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13,349 results for “mice”
Mueller matrix imaging combining optical parameters of mice non-melanoma skin cancer tissue
<p>The dataset consists of the Mueller matrix elements and optical parameters acquired from the backscattered light using a CCD camera and Mueller matrix imaging technique.</p><p>This dataset contains 90 samples including 20 feature vectors for SCC, 33 feature vectors for normal and 37 feature vectors for papilloma.</p>
Four lipidomics datasets (mouse liver, mouse pancreatic islets, mouse soleus muscle and mouse visceral adipose tissue), generated for the publication Mehl et al., "A multiorgan map of metabolic, signalling, and inflammatory pathways that coordinately control fasting glycemia in mice"
<p>Mehl, Thorens et al present a multiomics study aimiing to<span> identify the pathways that are coordinately regulated in pancreatic </span><span>b</span><span>-cells, muscle, liver, and fat to control fasting glycemia we fed C57Bl/6, DBA/2 and Balb/c mice a regular chow or a high fat diet for 3, 10 and 30 days. We measured fasted glycemia, insulinemia and whole-body insulin resistance. Transcriptomic and lipidomic analysis were used in a data fusion approach to identify organ-specific pathways related to the glycemic levels across all conditions investigated. In pancreatic islets, constant insulinemia despite higher glycemic levels were associated with reduced expression of mRNAs encoding hormone and neurotransmitter receptors as well as OXPHOS, cadherins, integrins and gap junction proteins. Higher glycemia and whole-body insulin resistance were associated, in muscle, with reduced expression of mRNAs encoding insulin signaling proteins and enzymes of the glycolysis, Krebs’ cycle and OXPHOS pathways, as well as endocytosis and exocytosis proteins; in hepatocytes, with lower expression of mRNAs of the insulin signaling pathway, of branched chain amino acid catabolism and of OXPHOS; in adipose tissue, with increased expression of mRNAs of innate immunity and lipid catabolism. These data provide a map of the pathways that are coordinately recruited in the investigated tissues to control fasting glycemia and a resource for further studies of interorgan communication in glucose homeostasis. </span></p>
Transcriptomic atlas reveals organ-specific disease tolerance in sickle cell mice: dataset bone marrow HbAA mice injected or not with heme
<p>The objective of this experiment was to explore the transcriptome of the HbSS Townes mouse model of sickle cell disease. Townes model mice carry several human hemoglobin knock-in genes replacing the endogenous mouse genes and may be useful in studying sickle cell disease. All mice were genotyped, age- and sex-matched littermates. All HbAA (control, normal human hemoglobin) vs HbSS (sickle cell disease, mutated human hemoglobin) mice were used for experimentations at 6-8 weeks of age, to limit intra-group heterogeneity. Hemin (Ferriprotoporphyrin IX) was purchased from Frontiers Scientific and injected intravenously (iv.) in a retroorbital sinus at a concentration of 24 µmol/kg. Control mice received PBS instead. Mice were anesthetized with isoflurane 2-3% for injections, blood collection and sacrifice. All mice were sacrificed by cervical dislocation, 4 hours after injection.</p> <p>This dataset contains the results of the HbAA mice with and without heme.</p> <p>The corresponding HbSS mice with and without heme are deposited under number 10.5281/zenodo.10962782</p> <p>Bone marrow RNA was extracted by Macherey Nagel kit, according to the manufacturer’s instructions. The quality and quantity of mRNA were evaluated using a 2100<br>bioanalyzer with TNA 6000 NanoKits (all Agilent Technologies, Palo Alto, CA, USA). RNA Integrity Numbers superior to 7 were eligible for subsequent reverse transcription into cDNA. RNAseq was performed at the GenomIC plateform Cochin Institute INSERM U1016. After RNA extraction, RNA quality (RNA integrity number) was estimated. 1μg of high-quality total RNA sample (RIN &gt;7) was processed to build up the libraries, using TruSeq Stranded mRNA kit (Illumina) according to manufacturer instructions. Briefly, purified poly-A containing mRNA molecules were fragmented and reverse-transcribed using random primers. Replacement of dTTP by dUTP during second strand synthesis allowed us to achieve strand specificity. Addition of a single A base to the cDNA was followed by ligation of Illumina adapters.<br>Libraries were quantified by qPCR using KAPA Library Quantification Kits for Illumina Libraries (KapaBiosystems, Wilmington, MA). Library profiles were assessed using DNA High Sensitivity LabChip kits on an Agilent Bioanalyzer. Libraries were sequenced on an Illumina Nextseq 500 instrument using 75 base-lengths read V2 chemistry in a paired-end mode. After sequencing, primary analysis based on AOZAN software (ENS, Paris), was applied to demultiplex and control the quality of the raw data (based of FastQC modules / version 0.11.5).</p> <p>The dataset here represents 4 groups of mice, 4 mice per group as follows: HbAA PBS, HbAA heme, HbSS PBS, HbSS heme. </p> <p> </p>
