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12,799 results for “Immunization”
Spatial mapping of the hepatocellular carcinoma landscape identifies unique intratumoural perivascular-immune neighbourhoods
<p>The uploaded data includes results from imaging mass cytometry (IMC) data collected from hepatocellular carcinoma patients. The associated publication can be found <a href="https://journals.lww.com/hepcomm/fulltext/2024/11010/spatial_mapping_of_the_hcc_landscape_identifies.11.aspx">here</a> (Marsh-Wakefield <em>et al.</em>, 2024, <em>Hepatology Communications</em>).</p> <p>The CSV file contains segmented cells from IMC data. This includes the Patient, ROI, and Group each cell is assigned. Marker signal intensities underwent arcsine transformation and were rescaled. The “simprof_cluster” column contains the final iteration of clustering following initial X-shift clustering.</p> <p>Notes on additional columns:</p> <ul> <li>“Sample” is barcoded such that the first three digits are the ablation number, followed by the region on the TMA, the group, and the patient. I.e., “[ablation.number]_[TMA.location]_[group]_[patient]”.</li> <li>“x” and “y” refer to the coordinates of samples.</li> <li>“Group” refers to the tissue type. Included non-tumour (NT), invasive margin (IM), and tumour (T) regions.</li> <li>The area for each ROI has been calculated (µm^2 and mm^2).</li> <li>“Batch” refers to TMA.</li> <li>In most cases each area from each patient has three ablation sites. Three samples have an extra ablation site due to technical difficulties during the ablation, and hence have a “split” sample.</li> </ul> <p>The PDF file contains patient information associated with the IMC data.</p> <p>DOI of dataset:</p> <p>10.5281/zenodo.10622397</p> <p>Any further questions can be addressed to Felix Marsh-Wakefield felix.marsh-wakefield@sydney.edu.au</p>
Generative AI in the Advancement of Viral Therapeutics for Predicting and Targeting Immune-Evasive SARS-CoV-2 Mutations
<p>This dataset <strong>encompasses</strong> and describes the following features:</p> <ul> <li>Mutations in viruses like SARS-CoV-2 can make them escape vaccines and treatments.</li> <li>Accurately predicting these mutations is crucial for developing effective countermeasures.</li> <li>The study uses a type of AI called a Generative Adversarial Network (GAN) to analyze the virus's spike protein, which plays a key role in infection.</li> <li>The GAN generates protein sequences similar to natural ones, but which are also likely to evade immune responses.</li> <li>By analyzing these generated sequences, the researchers improve their AI model's ability to predict real-world escape mutations.</li> <li>This improved prediction could help design better vaccines and treatments, and prepare for future viral threats.</li> </ul>
Leeches Baicalobdella torquata feed on hemolymph but have a low effect on the cellular immune response of amphipod Eulimnogammarus verrucosus from Lake Baikal
Open the record for dataset details and reuse information.
A multi-omics systems vaccinology resource to develop and test computational models of immunity: 1st challenge dataset and submissions
<p>The goal of the CMI-PB prediction contest is to foster a collaborative research community that can collectively tackle challenges and accelerates scientific progress beyond the capabilities of individual researchers or groups. The CMI-PB consortium has curated multi-source data from multiple individuals, encompassing Ab titers (around four antibodies/features), cell frequency (approximately 20 cell types/features), gene expression (roughly 50,000 RNA transcripts/features), and plasma proteomics (around 50 proteins/features). The challenge requires integrating these diverse data sources to predict different immune responses or tasks. Specifically, you will utilize multi-source data from several individuals on day 0 (baseline) to predict specific immune responses at later time points (1, 3, 7, and 14 days post-booster vaccination).</p> <p>The first CMI-PB challenge, which is an internal challenge, was conducted using datasets from 2020 (train) and 2021 (test). In the following sections, we provide detailed information on the datasets, challenge tasks, submission format, descriptions, and access to the necessary data files for participants to develop their models and make predictions.