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5,472 results for “mouse cells”
Data for Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex
<p>Data for the paper: Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex, Nature 640, 2025</p> <p>In brief, this data archive includes information about the skeleton morphology and synaptic features of neurons whose cell bodies fell within a 100 micron by 100 micron column spanning all layers of mouse visual cortex. See <a href="https://www.microns-explorer.org/cortical-mm3">MICrONs-Explorer</a> for a full description of the broader volume and how it was collected.</p> <p>The data here include both data tables of cell locations, neuronal features, synapse lists, and more, as well as files containing morphological descriptions of all neurons used for the analysis in the initial version of the preprint. See the README.md file for more complete information about the individual files.</p> <p>Note: Data has been updated with post-publication files.</p>
Cell metadata for "The emergent landscape of the mouse gut endoderm at single-cell resolution"
<p>Cell metadata for the data published in "The emergent landscape of the mouse gut endoderm at single-cell resolution"</p> <p> </p>
Data set for "Cell-type-specific nicotinic input disinhibits mouse barrel cortex during active sensing"
<p>Data set for: Gasselin C, Hohl B, Vernet A, Crochet C, Petersen CCH (2021) Cell-type-specific nicotinic input disinhibits mouse barrel cortex during active sensing. Neuron doi: 10.1016/j.neuron.2020.12.018</p> <p>There are 2 files in this upload:</p> <p>1. The file named "2021_Gasselin_Neuron.pdf" is the Open Access pdf of the online publication in Neuron.</p> <p>2. The file named "Gasselin_data_code.zip" (~9 GB) is a zipped version of a folder "Gasselin_data_code" (~13 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. To access the data and the code, first unzip the file. Then add the folder with subfolders to the Matlab path and run the different codes. The current folder must be the main folder (‘Gasselin_data_code’). Each code computes and plots the results used in the corresponding figure. Figures and Tables are saved in the subfolder ‘Figures’.</p> <p>The subfolder ‘Functions’ contains functions called by the main codes.</p> <p>The main folder contains the following codes:</p> <p><em>Gasselin_Figure1: computes and plots the results for the panels D, E and F of figure 1.</em></p> <p><em>Gasselin_Figure2: computes and plots the results for the panels B and C of figure 2.</em></p> <p><em>Gasselin_Figure3: computes and plots the results for the panels B, C and D of figure 3.</em></p> <p><em>Gasselin_Figure4: computes and plots the results for the panels A, B and C of figure 4.</em></p> <p><em>Gasselin_FigureS1: computes and plots the results for the panels A, B and C of figure S1.</em></p> <p><em>Gasselin_FigureS2: computes and plots the results for the panels A and B of figure S2.</em></p> <p> </p> <p>The subfolder ‘Data’ contains the data structures used for the different figures:</p> <p><em>data_figure1.mat</em></p> <p><em>data_figure2.mat</em></p> <p><em>data_figure3.mat</em></p> <p><em>data_figure4_MECA.mat</em></p> <p><em>data_figure4_Activation.mat</em></p> <p><em>data_figure4_Inactivation.mat</em></p> <p><em>data_figureS2_Activation.mat</em></p> <p><em>data_figureS2_Inactivation.mat</em></p> <p><em>data_Axon.mat</em></p> <p> </p> <p>The data structures contain the following fields:</p> <p><em>Mouse_Name</em> : name of the mouse.</p> <p><em>Mouse_DateOfBirth</em>: date of birth of the mouse (YMD).</p> <p><em>Mouse_Sex</em>: sex of the mouse (F or M).</p> <p><em>Mouse_Genotype</em>: genotype of the mouse.</p> <p><em>Mouse_Drug</em>: experimental condition of the recording (control = ‘No Drug’; blockade of glutamatergic transmission = ‘CNQX_DAPV’; blockade of glutamatergic transmission and nicotinic receptors = ‘CNQX_DAPV_MECA’; blockade of nicotinic receptors only = ‘MECA’).</p> <p><em>Mouse_Virus</em>: virus injected if any.</p> <p><em>Cell_Counter</em>; cell recorded in a given mouse.</p> <p><em>Cell_Type</em>: type of the recorded cell based on 2P imaging. (EXC, VIP, PV, SST, 5HT3aR_non_VIP).</p> <p><em>Cell_Depth</em>: depth of the recorded cell relative to pia (µm).