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111,247 results for “cells”
Advanced Non-Clear Cell Renal Cell Carcinoma Treatments and Survival: A Real-World Single-Centre Experience
<p>Dataset of the paper "Advanced Non-Clear Cell Renal Cell Carcinoma Treatments and Survival: A Real-World Single-Centre Experience"</p>
Supplementary material - Optical Diffraction Tomography and Raman Confocal Microscopy for the Investigation of Vacuoles Associated with Cancer Senescent Engulfing Cells
<p>Supplementary material containing the data used in the manuscript "Optical Diffraction Tomography and Raman Confocal Microscopy for the Investigation of Vacuoles Associated with Cancer Senescent Engulfing Cells"</p>
Deep learning segmentation projects of FIB-SEM dataset of U2-OS cell
<p>This submission includes ground truth datasets that were used to segment the nuclear envelope (NE), mitochondria, endoplasmic reticulum (ER) and Golgi from a human bone osteosarcoma epithelial cell (U2-OS) imaged using focused-ion beam scanning electron microscopy (FIB-SEM).</p><p>The full FIB-SEM dataset is deposited to EMPIAR (<a href="https://www.ebi.ac.uk/empiar">https://www.ebi.ac.uk/empiar</a>, EMPIAR-11746). </p>
Identification of biomarkers for the early detection of non-small cell lung cancer: a systematic review and meta-analysis
<p>We sought to identify the best biomarkers for the early diagnosis of LC, using a systematic review of seven databases. We identified 79 articles that focused on the identification and assessment of diagnostic biomarkers and then performed a meta-analysis. This work has been submitted for publication.</p>
Data for a publication "Characterization of hFOB 1.19 cell line for studying Zn-based degradable metallic biomaterials"
<div> <p>These data are published as part of the paper: “Characterization of hFOB 1.19 cell line for studying Zn-based degradable metallic biomaterials” published in journal: “Materials”. </p> </div> <div> <p>This repository contains one folder, namely: “concentration ICP_MS” </p> </div> <div> <p>This folder contains further data relevant to the results published in the paper, which are described in a separate file inside. </p> <p> </p> <p><strong>Preprint evolution (versions).</strong></p> <p><strong>2024-01-31-V2</strong>; <a href="https://doi.org/10.20944/preprints202401.2053.v2" target="_blank" rel="noopener">(https://doi.org/10.20944/preprints202401.2053.v2</a>) - the acknowledgement was modified as well as the data availability mentioning the Zenodo repository with the dataset as well as the availability of the datasets generated during and/or analyzed during the current study on reasonable request from corresponding author.</p> </div> <div> <p> </p> </div>
Cell-cell interactome of the hematopoietic niche and its changes in acute myeloid leukemia
<p>This repository contains data described in this study: Cell-cell interactome of the hematopoietic niche and its changes in acute myeloid leukemia. <em>Ennis S et. al., iScience, 2023. DOI: </em><a href="https://doi.org/10.1016/j.isci.2023.106943">10.1016/j.isci.2023.106943</a><em>.</em></p> <p> </p> <p><strong>Contents:</strong></p> <ul> <li>bone_marrow.h5ad - AnnData file with the integrated dataset</li> <li>ref_model_final.tar.gz - A zipped folder containing the scVI model for the integrated dataset</li> <li>Supplemental_material.tar.gz - A zipped folder containing the supplemental figures and tables from the publication</li> </ul>
Gene expression count matrix for 4 T cell subtypes from ROSMAP participants
<p><span>Peripheral blood mononuclear cells (PBMCs) from participants in the Rush Religious Orders Study/Memory and Aging Project (ROSMAP) were isolated by Ficoll gradient centrifugation, then sorted by high-speed flow cytometry into the following T cell subtypes:<span> </span>CD4+CD45RO-, CD4+CD45RO+, CD8+CD45RO-, and CD8+CD45RO+.<span> </span>Total RNA was extracted using buffer TCL (Qiagen), then RNA-seq libraries were prepared according to the Single Cell RNA Barcoding and Sequencing method originally developed for single-cell RNA-seq</span><span>, adapted for extracted total RNA.<span> </span>RNA libraries were collected on a single 384-well plate and sequenced on the Illumina HiSeq </span><span>using the High-throughput 3<span>’</span> Digital Gene Expression (DGE) library</span><span>.<span> The "RNA count matrix" file is the raw counts from the 384-well plate, while the "ROSMAP_Tcell_DGE_PlateMap" file contains metadata for the wells on the plate, by well position.</span></span></p>