</p> <p><br><strong>A) Multiomics CMI-PB dataset:</strong></p> <p>We propose a study design that enables a systems-level understanding of the immune responses through computational modeling. Our cohort comprises aP vs. wP infancy-primed subjects boosted with Tdap. We recruit individuals born before 1995 (wP) and after 1996 (aP), collect baseline plasma and blood samples, and then at 1, 3, 7 and 14 days post booster vaccination.</p> <p>With the obtained samples processed, we generated omics data by:</p> <ul> <li> <p>Bulk PBMCs transcriptomics,</p> </li> <li> <p>Plasma proteomics using Olink, which provides a quantitative readout of cytokines, chemokines, and other immune factors,</p> </li> <li> <p>Cell frequency in PBMCs using flow cytometry,</p> </li> <li> <p>Tdap-specific antibodies levels</p> </li> </ul> <p><strong>B) List of tasks can be accessed using the “List of tasks for challenge 1.docx” file, and submissions need to submit in provided format here: “submission template challenge 1.tsv”</strong></p> <p><strong>C) Datasets for model building and making predictions:</strong></p> <p> Data files are divided into two categories: 1) raw dataset and 2) computable matrices.</p> <ol> <li> <p><strong>Raw dataset: </strong>This raw-most dataset is divided into training and test datasets. </p> </li> <li> <p><strong>Computable matrices: </strong>There are three different types of computable matrices. a) Full: These files are generated by dividing raw files into sub-files specific to planned days specific to vaccination. b) harmonized: These are generated by preserving only overlapping features between train and test datasets. b) imputed: MICE imputation is performed to impute missing values in the dataset.</p> </li> </ol> <p><strong>D) Submission evaluation</strong></p> <p>This folder contains all submitted models with ranking files and code for evaluating these models.</p> <p><strong>To learn more about the CMI-PB prediction challenge, visit our website at www.cmi-pb.org.</strong></p>
Data from: DGKα/ζ inhibition lowers the TCR affinity threshold and potentiates anti-tumor immunity
<p>Checkpoint blockade immunotherapies expand neoantigen- or virus-specific T cells, and poor responsiveness to immunotherapy is associated with lower mutational burden in tumors of non-viral origin. Although mouse models demonstrate that lower affinity T cells recognizing self-antigens can contribute to tumor control if sufficiently activated, therapeutic options for enhancing T cell priming are limited. Diacylglycerol kinases (DGKs) attenuate DAG signaling by converting DAG to phosphatidic acid, thereby suppressing pathways downstream of TCR signaling. Using a novel dual DGK alpha and zeta inhibitor (DGKi), tumor-specific CD8 T cells with different affinities (TRP1high and TRP1low), and a series of altered peptide ligands, we demonstrate that inhibition of DGKα/ζ can lower the signaling threshold for T cell priming. TRP1high and TRP1low CD8 T cells produced more IL-2, IFNγ, and other effector cytokines in the presence of cognate antigen and DGKi. Effector TRP1high- and TRP1low-mediated cytolysis of tumor cells with low antigen load was MHC-restricted, mediated by IFNγ, and augmented by DGKi. Adoptive T cell transfer into mice bearing pancreatic or melanoma tumors synergized with single-agent DGKi or DGKi and αPD1, with increased expansion of low-affinity T cells and increased cytokine production observed in tumor infiltrates of treated mice. Collectively, our findings highlight DGKα/ζ as therapeutic targets for augmenting tumor-specific CD8 T cell function.</p>
Characterization of the tumor-immune microenvironment in hepatocellular carcinoma by highly multiplexed imaging mass cytometry
<p>Imaging mass cytometry data of 54 HCC patients. </p> <ul> <li>DC_img_normalized: Preprocessed and normalized multistack .tiff images. Each stack represents one channel. Channel annotations are stored in the ICICohort_panel.csv file. ROIs are located in the tumor, interface and adjacent liver as indicated in the file name.</li> <li>DC_cellmasks: Masks identifying individual cells on the images.</li> <li>DC_stromamasks: Masks identifying stromal and parenchymal regions on the image.</li> <li>DCCohort_panel.csv: table containing channel information (metal tag and marker).</li> </ul> <p>Patient metadata may be found as supplementary table 2 of DOI <a href="https://doi.org/10.1136/gutjnl-2024-332837" target="_blank" rel="noopener noreferrer"> 10.1136/gutjnl-2024-332837 </a>.</p>
