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5,472 results for “mouse cells”
Data from: Effect of stretching on inflammation in a subcutaneous carrageenan mouse model analyzed at single-cell resolution
<p>Understanding the factors that influence the biological response to inflammation is crucial, due to its involvement in physiological and pathological processes, including tissue repair/healing, cancer, infections, and autoimmune diseases. We have previously demonstrated that in vivo stretching can reduce inflammation and increase local pro-resolving lipid mediators in rats, suggesting a direct mechanical effect on inflammation resolution. Here, we aimed to explore further the effects of stretching at the cellular/molecular level in a mouse subcutaneous carrageenan-inflammation model. Stretching for 10 minutes twice a day reduced inflammation, increased the production of pro-resolving mediator pathway intermediate 17-HDHA at 48h post carrageenan injection, and decreased both pro-resolving and pro-inflammatory mediators (e.g., PGE<sub>2</sub> and PGD<sub>2</sub>) at 96h. ScRNAseq analysis of inflammatory lesions at 96h showed that stretching increased the expression of both pro-inflammatory (<em>Nos2</em>) and pro-resolution (<em>Arg1</em>) genes in M1 and M2 macrophages at 96 hours. An intercellular communication analysis predicted specific ligand-receptor interactions orchestrated by neutrophils and M2a macrophages, suggesting a continuous neutrophil presence recruiting immune cells such as activated macrophages to contain the antigen while promoting resolution and preserving tissue homeostasis.</p>
Real-time influence of intracellular acidification and Na+/H+ exchanger inhibition on in-cell pyruvate metabolism in the perfused mouse heart: A 31P-NMR and hyperpolarized 13C-NMR study
<p>This dataset contains primary data for DOI: 10.1002/nbm.4993</p> <p>NMR in Biomedicine. 2023; 10;e4993</p> <p>Title: Real-time influence of intracellular acidification and Na+/H+ exchanger inhibition on in-cell pyruvate metabolism in the perfused mouse heart: A 31P-NMR and hyperpolarized 13C-NMR study</p> <p>Authors: David Shaul, Naama Lev-Cohain, Gal Sapir, Jacob Sosna, J. Moshe Gomori, Leo Joskowicz, Rachel Katz-Brull</p> <p> </p> <p>Description:</p> <p>These primary datasets contains data presented in the above publication and consist of 31P- (at thermal equilibrium) and hyperpolarized 13C-NMR spectra. </p> <p>Please consult the Archive Guide.</p>
Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data
<p>Hereditary diseases manifest clinically in certain tissues, however their affected cell types typically remain elusive. Single-cell expression studies showed that overexpression of disease-associated genes may point to the affected cell types. Here, we developed a method that infers disease-affected cell types from the preferential expression of disease-associated genes in cell types (PrEDiCT). We applied PrEDiCT to single-cell expression data of six human tissues, to infer the cell types affected in 1,459 hereditary diseases. Overall, we identified 114 cell types affected by 1,140 diseases. We corroborated our findings by literature text-mining and recapitulation in mouse corresponding tissues. Based on these findings, we explored features of disease-affected cell types and cell classes, highlighted cell types affected by mitochondrial diseases and heritable cancers, and identified diseases that perturb intercellular communication. This study expands our understanding of disease mechanisms and cellular vulnerability.</p>
Expression and function of Twitch-2B in the granulosa cells of mouse ovarian follicles
<p>Briefly, antral follicles were dissected from 23- to 26-d-old <em>RR8; Bact_Cre mice</em>. Follicles were cultured for 24–30 h on organotypic membranes (Millipore; cat. No. PICMORG50) in the presence of follicle-stimulating hormone. The follicle was held in a perfusion slide consisting of a plastic slide (ibidi) and a glass coverslip and assembled using silicon grease. The slide was constructed such that medium containing ovine LH (National Hormone and Peptide Program; 10 μg/mL) could be perfused through a 200-μm-deep channel holding the follicle. Temperature was maintained at 30–34 °C, by use of a warm air blower (Nevtek). Preovulatory Follicles were imaged using a Zeiss Pascal confocal microscope with a 40X/1.2 NA objective. Images were collected every 10 seconds. Measurements were corrected for autofluorescence and for spectral bleed-through of CFP into the YFP channel. Ratios were calculated by dividing the mean CFP intensity in each region of interest by the mean YFP intensity. Data analysis was done using ImageJ and Excel software. Data is representative of 4 follicles.</p>
