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111,247 results for “cells”
Fluorescent images of actin and DAPI-labelled MCF10A, MCF7 and MDA-MB-231 cell lines
<p>This dataset of cell images was generated to understand the morphological changes between less and more metastic cancer cells and between normal and cancerous cells. They have been used in the linked publications.</p>
Dataset for "Pan-cancer integrative analyses dissect the remodeling of endothelial cells in human cancers"
<p>This is the dataset for "Pan-cancer integrative analyses dissect the remodeling of endothelial cells in human cancers".</p> <p> </p> <p>File "NSR.panE.exprs.all.h5ad.gz" contains processed expression .h5ad data.</p> <p>File "NSR.panE.obs.meta.csv" contains the meta data for this study.</p> <p>File "umap.zip" contains the .csv files for umap coordinates.</p> <p> </p> <p> </p>
Closed loop recordings from IPSC cell culture using planar MEA
<p>Recordings from IPSC cell culture using planar MEA in closed loop with the AlzModel. </p> <p>The script 'alzgraphs.py' is used to display summary statistics. </p>
Demo Live Cell Imaging Dataset for PetaKit5D
<p>Demo dataset for PetaKit5D (<a href="https://github.com/abcucberkeley/LLSM5DTools">https://github.com/abcucberkeley/PetaKit5D</a>). </p> <p>The dataset is for the large field of view live cell imaging of LLC-PK1 cells of nuclei and endoplasmic reticulum (ER). It contains two time points with two channels. Each time point and channel contains 4 volumetric tiles. Some demos in PetaKit5D use this dataset to demonstrate the usage.</p> <p>Please cite our paper (<a href="https://doi.org/10.1101/2023.12.31.573734">https://doi.org/10.1101/2023.12.31.573734</a>) if you use this dataset in your research:</p> <p><code>Xiongtao Ruan, Matthew Mueller, Gaoxiang Liu, Frederik Görlitz, Tian-Ming Fu, Daniel E. Milkie, Joshua L. Lillvis, Alexander Kuhn, Chu Yi Aaron Herr, Wilmene Hercule, Marc Nienhaus, Alison N. Killilea, Eric Betzig, Srigokul Upadhyayula. Image processing tools for petabyte-scale light sheet microscopy data. bioRxiv 2023.12.31.573734; doi: <a href="https://doi.org/10.1101/2023.12.31.573734">https://doi.org/10.1101/2023.12.31.573734</a></code></p>
Secondary data_Cell density distributions histograms
<p>Dataset containing secondary data regarding cell density. This data was used for the generation of histograms of cell density related to the article: <strong>The distinctive chemotactic responses of three marine herbivore protists to DMSP and related compounds.</strong> Therefore, provides information about the spatial distribution of three cultured dinoflagellates (<em>Karlodinium armiger, Gyrodinium dominans</em> and<em> Oxyrrhis marina</em>) responding to three chemical cues diffusing from a microcapillary. </p> <p>Nomenclature:</p> <p>bin_edge_um: spatial postion (um2) / counts: cell/um2 (density9 / concentration: uM</p>
Data for "CryoDRGN-ET: Deep reconstructing generative networks for visualizing dynamic biomolecules inside cells"
<p>Trained models weights, training parameters, sampled density maps, reconstructed density maps for featured classes, and plotting scripts are included for each of the following datasets and training runs:</p> <ul> <li><em>M. pneumoniae</em> ribosome, initial training run with all 18,466 particles, 1 tilt per particle</li> <li><em>M. pneumoniae</em> ribosome, training run with 16,655 filtered particles and 10 tilts per particle</li> <li><em>M. pneumoniae</em> ribosome, training run with 16,655 filtered particles and 41 tilts per particle</li> <li><em>S. cerevisiae </em>ribosome, initial training run with all 119,031 particles, 10 tilts per particle</li> <li><em>S. cerevisiae </em>ribosome, training run with 93,281 filtered particles, 10 tilts per particle</li> <li><em>S. cerevisiae</em> ribosome, training run with 30,657 particles in the non-rotated state, 10 tilts per particle</li> <li><em>S. cerevisiae </em>ribosome, training run with 62,624 particles in the rotated state, 10 tilts per particle</li> <li><em>S. cerevisiae </em>fatty acid synthase, initial training run with all 33,492 particles, 10 tilts per particle</li> <li><em>S. cerevisiae </em>fatty acid synthase, training run with all 5,239 filtered particles, 10 tilts per particle</li> </ul>
Figure 6 in Potential neuroprotective of trans-resveratrol a promising agent tempeh and soybean seed coats-derived against beta-amyloid neurotoxicity on primary culture of nerve cells induced by 2-methoxyethanol
Figure 6. Treatment Group with resveratrol standard after induced by Beta-Amyloid (10 x10).