Respiratory Complex I Regulates Dendritic Cell Maturation in Explant Model of Human Tumor Immune Microenvironment
<p>Source data for the paper, "Respiratory Complex I Regulates Dendritic Cell Maturation in Explant Model of Human Tumor Immune Microenvironment."<br><br>This includes nanostring gene expression profiling of primary human tumor material ("fresh" in the data, these samples are taken directly after enzymatic digestion) and the corresponding 3D Patient-Derived Explant Culture (PDEC). <br><br>transfer_257851_files_0a119f4d.zip refers to the mouse spatial transcriptomics data.<br><br>DE_results_Metformin/LPS files refer to differentially expressed genes of human monocyte-derived dendritic cells of 6 individual donors to control untreated dendritic cells after 24hr treatment. <br><br> Also includes the original Seurat.rds file for the scSEQ of primary tumor material (fresh) vs. PDEC<br><br><br><br><br> </p>
Investigating the causal association between immune cell phenotypes and allergic diseases and non-allergic asthma using conventional Two-sample and Bayesian weighted Mendelian randomization
<p>Investigating the causal association between immune cell phenotypes and allergic diseases and non-allergic asthma using conventional Two-sample and Bayesian weighted Mendelian randomization</p>
Lower baseline immunity in invasive Egyptian Goose compared to sympatric native waterfowls
<p>Successful invasive species out-compete native species for vital resources. To increase their spreading success, invasive species need to trade off nutritional and metabolic resources allocated to reproduction and range expansion with other costly body functions. One proposed mechanism for the reallocation of resources is a trade-off with the immune function. According to the evolution of the increased competitive ability hypothesis, a reduced investment in immunity would be favoured by parasite loss in colonised habitats. It is also suggested that invaders would reallocate resources among different immune effectors depending on the new pathogens they encounter in the new habitat. Reallocation of resources may also involve the regulation of oxidative status given its fundamental link with the immune system. Relying on a panel of blood-based markers of immune function and oxidative status quantified in an invasive species (Egyptian Goose) and two native competing species (Mallard and Mute Swan) in Germany, we tested the hypothesis that the invasive species would have a lower investment in immune function compared to the native species. We predicted lower levels of baseline immune markers associated with systemic inflammatory response, and higher levels of certain low-cost immunological effectors (e.g. humoral effectors) in the invasive species compared to the two native species. If Egyptian Geese reduced their investment in immune function, we would also expect that geese generated less oxidative damage and had a lower expression of antioxidant defences than native species. We found lower levels of several immune markers associated either with inflammatory response or humoral innate and adaptive immunity in the invasive species compared to the two native species. The bacteria-killing ability of plasma was the only immune marker that was upregulated in Egyptian Geese. Markers of oxidative status were higher in Mallards compared to the other species. The results of our study point to an overall reduced investment in immune function in the invasive species as a possible energy-saving immunological strategy due to the loss of parasites in the newly colonised habitats, as observed in a previous study. Thus, a lower investment in immune function may benefit other energy-demanding activities, such as reproduction, dispersal, and territoriality.</p>
Raman spectra from "Discrimination of immune cell activation using Raman micro-spectroscopy in an in-vitro & ex-vivo model"