Intermittent ERK oscillations downstream of FGF in mouse embryonic stem cells
<p>Signal transduction networks generate characteristic dynamic activities to process extracellular signals and guide cell fate decisions such as to divide or differentiate. The differentiation of pluripotent cells is controlled by FGF/ERK signaling. However, only a few studies have addressed the dynamic activity of the FGF/ERK signaling network in pluripotent cells at high time resolution. Here, we use live cell sensors in wild-type and Fgf4-mutant mouse embryonic stem cells to measure dynamic ERK activity in single cells, for defined ligand concentrations and differentiation states. These sensors reveal pulses of ERK activity. Pulsing patterns are heterogeneous between individual cells. Consecutive pulse sequences occur more frequently than expected from simple stochastic models. Sequences become more prevalent with higher ligand concentration, but are rarer in more differentiated cells. Our results suggest that FGF/ERK signaling operates in the vicinity of a transition point between oscillatory and non-oscillatory dynamics in embryonic stem cells. The resulting heterogeneous dynamic signaling activities add a new dimension to cellular heterogeneity that may be linked to divergent fate decisions in stem cell cultures.</p>
Object for single-cell mouse HSC development data
<p>This file contains SingleCellExperiment object generated from mouse HSC development data, including raw and log-normalized counts, gene regulatory network inferred by PUIC, transcription factor activity matrix estimated by metaRegulon.</p>
An optimized 4C-seq protocol based on cistrome and epigenome data in the mouse RAW264.7 macrophage cell line
<p>In this protocol, we describe the 4C-seq method in detail using RAW264.7 cells, a mouse macrophage cell line widely used to study acute and metabolic inflammation. We specifically outline how cistrome and epigenome data can be integrated into the primer designing step, critical for the entire protocol. Because intra-TAD chromatin loops are facilitated by transcription factors and coregulators, many of which are co-localized in open chromatin regions, the binding centers of those factors can be obtained by chromatin immunoprecipitation sequencing (ChIP-seq). The binding sites of these factors reflect the coherent loci of the chromatin loops and therefore can be used as references to improve the accuracy of the 4C primers. We specifically describe the 4C protocol using examples of recently identified Ccl2 enhancer and silencer as bait. <br> </p>
Single-Cell RNA-sequencing of neural precursor cells from an Alzheimer's mouse model, wild-type mice, and Alzheimer's mice rescued with Usp16 haploinsufficiency
<p class="MsoNormal">Alzheimer's disease (AD) is a progressive neurodegenerative disease observed with aging that represents the most common form of dementia. To date, therapies targeting end-stage disease plaques, tangles, or inflammation have limited efficacy. Therefore, we set out to identify an earlier targetable phenotype. Utilizing a mouse model of AD we found that cell intrinsic neural precursor cell (NPC) dysfunction precedes widespread inflammation and amyloid plaque pathology, making it one of the earlier defects in the evolution of the disease. We demonstrate that reversing impaired NPC self-renewal via genetic reduction of USP16, a histone modifier and critical physiological antagonist of the Polycomb Repressor Complex 1, can prevent downstream cognitive defects and decrease astrogliosis in vivo. To delineate potential self-renewal pathways that might contribute to the defect and rescue of Tg-SwDI NPCs and Tg-SwDI/<em>Usp16<sup><span>+/-</span></sup></em> NPCs, respectively, we performed single-cell RNA-seq and gene set enrichment analysis (GSEA) on lineage depleted primary FACS-sorted CD31<sup><span>-</span></sup>CD45<sup><span>-</span></sup>Ter119<sup><span>-</span></sup>CD24<sup><span>-</span></sup> NPCs from Tg-SwDI, WT, and Tg-SwDI/<em>Usp16<sup><span>+/-</span></sup></em> mice at 3-4 months and 1 year of age. Using the GSEA Hallmark gene sets, we found only three gene sets that were enriched in Tg-SwDI mice over WT mice and rescued in the Tg-SwDI/<em>Usp16<sup><span>+/-</span></sup> </em>mice at both ages: TGF-ß pathway, oxidative phosphorylation, and Myc Targets. The TGF-ß pathway consistently had the highest normalized enrichment score in pairwise comparisons between Tg-SwDI vs WT and Tg-SwDI vs Tg-SwDI/<em>Usp16<sup><span>+/-</span></sup> </em>of the three rescued pathways. These data suggest that USP16 may regulate neural precursor cell function in part through the BMP pathway.</p>