Development of Multiomics in situ Pairwise Sequencing (MiP-Seq) for Single-cell Resolution Multidimensional Spatial Omics
<p>The original data used in the article: Development of Multiomics in situ Pairwise Sequencing (MiP-Seq) for Single-cell Resolution Multidimensional Spatial Omics</p> <p>Delineating the spatial multiomics landscape will pave the way to understanding the molecular basis of physiology and pathology. However, current spatial omics technology development is still in its infancy. Here, we developed a high-throughput targeted in situ sequencing strategy, multiomics in situ pairwise sequencing (MiP-Seq), to efficiently decipher multiplexed DNAs, RNAs, proteins, and small biomolecules at subcellular resolution. MiP-Seq simultaneously sequenced the dual barcode base of padlock probes, dramatically increasing the detection capacity to 10N by N rounds of sequencing. We delineated spatial gene profiles in the hypothalamus using MiP-Seq. Moreover, MiP-Seq was unitized to detect tumor gene mutations and allele-specific expression of parental genes and to differentiate sites with and without the m6A RNA modification at specific sites. MiP-Seq was combined with in vivo Ca2+ imaging and Raman imaging to obtain a spatial multiomics atlas correlated to neuronal activity and cellular biochemical fingerprints. Importantly, we proposed a “signal dilution strategy” to resolve the crowded signals that challenge the applicability of in situ sequencing. Together, our method improves spatial multiomics and precision diagnostics, and facilitates analyzing cell function in connection with gene profiles.</p>
Data description: Deprivation of loading during early healing of rat Achilles tendons affects extracellular matrix composition and structure, and reduces cell density and cell alignment
<p><a name="_Hlk158643946"></a><strong>Data description: Deprivation of loading during early healing of rat Achilles tendons affects extracellular matrix composition and structure, and reduces cell density and cell alignment</strong></p> <p><em>Malin Hammerman, Maria Pierantoni, Hanna Isaksson<sup> *</sup>, Pernilla Eliasson <sup>*</sup></em></p> <p><em><sup>* </sup></em><em>joint<sup> </sup>last authors</em></p> <p>This dataset contains microscope images obtained from sections of healing and intact rat Achilles tendons undergoing different in vivo loading protocols and different time points post-transection. The data presented are the full resolution microscope images available in lower resolution in the accompanying manuscript’s Supplementary Figures 4-6.</p> <p>Each zipped folders contain images (tif-files) from all time-points for each respective staining and loading group. </p> <ul> <li>Col1: Sections stained with Collagen 1 antibodies</li> <li>Col3: Sections stained with Collagen 3 antibodies</li> <li>Elastin: Sections stained with Elastin antibodies</li> <li>Full_loading: Free cage activity</li> <li>Reduced_loading: Paralysis of the calf muscle with Botox</li> <li>Minimal_loading: Botox combined with joint fixation using a steel-orthosis</li> <li>Intact_reference: Contralateral uninjured Achilles tendons, used as reference</li> </ul> <p>More description of the datasets inside the zipped files are available below and in the file 'Data Description.pdf'</p> <p> </p> <p><strong>Brief re-cap of methods</strong></p> <p>Histological analysis was performed on healing Achilles tendons from Female Sprague-Dawley rats, specific-pathogen free (11-12 weeks, weight 299 ± 15 g), that had undergone full transection [13] of the right Achilles tendon, and been exposed to different levels of loading. Altered loading was imposed through two mechanisms. Reduced loading involved intramuscular Botox injections in the right calf muscles to induce plantar flexor muscle paralysis [24]. Additionally, the rats in the minimal loading group received a steel-orthosis around their right hindlimb directly after surgery [24].