Figure 3 in Potential neuroprotective of trans-resveratrol a promising agent tempeh and soybean seed coats-derived against beta-amyloid neurotoxicity on primary culture of nerve cells induced by 2-methoxyethanol
Figure 3. Treatment group: 2- ME + Resveratrol isolated from Tempeh (10 x10).
Matrix stiffness influences response to chemo and targeted therapy in brain metastatic breast cancer cells
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Metabolomic analysis of cultured TRAMP-C2 cells in the presence or absence of PD-L1 expression
<p>The interaction between the immune inhibitory receptor PD-1 and its ligand PD-L1 is a critical mechanism for altering immune responses, especially during chronic antigen exposures such as cancer. While much research has focused on the PD-1 receptor, recent evidence suggests that PD-L1 can have cell-intrinsic effects in cancer and immune cells. These functions are distinct from its ability to bind and trigger PD-1 activity and are notable given that PD-L1 is widely expressed in mammals. One such cell-intrinsic function is the modulation of cellular metabolism, including regulation of mTOR activity and glycolysis. As part of our investigation into PD-L1 function, we analyzed the metabolome of cultured mouse prostate cancer cells (TRAMP-C2) expressing PD-L1 or with PD-L1 deleted via CRISPR/Cas9. We quantified 186 water-soluble metabolites from TRAMP-C2 cells expressing PD-L1 or not to better understand what metabolic pathways and processes are regulated by PD-L1 expression/activity. We found a broad range of differentially abundant metabolites, most notably a decreased abundance of glycolytic metabolites when PD-L1 expression is knocked out. In our manuscript, we show that this has a functional outcome on viral infection and cytokine signaling.</p>
Impact of low pH on glutamine incorporation in CD8+ T cells
<p>OT-I CTLs were cultured for 4 hours with <sup><span>13</span></sup><span>C</span>-glutamine in the presence, or absence, of IL-2 at pH7.4 or pH6.6. Metabolites that incorporated isotopic glutamine were identified by HILIC-HRMS.</p>
Identifying cell states in single-cell RNA-seq data at statistically maximal resolution
<p>In this repository we provide the datasets for the results of the Cellstates method as shown in the article: “Identifying cell states in single-cell RNA-seq data at statistically maximal resolution” by Pascal Grobecker, Thomas Sakoparnig, and Erik van Nimwegen.</p> <p>A preprint is available under the following DOI: https://doi.org/10.1101/2023.10.31.564980</p> <p>There is a README file that describes the contents and formats of each of the tab-separated values files. There is one subdirectory containing files with Cellstates' results on the dataset of Zeisel et al. (DOI: 10.1016/j.cell.2018.06.021) which is accompanied by another README file describing the formats and contents of these files. </p>
Sample data for "Live Cell Fluorescence Microscopy – An End-to-End Workflow for High-Throughput Image and Data Analysis"
<p>This repository contains:</p> <ul> <li> <p>Sample data for the "Live Cell Fluorescence Microscopy – From Sample Preparation to Numbers and Plots" methodology paper by Zahumensky & Malinsky. The paper describes the preparation of live yeast cell samples for microscopy, the subsequent semi-automatic analysis of the microscopy images using our custom-written Fiji macros, and automatic processing of the output (Results table) from the image analys using custom-written R scripts. The data provided here are real experimental data from two publications of our group: Zahumensky et al., 2022 and Vesela et al., 2023</p> </li> <li> <p>"Results tables" from the Fiji based analysis</p> </li> <li> <p>Outputs of the processing of these Results tables using our R scripts, in the form of summary tables, graphs, and statistical analyses</p> </li> </ul>
Spatial dynamics of CD39⁺CD8⁺ exhausted T cells reveal tertiary lymphoid structures-mediated response to PD-1 blockade in esophageal cancer