</p> <p><em>Cell_TargetedBrainArea</em>: cortical area targeted (C2 column of the barrel cortex = C2).</p> <p><em>Cell_Fluorescence</em>: expression of the genetically encoded fluorophore (FALSE or TRUE). A neuron recorded in a VIP_IRES_Cre x LSL_tdTomato (cf <em>Mouse_Genotype</em>) with <em>Cell_Fluorescence</em>=TRUE is considered as a VIP neuron (cf <em>Cell_Type</em>).</p> <p><em>Sweep_Counter</em>: number of the sweep recorded for a given neuron (data were acquired across successive continuous sweeps of 30-60 s).</p> <p><em>Sweep_Type</em>: experimental condition during that sweep (Only spontaneous whisking onset = ‘Onset’; Whisking onset and whisker stimulus = ‘Onset_Whisker_Stim’ ; Optogenetic stimulation = ‘Opto_Stim’; Optogenetic activation = ‘Opto_Activation’; Optogenetic inactivation = ‘Opto_Inactivation’; ).</p> <p><em>Sweep_Start_Time</em>: time at the beginning of the sweep recording (YMDHms).</p> <p><em>Sweep_WhiskerAngle</em>: C2 whisker angular position extracted from simultaneous high-speed video filming (deg).</p> <p><em>Sweep_WhiskerAngle_SamplingRate</em>: sampling rate of the whisker angle trace.</p> <p><em>Sweep_WhiskingOnset_Time</em>: time of identified whisking onset - excluding any whisker stimulus shortly before or after (s).</p> <p><em>Sweep_MembranePotential</em>: membrane potential recording (mV) after cutting of the APs.</p> <p><em>Sweep_MembranePotential_SamplingRate</em>: sampling rate of the membrane potential signal (pt.s<sup>-1</sup>).</p> <p><em>Sweep_CurrentInjected</em>: current injected into the cell (pA).</p> <p><em>Sweep_CurrentInjected_SamplingRate</em>: sampling rate of current signal (pt.s<sup>-1</sup>).</p> <p><em>Sweep_WhiskerStim_Name</em>: whisker to which the magnetic stimulus was applied to (C2 or B2&C2).</p> <p><em>Sweep_WhiskerStim_Time</em>: onset times of the whisker stimulus (s).</p> <p><em>Sweep_OptoStim_Power</em>: light power applied for optogenetic manipulations (% of the max power).</p> <p><em>Sweep_OptoStim_Time</em>: onset times of the light pulses for optogenetic manipulations (s).</p> <p><em>Cell_ID</em>: unique cell identifier (= <em>Mouse_Name</em>+<em>Cell_Counter</em>).</p> <p><em>SpikeThreshold</em>: spike threshold used to detect APs (mV).</p> <p><em>Trial_WhiskingOnset</em>: data structure containing the cut signals used to compute averaged responses around whisking onset times.</p> <p><em>Trial_WhiskerStim</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times.</p> <p><em>Trial_WhiskerStim_QuietTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without whisker movements.</p> <p><em>Trial_WhiskerStim_WhiskingTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with whisker movements.</p> <p><em>Trial_Opto</em>: data structure containing the cut signals used to compute averaged responses around optogenetic stimulus onset times.</p> <p><em>Trial_OptoAndWhisker_QuietTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with optogenetic manipulation and no whisker movements.</p> <p><em>Trial_OptoAndWhisker_WhiskingTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with optogenetic manipulation and with whisker movements.</p> <p><em>Trial_OnlyWhisker_QuietTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without optogenetic manipulation and without whisker movements.</p> <p><em>Trial_OnlyWhisker_WhiskingTrials</em>: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without optogenetic manipulation and with whisker movements.</p> <p><em>Trial_OnlyOpto</em>: data structure containing the cut signals used to compute averaged responses around optogenetic stimulus onset times in trials without whisker stimulus.</p>
Control T-cell receptor (TCR) alpha and beta chain nucleotide and amino acid sequences from human and mouse