</p> <p>Snap frozen tendons in OCT were sectioned longitudinally (7 μm thickness) and stained with immunofluorescent staining for collagen 1, collagen 3, or elastin. Sections were counterstained with DAPI followed by mounting. The tissue sections were imaged under a microscope (DMi8, Leica Microsystems, Wetzlar, Germany, with a Hamamatsu Orca LT Flash sCMOS camera) where fluorescence was detected at 550 nm (secondary antibody Alexa Fluor 594), 470 nm (secondary antibody Alexa Fluor 488) and 385 nm (DAPI), and exposure time was held constant for each color channel regarding magnification and staining.</p> <p>Mapping images of the entire tendon were obtained for one section per group (n=1 per healing time, loading group and ECM matrix protein). All images were adjusted to the negative control, where the primary antibody was omitted, to correct for unspecific antibody detection.</p> <p><strong>Microscope images and description of file-names </strong></p> <p>All data is presented in the form of .tif files. Please refer to the scale bars in the images. All image-files are named using the following abbreviations, as described below. As an example, the file name “Tendon_col1_FL_1W_col1.tif” refers to a tendon section stained for collagen 1 from a rat exposed to full loading for a period of 1 week after tendon transection, where only the channel for collagen 1 is shown, whereas “Tendon_col1_FL_1W_merged.tif” includes the channels for both staining for collagen 1 and DAPI of the same section.</p> <p>Col1: Sections stained with Collagen 1 antibodies<br>Col3: Sections stained with Collagen 3 antibodies<br>Elastin: Sections stained with Elastin antibodies<br>dapi: Sections stained with 4',6-Diamidino-2-Phenylindole Dihydrochloride.<br>FL: Full loading (free cage activity),<br>RL: Reduced loading (paralysis of the calf muscle with Botox),<br>ML: Minimal loading (Botox combined with joint fixation using a steel-orthosis)<br>IT: Intact contralateral Achilles tendons, used as reference.</p> <p>1W: Healing time point 1 week after transection<br>2W: Healing time point 2 weeks after transection<br>3W: Healing time point 3 weeks after transection<br>20W: Healing time point 20 weeks after transection</p> <p><strong>Settings for brightness and contrast</strong></p> <p><em>Collagen 1</em><br>1w FL 2000-12 000, UL 4000-10 000, ML 4000-12 000<br>2w FL 2500-10 000, UL 4000-10 000, ML 5000-12 000<br>3w FL 2000-12 000, UL 3500-13 000, ML 3500-14 000<br>12w FL 3000-12 000<br>20w FL 3000-11 000<br>IT 2000-8 000</p> <p>Collagen 3<br>1w FL 3000-12 000, UL 4000-10 000, ML 4000-13 000<br>2w FL 2000 - 7 000, UL 2500-12 000, ML 2000-12 000<br>3w FL 2000-12 000, UL 3500-13 000, ML 3500-14 000<br>12w FL 3000-12 000<br>20w FL 2000-12 000<br>IT 3000-12 000</p> <p>Elastin<br>1w FL 4000-10 000, UL 5000 - 8000, ML 3500-12 000<br>2w FL 3000-12 000, UL 3000-12 000, ML 3000-12 000<br>3w FL 2500-12 000, UL 2000-12 000, ML 2500-12 000,<br>12w FL 3500-12 000<br>20w FL 3500-12 000<br>IT 2000-12 000</p>
scGraph2Vec: a deep generative model for gene embedding augmented by Graph Neural Network and single-cell omics data
<p>This repository contains the training data and source code to reproduce the results of our paper:<br>scGraph2Vec: a deep generative model for gene embedding augmented by Graph Neural Network and single-cell omics data</p> <p>More description can be also found in GitHub (https://github.com/LPH-BIG/scGraph2Vec).</p>
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>
Dataset for a publication: "PLLA honeycombs activated by plasma and high energy excimer laser for stem cell support"
