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450 results for “neuronal differentiation”
Neurothreads: development of supportive carriers for mature dopaminergic neuron differentiation and implantation
<p>Raw data for the publication:</p> <p><strong>Neurothreads: development of supportive carriers for mature dopaminergic neuron differentiation and implantation</strong></p>
Dataset: Measuring stimulus-evoked neurophysiological differentiation in distinct populations of neurons in mouse visual cortex
<p>This dataset contains the calcium imaging and behavioral data analyzed in our paper, "Measuring stimulus-evoked neurophysiological differentiation in distinct populations of neurons in mouse visual cortex".</p> <p>These data were obtained at the Allen Brain Observatory as part of the <em>OpenScope</em> project, which is operated by the Allen Institute.</p> <p>Analysis code is available at <a href="https://github.com/wmayner/openscope-differentiation">https://github.com/wmayner/openscope-differentiation</a>.</p>
Electrophysiological data of the paper 'Serotonergic and dopaminergic neurons in the dorsal raphe are differentially altered in a mouse model for parkinsonism'
<p>This excel data set contains the electrophysiological data presented in the paper including figure 1I, 1J, figure 3, figure 5, suppl. figure 2A, suppl. figure 3, suppl. figure 6E, 6G, 6L & 6N.</p> <p> </p> <p>More information about how the data was extracted can be found in the materials and methods section of the paper. </p>
RNAseq data: Analysis of circRNA expression in human neuronal differentiation
<p>This dataset contains sequencing read count data related to samples from differentiating human neuroepithelial stem cells (NES) collected at days zero (NES), five (D5) and 28 (D28) of differentiation. Details on how samples were collected and how data was generated and processed are described below.</p> <p> </p> <p><em>Sample preparation</em></p> <p>NES were seeded on tissue culture flasks coated with 20 μg/ml poly-L-ornithine (Sigma-Aldrich P3655), and 1 μg/ml laminin (Sigma-Aldrich L2020). Cells were grown in DMEM/F12+GlutaMAX medium (ThermoFisher 31331093) supplemented with 0.05X B27 (ThermoFisher 17504044), 1X N2 (ThermoFisher 17502001), 10 ng/ml bFGF (fisher scientific CTP0261), 10 ng/ml EGF (PeproTech AF-100-15) and 10 U/ml penicillin/streptomycin (ThermoFisher 15140122). Medium was exchanged 50% daily and cells maintained in 5% CO2 at 37ºC, passaging once 100% confluent and seeding at a density of 5x104 cells/cm2. Neural differentiation was induced by growth factor withdrawal the day after plating with media B27 concentration increased to 0.5X. Media was exchanged 50% every second day up until D15, after which media was supplemented with 0.4 ug/ml laminin and exchanged 50% every three days. </p> <p> </p> <p><em>RNA extraction and sequencing</em></p> <p>Cells were lysed in TRIzol reagent (ThermoFischer 15596026) before separating with chloroform and mixing the aqueous phase with isopropanol as per manufacturer directions. RNA was then isolated from the isopropanol/chloroform solution using the ReliaPrep RNA Cell Miniprep kit (Promega Z6010). Libraries were prepared with Illumina Truseq Stranded total RNA RiboZero GOLD kit and sequenced on the NovaSeq6000 platform with a 2x151 setup using NovaSeqXp workflow in S4 mode flowcell.</p> <p> </p> <p><em>Data generation</em></p> <p>Raw reads were processed using cutadapt v3.2 to trim adaptor sequences and low-quality base pairs and discard short reads (options: -m 20 -e 0.1 -q 20 -O 1). The GRCh37 genome assembly was used for all alignment, annotation, and downstream analysis steps. Trimmed read weres alignment to the GRCh37 genome assembly using TopHat v2.0.9 tophat_fusion (with Bowtie v1.1.2 and Samtools v0.1.19) with –fusion-min-dist 200. BAM files have been anonymised by removal of potentially identifiable genetic variant information using BAMboozle v0.5.0 (Ziegenhain & Sandberg, 2021) with default settings. This BAM files and corresponding index (.bai) files are provided here with naming convention "<em>label.</em>bam" Information on sample labels and corresponding conditions is provided in the file 'metadata.txt'.</p> <p><br> </p>
