</p> </li> <ul> <li> <p>PANDA_exp_files_array.txt: Text files with files paths to expression inputs for PANDA gene regulatory networks.</p> </li> <li> <p>mm10_TFmotifs.txt: Mouse TF motif input for PANDA gene regulatory networks. Previously published in Whitlock et al. 2023 </p> </li> <li> <p>mm10_ppi.txt: Mouse protein-protein interaction information from SringDB input for PANDA gene regulatory networks. Previously published in Whitlock et al. 2023 </p> </li> </ul> <li> <p>ccc/</p> </li> <ul> <li> <p>nichenet_v2_prior/</p> </li> <ul> <li> <p>gr_network_mouse_21122021.rds : accessed in December 2023, gene regulation network – gene regulatory information from MultiNicheNet</p> </li> <li> <p>ligand_target_matrix_nsga2r_final_mouse.rds: accessed in December 2023, ligand target matrix for mouse from MultiNicheNet.</p> </li> <li> <p>ligand_tf_matrix_nsga2r_final_mouse.rds: accessed in December 2023, mouse ligand tf matrix for signaling path determination from MultiNicheNet</p> </li> <li> <p>lr_network_mouse_21122021.rds : accessed in December 2023, ligand-receptor matrix from MultiNicheNet</p> </li> <li> <p>signaling_network_mouse_21122021.rds : accessed in December 2023, signaling network – protein-protein interaction information from MultiNicheNet for mouse</p> </li> <li> <p>weighted_networks_nsga2r_final_mouse.rds : accessed in October 2023, networks weighted by literature evidence from MultiNicheNet for mouse</p> </li> </ul> <li> <p>multinichenet_output.rds : MultiNicheNet output for 3xTg-AD snRNA-seq data</p> </li> <li> <p>12m_signaling_igraph_objects.rds : list of igraph objects for 93 LRTs and their signaling mediators at 12 months</p> </li> <li> <p>6m_signaling_igraph_objects.rds :list of igraph objects for 2 LRTs and their signaling mediators at 6 months</p> </li> </ul> <li> <p>elisa/: CSV files of measured OD values for every ELISA.</p> </li> <ul> <li> <p>240319_ELISA_Ab40.csv: OD measurements for Ab40</p> </li> <li> <p>240319_ELISA_Ab42.csv: OD measurements for Ab42</p> </li> <li> <p>240319_ELISA_total_tau.csv: OD measurements for Total Tau</p> </li> </ul> <li> <p>panda/: PANDA gene regulatory networks for each time point and condition in excitatory and inhibitory neurons. Used for differential gene targeting.</p> </li> <ul> <li> <p>excitatory_neurons_AD12.Rdata</p> </li> <li> <p>excitatory_neurons_AD6.Rdata</p> </li> <li> <p>excitatory_neurons_WT12.Rdata</p> </li> <li> <p>excitatory_neurons_WT6.Rdata</p> </li> <li> <p>inhibitory_neurons_AD12.Rdata</p> </li> <li> <p>inhibitory_neurons_AD6.Rdata</p> </li> <li> <p>inhibitory_neurons_WT12.Rdata</p> </li> <li> <p>inhibitory_neurons_WT6.Rdata</p> </li> </ul> <li> <p>pseudobulk/: includes pseudo bulk matrices for every cell type which were used for downstream analyses. Each matrix also includes metadata information on condition and time point.</p> </li> <ul> <li> <p>all_counts_ls.rds: List of all the pseudo bulk matrices (below).</p> </li> <li> <p>astrocytes.rds: pseudobulk matrix for astrocytes. Include time point and condition information for downstream analyses.</p> </li> <li> <p>endothelial_cells.rds: pseudobulk matrix for endothelial cells. Include time point and condition information for downstream analyses.</p> </li> <li> <p>ependymal_cells.rds: pseudobulk matrix for ependymal cells. Include time point and condition information for downstream analyses.</p> </li> <li> <p>excitatory_neurons.rds: pseudobulk matrix for excitatory neurons. Include time point and condition information for downstream analyses.