<p>The dataset accompanies the article <em>"PLLA Honeycombs Activated by Plasma and High-Energy Excimer Laser for Stem Cell Support."</em> It is organized into several subfolders, each corresponding to a different analytical method used in the study, with data presented in the manuscript. The main folder is structured as follows:</p> <ol> <li><strong>AFM</strong></li> <li><strong>Contact Angle</strong></li> <li><strong>Zeta Potential</strong></li> <li><strong>SEM</strong></li> <li><strong>EDS</strong></li> <li><strong>XPS</strong></li> <li><strong>Cytocompatibility</strong></li> </ol> <p>Each subfolder contains the relevant data associated with the specific analysis.</p> <p> </p> <p>For more details, please read the <strong>README - Description of data and analysis informations_PS.txt</strong> file.</p> <p> </p> <p><strong> </strong></p> <p><strong>Dataset versions:</strong></p> <p>There are no newer versions so far.</p>
Datasets: Performance Characterization of Lithium-Ion Battery Cells Within Restricted Operating Range Using an Extended Ragone Plot
<h1>Documentation</h1> <p>This repository contains the measurement data presented in <strong>"Performance Characterization of Lithium-Ion Battery Cells Within Restricted Operating Range Using an Extended Ragone Plot."</strong></p> <p>The performance characterization was conducted on three lithium-ion battery cell types, each with two samples. In the publication only datasets from the following cells are included: #1: SCiB-23Ah-01, #2: SLPB8644143-353, #3: M1B-1223-01. The measurements were performed using a Scienlab SL60/300/18BT2C battery test system in combination with a BINDER type MK 720 temperature chamber. Further details about the battery cells, the experimental setup and procedures are available in the publication. An uncertainty analysis for these measurements is included in the supplementary material of the publication.</p> <blockquote> <p><strong>Note:</strong> Please cite the referenced publication when using these datasets in your work.</p> </blockquote> <h2>Datasets Overview</h2> <p><strong>Toshiba SCiB™ 23 Ah (prismatic)<br></strong></p> <ul> <li>SCiB-23Ah-01<br> <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1<br> <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (2.7 V); 2 (2.55 V); 3 (2.4 V); 4 (2.25 V); 5 (2.1 V).</em></li> </ul> </li> </ul> </li> <li>SCiB-23Ah-02 <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (2.7 V); 2 (2.55 V); 3 (2.4 V); 4 (2.25 V); 5 (2.1 V).</em></li> </ul> </li> </ul> </li> </ul> <p><strong>Shenzen Melasta Battery SLPB8644143 (pouch)<br></strong></p> <ul> <li>SLPB8644143-353<br> <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (4.2 V).</em></li> </ul> </li> <li>PowerTemperatureTest_dis-Umax#2 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 2 (4.1 V); 3 (4.0 V); 4 (3.9 V); 5 (3.8 V).</em></li> </ul> </li> </ul> </li> <li>SLPB8644143-45 <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (4.2 V).</em></li> </ul> </li> <li>PowerTemperatureTest_dis-Umax#2 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 2 (4.1 V); 3 (4.0 V); 4 (3.9 V); 5 (3.8 V).</em></li> </ul> </li> </ul> </li> </ul> <p><strong>LithiumWerks (A123) ANR26650m1B (cylindrical)<br></strong></p> <ul> <li>M1B-1223-01<br> <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (3.6 V); 2 (3.45 V); 5 (3.3 V).</em></li> </ul> </li> <li>PowerTemperatureTest_dis-Umax#2<br> <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 3 (3.4 V); 4 (3.35 V).</em></li> </ul> </li> </ul> </li> <li> M1B-1223-02 <ul> <li>PowerTemperatureTest_dis-Umax#2 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 3 (3.4 V); 4 (3.35 V).</em></li> </ul> </li> </ul> </li> </ul> <h2>Usage instructions</h2> <ol> <li>Raw test reports are stored in the <code>test reports</code> directory as <code>*.csv</code> and <code>*.info.txt</code> files. These files contain the following measurement signals: <ul> <li>Time, Test step, ind_T, ind_U, ind_xP, E [J], Eneg [J], Epos [J], I [A], P [W], Q [As], Qneg [As], Qpos [As], T_1 [°C], T_2 [°C], T_3 [°C], T_Clima [°C], U [V]</li> </ul> </li> <li>The test reports are structured and consolidated into an HDF5 file (<code>Scienlab.h5</code>) for efficient storage and analysis. This HDF5 file can be processed using the <strong>HDF5 Data Analysis and Visualization Toolkit</strong>, provided in this repository. <ul> <li>The Python script <code>hdf5_main.py</code> is included for processing and visualizing the datasets.</li> <li>PowerTemperatureTest_dis-Umax datasets contain cyclization data from multiple constant power (CP) discharges and standardized constant current constant voltage (CCCV) charges, with varying end-of-charge voltages.</li> <li>OCVTest datasets contain low-current galvanostatic data required for performance characterization via reconstruction-based approaches. The test protocol is extensively described in the publication.</li> </ul> </li> </ol>