The oxygen initial dip in the brain of anesthetized and awake mice
<p>Datasets containing the data used to generate the figures and results from <a href="https://doi.org/10.1073/pnas.2200205119"><em>The oxygen initial dip in the brain of anesthetized and awake mice</em>, Aydin et al. (PNAS, 2022)</a>, along with the corresponding MATLAB code on <a href="https://github.com/alike-aydin/InitialDip_AydinEtAl">GitHub</a>. Download the file, and unzip it in the same folder as the scripts from the GitHub repository.</p>
Raman spectra of the Adenoma-Carcinoma-Sequence in a mice model
<p>In the following, a short desciption for each csv files:</p> <ol> <li>Meta data: includes information about mice ID, scans collected from each mouse, location of extracted scans, activity of P53 gene, mouce gender, tissue type.</li> <li>MSpectra: contains mean spectra of tissue types with respect to each extracted scan.</li> <li>TissueLabels: describes different divisions of tissue types;e.g. normal vs abnormal, normal vs HB vs Karzinom, normal vs HB vs adenoma vs carcinoma</li> <li>Wavenumbers: includes Raman spectra wavenumbers. </li> </ol>
Data associated with "A weakened recurrent circuit in the hippocampus of Rett syndrome mice disrupts long-term memory representations"
<p><strong>Datasets used in <em>A weakened recurrent circuit in the hippocampus of Rett syndrome mice disrupts long-term memory representations.</em></strong></p> <p><strong>Datatypes:</strong></p> <ol> <li>Multi-index pandas dataframe (.pkl)</li> <li>Numpy array (.npy)</li> <li>Collection of numpy arrays (.npz)</li> <li>Python dictionary objects (.pkl)</li> </ol> <p><strong>Datasets:</strong></p> <p><strong>alignments.pkl: A dataframe containing numpy arrays of image displacements for each mouse in each memory context.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id. The columns are ['T', 'F1', 'N1', 'F2', 'N2'] for the training, recall 1-hour, neutral, recall 1-day, neutral day 2 memory contexts respectively. Each element of this dataframe is a numpy array of shape images x 2 that hold x and y image displacements respectively. These alignments are computed after the inscopix software motion correction and are used in Supplemental Figure 2 of the paper.</p> <p><strong>behavior_df.pkl: A dataframe of behavior readouts recorded by a camera positioned above the mice in each context chamber.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id.The columns are; sample times (*_time), freezing boolean arrays (*_freeze), x-positions in context chamber (*_x) and y-positions in the context chamber (*_y) for each context (*) in ('Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2').</p> <p><strong>correlated_pairs_df.pkl: A dataframe containing arrays of neuron indices that have a correlation in activity pattern > 0.3.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id and treatment ('NA'). The columns contain ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] representing each memory context. Each element of the dataframe is a numpy array with three columns. The first two columns are the neuron indices that are correlated and the last column is the strength of the correlation.</p> <p><strong>dredd_freezes_df.pkl: A dataframe containing freezing percentages for SOM-Cre and RTT-SOM-Cre mice treated with DREADDS.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id and treatment (mcherry, hm3d, hm4d). The columns contain one of ['Neutral', 'Fear', 'Fear_2']. Each element of the dataframe is a freezing percentage for a single mouse. This dataframe is built from reading the dredd_behavior.xlsx excel file. This is used to generate figure 5E of the paper.</p> <p><strong>high_degree_df.pkl: A dataframe containing list of high degree neuron indices.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id and treatment ('NA'=not applicable since no DREADD used). The columns contain ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] representing each memory context. Each element of the dataframe is a list of neuron indices that are high-degree cells.</p> <p><strong>N006_wt_basis.npz: a dict containing three numpy arrays representing the basis images for mouse N006 of genotype wild-type.</strong></p> <p>This dict has three arrays stored under the variable names 'U', 'sigma' and 'img_shape'. U is a matrix of column vector basis images. Each column is the vector representation of a basis image (row pixels x column pixels). There are 220 basis images (columns) in U. The sigma variable is the singular value associated with each basis image vector in U. img_shape can be used to reshape each basis column vector into a 2-D image for viewing. This data is used in Supplemental Figure 2 of the paper.</p> <p><strong>N006_wt_cxtbasis.pkl: A dictionary containing arrays for basis images and singular values for each context.