</p> </li> <li> <p>fibroblasts.rds: pseudobulk matrix for fibroblasts. Include time point and condition information for downstream analyses.</p> </li> <li> <p>inhibitory_neurons.rds: pseudobulk matrix for inhibitory neurons. Include time point and condition information for downstream analyses.</p> </li> <li> <p>meningeal_cells.rds: pseudobulk matrix for meningeal cells. Include time point and condition information for downstream analyses.</p> </li> <li> <p>microglia.rds: pseudobulk matrix for microglia. Include time point and condition information for downstream analyses.</p> </li> <li> <p>oligodendrocytes.rds: pseudobulk matrix for oligodendrocytes. Include time point and condition information for downstream analyses.</p> </li> <li> <p>opcs.rds: pseudobulk matrix for oligodendrocyte progenitor cells. Include time point and condition information for downstream analyses.</p> </li> <li> <p>percicytes.rds: pseudobulk matrix for pericytes. Include time point and condition information for downstream analyses.</p> </li> <li> <p>rgcs.rds: pseudobulk matrix for retinal ganglion cells. Include time point and condition information for downstream analyses.</p> </li> </ul> <li> <p>pseudobulk_split/: Includes pseudo bulk count matrices split by time point and condition. Used for input to PANDA for gene regulatory network construction.</p> </li> <ul> <li> <p>excitatory_neurons_AD12.Rdata</p> </li> <li> <p>excitatory_neurons_AD6.Rdata</p> </li> <li> <p>excitatory_neurons_WT12.Rdata</p> </li> <li> <p>excitatory_neurons_WT6.Rdata</p> </li> <li> <p>inhibitory_neurons_AD12.Rdata</p> </li> <li> <p>inhibitory_neurons_AD6.Rdata</p> </li> <li> <p>inhibitory_neurons_WT12.Rdata</p> </li> <li> <p>inhibitory_neurons_WT6.Rdata</p> </li> </ul> <li> <p>seurat_preprocessing/</p> </li> <ul> <li> <p>filtered_seurat.rds : merged and filtered seurat object</p> </li> <li> <p>integrated_seurat.rds : seurat object integrated using harmony</p> </li> <li> <p>clustered_seurat.rds : clustered seurat object</p> </li> <li> <p>processed_seurat.rds : processed seurat object with final cell type assignments at specified resolution</p> </li> </ul> </ul> <p> </p> <p>Raw data publicly available on GEO under series accession: GSE261596</p>
Dataset: Fear learning induces synaptic potentiation between engram neurons in the rat lateral amygdala
<p><a name="_Hlk112677821"></a>The lateral amygdala (LA) encodes fear memories by potentiating sensory inputs associated with threats and, in the process, recruits 10-30% of its neurons per memory engram. However, how the network within the LA processes this information, and whether it also plays a role in storing it, is still largely unknown. Using <em>ex vivo</em> 12 patch clamp and <em>in-vivo</em> 32 electrode electrophysiological recordings, in combination with activity-dependent fluorescent and optogenetic tagging and recall, we identified between principal LA neurons in rats a sparsely connected network that is organized in clusters and in which connections between recruited neurons specifically potentiate after fear learning. Our findings of synaptic plasticity in an auto-associative excitatory network of the LA suggest a basic cortical principle through which a small number of pyramidal neurons could encode a large number of memories.</p>
Functional efficacy of the MAO-B inhibitor safinamide in murine substantia nigra pars compacta dopaminergic neurons in vitro: a comparative study with tranylcypromine
Open the record for dataset details and reuse information.