scRNA-seq data for article: Kupffer cell and recruited macrophage heterogeneity orchestrate granuloma maturation and hepatic immunity in visceral leishmaniasis
<p>Single-cell RNA-seq dataset from sorted CD11bInt, F4/80Hi, CD64+ mouse liver cells in naive or Leishmania infantum-infected animals at 42 d.p.i.. Data analyses and results are described in manuscript: "Kupffer cell and recruited macrophage heterogeneity orchestrate granuloma maturation and hepatic immunity in visceral leishmaniasis". Data files are Seurat objects in RDS format. Filtered-out potential doublets, low quality cells and dying cells (excluded cells with <1000 genes detected, cells with >6000 genes detected, cells with mitochondrial gene expression > 10% and cells with <5000 transcript molecules). Data normalization, scaling and integration performed using Seurat.</p> <p>Filtered dataset containing all KCs and macrophages is in the "pessenda_KC_Macro_seurat" file.</p> <p>Our data were then mapped onto a reference dataset published by Remmerie et al. (DOI: 10.1016/j.immuni.2020.08.004) for annotation consistent with the literature. The reference mapped object can be found in the "pessenda_refmap_KC_Macro_seurat" file.</p> <p>Dataset containing the additional analysis of CLEC4F-TIM4+ FACS-sorted KCs can be found in the "pessenda_refmap_KCTimPos_seurat" file.</p>
m6Am landscape of human cell lines
<p>Tables of m6Am profiling results by CROWN-seq. </p> <p>Cell lines in included in this version are:</p> <ol> <li>HEK293T (wild-type, PCIF1 KO, FTO KO)</li> <li>A549</li> <li>HepG2</li> <li>Huh-7</li> <li>CCD841 CoN</li> <li>HT-29</li> <li>HCT-116</li> <li>K562</li> <li>Jurkat E6.1</li> </ol> <p>ReCappable-seq data for HEK293T and A549 (WT and PCIF1 KO) are also included.</p> <p>Description of the table columns can be found in <code>README.md</code>.</p>
Arbitrary and active colouring of solar cells with negligible loss of efficiency
<p>This is all the data associated with the journal article. The dataset is organized on a figure-by-figure basis within a compressed ZIP file for ease of access.</p> <ul> <li><strong>Graph Data</strong>: Available in <code>.txt</code> and <code>.xlsx</code> formats, providing raw and processed data used to generate the figures.</li> <li><strong>Images</strong>: All images included in the article are provided in <code>.jpg</code> format.</li> <li><strong>Figure Graphs</strong>: All complete figure graphs are supplied as <code>.pdf</code> files.</li> </ul>
Additional data: Longitudinal single-cell multiomic atlas of high-risk neuroblastoma reveals chemotherapy-induced tumor microenvironment rewiring
<p>This repository provides additional data for the manuscript titled "Longitudinal single-cell multiomic atlas of high-risk neuroblastoma reveals chemotherapy-induced tumor microenvironment rewiring", currently under revision at Nature Genetics. The primary data cohort has been deposited in the HTAN data portal. This repository includes processed 10x Xenium spatial transcriptomic data for six TH-MYCN mice (three chemotherapy-treated and three treatment-naive) as well as processed scRNA-seq data for CHLA15 and CHLA20 neuroblastoma (NBL) cells. The scRNA-seq data includes mono-cultured, co-cultured cells with THP-1 macrophages, and co-culture cells treated with Afatinib/CRM197. </p>
Untargeted metabolomics analysis of RPE cells during six month in culture