<p>A dataset of pooled T-cell receptor (TCR) sequences for TCR alpha and beta chains of human and mouse.</p> <p>Sequences are obtained from various samples of healthy individuals/mice using our conventional protocols: see for example [Britanova et al "Dynamics of individual T cell repertoires: from cord blood to centenarians" The Journal of Immunology 2016] and [Izraelson et al. "Comparative analysis of murine T‐cell receptor repertoires." Immunology 2018].</p> <p>The sequences are stored as gzipped clonotype tables in VDJtools format, see [https://vdjtools-doc.readthedocs.io/en/master/input.html#vdjtools-format].</p> <p>This control dataset can be used as a proxy for a generative VDJ rearrangement model to estimate the expected frequency distribution of TCRs and check for enrichment of rare TCR clonotypes and groups of similar TCR sequences. For the implementation of the enrichment analysis, please see CalcDegreeStats routine from VDJtools software, see [https://vdjtools-doc.readthedocs.io/en/master/annotate.html#calcdegreestats].</p> <p>Files named "human.tra.strict.txt.gz", etc are pools of random/naive TCR clonotypes containing unique V/J/CDR3 nucleotide sequence combinations observed in data. The pools.zip file is used for TCR motif inference in VDJdb database [https://github.com/antigenomics/vdjdb-motifs], it contains human.tra.aa.txt, etc files that contain random/naive TCR clonotypes grouped by CDR3 amino acid sequence with the most frequent representative V and J.</p>
Human-mouse syntenic Long Range Interactions in Neural Stem Cells
<p>This repository contains the list of human DNA regions corresponding to long-range interactions in RNA polII-mediated long-range interactions in mouse. They are named human-mouse syntenic Long Range Interactions (hmsLRI) and were obtained via synteny from mouse neonatal forebrain stem cells. They were also annotated for their overlap with DNA sequence variants (SNPs; CNVs) associated with human neurodevelopmental disease (NDD). The lists of genes identified via inferred that are potentially involved in NDD, eye-development, and traits (schizophrenia, bipolar disorder, intelligence).</p> <p>This is a resource for exploring the potential of the non-coding regions of DNA, the largest part of the genome (~98%), in NDD. Indeed, despite the numerous NDD-causative genes identified, only 42% of patients with severe developmental disorders carry pathogenic <em>de novo</em> mutations within coding sequences. More than a half of patient could be diagnosed and treated with further research and technologies, one option is studying the non-coding genome and how alterations affect genes. </p> <p>Tracks for visualization onto UCSC Genome Browser and WashU, are also provided.<br> See README for more details. </p> <p>The peer-reviewed publication for this dataset has now been published in International Journal of Molecular Science, available OA at: <a href="https://www.mdpi.com/1422-0067/23/14/7964">https://www.mdpi.com/1422-0067/23/14/7964</a>. Please cite this when using the dataset.</p>
Single-cell transcriptomic profiling unveils dysregulation of cardiac progenitor cells and cardiomyocytes in a mouse model of maternal hyperglycemia
<p>Congenital heart disease (CHD) is the most prevalent structural malformations of the heart affecting ∼1% of live births. To date, both damaging genetic variations and adverse environmental exposure such as maternal diabetes have been found to cause CHD. Clinical studies show ∼fivefold higher risk of CHD in the offspring of mothers with pregestational diabetes. Maternal pregestational diabetes affects the gene regulatory networks key to proper cardiac development in the fetus. However, the cell-type specificity of these gene regulatory responses to maternal diabetes and their association with the observed cardiac defects in the fetuses remains unknown. To uncover the transcriptional responses to maternal diabetes in the early embryonic heart, we used an established murine model of pregestational diabetes. In this model, we have previously demonstrated an increased incidence of CHD. Here, we show maternal hyperglycemia (matHG) elicits diverse cellular responses during heart development by single-cell RNA-sequencing in embryonic hearts exposed to control and matHG environment. Through differential gene-expression and pseudotime trajectory analyses of this data, we identified changes in lineage specifying transcription factors, predominantly affecting Isl1+ second heart field progenitors and Tnnt2+cardiomyocytes with matHG. Using in vivo cell-lineage tracing studies, we confirmed that matHG exposure leads to impaired second heart field-derived cardiomyocyte differentiation. Finally, this work identifies matHG-mediated transcriptional determinants in cardiac cell lineages elevate CHD risk and show perturbations in Isl1-dependent gene-regulatory network (Isl1-GRN) affect cardiomyocyte differentiation. Functional analysis of this GRN in cardiac progenitor cells will provide further mechanistic insights into matHG-induced severity of CHD associated with diabetic pregnancies.</p>