Supplementary materials of Brain Extract of Subacute Traumatic Brain Injury Promotes Neuronal Differentiation of Human Neural Stem Cells Via Autophagy
<p>Figure S1: Characterization of hNSCs. In proliferate medium, the embryo derived hNSCs could form the neurospheres (A). Markers of NSCs (NES, SOX2, SOX1) were detected (B). The SOX2<sup>+</sup> cells accounted for >95% of all cells (C). Bar scale: 60μm;</p> <p>Figure S2: The proliferation of hNSCs in TBI brain extracts with different phases. Differentiated hNSCs could be labeled with EdU in TBI brain extracts with different phases (A). Ratio of EdU<sup>+</sup> cells was decreased with the progression of days post-injury (B). Bar scale: 75μm; ****: P<0.0001;</p> <p>Figure S3: The GO annotations and KEGG pathway analysis of different expressed pro-teins in acute brain extract and subacute brain extract;</p> <p>Table S1: The protein profiles of brain extract in acute phase and subacute phase after TBI.</p>
Somatic mutations alter the differentiation outcomes of iPSC-derived neurons (Metadata and AnnData/H5AD files)
<p><strong>Data S1:</strong> Metadata information for the 828,937 processed cells from the DN dataset: donor identity, cell type annotation, pool identifier, 10x sample, time point and replicate information. Related to STAR Methods: Reanalysis of pooled single-cell data (DA).</p> <p><strong>Data S2-S4:</strong> AnnData/H5AD files containing the single-cell gene expression matrices and the metadata for day 11, day 30 and day 52, respectively. The gene expression is normalised and log-transformed, but not scaled. md5 files are also included. Related to STAR Methods: DE analysis between failed and successful lines.</p> <p>#File names:</p> <p><strong>File-Data S1: </strong>suppData1.RDS</p> <p><strong>File-Data S2: </strong>allpools.scanpy.D11.wMetaClustUmapGraph.exprLogNormNotScaled_notKO.h5ad</p> <p><strong>File-Data S3: </strong>allpools.scanpy.D30.wMetaClustUmapGraph.exprLogNormNotScaled_notKO.h5ad</p> <p><strong>File-Data S4: </strong>allpools.scanpy.D52.wMetaClustUmapGraph.exprLogNormNotScaled_notKO.h5ad</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>
Clonally related, Notch-differentiated spinal neurons integrate into distinct circuits EPhys raw data
<p>Shared lineage has diverse effects on patterns of neuronal connectivity. In mammalian cortex, excitatory sister neurons assemble into shared microcircuits, whereas throughout the <em>Drosophila</em> nervous system, Notch-differentiated sister neurons diverge into distinct circuits. Notch-differentiated sister neurons have been observed in vertebrate spinal cord and cerebellum, but whether they integrate into shared or distinct circuits remains unknown. Here we evaluate the connectivity between sister V2a/b neurons in the zebrafish spinal cord. Using an <em>in</em> <em>vivo</em> labeling approach, we identified pairs of sister V2a/b neurons born from individual Vsx1+ progenitors and observed that they have similar axonal trajectories and proximal somata. However, paired whole-cell electrophysiology and optogenetics revealed that sister V2a/b neurons receive input from distinct presynaptic sources, do not communicate with each other, and connect to largely distinct targets. These results resemble the divergent connectivity in <em>Drosophila</em> and represent the first evidence of Notch-differentiated circuit integration in a vertebrate system.</p>