</strong></p> <p>This dictionary has keys, ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] representing the memory contexts. Each value is a 2 element list containing the U-basis images as column vectors and singular values, one per basis image in U. The shape of the basis images is the same shape stored in N006_wt_basis.pkl. This dataset is used in Supplementary Figure 2 to track cells across contexts of the CFC task (see also N006_wt_cxtsources.pkl)</p> <p><strong>N006_wt_cxtsources.pkl: A dictionary containing the independent component source images computed from the basis images for automatically identifying regions of interest (ROIs). </strong></p> <p>The dictionary is keyed on ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] contexts. Each value in the dictionary at a given key is a 3-D numpy array of shape sources x height x width. These data were used to construct the source images and max intensity projection image of the sources in Supplemental Figure 2F-J of the paper.</p> <p><strong>N006_wt_rois.pkl: A dictionary containing the boundaries and annuli coordinates of all rois for mouse N006 of genotype wild-type.</strong></p> <p>This dictionary is keyed on ['boundaries', 'annuli'] contexts and each value is a 179 element list of arrays of boundary line coordinates or annulus point coordinates one per ROI detected for this mouse.</p> <p><strong>N006_wt_sources.npy: A numpy array containing all source images computed from all contexts of the CFC task for mouse N006 of genotype wild-type.</strong></p> <p>This numpy array has shape n x height x width where n=205 source images, height=517 pixels and width=704 pixels. This data was used to construct Supplemental Figure 3F.</p> <p><strong>N019_wt_basis.npz: a dict containing three numpy arrays representing the basis images for mouse N019 of genotype wild-type.</strong></p> <p>This dict has three arrays stored under the variable names 'U', 'sigma' and 'img_shape'. U is a matrix of column vector basis images. Each column is the vector representation of a basis image (row pixels x column pixels). There are 220 basis images (columns) in U. The sigma variable is the singular value associated with each basis image vector in U. img_shape can be used to reshape each basis column vector into a 2-D image for viewing. This data is used in Figure 1C of the paper.</p> <p><strong>N019_wt_sources.npy: A numpy array containing all source images computed from all contexts of the CFC task for mouse N019 of genotype wild-type.</strong></p> <p>This numpy array has shape n x height x width where n=204 source images, height=516 pixels and width=698 pixels. This data was used to construct Figure 1C of the paper.</p> <p><strong>P80_animals.pkl: A pandas multi-index object containing the genotype, mouse_id and treatment of the top 80% behavioral performance animals.</strong></p> <p>In this study, we drop the lowest 20% performing WT and RTT animals based on freezing percentage during the recall contexts. This multi-index is used to filter the data before each computation or plot in this study. So for example Figure 1B contains only the top 80% performing WT and RTT mice.</p> <p><strong>pc_sipscs_amps.pkl: A dictionary containing the amplitudes of spontaneous IPSCs recorded in pyramidal cells of WT and RTT mice.</strong></p> <p>This dictionary is keyed on ['wt', 'mecp2_pos', 'mecp2_neg'] representing whether the pyramidal cell was recorded from a wild-type mouse ('wt') or is an MeCP2 negative or MeCP2 positive RTT cell. This value under each key is an array of IPSC amplitudes, one per recorded cell. This data was used to construct Figure 4C in the paper.</p> <p><strong>pc_sipscs_freqs.pkl: A dictionary containing the frequencies of spontaneous IPSCs recorded in pyramidal cells of WT and RTT mice.</strong></p> <p>This dictionary is keyed on ['wt', 'mecp2_pos', 'mecp2_neg'] representing whether the pyramidal cell was recorded from a wild-type mouse ('wt') or is an MeCP2 negative or MeCP2 positive RTT cell. This value under each key is an array of IPSC frequencies, one per recorded cell. This data was used to construct Figure 4C in the paper.</p> <p><strong>rois_df.pkl: A multi-index dataframe containing all ROI information for each non-DREADD treated cell in this study (Figures 1-3).</strong></p> <p>This dataframe index contains the genotype ('wt', 'het'), the mouse_id, the treatment ('NA'=not applicable since no DREADD used), and the cell index starting from 0. The columns are ['centroid', 'cell_boundary', 'annulus_boundary']. The centroid for each cell is a 2-tuple of row, column pixel centroid coordinates. The cell_boundary is a two-column array of row, col boundary points for each ROI. The annulus_boundary is a two-column array of row, column interior points in the annulus. The annulus region excludes points of overlap with nearby cell bodies (See STAR methods of the paper).