<p>Primary RPE cell cultures were established with human fetal RPE cells acquired from ScienCell (Cat. No, 6540) seeded at passage 3 (P3) in 12-well Transwell<sup>®</sup> inserts (Corning Inc., Cat. No. CLSS3460) coated with 2% v/v Geltrex<sup>®</sup> matrix (Thermo Fisher Scientific; Cat. No. A1413202). Cells were cultured for a total time of 6 months (25 weeks), following the same protocol as previous works (16,59). Samples were acquired for protein immunolocalization, transcriptomic and metabolomics analysis from independent cell cultures along the total 6 months culture time (specifically at 4, 12, 17 and 25 weeks in culture), collecting 3 biological replicates per type of analysis and time point.<em> </em>Cell metabolites were quenched and extracted using MeOH:H<sub>2</sub>O (80:20, -20ºC) containing a spiked solution of isotopically enriched low molecular mass internal standard<strong>. </strong>Quality control (QC) samples were prepared by creating a pool of equal volumes from each biological replicate and were analyzed after a blank solution every fifth sample to monitor the performance, stability, and reproducibility of the LC-MS run. A liquid chromatography (LC) system 1290 Infinity II (Agilent Technologies) was coupled to an Agilent 6560B Ion Mobility quadrupole-time-of-flight mass spectrometer (IM-QTOF-MS) equipped with an Agilent G1607A dual jetstream ESI source and the MassHunter WorkSation 11.0. A reference solution containing purine and hexakis(1H,1H,3H-tetrafluoropropoxy)phosphazene for mass correction was added using the second ESI source. Chromatographic, ion source and MS conditions were optimized using QCs and selected parameters are shown in Supporting Information. MS analysis was conducted with an untargeted approach, operating the instrument in both positive and negative ionization modes. All samples were analysed in a randomized order. Raw data was processed using the software Profinder B10.00 (Agilent) and refined data were exported as CEF files<strong>. </strong>Data were converted to mzml files using MS convert.</p>
Time-lapse 3D confocal microscopy videos of mitochondrial dynamics in human alveolar epithelial cells (A549-DsRed) infected with Mycobacterium marinum (Mmar) strains
<div>The dataset consists of time-lapse, 3D confocal images of mitochondrial dynamics in human alveolar epithelial cells (A549-DsRed) infected with Mycobacterium marinum (Mmar) strains. Images were captured at 60X magnification in an environmental chamber at 35°C for live-cell imaging. Host cell mitochondria were labeled with red fluorescent protein (RFP) and infected with both wildtype (wt) and ESAT-6 operon knockout mutant labeled with green fluorescent protein (GFP) at MOI of 100 for 24 hours at 35°C. Infected cells were identified and analyzed to explore the effect of pathogenic mycobacteria on mitochondrial morphology over time.</div> <div> </div> <div>More details available in this preprint: <a href="https://doi.org/10.48550/arXiv.2411.06035">https://doi.org/10.48550/arXiv.2411.06035</a></div>
Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens
<p>This repo contains Seurat objects, differential expression analysis results, and pathway gene lists for the manuscript "Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens"<br>List of files:</p> <p>1. Seurat_object_IFNB_Perturb_seq.rds: Seurat object of the Perturb-seq data for Interferon-beta pathway<br>2. Seurat_object_IFNG_Perturb_seq.rds: Seurat object of the Perturb-seq data for Interferon-gamma pathway<br>3. Seurat_object_TNFA_Perturb_seq.rds: Seurat object of the Perturb-seq data for TNF-alpha pathway<br>4. Seurat_object_TGFB1_Perturb_seq.rds: Seurat object of the Perturb-seq data for TGF-beta1 pathway<br>5. Seurat_object_INS_Perturb_seq.rds: Seurat object of the Perturb-seq data for insulin pathway<br>6. Pathway_genelist.rds: The pathway gene lists from MultiCCA analysis<br>7. Pathway_Exclusive_genelist.rds: The pathway exclusive gene lists generated from Pathway_genelist.rds<br>8. HClust_Pathway_celltype_specific_genelist.rds: The cell-line specific pathway gene lists from hierarchical clustering analysis independently done on each cell line<br>9. DE_results_all_pathway.zip: The DE test results for all the regulators, cell lines, and pathways (from Mixscale weighted DE test.)<br>10. Bulk_RNAseq_Seurat_object_IFNG_and_TGFB_stim.rds: Seurat object for the bulk RNA-seq data for interferon-gamma and TGF-beta stimulation experiments<br>11. Parse_Guide_Capture_Protocol.pdf: The guide RNA capture protocol developed for Parse Evercode Whole Transcriptome kit</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.