Single Cell RNAseq of Mouse Testis
<p>This is the processed data from the publication: "Unified single-cell analysis of testis gene regulation and pathology in 5 mouse strains" (https://doi.org/10.1101/393769)</p> <p>The raw data is avaliable at GEO: GSE113293</p> <p>Associated software is at https://zenodo.org/badge/latestdoi/140632831</p> <p>SDA_objects.zip contains key tables required for many functions, download this to use the shiny app. Contents:</p> <ul> <li>cell_data: data.table containing metadata of the cells. 80 columns including cell id, SDA component cell scores, Tsne coordinates, pseudotime, experimental group etc.</li> <li>data: a sparse matrix of normalised read counts (20322 cells by 19262 genes)</li> <li>gene_annotations: data.table containing gene locations, enrichment vs other tissues from bulk data, infertility gene status</li> <li>GO_enrich: data.table containing gene ontology enrichments for each component, the genes enriched, p.values and enrichment values</li> <li>principal_curves: list of output of princurve() containing the pseudotime trajetories</li> <li>SDAresults: output of SDA run loaded using SDAtools::load_results()</li> </ul> <p>Other R objects include:</p> <ul> <li>QC_count_matrix: Sparse matrix of raw count values for QC cells and genes</li> <li>cell_imputation_AUCs: data.table of PRAC AUC values for unimputed (train), mean cell, SDA (predict), ICA, PCA, NNMF, and MAGIC</li> <li>HocomocoV11_motifProbs_matrix: Matrix of regulation probabilities from MotifFinder using fixed motifs from HocomocoV11 database</li> <li>motifFinder_denovo: list of results of MotifFinder denovo motifs from promoters regions of genes from each component</li> <li>motifFinder_denovo_fixed: list of results of MotifFinder using 125 denovo motifs with fixed motif on all genes.</li> <li>tomtom_matched_motifs: data.table of TOMTOM matches of denovo motifs, plus metadata</li> </ul>
Data set for "Cell class-specific long-range axonal projections of neurons in mouse whisker-related somatosensory cortices"
<p>Data set for: Liu Y, Bech P, Tamura K, Délez LT, Crochet S, Petersen CCH (2024) Cell class-specific long-range axonal projections of neurons in mouse whisker-related somatosensory cortices. eLife 13: RP97602. https://doi.org/10.7554/eLife.97602</p> <p>There are 3 files in this upload:</p> <p>1. The file named "2024_Liu_eLife.pdf" is the Open Access pdf of the online publication in eLife.</p> <p>2. The file named "Liu_anatomy_data_code.zip" (~35 GB) is a zipped version of a folder "Liu_anatomy_data_code" (~111 GB), which contains the anatomical data analysed in the study along with the Python codes used to generate the published figures 1-7 and their associated figure supplements. </p> <p>3. The file named "Liu_function_data_code.zip" (~10 GB) is a zipped version of a folder "Liu_function_data_code" (~35 GB), which contains the functional data analysed in the study along with the Python codes used to generate the published figure 8 and its associated figure supplement. </p> <p>After unzipping, the Python codes should run as a Jupyter notebook (anatomy .ipynb code) or Python code (function .py code) in Anaconda.</p>
Differential response of α-synuclein expression to bacterial ligands and metabolites in mouse enteroendocrine cells
<p>Dataset for manuscript <em>"<strong> </strong></em><strong>α</strong><strong>-synuclein expression in response to bacterial ligands and metabolites in gut enteroendocrine cells</strong><em>". </em>Tabs in excel file are title with the figure number. </p>
Data set for "Pathway-, layer- and cell-type-specific thalamic input to mouse barrel cortex"