</p> <p><strong>signals_df.pkl: A multi-index dataframe containing calcium signals, inferred spikes and metadata for all Non-DREADD experiments used in this study (Figs 1-3).</strong></p> <p>This dataframe index contains the genotype ('wt', 'het'), the mouse_id, the treatment ('NA'=not applicable since no DREADD used), and the cell index starting from 0 and going up to 5771 cells. The columns are ['channels', 'channel', 'num_pages', 'width', 'height', 'bits', 'Train_signals', 'Fear_signals', 'Neutral_signals', 'Cue_signals', 'Fear_2_signals', 'Neutral_2_signals', 'Cue_2_signals', 'Train_spikes', 'Fear_spikes', 'Neutral_spikes', 'Cue_spikes', 'Fear_2_spikes', 'Neutral_2_spikes', 'Cue_2_spikes', 'sample_rate']. The channels are all the recorded channels, the channels is the channel on which ROIs were detected, the width and height are the image dimensions, the bits is the image bit depth of the calcium movie. The *_signals' are the df/f signals for each cell in each context. Each signal is a numpy array with the first 800 samples have been set to NAN due to settling time of the miniscope. The '*_spikes' are the inferred spikes for each cell stored as an image index. This signal and spike indices can be converted to time using the sample column. This dataframe is used in the construction of Figures 1-3 in the paper.</p> <p><strong>som_behavior_df.pkl: A dataframe of behavior readouts recorded by a camera positioned above the mice in each context chamber.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id. The columns are; sample times (*_time), freezing boolean arrays (*_freeze), x-positions in context chamber (*_x) and y-positions in the context chamber (*_y) for each context in *=('Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'). This dataframe was not used in the paper but may still be useful for further analysis.</p> <p><strong>som_sepsc_amplitudes:</strong> <strong>A dictionary containing the amplitudes of spontaneous EPSCs recorded in SOM cells of WT and RTT mice with and without MeCP2.</strong></p> <p>A dictionary with keys ['som', 'som_rett_pos', 'som_rett_neg'] for WT SOM and RTT-SOM cells with and without MeCP2 respectively. Each value is a list of sEPSC amplitudes. This data was used in Figure 4E-G.</p> <p><strong>som_sepsc_freqs:</strong> <strong>A dictionary containing the amplitudes of spontaneous EPSCs recorded in SOM cells of WT and RTT mice with and without MeCP2.</strong></p> <p>A dictionary with keys ['som', 'som_rett_pos', 'som_rett_neg'] for WT SOM and RTT-SOM cells with and without MeCP2 respectively. Each value is a list of sEPSC frequencies. This data was used in Figure 4E-G.</p> <p><strong>som_signals_df.pkl: A multi-index dataframe containing calcium signals, inferred spikes and metadata for all Non-DREADD SOM cell recordings used in this study (Figs 5).</strong></p> <p>This dataframe index contains the genotype ('wt', 'het'), the mouse_id, the treatment ('NA'=not applicable since no DREADD used), and the cell index starting from 0 and going up to 710 cells. The columns are ['channels', 'channel', 'num_pages', 'width', 'height', 'bits', 'Train_signals', 'Fear_signals', 'Neutral_signals', 'Cue_signals', 'Fear_2_signals', 'Neutral_2_signals', 'Cue_2_signals', 'Train_spikes', 'Fear_spikes', 'Neutral_spikes', 'Cue_spikes', 'Fear_2_spikes', 'Neutral_2_spikes', 'Cue_2_spikes', 'sample_rate']. The channels are all the recorded channels, the channels is the channel on which ROIs were detected, the width and height are the image dimensions, the bits is the image bit depth of the calcium movie. The *_signals' are the df/f signals for each cell in each context. Each signal is a numpy array with the first 800 samples have been set to NAN due to settling time of the miniscope. The '*_spikes' are the inferred spikes for each cell stored as an image index. This signal and spike indices can be converted to time using the sample column. This data was used to construct Figure 5B-C.</p> <p><strong>ssn33_sstcre_basis.npz: a dict containing three numpy arrays representing the basis images for mouse ssn33 of genotype sst-cre.</strong></p> <p>This dict has three arrays stored under the variable names 'U', 'sigma' and 'img_shape'. U is a matrix of column vector basis images. Each column is the vector representation of a basis image (row pixels x column pixels). There are 100 basis images (columns) in U. The sigma variable is the singular value associated with each basis image vector in U. img_shape can be used to reshape each basis column vector into a 2-D image for viewing. This data is used in Figure 5A of the paper.</p> <p><strong>ssn33_sstcre_sources.npy: A numpy array containing all source images computed from all contexts of the CFC task for mouse N019 of genotype wild-type.</strong></p> <p>This numpy array has shape n x height x width where n=86 source images, height=516 pixels and width=654 pixels. This data was used to construct Figure 5A of the paper.</p>
Spatial characterization of the motor and non-motor somal and axonal transcriptome in adult healthy and mutant FUS mice