<p>The uploaded files are data from Chaudhary et al, 2021 (Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, Discrimination of immune cell activation using Raman micro-spectroscopy in an in-vitro & ex-vivo model, https://doi.org/10.1016/j.saa.2020.119118).</p> <p>There are two files in .mat format. In one (Preprocessed.mat) the data has been completely pre-processed according to the methods described in the paper.</p> <p>In the second (Unpreprocessed.mat) the data has been calibrated using the methods described in the paper, but has not received further pre-processing.</p> <p>Within both files there are datasets for the spectral measurement from each cell (‘spectra’), together with the treatment which was applied to each sample (‘treatment’) and the wavenumber at which the spectral measurements were made (‘wavenumber’).</p>
Data for the paper titled 'Dynamic Molecular Atlas for Cardiac Fibrosis at Single-Cell and Spatial Resolution: CD248 in Orchestrating Fibroblast-Immune Interaction'
<p>The deposited data were employed to generate the figures concerning single-cell RNA (scRNA) and spatial transcriptomic analyses in the paper titled 'Dynamic Molecular Atlas for Cardiac Fibrosis at Single-Cell and Spatial Resolution: CD248 in Orchestrating Fibroblast-Immune Interaction'.</p>
Autoimmunity-associated allele of tyrosine phosphatase gene PTPN22 enhances anti-viral immunity
<p>The 1858C>T allele of the tyrosine phosphatase <em>PTPN22</em> is present in 5-10% of the North American population and is strongly associated with numerous autoimmune diseases. Although research has been done to define how this allele potentiates autoimmunity, the influence <em>PTPN22</em> and its pro-autoimmune allele have in anti-viral immunity remains poorly defined. Here, we use single-cell RNA-sequencing and functional studies to interrogate the impact of this pro-autoimmune allele on anti-viral immunity during Lymphocytic Choriomeningitis Virus clone 13 (LCMV-cl13) infection. Mice homozygous for this allele (PEP-619WW) clear the LCMV-cl13 virus whereas wildtype (PEP-WT) mice cannot. This is associated with enhanced anti-viral CD4 T cell responses and a more immunostimulatory CD8a<sup>-</sup> cDC phenotype. Adoptive transfer studies demonstrated that PEP-619WW enhanced anti-viral CD4 T cell function through virus-specific CD4 T cell-intrinsic and extrinsic mechanisms. Taken together, our data show that the pro-autoimmune allele of <em>Ptpn22</em> drives a beneficial anti-viral immune response thereby preventing what is normally a chronic virus infection.</p>
AlphaFold Structures for: A wheat tandem kinase activates an NLR to trigger immunity
<p>AlphaFold Predictions used for analysis in "A wheat tandem kinase activates an NLR to trigger immunity".</p>
Regulatory T cell therapy is associated with distinct immune regulatory lymphocytic infiltrates in kidney transplants: Spatial transcriptomic dataset and images
<p>The outputs of the NanoString GeoMx DSP platform were concatenated into three xlsx files, each illustrating a separate experiment along with their sample annotations. This technique analyzes protein or RNA abundance within regions of interest (ROIs) or specific cell segments selected based on histological features and immunofluorescence. In this repository, the concatenated GeoMx output files are presented, along with PowerPoint presentations for each biopsy that show immunofluorescence images of the selected ROIs and/or cell segments.</p> <ul> <li><strong>Protein_Full ROI:</strong> This experiment measured the abundance of 41 proteins in discrete regions of interest (ROIs) within transplant kidney biopsies.</li> <li><strong>Protein_Rare cell:</strong> This experiment measured the abundance of 40 proteins in specific cell segments, such as CD4+FoxP3- cells vs. CD4+FoxP3+ cells, within transplant kidney biopsies.</li> <li><strong>RNA:</strong> This experiment measured the abundance of 90 genes in discrete ROIs within transplant kidney biopsies.</li> </ul>
Card9 and MyD88 differentially regulate Th17 immunity to the commensal yeast Malassezia in the murine skin
<p><span>Raw data associated with Tuor et al., Mucosal Immunology, 2024. doi: 10.1016/j.mucimm/2024.11.004 (Figures 5 - 6 and associatd supplementary Figures S7 - S8)</span></p>
Card9 and MyD88 differentially regulate Th17 immunity to the commensal yeast Malassezia in the murine skin
<p>Raw data associated with Tuor et al., Mucosal Immunology, 2024. doi: 10.1016/j.mucimm/2024.11.004 (Figures 1 to 4 and associatd supplementary Figures S1 - S6)</p>