Hindbrain modules differentially transform activity of single collicular neurons to coordinate movements
<p>Seemingly simple behaviors such as swatting a mosquito or glancing at a signpost involve the precise coordination of multiple body parts. Neural control of coordinated movements is widely thought to entail transforming a desired overall displacement into displacements for each body part. Here we reveal a different logic implemented in the mouse gaze system. Stimulating superior colliculus (SC) elicits head movements with stereotyped displacements but eye movements with stereotyped endpoints. This is achieved by individual SC neurons whose branched axons innervate modules in medulla and pons that drive head movements with stereotyped displacements and eye movements with stereotyped endpoints, respectively. Thus, single neurons specify a mixture of endpoints and displacements for different body parts, not overall displacement, with displacements for different body parts computed at distinct anatomical stages. Our study establishes an approach for unraveling motor hierarchies and identifies a logic for coordinating movements and the resulting pose.</p>
Differentiation trajectories of the Hydra nervous system reveal transcriptional regulators of neuronal fate
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Clonally related, Notch-differentiated spinal neurons integrate into distinct circuits EPhys raw data
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Transcriptome-wide alternative mRNA splicing analysis reveals post-transcriptional regulation of neuronal differentiation
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Endocannabinoid signalling in stem cells and cerebral organoids drives differentiation to deep layer projection neurons via CB1 receptors
<p>The endocannabinoid (eCB) system, <i>via</i> cannabinoid CB<sub>1</sub> receptor, regulates neurodevelopment by controlling neural progenitor proliferation and neurogenesis. CB<sub>1</sub> receptor signalling <i>in vivo</i> drives corticofugal deep layer projection neuron development through the regulation of <span>BCL11B </span>and <span>Satb2</span> transcription factors. Here, we investigated the role of eCB signalling in mouse pluripotent embryonic stem cell-derived neuronal differentiation. Characterization of the eCB system revealed increased expression of eCB-metabolizing enzymes, eCB ligands and CB<sub>1</sub> receptors along neuronal differentiation. CB<sub>1</sub> receptor knockdown inhibited neuronal differentiation of deep layer neurons and increased upper layer neuron generation, and this phenotype was rescued by CB<sub>1</sub> re-expression. Pharmacological regulation with CB<sub>1</sub> receptor agonists or elevation of eCB tone with a monoacylglycerol lipase inhibitor promoted neuronal differentiation of deep layer neurons at the expense of upper layer neurons. Patch-clamp analyses revealed that enhancing cannabinoid signalling facilitated neuronal differentiation and functionality. Noteworthy, incubation with CB<sub>1</sub> receptor agonists during human iPSC-derived cerebral organoid formation also promoted the expansion of BCL11B<sup>+</sup> neurons. These findings unveil a cell-autonomous role of eCB signalling that, <i>via</i> CB<sub>1</sub> receptor, promotes mouse and human deep layer cortical neuron development.</p>
Cilia structure and intraflagellar transport differentially regulate sensory response dynamics within and between C. elegans chemosensory neurons