<table> <tbody> <tr> <td> <p>Here we investigated the transcriptome of motor and non-motor axons and cell bodies in the context of mutant FUS-related amyotrophic lateral sclerosis (ALS). We applied Nanostring GeoMX Digital Spatial Profiler platform to profile the transcriptome of subcellular compartments in the lower motor circuitry of a mouse model ricapitulating ALS motor symptoms. This work sheds light for the first time on the transcriptomic alterations in axons and in somas which may contribute to axonal degeneration and neuromuscular junction denervation, early features of ALS.</p> </td> </tr> </tbody> </table>
Third harmonic generation images of the lacuno-canalicular network in bone femoral diaphysis of mice from the BionM1 project (space flight)
<p>Data set for 11 samples in 3 groups of Control, Space Flight and Synchro (ground control with space flight housing and feeding conditions). Contains THG images in tif format of 2D mosaic of selected samples and 3D stacks in selected anatomical regions of interest. See readme file for more information.</p>
Confocal fluorescence microscopy images of the lacuno-canalicular network in bone femoral diaphysis of mice from the BionM1 project (space flight)
<p>This data set provides complementary measurements to a separate THG data set of the same study: doi: 10.5281/zenodo.1475906</p> <p>Data set for 1 sample of each of the 3 groups: Control, Space Flight and Synchro (ground control with space flight housing and feeding conditions). Contains confocal fluorescence microscopy images in tif format of 2D mosaic of selected samples and 3D stacks in selected anatomical regions of interest. See readme file for more information.</p>
Experimental Factors Influence Diversity Metrics of the Gut Microbiome in Laboratory Mice
<p>Abstract<br> Introduction</p> <p>Gut microbiome studies often overlook experimental factors that could influence gut microbiome diversity and could impact findings. Large-scale studies investigating these experimental factors are lacking. Thus, we aimed to determine which experimental factors influence the gut microbiome diversity in pre-clinical animal model studies.</p> <p><br> Methods</p> <p>We extracted DNA and sequenced the V4 region of the 16S rRNA gene of a total of 538 samples from various sections of the gastrointestinal tract of 303 young and aged male and female C57BL/6J mice of three different genotypes on five diets from three animal house facilities. As a proof-of-concept in a disease model, some mice were treated with sham or angiotensin II, a commonly studied agent used as a hypertension model. Some samples were sequenced twice as a matched-comparison group.</p> <p>Results</p> <p>Using over 17 million sequencing reads, we found that experimental factors such as animal house facility, genotype, diet, age, sex, sampling site, and technical factor (i.e., sequencing batch) affected both α- and β-diversity (weighted and unweighted UniFrac), and were associated with compositional changes in the microbiome at varying magnitude, with diet and sampling site having the largest effect. After adjustment by these factors, treatment with angiotensin II had no impact on α-diversity and was only significant in unweighted UniFrac (presence/absence of bacteria) analyses.</p> <p><br> Conclusion</p> <p>Our data identified several key experimental and technical factors that affect the gut microbiome in laboratory mice. Our findings support that not accounting or adjusting for these factors may lead to false-positive discoveries and non-biologically relevant findings in the gut microbiome field.</p>
Dataset corresponding to scientific paper "Improved reperfusion following alternative surgical approach for experimental stroke in mice"
<p>Acquired raw experimental data using laser speckle contrast imaging (LSCI) following middle cerebral artery occlusion (MCAO) in mice. Data obtained from mice undergoing standard CCA ligation technique and mice undergoing CCA vessel repair technique<sup>1</sup>.</p> <p>The dataset is linked to paper "Improved reperfusion following alternative surgical approach for experimental stroke in mice". </p> <p>The dataset consists of the following:</p> <ul> <li>Raw LSCI flux values, from ipsilateral and contralateral hemispheres, measured at baseline, 24hours post-MCAO and 48hours post-MCAO. </li> <li>Normalised data expressing ispilateral hemisphere as a % of the control contralateral hemisphere.</li> <li>Mean normalised values for each subject. </li> </ul> <p> </p> <p><strong>References</strong></p> <ol> <li>Trotman-Lucas,M., Kelly, M.E., Janus, J., Fern, R., Gibson, C.L. (2017) 'An alternative surgical approach reduces variability following filament induction of experimental stroke in mice'. <em>Disease Models & Mechanisms,</em> 10, 931-938.</li> </ol> <p> </p>
Data set for "Optogenetic stimulation of cortex to map evoked whisker movements in awake head-restrained mice"