Impact of CD4 T cells on intratumoral CD8 T cell exhaustion and responsiveness to PD-1 blockade therapy in mouse brain tumors
<p>scRNA-seq data (Cellranger filtered feature-barcode matrices) and scVDJ-seq data (Cellranger filtered_contig_annotations.csv files) for publication listed above.</p>
ROS-specific Huntingtin Interactions: GFP reactivation assay WT vs HD mouse striatal cells second attempt
<p>Optimization step in the lead up to phenotypic analysis of ROS-dependent huntingtin interacting proteins.</p>
ROS-specific Huntingtin Interactions: GFP reactivation assay WT vs HD mouse striatal cells
<p>Optimization step in the lead up to phenotypic analysis of ROS-dependent huntingtin interacting proteins.</p>
ROS-Specific Huntingtin Interactions: ROS Source Optimization in Mouse Striatal Cells
<p>Optimization step in the lead up to mass spec identification of ROS-specific huntingtin protein-protein interactions. </p>
FLAIR2 results from LRGASP R2C2 sequencing of Human WTC11 and Mouse ES cell lines
<p>FLAIR2 results from Oxford Nanopore sequencing of R2C2 libraries generated from Human WTC11 and Mouse ES cell lines from the Long-read RNA-seq Genome Annotation Assessment Project (LRGASP) (Pardo-Palacios et al. Nature Methods 2024). FLAIR2 high-confidence isoforms are provided for WTC11 and ES cells. For Mouse ES cells, sequence variants were identified with Longshot and incorporated into transcript isoforms using FLAIR2. These data are associated with the FLAIR2 publication (Tang et al. 2024)</p>
Data for Cell-type-specific alternative splicing in the cerebral cortex of a Schinzel-Giedion Syndrome patient variant mouse model
<p><span><strong>data.tar.gz </strong>contains all files from the data directory (except for sam outputs from STAR) associated with the 230926_EJ_Setbp1_AlternativeSplicing GitHub project and includes the following files:</span></p> <p> </p> <p><span><strong>./marvel: </strong>- </span><span>This directory contains rds and Rdata objects that were created using the MARVEL R package</span></p> <p><span>cell_type_goresults.rds - This is the go results split by cell type</span></p> <p><span>marvel_04_split_counts.Rdata - This R data includes all environment objects from MARVEL script 04, and is used for downstream plotting</span></p> <p><span>normalized_sj_expression.Rds - This object is the normalized splice junction expression</span></p> <p><span>Setbp1_marvel_aligned.rds - Final prepared MARVEL object before any SJU analyses have been run</span></p> <p><span>significant_tables.RData - For those who do not want to load multiple massive files, this includes all significant SJU results for each cell type</span></p> <p><span>sj_usage_cell_type.rds - This data object has splice junction usage calculated for each cell type</span></p> <p><span>sj_usage_condition.rds - This data object has splice junction usage calculated for each cell type and also split by condition</span></p> <p> </p> <p><strong><span>./seurat: </span></strong><span>- This directory contains all intermediate and final Seurat single-cell gene expression objects</span></p> <p><span>annotated_brain_samples.rds - This is the final iteration of the processing in Seurat for a final annotated object. Please use this object for any Seurat or single-cell gene expression analyses.</span></p> <p><span>clustered_brain_samples.rds - This is the clustered Seurat object, before cell type annotation based on canonical markers.</span></p> <p><span>filtered_brain_samples_pca.rds - This is the filtered Seurat object, before clustering but after PCA.</span></p> <p><span>filtered_brain_samples.rds - This is the filtered Seurat object, before PCA.</span></p> <p><span>integrated_brain_samples.rds - This the integrated Seurat object, before other steps.</span></p> <p> </p> <p><span><strong>./star: </strong>- </span><span>All files in the STAR directory are outputs from STARsolo, as described in our methods. Each output directory contains the same files, so only one example is included here for brevity. Intermediate SAM files were removed to optimize space.