<p><strong>Data related to the paper</strong>: <em>"Spatial dynamics of CD39+CD8+ exhausted T cells reveal tertiary lymphoid structures-mediated response to PD-1 blockade in esophageal cancer,”</em> <em>Nature Communications</em> (2024)</p> <p>The repository data consists of two main folders: <strong>IMC_dataset</strong> and <strong>MC_normalized_dataset</strong>.</p> <p><strong>IMC_dataset</strong> includes:</p> <ol> <li> <p><strong>IMC_denoised_dataset</strong>: This folder contains cell mask images and noise-reduced images for each sample.</p> </li> <li> <p><strong>IMC_raw_dataset</strong>: This folder contains raw, unprocessed data.</p> </li> <li> <p><strong>IMC_processed_data</strong>: This folder contains standardized single-cell information and spillover-corrected FCS files, along with the compensation matrix.</p> </li> </ol> <p>The <strong>MC_normalized dataset</strong> includes FCS files that have been sorted by barcode.</p> <p><strong>Please note</strong> that in the IMC dataset, the following mass channels are blank:</p> <ul> <li><strong>Tumor-ROI</strong>: 80Ar, 127I, 131Xe, 145Nd, 146Nd, 149Sm, 160Gd, 171Yb, 174Yb, 176Yb, 190Os</li> <li><strong>SLO-ROI</strong>: 80Ar, 127I, 131Xe, 145Nd, 146Nd, 149Sm, 160Gd, 176Yb, 190Os</li> </ul> <p>The names attached to the file names are IDs.</p>
Identification of genetic variants associated with clinical features of sickle cell disease
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CXCL12-loaded-hydrogel (CLG): a new device for metastatic Circulating Tumor Cells (CTCs) capturing and characterization
<p><strong>Background</strong>: Circulating Tumor Cells (CTCs) represent a small, heterogeneous population that comprise the minority of cells able to develop metastasis. To trap and characterize CTCs with metastatic attitude, a CXCL12-loaded hyaluronic-gel (CLG) was developed. CXCR4+cells with invasive capability would infiltrate CLG.<br><strong>Methods</strong>: Human colon, renal, lung and ovarian cancer cells (HT29, A498, H460 and OVCAR8 respectively) were seeded on 150 µl Empty Gels (EG) or 300 ng/ml CXCL12 loaded gel (CLG) and allowed to infiltrate for 16 hours. Gels were then digested and fixed with 2% FA-HAse for human cancer cell enumeration or digested with HAse and cancer cells recovered. CLG-recovered cells migrated toward CXCL12 and were tested for colonies/spheres formation. Moreover, CXCR4, E-Cadherin and Vimentin expression was assessed through flow cytometry and RT-PCR. The clinical trial “TRAP4MET” recruited 48 metastatic/advanced cancer patients (8 OC, 8 LC, 8 GBM, 8 EC, 8 RCC and 8 EC). 10 cc whole blood were devoted to PBMCs extraction (7cc) and ScreenCell™ filters (3cc) CTCs evaluation. Ficoll-isolated patient’s PBMCs were seeded over CLG and allowed to infiltrate for 16 hours; gels were digested and fixed with 2% FA-HAse, cells stained and DAPI+/CD45-/pan-CK+ cells enumerated as CTCs.<br><strong>Results</strong>: Human cancer cells infiltrate CLG more efficiently than EG (CLG/EG ratio 1.25 for HT29/ 1.58 for A498/ 1.71 for H460 and 2.83 for OVCAR8). CLG- recovered HT29 cells display hybrid-mesenchymal features [low E-cadherin (40%) and high vimentin (235%) as compared to HT29], CXCR4 two-fold higher than HT29, efficiently migrate toward CXCL12 (two-fold higher than HT29) and developed higher number of colonies (171±21 for HT29-CLG vs 131±8 colonies for HT29) /larger spheres (spheroid area: 26561±6142 µm2 for HT29-CLG vs 20297±7238 for HT29). In TRAP4MET clinical trial, CLG-CTCs were isolated in 8/8 patients with OC, 6/8 with LC, 6/8 with CRC, 8/8 with EC, 8/8 with RCC cancer and 5/8 with GBM. Interestingly, in OC, LC and GBM, CLG isolated higher number of CTCs as compared to the conventional ScreenCell™ (CLG/SC ratio=1.88 for OC, 2.47 for LC and 11.89 for GBM). Bland and Altman blot analysis and Passing and Bablok regression analysis showed concordance between the methodological approaches but indicate that SC and CLG are not superimposable suggesting that the two systems select cells with different features.<br><strong>Conclusion</strong>: CLG might represent a new and easy tool to isolate invasive CTCs in multiple cancers such as OC, LC and GBM at today orphan of reliable methods to consistently detect CTCs. </p>