<p>Data set for: Auffret M, Ravano VL, Rossi GMC, Hankov N, Petersen MFA, Petersen CCH (2017) Optogenetic stimulation of cortex to map evoked whisker movements in awake head-restrained mice. Neuroscience, http://dx.doi.org/10.1016/j.neuroscience.2017.04.004</p> <p>There are 9 files in this data upload:</p> <ol> <li>'2017_Auffret_Neuroscience.pdf' - this is a pdf version of the online publication.</li> <li>'Auffret_data.mat' - this is a Matlab data structure, which contains all the data for the publication.</li> <li>'Auffret_data.npy' - this is a Python data structure, which contains all the data for the publication. The Python data was generated from 'Auffret_data.mat' by 'DataViewer.py'.</li> <li>'Auffret_data.xlsx' - this is an Excel file, which contains all the data for the publication. This Excel file was generated from 'Auffret_data.mat'.</li> <li>'DataViewer.fig' - this is a Matlab Figure file, which is the GUI layout for 'DataViewer.m'.</li> <li>'DataViewer.m' - this is a Matlab Code, which displays the data contained in 'Auffret_data.mat'.</li> <li>'DataViewer.py' - this is a Python Code, which generates 'Auffret_data.npy' from 'Auffret_data.mat', and displays an example trial.</li> <li>'FigureViewer.fig' - this is a Matlab Figure file, which is the GUI layout for 'FigureViewer.m'.</li> <li>'FigureViewer.m' - this is a Matlab Code, which analyses the data in 'Auffret_data.mat', and displays the results in the same way as the published figures (Auffret et al., 2017).</li> </ol>
Source Data for Manuscript "Sodium salicylate improves detection of amplitude-modulated sound in mice"
<p>This repository contains the source data for our papers <strong>Sodium salicylate improves detection of amplitude-modulated sound in mice </strong>(van den Berg*, Wong*, Houtak, Williamson, Borst). The code to generate figure panels can be found in our github repository at https://github.com/aaronbwong/salicylateonam</p>
Reward perseveration is shaped by GABAA-mediated dopamine pauses: Histology from Behavior Data Mice
<p>Histology images for the experimental +HTP behavior mice in the dataset 10.5281/zenodo.10903566. This histology was used to generate the following figures in the paper "Reward perseveration is shaped by GABAA-mediated dopamine pauses":</p> <ul> <li>Ext. Fig 3, panel d</li> </ul> <p>Specifics of the data:</p> <ul> <li>HTP_Histo_Cohort_VX.zip contains the raw images (Olympus .vsi files) and drawn ROIs and fluorescence analysis (MATLAB files) for each mouse/tissue section in cohort VX</li> <li>HTP_Histo_Cohort_VZ.zip contains the raw images (Olympus .vsi files) and drawn ROIs and fluorescence analysis (MATLAB files) for each mouse/tissue section in cohort VZ</li> <li>HTP_Histo_Cohort_VAJ.zip contains the raw images (Olympus .vsi files) and drawn ROIs and fluorescence analysis (MATLAB files) for each mouse/tissue section in cohort VAJ</li> <li>HTP_Histo_Cohort_VAK.zip contains the raw images (Olympus .vsi files) and drawn ROIs and fluorescence analysis (MATLAB files) for each mouse/tissue section in cohort VAK</li> <li> Behavior_summary_data_HTP_Histology.csv contains the summarized fluorescence quantification per animal</li> </ul>
Single-cell RNA-seq profiles of tumor-bearing mice treated with PAGln with or without anti-PD-1
<p>single-cell RNA sequencing (scRNA-seq) profiles of tumor-bearing mice treated using Phenylacetylglutamine (PAGln) with or without anti-PD-1 were performed to compare the alterations of immune microenvironment affected by PAGln under the condition of anti-PD-1 treatment.</p>
Open data repository, Knab et al., Prediction of stroke outcome in mice based on non-invasive MRI and behavioral testing
<p><strong>Open data repository, Knab et al., Prediction of stroke outcome in mice based on non-invasive MRI and behavioral testing</strong></p> <p><strong>Latest version of files: repository_v2.0.zip, Behavior Data_v2.0.xlsx and MRI IDs Testing&Replication Cohort.xlsx (please ignore repository.zip)</strong></p> <p>Open data repository Knab et al. Prediction of stroke outcome in mice based on non-invasvive MRI and behavioral testing</p> <p>Open code and documentation of prediction models available via <a href="https://github.com/major-s/mouse-mcao-outcome-predictor">https://github.com/major-s/mouse-mcao-outcome-predictor</a></p> <p><strong>Content:</strong></p> <p>README.txt</p> <p>This information</p> <p><strong>dat</strong></p> <p>Contains MRI data in NIFTI format and secondary data from atlas registration. For documentation of atlas registration files see https://pubmed.ncbi.nlm.nih.gov/28829217/<br>Files used for the manuscript:<br>t2.nii: t2 weighted image acquired 24 h post stroke<br>masklesion.nii: manually delineated lesion<br>x_masklesion.nii: lesion in atlas space<br>ix_ANO.nii: Allen brain atlas in native space (i.e. matching t2.nii)<br>Lesion volume was calculated by volume of voxels unequal 0 in x_masklesion.nii<br>Overlap of regions defined by ix_ANO.nii with masklesion.nii were used for calculating percent damage in each atlas region</p> <p><strong>prediction_models</strong></p> <p>Contains separated training and test data as xlsx and csv files with lesion volumes in cubic mm of the Allen brain atlas space, percent damage per atlas region and behavioral data. The training data was used as input for training prediction models in MATLAB, the results were created using the test data.