Immune repertoires of de-identified TCGA tumor samples assembled by TRUST4
<p>We applied TRUST4 to assemble the immune repertoire data from TCGA tumor samples. Because TCGA has restricted access permission, the sample IDs are de-identified and the sequence is at the amino acid level. The data is used in the study of "Comprehensive characterizations of immune receptor repertoire in tumors and cancer immunotherapy studies". The format is:</p> <p>CancerType_RandomID Chain_Type CDR3_AminoAcid VGene JGene ConstantGene Abundance</p> <p>(Note that the deidentified RandomID is different from the previous version (version 1))</p>
Dataset used in "Helper NLR immune protein NRC3 evolved to evade inhibition by a cyst nematode virulence effector"
<p><strong>[Figs 1 and S2]</strong></p> <p> </p> <p><strong>00_cloned_NRC123.fasta</strong></p> <p> </p> <p>FASTA file containing NRC1, NRC2 and NRC3 sequences tested in HR cell death assay.</p> <p> </p> <p><strong>01_NRCX0123_4species.fasta</strong></p> <p> </p> <p>FASTA file containing NRC0, NRC1, NRC2, NRC3 and NRCX of <em>N. benthamiana</em>, <em>C. annuum</em> (pepper), <em>S. tuberosum</em> (potato) and <em>S. lycopersicum</em> (tomato). In addition to a previously published dataset (Selvaraj et al., 2023), we included the NbNRC2, CaNRC3 and StNRC3 sequences from 00_cloned_NRC123.fasta.</p> <p> </p> <p><strong>02_NRCX0123_4species.local_aln.fasta</strong></p> <p> </p> <p>FASTA file containing the protein sequence alignment of 01_NRCX0123_4species.fasta. We used MAFFT for the alignment (Katoh & Standley, 2013).</p> <p> </p> <p><strong>03_NRCX0123_4species.local_aln.clip.fasta</strong></p> <p> </p> <p>FASTA file containing the trimmed protein sequence alignment of 02_NRCX0123_4species.local_aln.fasta. We used ClipKIT for trimming (Steenwyk et al., 2020).</p> <p> </p> <p><strong>04_NRCX0123_4species.local_aln.clip.fasta.treefile</strong></p> <p><strong> </strong></p> <p>Newick file containing the phylogenetic tree reconstructed based on 03_NRCX0123_4species.local_aln.clip.fasta. We used IQ-TREE to create a phylogenetic tree (Minh et al., 2020).</p> <p> </p> <p><strong>[Fig 2B]</strong></p> <p><strong> </strong></p> <p><strong>05_cloned_NRC123.local_aln.fasta</strong></p> <p><strong> </strong></p> <p>FASTA file containing the protein sequence alignment of 00_cloned_NRC123.fasta. We used MAFFT for the alignment (Katoh & Standley, 2013).</p> <p> </p> <p><strong>[Fig 5 and Table S1]</strong></p> <p> </p> <p><strong>06_NRCH_cds_23-06-20.min2400max2800.fasta</strong></p> <p><strong> </strong></p> <p>FASTA file containing the nucleotide sequences of helper NRC sequences from 124 Solanaceae genomes (Sugihara et al., 2023; Huang et al., 2023). We filtered out sequences shorter than 2,400 or longer than 2,800 bases, resulting in 1,748 sequences.</p> <p> </p> <p><strong>07_NRCH_cds_23-06-20.min2400max2800.aa.fasta</strong></p> <p> </p> <p>FASTA file of the amino acid sequences translated from 06_NRCH_cds_23-06-20.min2400max2800.fasta.</p> <p> </p> <p><strong>08_NRCH_cds_23-06-20.min2400max2800.aa.NBARC.fasta</strong></p> <p> </p> <p>FASTA file containing the amino acid sequences of NB-ARC module corresponding to the sequences in 07_NRCH_cds_23-06-20.min2400max2800.aa.fasta.</p> <p> </p> <p><strong>09_NRCH_cds_23-06-20.min2400max2800.aa.NBARC.local_aln.clip.fasta</strong></p> <p> </p> <p>FASTA file containing the trimmed protein sequence alignment of 02_NRCX0123_4species.local_aln.fasta. We used MAFFT and ClipKIT for the alignment and trimming, respectively (Katoh & Standley, 2013; Steenwyk et al., 2020).</p> <p> </p> <p><strong>10_NRCH_cds_23-06-20.min2400max2800.aa.NBARC.local_aln.clip.fasta.treefile</strong></p> <p> </p> <p>Newick file containing the phylogenetic tree reconstructed based on 09_NRCH_cds_23-06-20.min2400max2800.aa.NBARC.local_aln.clip.fasta. We used IQ-TREE to create a phylogenetic tree (Minh et al., 2020).</p> <p> </p> <p><strong>11_NRCX123_cds_23-06-20.min2400max2800.fasta</strong></p> <p> </p> <p>FASTA file containing the the nucleotide sequences of NRC1/2/3X clades identified based on 10_NRCH_cds_23-06-20.min2400max2800.aa.NBARC.local_aln.clip.fasta.treefile.