Data from "A population of neurons selective for human voice in the monkey brain"
<p>Preprocessed datasets associated to the research article "A population of neurons selective for human voice in the monkey brain", PNAS.</p>
Data and Images for Tonic dendritic GABA release by substantia nigra dopaminergic neurons
<p>Figure1_data.xlsx</p> <p>Numerical data for the charts in Figure 1C and Figure 1E</p> <p><br>Figure1A_GP.tif</p> <p>Fluorescent image (red channel, Chronos) for Figure 1A. </p> <p><br>Figure2_data.xlsx</p> <p>Numerical data for the charts in Figure 2B and Figure 2C</p> <p><br>Figure3_data.xlsx</p> <p>Numerical data for the charts in Figure 3A, 3B, and 3C.</p> <p><br>Figure4_data.xlsx</p> <p>Numerical data for the charts in Figure 4A and 4B.</p> <p><br>Figure5_data.xlsx</p> <p>Numerical data for the charts in Figure 5B and 5C.</p> <p><br>Figure6_data.xlsx</p> <p>Numerical data for the charts in Figure 6D, 6E, 6F, 6G and 6H.</p> <p> </p> <p>Figure6A_ALDH_Positive.tif</p> <p>DAT-Cre positive, ALDH1A1 positive axons of SNc neurons in Rostral SNr. </p> <p><br>Figure6A_ALDH_Negative.tif</p> <p>DAT-Cre positive, ALDH1A1 negative axons of SNc neurons in Rostral SNr.</p> <p><br>Figure6B_ALDH_Positive.tif</p> <p>DAT-Cre positive, ALDH1A1 positive axons of SNc neurons in Caudal SNr. </p> <p><br>Figure6B_ALDH_Negative.tif</p> <p>DAT-Cre positive, ALDH1A1 negative axons of SNc neurons in Caudal SNr.</p> <p><br>Figure6C_shRNA.tif</p> <p>Fluorescent image (red channel: ALDH_shRNA) for Figure 6C.</p> <p><br>Figure7_data.xlsx</p> <p>Numerical data for the charts in Figure 7D and 7E.</p> <p><br>Figure7A_10x.tif</p> <p>Confocal image (red channel: PSAM_FusionRed; green channel: anti-TH) for Figure 7A.</p> <p><br>Figure7B_60x.tif</p> <p>Confocal image (red channel: PSAM_FusionRed; green channel: anti-TH) for Figure 7B. </p> <p><br>Figure8_data.xlsx</p> <p>Numerical data for the charts in Figure 8A and 8B. </p> <p> </p> <p>Figure_S1_data.xlsx</p> <p>Numerical data for the chart in Figure S1</p> <p><br>Figure_S2_data.xlsx</p> <p>Numerical data for charts in Figure S2 C - E</p>
Comparative basolateral amygdala connectomics reveals dissociable single-neuron projection patterns to frontal cortex in macaques and mice
<p>Zeisler et al., (2024) <em>Current Biology</em></p> <p> </p> <p>This repository contains data and code necessary to generate the main figures contained in the paper.</p> <p> </p> <p>The macaque data used is available at: https://zenodo.org/records/8319819</p>
Excessive Firing of Dyskinesia-Associated Striatal Direct Pathway Neurons is Gated By Dopamine and Excitatory Synaptic Input