<br>The files have following sturcture:<br>Column 1: animal ID<br>Columns 2-537: MRI regions (column title corresponds to the region number as used in the Allen common coordinate framework)<br>Column 538: lesion volume<br>Column 539: initial performance (subacute deficit) = mean performance/deficit on days 2-6<br>Column 540: mean performance/deficit on days 2-6 = initial performance (subacute deficit) - this column equals column 539 but has different header which was used to train the residual from initial deficit<br>Column 541: residual performance/deficit<br>Column 542: test or training group<br>Consecutive rows contain data for each animal specified by the animal id</p> <p>The repository also contains all trained models, prediction results for the test data and tables with resulting median absolute error (MedAE) and 5th, 25th, 75th and 95 absolute error quantiles for each model.<br>The model files end with '_models.mat' and contain 50 independently trained models each. Each model version is specified by number 1-50.<br>The result files end with '_test_results.mat' or '_test_results.xlsx', files with MedAE and quantiles end with '_test_errors.xlsx' or '_test_errors.csv. The common part of filenames specifies the used paradigm<br>Folder 'subacute deficit prediction' contains:<br> - initial_performance_from_lesion_volume: prediction of subacute deficit using lesion volume<br> - initial_performance_from_segmented_mri: prediction of subacute deficit using segmented mri<br>Folder 'long-term outcome prediction' contains:<br> - lesion_volume: prediction of residual deficit using lesion volume<br> - segmented_mri: prediction of residual deficit using segmented_mri<br> - initial_performance: prediction of residual deficit using subacute deficit<br>Folder 'mri_inc_oob_imp' contains models trained using increasing number of mri segments sorted according to the out-of-bag importance. The number of used segments is given in the file name. The models, results and errors are separated in subfolders.</p> <p>Files with equal file name and different extension always contain the same data</p> <p><strong>templates</strong><br>Allen atlas, template, brain mask, hemisphere masks, tissue probability masks in NIFTI format including annotations of region IDs and parameter.m file for use in MATLAB toolbox ANTx2<br> </p>
The gut microbiota of environmentally enriched mice regulates visual cortical plasticity
<p>ABSTRACT</p> <p>The complexity of brain circuits is sculpted both by innate genetic programs and environmental stimuli. Since the 1960s scientists have noticed that raising rodents in an enriched environment (EE) is able to improve all aspects of brain plasticity, from learning and memory to visual plasticity in adult and developing animals. Importantly, EE has also been shown to have beneficial effects on a variety of preclinical models of central nervous system diseases: Alzheimer’s and Parkinson’s disease, Rett syndrome, epilepsy etc, prompting intervention protocols in humans. However, the “enrichment derived key signals” through which this special environment performs its broad positive effects on brain health have not been completely elucidated yet. Here, we focused on signals coming from the body periphery and in particular on the gut microbiota. We found that the intestinal microbiota composition of EE mice is significantly different from the one of standard raised (ST) animals. Treatment of EE mice with an antibiotic cocktail completely prevented the EE-driven enhancement of OD plasticity. Strikingly, the fecal microbiota transplant from EE donors to adult ST mice was able to re-activate OD plasticity in the ST recipients. Thus, taken together our data suggest that experience-dependent changes in gut microbiota regulate brain plasticity.</p> <p>METHODS</p> <p>In the first dataset (Dataset1, files called zr2423) we report the raw data (.fastq) obtained from the sequencing of the fecal samples from C57BL/6J mice raised in EE or in ST from birth and collected at different time points during their lives.</p> <p>To analyze the composition of the microbiota of ST and EE mice at different ages, fresh faeces were collected longitudinally in the same subject at postnatal day (P)20 (n=6), P25 (n=6) and P90 (n=6). </p> <p>In the second dataset (Dataset2, files called zr2747) we report the raw data (.fastq) obtained from the sequencing of the fecal samples from C57BL/6J: adult donor mice living in EE (EE, n=8), adult recipient mice living in ST condition before the fecal transplantation (preFT, n=8) and 4 weeks after the fecal transplantation (postFT, n=8).