</span></p> <p><span>J1/ - This directory contains outputs for brain sample J1</span></p> <p><span>J13/ - This directory contains outputs for brain sample J13</span></p> <p><span>J15/ - This directory contains outputs for brain sample J15</span></p> <p><span>J2/ - This directory contains outputs for brain sample J2</span></p> <p><span>J3/ - This directory contains outputs for brain sample J3</span></p> <p><span>J4/ - This directory contains outputs for brain sample J4</span></p> <p><span>K1/ - This directory contains outputs for kidney sample K1</span></p> <p><span>K2/ - This directory contains outputs for kidney sample K2</span></p> <p><span>K3/ - This directory contains outputs for kidney sample K3</span></p> <p><span>K4/ - This directory contains outputs for kidney sample K4</span></p> <p><span>K5/ - This directory contains outputs for kidney sample K5</span></p> <p><span>K6/ - This directory contains outputs for kidney sample K6</span></p> <p> </p> <p><span><strong>./star/genome:</strong> - This directory contains outputs from running STAR genomeGenerate. Detailed file descriptions available from</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>chrLength.txt</span></p> <p><span>chrNameLength.txt</span></p> <p><span>chrName.txt</span></p> <p><span>chrStart.txt</span></p> <p><span>exonGeTrInfo.tab</span></p> <p><span>exonInfo.tab</span></p> <p><span>geneInfo.tab</span></p> <p><span>Genome</span></p> <p><span>genomeParameters.txt</span></p> <p><span>Log.out</span></p> <p><span>SA</span></p> <p><span>SAindex</span></p> <p><span>sjdbInfo.txt</span></p> <p><span>sjdbList.fromGTF.out.tab</span></p> <p><span>sjdbList.out.tab</span></p> <p><span>transcriptInfo.tab</span></p> <p> </p> <p><span><strong>./star/J1:</strong> - This is the head STAR directory for sample J1. It contains logs, basic QC, and gene and splice junction counts. For more information about the STAR pipeline and its outputs, please refer to the STAR documentation</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>Log.final.out</span></p> <p><span>Log.out</span></p> <p><span>Log.progress.out</span></p> <p><span>SJ.out.tab</span></p> <p><span>Solo.out/</span></p> <p><span>STARgenome/</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out:</strong>- This directory contains the outputs used for downstream analysis</span></p> <p><span>Barcodes.stats</span></p> <p><span>GeneFull_Ex50pAS/</span></p> <p><span>SJ/</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS: </strong>- This directory contains the filtered and raw barcodes, features, and matrix files for gene expression (including introns)</span></p> <p><span>Features.stats</span></p> <p><span>filtered/</span></p> <p><span>raw/</span></p> <p><span>Summary.csv</span></p> <p><span>UMIperCellSorted.txt</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS/filtered: </strong>- This directory contains the filtered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages)</span></p> <p><span>barcodes.tsv.gz - This file contains filtered cell barcodes</span></p> <p><span>features.tsv.gz - This file contains filtered features (genes)</span></p> <p><span>matrix.mtx.gz - This file contains the filtered cell by gene expression count matrix</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/GeneFull_Ex50pAS/raw: </strong>- This directory contains the unfiltered tsv and mtx gene expression files required for creating a Seurat object (or other single cell packages). Files are the same as previously described for filtered.</span></p> <p><span>barcodes.tsv</span></p> <p><span>features.tsv</span></p> <p><span>matrix.mtx</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/SJ: </strong>- This directory contains the QC and raw barcodes, features, and matrix files for splice junction expression</span></p> <p><span>Features.stats</span></p> <p><span>raw/</span></p> <p><span>Summary.csv</span></p> <p> </p> <p><span><strong>./star/J1/Solo.out/SJ/raw:</strong> - This directory contains the raw barcodes, features, and matrix files for splice junction expression</span></p> <p><span>barcodes.tsv - This file contains filtered cell barcodes</span></p> <p><span>features.tsv - This file contains filtered features (splice junctions)</span></p> <p><span>matrix.mtx - This file contains the filtered cell by gene expression count matrix</span></p> <p> </p> <p><span><strong>./star/J1/_STARgenome:</strong> - This directory contains the STARgenome created and used by STAR for this sample. Detailed file descriptions available from</span><a href="https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf"><span> </span><span>https://github.com/alexdobin/STAR/blob/master/doc/STARmanual.pdf</span></a><span> </span></p> <p><span>exonGeTrInfo.tab</span></p> <p><span>exonInfo.tab</span></p> <p><span>geneInfo.tab</span></p> <p><span>sjdbInfo.txt</span></p> <p><span>sjdbList.fromGTF.out.tab</span></p> <p><span>sjdbList.out.tab</span></p> <p><span>transcriptInfo.tab</span></p>
Systematic spatio-temporal mapping reveals divergent cell death pathways in three mouse models of hereditary retinal degeneration