<p>Data set for: Sermet BS, Truschow P, Feyerabend M, Mayrhofer JM, Oram TB, Yizhar O, Staiger JF, Petersen CCH (2019) Pathway-, layer- and cell-type-specific thalamic input to mouse barrel cortex. eLife 8: e52665. https://doi.org/10.7554/eLife.52665</p> <p>There are 2 files in this upload:</p> <p>1. The file named "2019_Sermet_eLife.pdf" is the Open Access pdf file of the manuscript published in eLife.</p> <p>2. The file named "Sermet_data_code.zip" (~5 GB) is a zipped version of a folder "Sermet_data_code" (~5 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. When unzipped, the folder contains 8 Matlab '.m' files with analysis code and one '.mat' data file. In order to run the analysis of the data set, you need to execute 'PopPlot.m'.</p>
GFP-GOWT1 mouse stem cells TIFF file
<p>This is accompanying file to the CellMAPtracer software (<a href="https://doi.org/10.5281/zenodo.3878087">https://doi.org/10.5281/zenodo.3878087</a>).</p>
Single-cell RNA sequencing identifies shared differentiation paths of mouse thymic innate T cells
<p>scRNA sequencing datasets used in the paper titled 'Single-cell RNA sequencing identifies shared differentiation paths of mouse thymic innate T cells' published in Nature Communications<br> <br> https://www.nature.com/articles/s41467-020-18155-8</p>
Single-cell mouse and PC9 data for "TP53 loss with whole genome doubling mediates heterogeneous intra-patient therapy response through Chromosomal Instability"
<p>This repository includes the processed data (including copy number profiles and related analysis) for the E/EP mouse tumors and for the PC9 resistance cell lines for all the analyses of the manuscript "TP53 loss with whole genome doubling mediates heterogeneous intra-patient therapy response through Chromosomal Instability".</p><p>The code for the related analyses is available in GitHub at https://github.com/zaccaria-lab/TP53loss_WGD</p>
scGeneAI mouse eye cells dataset
<p>The input mouse eye cell dataset used in the full-size examples in scGenAI is uploaded here</p>
Cell type specificity of glucocorticoid signaling in the adult mouse hippocampus
<p>The present study is based on the 10X scRNA-seq dataset published by the Allen Institute for Brain Science and publicly available at: <a href="https://portal.brain-map.org/atlases-and-data/RNA-seq/mouse-whole-cortex-and-hippocampus-10x">https://portal.brain-map.org/atlases-and-data/RNA-seq/mouse-whole-cortex-and-hippocampus-10x</a>. The cells from the hippocampus region were selected from the gene count expression matrix and pre-processed in R v3.6.1 according to the Seurat v3.1.5 standard pre-processing workflow for quality control, normalization, and analysis of scRNA-seq data. Here we make the final seurat object and other datasets further used in the code (<a href="https://github.com/eviho/10XHip2021_VihoEMG">https://github.com/eviho/10XHip2021_VihoEMG</a>) available for download. </p>
DATA: Comparison between optical tissue clearing methods for detecting administered mesenchymal stromal cells in mouse lungs
<p>This data set includes all the raw data collected for the following article: "Comparison between optical tissue clearing methods for detecting administered mesenchymal stromal cells in mouse lungs".</p>
ETV6 represses Tumor Necrosis Factor During Stress Hematopoiesis to Regulate Mouse Stem Cell Function
<p>At the root of the blood system lies a heterogenous population of hematopoietic stem and progenitor cells (HSPCs), which reside in the bone marrow (BM) and give rise to diverse blood lineages. Maintaining this pool of self-renewing HSPCs is critical to uphold hematopoiesis during period of stress such as bleeding or infection<sup>1</sup>. ETS Variant Transcription Factor 6 (ETV6) is a transcriptional repressor that is highly expressed in HSPCs<sup>2</sup> where it is essential for development and maintenance of the adult hematopoiesis <sup>3</sup>. In 2015, our group<sup>4</sup> and others<sup>5-7</sup> identified germline pathogenic <em>ETV6</em> variants in families with predisposition to B-acute lymphoblastic leukemia (B-ALL) and thrombocytopenia, defining a new genetic syndrome known as Thrombocytopenia 5 (T5). Subsequently, we performed targeted germline <em>ETV6 </em>sequencing of remission blood samples from over 4,000 children with B-ALL and identified germline <em>ETV6</em> variants in ~1% of cases<sup>7</sup>. <em>In vitro</em> studies revealed that ETV6 variant proteins exhibit impaired repressor activity, reduced DNA binding, and aberrant subcellular localization<sup>4,5,7,8</sup>. Overall, these studies indicated that T5-associated germline <em>ETV6</em> variants negatively impact the repressor activity of ETV6. In support of this notion, transcriptional profiling of peripheral blood cells from T5 patients has revealed upregulation of interferon response genes<sup>9</sup>. Nevertheless, little remains known about the mechanisms by which ETV6 regulates the HSPC compartment and how T5-associated <em>ETV6</em> variants contribute to disease.