</p> <p> </p> <p><strong>12_NRCX123_nt_codon_ancseq_v1.2.1.zip</strong></p> <p> </p> <p>Results of ancestral sequence reconstruction. We used ancseq to perform ancestral sequence reconsturction (Sugihara, 2024). "NRCX123_cds_23-06-20.min2400max2800.nt_codon.local_aln.manual.clip.uniq.rm_4sp.fasta" is an input alignment and "NRCX123_cds_23-06-20.min2400max2800.nt_codon.local_aln.manual.clip.uniq.rm_4sp.fasta.treefile" is a tree file. Regarding the output files for ancseq, please refer to the <a href="https://github.com/YuSugihara/ancseq?tab=readme-ov-file#outputs">GitHub repository</a>.</p> <p> </p> <p><strong>[Fig S7]</strong></p> <p> </p> <p><strong>13_logo_plot.zip</strong></p> <p> </p> <p>Sequence alignments and script used in Fig S7. To generate the consensus sequence shown in Fig S7, we concatenated interfaces 1, 2 and 3 with SS15 and visualized the results using logomaker (Tareen and Kinney, 2020).</p> <p> </p> <p><strong>References</strong></p> <p> </p> <p>Huang C-Y, Huang Y-S, Sugihara Y, Wang H-Y, Huang L-T, Lopez-Agudelo JC, Chen Y-F, Lin K-Y, Chiang B-J, Toghani A, Kourelis J, Derevnina L, Wu C-H. 2023. Functional divergence shaped the network architecture of plant immune receptors. <em>bioRxiv</em>. 2023:2023.12.12.571219. DOI: 10.1101/2023.12.12.571219.</p> <p>Katoh K, Standley DM. 2013. MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability. <em>Molecular Biology and Evolution</em> 30:772–780. DOI: 10.1093/molbev/mst010.</p> <p>Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD, von Haeseler A, Lanfear R. 2020. IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era. <em>Molecular Biology and Evolution</em> 37:1530–1534. DOI: 10.1093/molbev/msaa015.</p> <p>Selvaraj M, Toghani A, Pai H, Sugihara Y, Kourelis J, Yuen ELH, Ibrahim T, Zhao H, Xie R, Maqbool A, Concepcion JCD la, Banfield MJ, Derevnina L, Petre B, Lawson DM, Bozkurt TO, Wu C-H, Kamoun S, Contreras MP. 2023. Activation of plant immunity through conversion of a helper NLR homodimer into a resistosome. <em>bioRxiv</em>. 2023:2023.12.17.572070. DOI: 10.1101/2023.12.17.572070.</p> <p>Steenwyk JL, Iii TJB, Li Y, Shen X-X, Rokas A. 2020. ClipKIT: A multiple sequence alignment trimming software for accurate phylogenomic inference. <em>PLOS Biology</em> 18:e3001007. DOI: 10.1371/journal.pbio.3001007.</p> <p>Sugihara Y. 2024. YuSugihara/ancseq: v1.2.1. <em>Zenodo</em>. DOI: 10.5281/zenodo.10808871.</p> <p>Sugihara Y, Toghani A, Kamoun S, Kourelis J. 2023. NLRome dataset from 124 genomes of plants in the Solanaceae family. <em>Zenodo</em>. DOI: 10.5281/zenodo.10354350.</p> <p>Tareen A, Kinney JB. 2020. Logomaker: beautiful sequence logos in Python. Bioinformatics 36:2272–2274. doi:10.1093/bioinformatics/btz921</p> <p> </p>
Scupa: Single-cell unified polarization assessment of immune cells using the single-cell foundation model
<p>This dataset contains the processed scRNA-seq data and code for single-cell unified polarization assessment. Please refer to the article "Scupa: Single-cell unified polarization assessment of immune cells using the single-cell foundation model" for the detailed data and method description.</p> <p>*.rds: the processed scRNA-seq datasets with Universal Cell Embeddings saved as a Seurat object. The Universal Cell Embeddings are saved in the assay 'uce'.</p> <ul> <li>immune_dict_uce.rds: Immune Dictionary.</li> <li>ifnb_treatment_uce.rds: human PBMCs treated with IFN-beta.</li> <li>macrophage_stimulation_uce.rds: human macrophages treated with one or two of IFN-beta, IFN-gamma, TNF-alpha and IL-4.</li> <li>il2_treatment_uce.rds: mouse spleen CD8+ T cells treated with IL-2 or anti-PD-L1.</li> <li>pan_cancer_myeloid_uce.rds: tumor-infiltrating myeloid cells across seven cancer types.</li> </ul> <p>notebooks.zip: Jupyter notebooks containing code for training models and applications to multiple datasets.</p>
A data repository for the study of Alpha-synuclein aggregates trigger anti-viral immune pathways and RNA editing in human astrocytes
<p><span>This repository contains data associated with the study:</span></p> <p><span><strong>"Alpha-synuclein Aggregates Trigger Anti-Viral Immune Pathways and RNA Editing in Human Astrocytes"</strong></span></p> <p><span>Published as a <strong>bioRxiv preprint</strong>: <a href="https://doi.org/10.1101/2024.02.26.582055"><span>DOI: 10.1101/2024.02.26.582055</span></a></span></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.