</p> <p>For further details about the sample names see the “Explanation Table”.</p> <p>Bacterial DNA was extracted using a specific kit (QIAamp Powerfecal DNA kit, Qiagen) following the manufacturer's protocol. The 16S rRNA sequencing and analysis was performed by a service offered by Zymo Research (Irvine, CA, USA). </p> <p><em>Targeted Library Preparation</em>: The DNA samples were prepared for targeted sequencing with the Quick-16S™ NGS Library Prep Kit (Zymo Research). The primer sets used were Quick-16S™ Primer Set V3-V4 (Zymo Research). The sequencing library was prepared using an innovative library preparation process in which PCR reactions were performed in real-time PCR machines to control cycles and therefore limit PCR chimera formation. The final PCR products were quantified with qPCR fluorescence readings and pooled together based on equal molarity. The final pooled library was cleaned up with the Select-a-Size DNA Clean & Concentrator™, then quantified with TapeStation® (Agilent Technologies, Santa Clara, CA) and Qubit® (Thermo Fisher Scientific, Waltham, WA). </p> <p><em>Sequencing:</em> The final library was sequenced on Illumina® MiSeq™ with a v3 reagent kit (600 cycles). The sequencing was performed with >10% PhiX spike-in.</p> <p> </p>
The contribution of genetic and environmental effects to Bergmann's rule and Allen's rule in house mice
<p>Data associated with the manuscript, "The contribution of genetic and environmental effects to Bergmann's rule and Allen's rule in house mice".</p> <p><strong>Abstract</strong>: Distinguishing between genetic, environmental, and genotype-by-environment effects is central to understanding geographic variation in phenotypic clines. Two of the best-documented phenotypic clines are Bergmann's rule and Allen's rule, which describe larger body sizes and shortened extremities in colder climates, respectively. Although numerous studies have found inter- and intraspecific evidence for both ecogeographic patterns, we still have a poor understanding of the extent to which these patterns are driven by genetics, environment, or both. Here, we measured the genetic and environmental contributions to Bergmann's rule and Allen's rule across introduced populations of house mice (<em>Mus musculus domesticus</em>) in the Americas. First, we documented clines for body mass, tail length, and ear length in natural populations, and found that these conform to both Bergmann's rule and Allen's rule. We then raised descendants of wild-caught mice in the lab and showed that these differences persisted in a common environment and are heritable, indicating that they have a genetic basis. Finally, using a full-sib design, we reared mice under warm and cold conditions. We found very little plasticity associated with body size, suggesting that Bergmann's rule has been shaped by strong directional selection in house mice. However, extremities showed considerable plasticity, as both tails and ears grew shorter in cold environments. These results indicate that adaptive phenotypic plasticity as well as genetic changes underlie major patterns of clinal variation in house mice and likely facilitated their rapid expansion into new environments across the Americas.</p> <p>Supplemental data files are provided below.</p> <p>Code associated with the analysis of these data can be found on GitHub at <a href="https://github.com/malballinger/Ballinger_allenbergmann_AmNat_2021">https://github.com/malballinger/Ballinger_allenbergmann_AmNat_2021</a>.</p>
Data_Supplemental_Tab3_Albendazole reduces endoplasmic reticulum stress induced by Echinococcus multilocularis in mice
<p>Data of supplemental Tab3, “Albendazole reduces endoplasmic reticulum stress induced by Echinococcus multilocularis in mice”</p> <p>The Dataset contains the original supplemental table 3 as PDF-format (PNTD-D-21-00134R2_S_T3.pdf). Related information (meta-data) are provided as one file in TXT format (31003A-179400_PNTD-D-21-00134R2 _FJ_MW_SS_Echinococcus_ST3_M_1.txt) and three files in PDF format (31003A-179400_PNTD-D-21-00134R2 _FJ_MW_SS_Echinococcus_ST3_M_1-3.pdf).</p>
Data_Supplemental_Fig4_Albendazole reduces endoplasmic reticulum stress induced by Echinococcus multilocularis in mice
<p>Data of supplemental Fig4, “Albendazole reduces endoplasmic reticulum stress induced by Echinococcus multilocularis in mice”</p> <p>The Dataset contains the original supplemental figure 4 as PNG-format (PNTD-D-21-00134R2_SFig4.png). Corresponding raw data and subsequent data analysis obtained from RT-PCR analysis provided as four files in CSV format (31003A-179400_PNTD-D-21-00134R2 _FJ_MW_SS_Echinococcus_SF4_1_3-4_1-4.csv), all further experiment related information provided as one meta-data-file in txt format (31003A-179400_PNTD-D-21-00134R2 _FJ_MW_SS_Echinococcus_SF4_1_3-4_M_1.txt) and three meta data files in pdf format (31003A-179400_PNTD-D-21-00134R2 _FJ_MW_SS_Echinococcus_SF4_M_1-3.pdf).</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.