</p> <p> </p> <p>To address these questions, we used CRISPR-Cas9 gene editing to generate a novel mouse model harboring a pathogenic heterozygous germline <em>Etv6 </em>variant, R355X, the murine equivalent to the human T5-associated variant R359X<sup>10</sup>. Through the comprehensive study of this model, we describe a novel role for ETV6 during aging and regenerative hematopoiesis and show that the heterozygous <em>Etv6</em><sup>R355X</sup> variant impairs HSC function <em>in vitro</em> and <em>in vivo</em>. Using genomic approaches to interrogate mouse and human HSCs, we identify new ETV6 targets, including the gene encoding Tumor Necrosis Factor (TNF) and genes involved in TNF signaling. Further, we show increased TNF production and cell cycling in <em>Etv6<sup>R355X/+</sup></em> mouse HSPCs post-BM transplantation. Finally, we demonstrate that genetic ablation of <em>Tnf</em> restores the long-term potential of <em>Etv6<sup>R355X/+</sup></em> cells in serial replating assays <em>in vitro</em>. Together, these findings provide novel insights into the pathways regulated by ETV6 and demonstrate how a pathogenic variant impacts ETV6 function in the context of hematopoietic stress.</p> <p>All bulk RNAseq, Cut&Run, ATACseq, and Hi-C data has been submitted to in the Gene Expression Omnibus (accession number GSE213597) and Sequence Read Archive (BioProject number PRJNA880871). Due to the journal’s limitation on submitted supplementary data as an Excel file, all post-analysis supplemental data files are deposited in Dryad data repository to be made available with this manuscript.</p>
3D reconstruction of an NIH/3T3 mouse fibroblast cell imaged by the SXT-100.
<p>This dataset (*.mrc file) is a reconstructed 3D volume obtained by soft X-ray tomography on NIH/3T3 cells. The cells were grown on a 200 mesh 3.05 mm EM finder grid with a Quantifoil Holey Carbon support. The sample was vitrified by plunge-freezing in liquid ethane. The tomogram was collected with a pixel size of 28.85 nm over the tilt range from -53 to 53.5 degrees with 1.5 deg step size and 116 s exposure per tilt.</p>
Confocal dataset of Purkinjie cells from clarified cerebellum slices from L7GFP mouse
<div> <div> <h3><span><span>TITLE: Confocal dataset of Purkinjie cells from larified cerebellum slices from L7GFP mouse </span></span><span> </span></h3> </div> <div> <p><span><span> </span></span><strong><span><span>SUMMARY:</span></span> </strong><span> </span></p> </div> <div> <p><span><span>We have processed cerebellar material from transgenic mice expressing the green fluorescent protein (GFP). Specifically, in L7GFP mice, the expression of GFP in Purkinje is specifically driven by the Pcp-2 promoter (Zhang, X., Baader, S. L., Biang, F., Müller, W., and Oberdick, J. (2001). High level Purkinje cell specific expression of green fluorescent protein in transgenic mice. </span></span><span><span>Histochem</span><span>. Cell Biol</span></span><span><span>. 115, 455–464)</span><span>. </span><span>The sample were firstly clarified using the optimization of the CLARITY protocol developed by our group, and then </span><span>acquired</span><span> using a confocal microscope</span><span>. </span></span><span> </span></p> </div> <div> <p><strong><span><span> </span></span><span><span>EXPERIMENTAL METHODS AND MATERIALS:</span></span></strong> <span> </span></p> </div> <div> <p><span><span>Data contained in this folder belong to an adult male L7GFP mouse, processed as in Magliaro et al., 2016 </span></span><span><span>(</span></span><a href="https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2016.00179/full#h3" target="_blank" rel="noreferrer noopener"><span><span>https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2016.00179/full#h3</span></span></a><span><span>).