Characterizing multidimensional cellular physiological states with quantitative three-dimensional shape descriptors for cell membranes
<h3>Supplementary dataset and code for the article "<em>Characterizing Cellular Physiological States with Three-Dimensional Shape Descriptors for Cell Membranes</em>"</h3> <p>CShaper Dataset.zip: The 3D cell regions reused from the previously published article <a href="https://doi.org/10.1038/s41467-020-19863-x">https://doi.org/10.1038/s41467-020-19863-x</a>.</p> <p>Cell Shape Descriptors - Code & Data.zip: The code (exemplifed by embryo Sample04) and data (including embryo Sample04-Sample20) of 12 3D shape descriptors for all 3D cell regions in the <em>CShaper</em> dataset.</p> <p>GUI.zip: The user-friendly software <em>Shape Descriptor Tool</em> is a Graphical User Interface based on <em>Matlab</em> for calculating 12 shape descriptors for a 3D cell region (exemplified by embryo Sample20, time point 14, ABpl cell in the <em>CShaper</em> dataset). The instruction guidebook is included in the Supplementary Material of the article.</p>
Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets
<p>The uploaded files are source datasets for the scMTNI algorithm. scMTNI is a multi-task learning framework that integrates the cell lineage structure, scRNA-seq and scATAC-seq measurements to enable joint inference of cell type-specific GRNs. See more details at Zhang, S., Pyne, S., Pietrzak, S. et al. Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets. Nat Commun 14, 3064 (2023). <a href="https://doi.org/10.1038/s41467-023-38637-9">https://doi.org/10.1038/s41467-023-38637-9</a></p> <p>The source data <a href="../api/files/13d4a93c-bf23-47c9-ae64-92bcfd9f8772/scMTNI_sourcedata.tar.gz?versionId=a19f69c1-5bb2-436e-9a30-a7efbf1ab3da">scMTNI_sourcedata.tar.gz</a> contains the following 3 parts:</p> <p>1) The cluster-specific scRNA-seq matrices and the prior networks for all three datasets and scMTNI inferred consensus networks.</p> <p>2) Gold standard human and mouse datasets for evaluation.</p> <p>3) Source data for scMTNI figures 2-8 and supplementary figures. The key for each figure and its corresponding file path is in SourceData_Key_v2.xlsx.</p> <p>The source data <a href="../api/files/13d4a93c-bf23-47c9-ae64-92bcfd9f8772/Buenrostro_Hematopoiesis.tar.gz">Buenrostro_Hematopoiesis.tar.gz</a> contains the scRNA-seq data for human hematopoietic differentiation downloaded from Data S2 of Buenrostro et al.</p> <p>The source data <a href="../api/files/5c75876b-c08a-4186-a08d-da9db78f64f3/RawMotifFiles.tar.gz">RawMotifFiles.tar.gz</a> contains the motif instance files and promoter files for human and mouse for generating prior networks using scATAC-seq data for scMTNI. Check <a href="https://github.com/Roy-lab/scMTNI/blob/master/Scripts/genPriorNetwork/readme.md">https://github.com/Roy-lab/scMTNI/blob/master/Scripts/genPriorNetwork/readme.md</a> for examples and scripts.</p> <p>The <a href="../api/records/11876980/draft/files/Buenrostro_priorNetwork_bamfiles.tar.gz/content">Buenrostro_priorNetwork_bamfiles.tar.gz</a> contains the raw bam files of scATAC-seq data for human hematopoietic differentiation downloaded from Buenrostro et al.</p>
Datasets for evaluating SCEMENT: Scalable and Memory Efficient Integration of Large-scale Single Cell RNA-sequencing Data
<p>This resource contains pre-processed A. thaliana root , the H. sapiens aortic valve datasets, PBMC Covid atlas and public 10x datasetse used in the paper, SCEMENT: Scalable and Memory Efficient Integration of Large-scale Single Cell RNA-sequencing Data. The raw datasets provided in the links below are pre-processed for quality control with respect to both cells and genes. </p> <p>A. thaliana datasets are sourced from the following locations at <a href="https://www.ebi.ac.uk/gxa/sc/home">Single-cell Gene expression Atlas </a>and <a href="https://www.ncbi.nlm.nih.gov/geo/">Gene Expression Omnibus (GEO)</a>:</p> <ol> <li>E-GEOD-121619 : <a title="E-GEOD-121619" href="https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-121619/results">https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-121619/results</a></li> <li>E-GEOD-152766 : <a