<p>Values for immuohistochemical analysis or enzymatic analysis of various markers theorised to have roles in retinal dystrophies in three models of mouse retinal degeneration with a control. Analysis has been broken down by marker, mouseline, age and region of the retina recorded from</p>
Data for "Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas"
<p>Data for "Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas". Preprint: https://www.biorxiv.org/content/10.1101/507293v2.</p> <p>Raw sequencing data is available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134378.</p>
Contributions of mirror-image hair cell orientation to mouse otolith organ and zebrafish neuromast function
<p>Dataset for Ono et al., 2024.<br>Contains all source data from the Tarchini laboratory, including illustrative images and raw data for quantification, as well as the data file containing statistical analysis. It also contains the data file produced by the Cullen laboratory reporting behavioral data. </p>
Membrane staining and segmentation of a developing mouse embryo from 4 to 26 cells
<h1>Intent</h1> <p>The role of this dataset is to provide an example of segmentable and trackable data using signal from cell membrane. This dataset contains one file with the pre-processed imaged embryo (imaging.zip -> imaging.tif) and one file with the outcome of a segmentation using Cellpose (segmentation.zip -> segmentation.tif).</p> <blockquote> <p>This embryo corresponds to the embryo "C1" in the <a href="https://doi.org/10.1126/science.adh1145" target="_blank" rel="noopener">original publication</a></p> </blockquote> <h1>Animal</h1> <p>This set of data represents a mouse embryo developing from the 4-cell stage. The embryo at the last processed timepoint has 26 cells. The mother and the father were both mTmG animals (tdTomato anchored at the membrane of the cells).</p> <blockquote> <p>Details can be found in the <a href="https://doi.org/10.1126/science.adh1145" target="_blank" rel="noopener">original publication</a></p> </blockquote> <h1>Culture conditions</h1> <p>The embryo was imaged in an inverted SPIM from Luxendo (now Bruker), laying at the bottom of a PFE imaging dish. The embryo developed in a small pocket made by deforming the PFE with a glass tip. We used approx. 150μL of KSOM-AA to cutlure the embryos at 37ºC ± 0.2ºC in 5% CO2 and 5% O2. The medium was covered with approx. 100μL of mineral oil.</p> <blockquote> <p>Details can be found in the <a href="https://doi.org/10.1126/science.adh1145" target="_blank" rel="noopener">original publication</a></p> </blockquote> <h1>Imaging conditions</h1> <p>The tdTomato was excited with a 561nm laser using as little intensity as possible (0% in the settings + a small fraction leaking through the shutter). We used a 561 LP filter to acquire the signal. Imaging was set with a lateral resolution of 0.208μm and a axial resolution of 1μm. Because of technical issues with the stage of the microscope at the time, the resulting axial resolution was ultimately 1.338μm. In total, 181 slices were acquired per time point. Two consecutive timepoints start with 15 minutes of interval.</p> <p>Although this embryo was imaged for a longer period of time, this dataset shows only the first 117 timepoints.</p> <blockquote> <p>Details can be found in the <a href="https://doi.org/10.1126/science.adh1145" target="_blank" rel="noopener">original publication</a></p> </blockquote> <h1>Processing conditions</h1> <table> <tbody> <tr> <td><strong>Operation</strong></td> <td><strong>Lateral resolution</strong></td> <td><strong>Axial resolution</strong></td> <td><strong>File</strong></td> </tr> <tr> <td><strong>Imaging</strong></td> <td>0.208 μm</td> <td>1.338 μm</td> <td>-</td> </tr> <tr> <td><strong>Cropping<br></strong></td> <td>0.208 μm</td> <td>1.338 μm</td> <td>-</td> </tr> <tr> <td><strong>Lateral binning (average)</strong></td> <td>0.416 μm</td> <td>1.338 μm</td> <td>-</td> </tr> <tr> <td><strong>Isotropic rescaling</strong></td> <td>0.416 μm</td> <td>0.416 μm</td> <td>-</td> </tr> <tr> <td><strong>Lateral binning (average)</strong></td> <td>0.832 μm</td> <td>0.832 μm</td> <td>imaging.tif</td> </tr> <tr> <td><strong>Segmentation (cellpose)</strong></td> <td>0.832 μm</td> <td>0.832 μm</td> <td>segmentation.tif</td> </tr> </tbody> </table>
Data and Code from: Dysregulation of zebrin-II cell subtypes is a shared feature across polyglutamine ataxia mouse models and human patients