<p><u>List of data with corresponding files</u></p> <p><strong>Figure 1</strong></p> <p>A) <em>File name:</em> Figure 1A- ASH_IFT_mutants_length_copy.pzfx</p> <p>B) <em>File names: </em>(Folder) ASH_1Mglycerol_IFT_mutants <em>and </em>(Folder) ASH_10-2IAA_IFT_mutants</p> <p>C) <em>File names: </em>(Folder) ASH_1Mglycerol_IFT_mutants > Peak_responses <em>and </em>(Folder) ASH_10-2IAA_IFT_mutants > Peak_responses - first pulse</p> <p>D) <em>File names: </em>Figure 1D – ASH Cher and Marianas Lengths Truncation osm-3(ts) 27 and 30 degree graph <em>and </em>Figure 1D – ASH Cher and Marianas Lengths Truncation osm-3(ts) 27 degrees</p> <p>E) <em>File name: </em>Figure 1E – ASH IFT Quantification.xlsx</p> <p>F) <em>File name: </em>(Folder) ASH_1Mglycerol_osm3ts</p> <p>G) <em>File names: </em>Figure 1 – Glycerol_Assays_Data.pzfx <em>and </em>Figure 1 – Glycerol_Assays_Data.xlsx</p> <p>H) <em>File name: </em>Figure 1H – ALL ASH Marianas and Cher Lengths che-3(nx159ts)</p> <p>I) <em>File names: </em>Figure 1- ASH IFT PY12007 v AP457 <em>and </em>Figure 1- ASH IFT PY12007 v AP457.xlsx</p> <p>J) <em>File name:</em> (Folder) ASH_1Mglycerol_che3ts</p> <p>K) <em>File names: </em>Figure 1 – Glycerol_Assays_Data.pzfx <em>and </em>Figure 1 – Glycerol_Assays_Data.xlsx</p> <p><strong>Figure 2</strong></p> <p>B) <em>File name: </em>(Folder) 10-7Diacetyl_Calcium_IFT_mutants</p> <p>C) <em>File names: </em>(Folder) 10-6Pyrazine_Calcium_IFT_mutants <em>and </em>(Folder) 10-7Pyrazine_Calcium_IFT_mutants</p> <p>D) <em> File names: </em>(Folder) Microfluidics > (Folder) 10-6pyrazine <em>and </em>(Folder) 10-7diacetyl</p> <p>E) <em>File names: </em>(Folder) Microfluidics > Diacetyl_Chemotaxis_index_osm6_osm3kap1.pzfx <em>and</em><em> </em>Pyrazine_Chemotaxis_index_pyrazine_osm3kap1_osm6.pzfx</p> <p>G) <em>File name: </em>(Folder) 10-6Pyrazine_Calcium_misexpression</p> <p><strong>Figure 3</strong></p> <p>A) <em>File name: </em>Figure 3A- AWA IFT AP303 v AP379.xlsx</p> <p>C) <em>File names: </em>(Folder) AWA_Calcium_Diacetyl <em>and </em>(Folder) AWA_Calcium_Pyrazine</p> <p>D) <em>File names: </em>(Folder) AWA_Calcium_Diacetyl <em>and </em>(Folder) AWA_Calcium_Pyrazine</p> <p>E) <em>File name: </em>Figure 3E- SRX64 and ODR10 within dendritic branches.pzfx</p> <p><strong>Figure 4</strong></p> <p>A) <em>File name: </em>(Folder) AWA_Calcium_Diacetyl_grk2</p> <p>B) <em>File name: </em>(Folder) AWA_Calcium_Pyrazine_grk2</p> <p>C) <em>File name: </em>(Folder) Microfluidics_diacetyl</p> <p>D) <em>File name: </em>(Folder) Microfluidics_pyrazine</p> <p>E) <em>File name: </em>Figure 4E- GRK2tRFP_kap1osm3ts.pzfx</p> <p><strong>Figure 5</strong></p> <p>A) <em>File names: </em>(Folder) AWA_Diacetyl_Adaptation_osm3ts <em>and </em>(Folder) AWA_Pyrazine_Adaptation_osm3ts</p> <p>B) <em>File name: </em>Figure 5B and S7B- FRAP.pzfx</p> <p>C) <em>File names: </em>Figure 5C- SRX64_bbs7jhu590.pzfx <em>and </em>Figure 5C- SRX64_bbs7ok1351.pzfx <em>and </em>Figure 5C- oy158_bbs7jhu590.pzfx <em>and </em>Figure 5C- oy158_bbs7ok1351.pzfx</p> <p>D) <em>File names: </em>(Folder) AWA_Diacetyl_Adaptation_bbs7 <em>and </em>(Folder) AWA_Pyrazine_Adaptation_bbs7</p> <p>E) <em>File name: </em>Figure 5E- Marianas_EV_ODR10_SRX64_osm3tskap1.pzfx</p> <p><strong>Figure S1</strong></p> <p>A) <em>File names: </em>(Folder) ASH_IFTmutants_0.5and2Mglycerol > (Folder) 0.5Mglycerol <em>and </em>(Folder) 2Mglycerol</p> <p>B) <em>File names: </em>(Folder) ASH_IFTmutants_quinine > (Folder) 5mM_quinine <em>and </em>(Folder) 10mM_quinine</p> <p>C) <em>File name: </em>ASH_IAA_t1/2</p> <p>D) <em>File names: </em>(Folder) ASH_IFTmutants_heptanol > (Folder) 10-3_heptanol <em>and </em>(Folder) 10-4_heptanol</p> <p>E) <em>File names: </em>(Folder) ASH_IFTmutants_2Mglycerol_GABA <em>and </em>(Folder) ASH_IFTmutants_2Mglycerol_bicuculline</p> <p><strong>Figure S2</strong></p> <p>B) <em>File name: </em>Figure S2B- Dye-filling_data</p> <p>C) <em>File name: </em>Figure S2C- ASH_IFT_mutants_length.pzfx</p> <p>D) <em>File names: </em>Figure S2D- ASH