href="https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-152766/results">https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-152766/results</a></li> <li>E-GEOD-158761 : <a href="https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-158761/results">https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-158761/results</a></li> <li>E-GEOD-123013 : <a href="https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-123013/results">https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-123013/results</a></li> </ol> <p>H. sapiens datasets are obtained from the NCBI database : <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA562645/">https://www.ncbi.nlm.nih.gov/bioproject/PRJNA562645/ </a></p> <ol> <li>GSE152766: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152766">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152766</a></li> <li>GSE158761: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE158761">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE158761</a></li> </ol> <p>All COVID atlas datasets are from: <a href="http://covid19.cancer-pku.cn">http://covid19.cancer-pku.cn</a> . covid_atlas_data1.zip contains the h5ad files and covid_atlas_data2.zip contains the Seurat rds files.</p> <p>PBMC datasets are from the following public sources:</p> <table> <tbody> <tr> <td>Dataset Name</td> <td>Chemistry Version</td> <td>Web Link</td> </tr> <tr> <td>10k Human PBMCs, 3' v3.1, Chromium X</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/10k-human-pbmcs-3-ht-v3-1-chromium-x-3-1-high">https://www.10xgenomics.com/datasets/10k-human-pbmcs-3-ht-v3-1-chromium-x-3-1-high</a></td> </tr> <tr> <td>20k Human PBMCs, 3' HT v3.1, Chromium X</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/20-k-human-pbm-cs-3-ht-v-3-1-chromium-x-3-1-high-6-1-0">https://www.10xgenomics.com/datasets/20-k-human-pbm-cs-3-ht-v-3-1-chromium-x-3-1-high-6-1-0</a></td> </tr> <tr> <td>10k Human PBMCs, 3' v3.1, Chromium Controller</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/10k-human-pbmcs-3-v3-1-chromium-controller-3-1-high">https://www.10xgenomics.com/datasets/10k-human-pbmcs-3-v3-1-chromium-controller-3-1-high</a></td> </tr> <tr> <td>Healthy PBMC Chromium Connect (channel 1)</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-chromium-connect-channel-1-3-1-standard-3-1-0">https://www.10xgenomics.com/datasets/peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-chromium-connect-channel-1-3-1-standard-3-1-0</a></td> </tr> <tr> <td>Healthy PBMC Chromium Connect (channel 5)</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-chromium-connect-channel-5-3-1-standard-3-1-0">https://www.10xgenomics.com/datasets/peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-chromium-connect-channel-5-3-1-standard-3-1-0</a></td> </tr> <tr> <td>10k PBMCs from a Healthy Donor (v3 chemistry)</td> <td>v3.0</td> <td><a href="https://www.10xgenomics.com/datasets/10-k-pbm-cs-from-a-healthy-donor-v-3-chemistry-3-standard-3-0-0">https://www.10xgenomics.com/datasets/10-k-pbm-cs-from-a-healthy-donor-v-3-chemistry-3-standard-3-0-0</a></td> </tr> <tr> <td>1k PBMCs from a Healthy Donor (v2 chemistry)</td> <td>v2.0</td> <td><a href="https://www.10xgenomics.com/datasets/1-k-pbm-cs-from-a-healthy-donor-v-2-chemistry-3-standard-3-0-0">https://www.10xgenomics.com/datasets/1-k-pbm-cs-from-a-healthy-donor-v-2-chemistry-3-standard-3-0-0</a></td> </tr> <tr> <td>1k PBMCs from a Healthy Donor (v3 chemistry)</td> <td>v3.0</td> <td><a href="https://www.10xgenomics.com/datasets/1-k-pbm-cs-from-a-healthy-donor-v-3-chemistry-3-standard-3-0-0">https://www.10xgenomics.com/datasets/1-k-pbm-cs-from-a-healthy-donor-v-3-chemistry-3-standard-3-0-0</a></td> </tr> <tr> <td>Fresh 68k PBMCs (Donor A)</td> <td>v1.0</td> <td><a href="https://www.10xgenomics.com/datasets/fresh-68-k-pbm-cs-donor-a-1-standard-1-1-0">https://www.10xgenomics.com/datasets/fresh-68-k-pbm-cs-donor-a-1-standard-1-1-0</a></td> </tr> <tr> <td>Frozen PBMCs (Donor A)</td> <td>v1.0</td> <td><a href="https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-a-1-standard-1-1-0">https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-a-1-standard-1-1-0</a></td> </tr> <tr> <td>Frozen