<div> <div> <div> <p>Abstract</p> <p>Spinocerebellar ataxia type 7 (SCA7) is a genetic neurodegenerative disorder caused by a CAG- polyglutamine repeat expansion. Purkinje cells (PCs) are central to the pathology of ataxias, but their low abundance in the cerebellum underrepresents their transcriptomes in sequencing assays. To address this issue, we developed a PC enrichment protocol and sequenced individual nuclei from mice and patients with SCA7. Single-nucleus RNA sequencing in SCA7-266Q mice revealed dysregulation of cell identity genes affecting glia and PCs. Specifically, genes marking zebrin-II PC subtypes accounted for the highest proportion of DEGs in symptomatic SCA7-266Q mice. These transcriptomic changes in SCA7-266Q mice were associated with increased numbers of inhibitory synapses as quantified by immunohistochemistry and reduced spiking of PCs in acute brain slices. Dysregulation of zebrin-II cell subtypes was the predominant signal in PCs of SCA7-266Q mice and was associated with the loss of zebrin-II striping in the cerebellum at motor symptom onset. We furthermore demonstrated zebrin-II stripe degradation in additional mouse models of polyglutamine ataxia and observed decreased zebrin-II expression in cerebellum of patients with SCA7. Our results suggest that a breakdown of zebrin subtype regulation is a shared pathological feature of polyglutamine ataxias.</p> <p>Data and Code Availability</p> <p>Here you will find data and code associated with our manuscript "Dysregulation of zebrin-II cell subtypes is a shared feature across polyglutamine ataxia mouse models and human patients", Bartelt et al., <em>Sci. Trans. Med. </em>16, eadn5449 (2024).</p> <p>The data file labeled "HuCb_filtered.rds" is a processed and annotated single-nucleus RNA-seq Seurat object, containing the gene-level count data for the multiplexed snRNA-seq experiment performed on post-mortem human cerebellar tissues from patients with SCA7 and unaffected controls. Data obtained from WT and SCA7-266Q mice as described in our paper can be accessed in the NIH Gene Expression Omnibus under accession number GSE269430.</p> <p>There are three code files numbered 00 through 02 which contain analysis code for snRNA-seq data applied to both the mouse and human datasets. These files are sequential and will take the user from CellRanger output, to filtered and annotated Seurat objects, and include details for subclustering analysis as well as our pseudobulk DEseq2 differential expression approach. There are places where the user may need to modify the code based on their computer system, version of R or Seurat, and whether they are processing the 5 week, 8 week, or human data sets; these locations in the code are marked with comments.</p> <ul> <li>The first file, 00_Preprocessing_MULTIseq, begins with CellRanger filtered_feature_barcode_matrix output, extracts cell barcodes, utilizes the MULTIseq deMULTIplex software to match cell barcodes to oligo barcodes from MULTIseq fastq files, and annotates the Seurat file with metadata. Cell type identification and annotation also takes place in this file. Note: the deMULTIplex step will likely need to be run on a high performance compute cluster.</li> <li>The second file, 01_Seurat_Analysis, uses the filtered and annotated Seurat file to calculate useful QC metrics, investigate disease signals, and perform cell type subclustering analyses.</li> <li>The third file, 02_Pseudobulk_DEseq2, contains custom analysis code to extract raw counts for each cell type and each animal from the Seurat file, and uses the DEseq2 package to calculate DEGs, taking into account biological replicates, and raw read count differences between control and SCA7 animals.</li> </ul> </div> </div> </div>
Discovery proteomics by mass spectrometry comparing secreted proteins from young and old mouse lung mesenchymal stromal cells
<p>Lung mesenchymal stromal cells (L-MSCs) were isolated from the young ( 3 months) and old (22-24 months) mice (N = 4 each) following collagenase digestion of the lungs and anchorage-dependent growth. Culture media was collected from young and old L-MSCs (10^6 cells) grown in culture for 24 hrs in serum-free condition. Culture media was then centrifuged to remove any cellular debris. Five (5) ml of supernatants were concentrated using 3kDa Molecular weight cut-off filters, and 10 micrograms of proteins were <span><span><span><span><span>run as multiple MW 1D PAGE fractions using a standard </span><span>GeLC</span><span> approach with a </span><span>nano</span><span>-HPLC in-line with a </span><span>Velos</span><span> Pro Orbitrap MS. </span></span></span></span></span><span><span><span><span><span>Individual data files were searched using </span><span>Sequest</span><span>, the results were then combined for each sample, followed by grouping, filtering, and quantifying by normalized spectral counts. </span></span></span></span></span></p> <p> </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.