<p>This folder contains data associated with a submitted manuscript: Ryan, Girasole et al, which was first posted on BioRxiv in 2022. https://www.biorxiv.org/content/10.1101/2022.10.31.514594v1.</p> <p><strong>Files associated with each figure: </strong></p> <p><strong>Figure 1 & Supp Figure 1: </strong></p> <ul> <li>Summary.mat</li> <li>Behavior.mat</li> <li>Data.mat</li> <li>TRAP_InVivo_Analysis.m</li> <li>TRAP_Remove_INs.m</li> </ul> <p><strong>Figure 2 & Supp Figure 2: </strong></p> <ul> <li>See BioStudies for rabies dataset: <a href="https://www.ebi.ac.uk/biostudies/studies/S-BSST1374?key=9a1b2103-25a6-49ed-b257-0269d3ea19a6" target="_blank" rel="noopener">https://www.ebi.ac.uk/biostudies/studies/S-BSST1374?key=9a1b2103-25a6-49ed-b257-0269d3ea19a6</a></li> </ul> <p><strong>Figure 3 & Supp Figure 3 & Supp Figure 4: </strong></p> <ul> <li> <p>Fig3_FigS3_FigS4_Data.xlsx</p> </li> </ul> <p><strong>Figure 4 & Supp Figure 5: </strong></p> <ul> <li>Fig4Data.xlsx</li> <li>RNAscopeAnalysis_D1_D2.m</li> <li>RNAdata.mat</li> <li>RNAscopeAnalysis_D1_pDyn.m</li> <li>RNAScopeAnalysis.m</li> <li>pDynRNAdata.mat</li> <li>pDynSummary.mat</li> </ul> <p><strong>Contributions</strong>: </p> <p>Behavioral data was collected and analyzed by Ally Girasole, Michael Ryan, and Emily Twedell.</p> <p>In vivo electrophysiological data was collected and analyzed by Michael Ryan and Ally Girasole.</p> <p>Ex vivo electrophysiological data was collected and analyzed by Ally Girasole, Michael Ryan, Emily Twedell, and Alexandra Nelson.</p> <p>Anatomical tracing and histology data was collected and analyzed by Ron Paletzki, Chip Gerfen, Michael Ryan, Ally Girasole and Rea Brakaj.</p> <p>RNA Scope data was collected and analyzed by Andrew Flores, Mike Ryan, and Tom Hnasko.</p>
Medium spiny neuron electrophysiological properties across male rats and female rats in different estrous cycle phases in the nucleus accumbens core: Excitatory synaptic input, action potential, and intrinsic properties
<p>This dataset presents electrophysiological properties from whole-cell patch clamped nucleus accumbens core medium spiny neurons from male rats and female rats recorded in different estrous cycle phases. Excitatory synaptic input, action potential, and intrinsic properties are presented in an analyzed format, as well as the original electrophysiological recordings of each neuron. These data were originally presented across two different papers (Proaño et al., 2018, 2020) and further analyzed in an additional paper (Long et al., 2024), all of which are cited below. These data were key for establishing that nucleus accumbens neuron electrical function changed across the estrous cycle in response to its associated hormones, programming a phase-dependent sex difference in adulthood. This finding provided a new framework for understanding how biological sex and hormone cyclicity regulate the neurons implicated in motivated behaviors and related functions and disorders.</p>
3D Reconstruction of Neuronal Allometry and Neuromuscular Projections in Asexual Planarians Using Expansion Tiling Light Sheet Microscopy dataset2
<p>wide type planarian 6G10 staining taken with TLSM</p>
Pathogenic LRRK2 causes age-dependent and region-specific deficits in ciliation, innervation and viability of cholinergic neurons_1
Open the record for dataset details and reuse information.