PBMCs (Donor B)</td> <td>v1.0</td> <td><a href="https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-b-1-standard-1-1-0">https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-b-1-standard-1-1-0</a></td> </tr> <tr> <td>Frozen PBMCs (Donor C)</td> <td>v1.0</td> <td><a href="https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-c-1-standard-1-1-0">https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-c-1-standard-1-1-0</a></td> </tr> <tr> <td>PBMCs from a Healthy Donor: Whole Transcriptome Analysis</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/pbm-cs-from-a-healthy-donor-whole-transcriptome-analysis-3-1-standard-4-0-0">https://www.10xgenomics.com/datasets/pbm-cs-from-a-healthy-donor-whole-transcriptome-analysis-3-1-standard-4-0-0</a></td> </tr> <tr> <td>PBMC 600K</td> <td>v1</td> <td><a href="https://www.ebi.ac.uk/gxa/sc/experiments/E-HCAD-4/downloads">https://www.ebi.ac.uk/gxa/sc/experiments/E-HCAD-4/downloads</a></td> </tr> <tr> <td>GSM4560071</td> <td>v2.0</td> <td><a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560071">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560071</a></td> </tr> <tr> <td>GSM4560074</td> <td>v2.0</td> <td><a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560074">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560074</a></td> </tr> <tr> <td>GSM4560070</td> <td>v2.0</td> <td><a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560070">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560070</a></td> </tr> </tbody> </table> <p>References for the Datasets :</p> <ol> <li>H. sapiens dataset: Kang Xu, Shangbo Xie,Yuming Huang,Tingwen Zhou, Ming Liu, Peng Zhu, Chunli Wang, Jiawei Shi, Fei Li,Frank W. Sellke and Nianguo Dong (2020) Cell-Type Transcriptome Atlas of Human Aortic Valves Reveal Cell Heterogeneity and Endothelial to Mesenchymal Transition Involved in Calcific Aortic Valve Disease.</li> <li>E-GEOD-152766: Shahan R, Hsu C, Nolan TM, Cole BJ, Taylor IW et al. (2020) A single cell Arabidopsisroot atlas reveals developmental trajectories in wild type and cell identity mutants.</li> <li>E-GEOD-121619: Jean-Baptiste K, McFaline-Figueroa JL, Alexandre CM, Dorrity MW, Saunders L et al. (2019) Dynamics of Gene Expression in Single Root Cells of Arabidopsis thaliana.</li> <li>E-GEOD-123013: Ryu KH, Huang L, Kang HM, Schiefelbein J. (2019) Single-Cell RNA Sequencing Resolves Molecular Relationships Among Individual Plant Cells.</li> <li>E-GEOD-158761: Gala HP, Lanctot A, Jean-Baptiste K, Guiziou S, Chu JC et al. (2020) A single cell view of the transcriptome during lateral root initiation in Arabidopsis thaliana.</li> <li>COVID Atlas Reference: Xianwen Ren, Wen Wen, Xiaoying Fan et.al. (2021) COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas</li> <li>PBMC data are downloaded from respective links</li> </ol>
Data from: Caenorhabditis elegans germ granules accumulate hundreds of low translation mRNAs with no systematic preference for germ cell fate regulators
<p>In animals with germ plasm, embryonic germline precursors inherit germ granules, condensates proposed to regulate mRNAs coding for germ cell fate determinants. In <em>C. elegans</em>, mRNAs are recruited to germ granules by MEG-3, a sequence non-specific RNA-binding protein that forms stabilizing interfacial clusters on germ granules. Using fluorescent <em>in situ </em>hybridization, we confirmed that 441 MEG-3-bound transcripts distribute in a pattern consistent with enrichment in germ granules. 13 are related to transcripts reported in germ granules in <em>Drosophila</em> or <em>Nasonia</em>. The majority, however, are low-translation maternal transcripts required for embryogenesis that are not maintained preferentially in the nascent germline. Granule enrichment raises the concentration of certain transcripts in germ plasm but is not essential to regulate mRNA translation or stability. Our findings suggest that only a minority of germ granule-associated transcripts contribute to germ cell fate in <em>C. elegans</em> and that the vast majority function as non-specific scaffolds for MEG-3.</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.