</span></span><span> </span></p> </div> <div> <p><span> </span><span><span>Briefly, the L7GFP mouse was anesthetized with a lethal dose of 7% chloral hydrate and then perfused with 20 mL of Phosphate Buffered Saline and then 20 mL of a solution, containing 4% acrylamide, 0.05% bis-acrylamide and 0.25% VA-044 thermally triggered initiator at 4°C, as described in Chung et al. (2013) </span></span><span><span>(Chung, K., Wallace, J., Kim, S.-Y., Kalyanasundaram, S., </span><span>Andalman</span><span>, A. S., Davidson, T. J., et al. (2013). Structural and molecular interrogation of intact biological systems. </span></span><span><span>Nature</span></span><span><span> 497, 332–337. </span><span>doi</span><span>: 10.1038/nature12107)</span></span><span><span>. The brain was extracted and submerged in 20 mL hydrogel solution for 3 days at 4°C in a 50 mL Falcon tube to allow gel diffusion into the tissue. Then a vacuum was applied to the tube for 10 min to enable hydrogel formation. Hydrogel polymerization was thermally </span><span>initiated</span><span> by incubating the infused tissue overnight at 37°C. At this point, the mouse cerebellum was isolated and then cut into 1 mm-thick coronal slices using a Leica VT1200S vibratome. The cut settings were reported in Mattei et al., 2015 (</span></span><span><span>Mattei, G., Cristiani, I., Magliaro, C., and Ahluwalia, A. (2015). Profile analysis of hepatic porcine and murine brain tissue slices obtained with a vibratome. </span></span><span><span>PeerJ</span></span><span> <span>3:e</span><span>932. </span><span>doi</span><span>: 10.7717/peerj.932</span></span><span><span>). Each slice was finally placed in a 50 mL Falcon tube at 37°C with 20 mL of CLARITY clearing solution (200 mM Boric Acid and 4% Sodium Dodecyl Sulphate, pH adjusted to 8.5 by adding 1 M NaOH dropwise) for 5 days, as this is demonstrated to be the optimal clearing time in Magliaro et al., 2016</span></span><span><span> (</span></span><a href="https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2016.00179/full#h3" target="_blank" rel="noreferrer noopener"><span><span>https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2016.00179/full#h3</span></span></a><span><span>).</span></span><span> </span></p> </div> <div> </div> <div> <p><strong><span><span>Confocal microscopy data</span></span></strong> <span> </span></p> </div> <div> <p><span><span>Image stacks of samples were mounted on a glass slide with </span><span>FocusClear</span><span>™ (</span><span>Celexplorer</span><span> Labs Co., Hsinchu, Taiwan) and then </span><span>acquired</span><span> with a confocal microscope (Nikon A1). </span></span><span><span> The confocal settings were the following: 4.84 W laser power, emission and excitation wavelengths of 488 nm and 502 </span><span>nm</span><span> respectively.</span></span><span> Refer to metadata.xlsx file for detailed info.</span></p> </div> <div> <p> </p> </div> <div> <p><strong><span><span>Ethical approval</span></span><span> </span></strong></p> </div> <div> <p><span><span>The mouse was obtained from the Department of Translational Research, New Technologies in </span><span>Medicine</span><span> and Surgery of the University of Pisa (Italy). Mice were used to perform the experiments, which were conducted in conformity with the European Communities Council Directive of 24 November 1986 (86/609/EEC and 2010/63/UE) and in agreement with the Italian DM26/14. Experiments were approved by the Italian Ministry of Health and Ethical Committee of the University of Pisa.</span></span><span><span> </span></span><span> </span></p> </div> <div> <p><span><span> </span></span><span> </span></p> </div> </div>
10X Single Cell RNA-Seq On WT and Mutation mouse models
<p>Four four-week-old mice, consisting of wild type, Pten mutation, MAP3K3 mutation, and MAP3K3+Pten mutation, were sampled at 4 weeks of age. Endothelial cells were enriched using CD31 magnetic beads, followed by 10X single-cell RNA sequencing.</p>
ScienceDex guides
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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.