A fluid-walled microfluidic platform for human neuron microcircuits and directed axotomy
<p><strong>ABSTRACT</strong></p> <div> <p>In our brains, different neurons make appropriate connections; however, there remain few in vitro models of such circuits. We use an open microfluidic approach to build and study neuronal circuits in vitro in ways that fit easily into existing bio-medical workflows. Dumbbell-shaped circuits are built in minutes in standard Petri dishes; the aqueous phase is confined by fluid walls – interfaces between cell-growth medium and an immiscible fluorocarbon, FC40. Conditions are established that ensure post-mitotic neurons derived from human induced pluripotent stem cells (iPSCs) plated in one chamber of a dumbbell remain where deposited. After seeding cortical neurons on one side, axons grow through the connecting conduit to ramify amongst striatal neurons on the other – an arrangement mimicking unidirectional cortico-striatal connectivity. We also develop a moderate-throughput non-contact axotomy assay. Cortical axons in conduits are severed by a media jet; then, brain-derived neurotrophic factor and striatal neurons in distal chambers promote axon regeneration. As additional conduits and chambers are easily added, this opens up the possibility of mimicking complex neuronal networks, and screening drugs for their effects on connectivity.</p> <p> </p> </div> <p><strong>FILE DESCRIPTIONS</strong></p> <p>Source Data.xlsx: Tabular datasets plotted on Main Figures 2, 3 and 6, and Supplementary Figures 1 and 2.</p> <p>Key Resources Table.xlsx: Table containing details on the key resources (antibodies, cell lines, virus strains, software, code, and protocols) used in this study.</p>
Patch clamp recording from rat hippocampal neuron culture using bulk and NEA chip
<p>Patch-clamp, in current-clamp mode, recordings from rat hippocampal neuron culture using stimulated nano electrode array chip in bulk and low-density NEA chip. All the uncompressed data are in easy-to-use .mat files divided for type of chip (Bulk and NEA); type of coating material and type of stimulation (only for Bulk).</p>
Physiological Characteristics of Neurons in the Mammillary Bodies align with Topographical Organization of Subicular Inputs
<p>Batch data is deposited in cell explorer format</p> <p>Please refer to https://cellexplorer.org/ for additional explanation about the data structure </p> <p>See https://github.com/buzsakilab/buzcode for code </p> <p> </p>
Impact of Neural Cyberattacks on a Realistic Neuronal Topology from the Primary Visual Cortex of Mice
<p>Dataset associated to the paper "Impact of Neural Cyberattacks on a Realistic Neuronal Topology from the Primary Visual Cortex of Mice"</p>
Adult-born neurons boost odor-reward association
<p>Row data from main manuscript figures</p>
Mean and variance of spike counts for area MT neurons in awake and anesthetized subjects
<p>These data are the means and variance of spike counts in an analysis window beginning at motion onset of the visual stimulus, within the receptive field of MT neurons recorded in anesthetized or alert non-human primates. Dot motion was presented in a range of motion directions, with 0 degrees being the preferred direction of each cell. The tuning of the variability in the response and a model accounting for these state-dependent changes is in press at PLoS Computational Biology, along with more details on the data collection and analysis. A preprint is available at: https://arxiv.org/abs/1710.10548</p> <p>These data consist of a MatLab data file with two saved data structures, ‘units’ and ‘autocorrelation’. <br> <br> ‘units’ is an 80-element structure, one entry for each recorded extracellular single unit (putative neuron) in the recording with the following fields:<br> ‘state’ indicates whether the subject was anesthetized or alert during the recording<br> ‘stimDir’ is the direction of motion, in degrees, of the moving dot stimulus<br> ‘latency’ is the latency, in milliseconds, to the response onset<br> ‘latAlign’ are the spike counts and variance in a 150ms window beginning at response onset<br> ‘stimAligned’ are the spike counts and variance in a 250ms window beginning at motion onset<br> ‘MI’ is the mutual information between motion directions between +/- 90 degrees and the cumulative spike counts at a range of time points since motion onset, given in MI_time_vec. The mutual information quantity is extrapolated to the infinite data size limit via quadratic extrapolation.<br> 'MI_time_vec' is a vector of times (in milliseconds) since motion onset used in the information calculation</p> <p><br> ‘autocorrelation’ is a scalar structure containing the fields:<br> 'alert' is the average autocorrelation (range +/- 1) of the spike counts (analyzed in a 2ms window and smoothed over 5 bins) in the alert units during presentation of the null (no dot motion) stimulus<br> 'anesthetized' is the average autocorrelation (computed as for the alert units) of the anesthetized units during presentation of the null stimulus<br> 'timePoints' are the time point shifts in milliseconds corresponding to the calculated autocorrelations in 'alert' and 'anesthetized'</p>
Dataset: Dopamine neurons projecting to medial shell of the nucleus accumbens drive heroin reinforcement.
<p>This dataset contains the data presented in the paper Dopamine neurons projecting to medial shell of the nucleus accumbens drive heroin reinforcement. (doi: 10.7554/eLife.39945 )</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.