IFT PY6345 v PY12007 <em>and </em>Figure S2D- ASH IFT Quantification</p> <p><strong>Figure S3</strong></p> <p>A) <em>File name: </em>(Folder) AWA_10-7Diacetyl_IFTmutants</p> <p>B) <em>File names:</em> (Folder) AWA_Calcium_heptanone > (Folder) 10-4_heptanone <em>and </em>(Folder) 10-5_heptanone</p> <p>C) <em>File name: </em>(Folder) AWA_10-5Pyrazine_IFTmutants</p> <p>D) <em>File names: </em>(Folder) AWA_Calcium_methylpyrazine > (Folder) 10-8_methylpyrazine <em>and </em>(Folder) 10-9_methylpyrazine</p> <p>E) <em>File name:</em> Figure S3- Chemotaxis IFT_mutants.pzfx</p> <p><strong>Figure S4</strong></p> <p>A) <em>File name: </em>Figure S4A – AP419_pyrazine_Chemotaxis.pzfx</p> <p>C) <em> File name: </em>Figure S4C – srx64SL2gfp_osm5.pzfx</p> <p><strong>Figure S6</strong></p> <p>A) <em>File names: </em>Diacetyl_prism_decay_grk2 <em>and </em>Pyrazine_prism_decay_grk2</p> <p><strong>Figure S7</strong></p> <p> A) <em>File name: </em>(Folder) AWA_Calcium_Adaptation_diacetyl_IFT_mutant</p> <p> B) <em>File name: </em>Figure 5B and S7B- FRAP</p> <p> C) <em>File name: </em>Figure S7C- oy158_grk2_rescue.pzfx</p> <p> F) <em>File names: </em>(Folder) AWA_Calcium_Adaptation_diacetyl_grk2 > (Folder) grk2 <em>and </em>(Folder) grk2bbs7double</p> <p><strong>Additional materials</strong></p> <p> All code is listed under Code from Github > MF.matR-master <em>and </em>Additional Methods</p>
Hedgehog induced oxidative phosphorylation rescues the neuronal differentiation defect of human enteric neural crest cells underlying Hirschsprung disease
<p>Hedgehog induced oxidative phosphorylation rescues the neuronal differentiation defect of human enteric neural crest cells underlying Hirschsprung disease</p>
Endocannabinoid signalling in stem cells and cerebral organoids drives differentiation to deep layer projection neurons via CB1 receptors
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Data from: Persistent firing in LEC III neurons is differentially modulated by learning and aging
Whether and how persistent firing in lateral entorhinal cortex layer III (LEC III) supports temporal associative learning is still unknown. In this study, persistent firing was evoked <em>in vitro</em> from LEC III neurons from young and aged rats that were behaviorally naïve or trained on trace eyeblink conditioning. Persistent firing ability from neurons from behaviorally naïve aged rats was lower compared to neurons from young rats. Neurons from learning impaired aged animals also exhibited reduced persistent firing capacity, which may contribute to aging-related learning impairments. Successful acquisition of the trace eyeblink task, however, increased persistent firing ability in both young and aged rats. These changes in persistent firing ability are due to changes to the afterdepolarization, which may in turn be modulated by the postburst afterhyperpolarization. Together, these data indicate that successful learning increases persistent firing ability and d ecreases in persistent firing ability contribute to learning impairments in aging.
Morphological/WB/ELISA/cell counting data of the paper 'Serotonergic and dopaminergic neurons in the dorsal raphe are differentially altered in a mouse model for parkinsonism'
<p>The files contain the data included in Figure 2B, Figure 2C, Figure 4B, Figure 4C, Figure 6B, Figure 6C, Suppl.Fig.4, Suppl. Figure 5, Suppl. Figure 6I, Suppl. Figure 6J.</p>
Ultra robust negative differential resistance memristor for hardware neuron circuit implementation
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Data from: Differential octopaminergic modulation of olfactory receptor neuron responses to sex pheromones in Heliothis virescens
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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.