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2,952 results for “CD8”
An mRNA-encoded, long-lasting Interleukin-2 restores CD8+ T cell neoantigen immunity in MHC class I-deficient cancers
<ul> <li><strong><span>Cd45Summit-IntgrAllgroups_finalUMAP_simplified_colorordered.RData </span></strong><span>contains all final result of the workflow (data integration and annotation) without subsetting the cell types into the different treatment groups.</span></li> <li><strong><span>Cd45Summit-IntgrAllgroups_finalUMAP_Only_CD8Tcells_subsetted.RData </span></strong><span>contains combined dataset of all present CD8+ T cell types (final UMAP, all annotated), which are subsetted into the four treatment groups.</span></li> <li><strong><span>Cd45Summit-IntgrAllgroups_finalUMAP_Only_ProlifMacros_subsetted.RData </span></strong><span>contains only the proliferating CD8+ T cells, which are subsetted into the four different treatment groups.</span></li> <li><strong><span>Cd45Summit-IntgrAllgroups_forJan_Macors_Gr1to4.RData </span></strong><span>contains only the combined macrophages of the dataset, subsetted into the four treatment groups.</span></li> </ul>
Blood memory CD8 T cell phenotypes in lung cancer patients predict immune checkpoint treatment responses
<p>Rscript for figure generation and data analysis:</p> <p>GenerateFigures.R</p> <p> </p> <p>Seurat objects containing processed data after quality control:</p> <p><a href="../api/records/10867209/draft/files/NCCS_For_Zenodo.RDS/content" target="_blank" rel="noopener noreferrer">NCCS_For_Zenodo.RDS</a> - NCCS discovery cohort.</p> <p><a href="../api/records/10867209/draft/files/Pavia_For_Zenodo.RDS/content" target="_blank" rel="noopener noreferrer">Pavia_For_Zenodo.RDS</a> - Pavia validation cohort.</p> <p> </p> <p>RDS files containing DEGs or differentially abundant surface markers:</p> <p>TestResults2Groups.rds - Cell type specific LTR vs Non Responder DEG </p> <p>TestResults2GroupsADT.rds - Cell type specific LTR vs Non Responder differential surface markers</p> <p>TestResults2GroupsLungOnly.rds - Cell type specific LTR vs Non Responder DEG on lung samples only</p> <p>TestResults2GroupsLungOnlyADT.rds - Cell type specific LTR vs Non Responder differential surface markers on lung samples only</p> <p>TestResults3Groups.rds - Cell type specific LTR vs R vs Non Responder differential DEG</p> <p>TestResults3GroupsGeneralADT.rds - Across cell type LTR vs R vs Non Responder differential surface markers</p> <p>TestResults2GroupsGeneralRNA.rds - Across cell type LTR vs Non Responder DEG </p> <p>TestResults2GroupsGeneralADT.rds - Across cell type LTR vs Non Responder differential surface markers</p> <p>TestResults2GroupsLungOnlyGeneralRNA.rds - Across cell type LTR vs Non Responder DEG on lung samples only</p> <p>TestResults2GroupsLungOnlyGeneralADT.rds - Across cell type LTR vs Non Responder differential surface markers on lung samples only</p> <p>TestResults3GroupsGeneralRNA.rds - Across cell type LTR vs R vs Non Responder differential DEG</p> <p>TestResults3GroupsGeneralADT.rds - Across cell type LTR vs R vs Non Responder differential surface markers</p> <p> </p> <p>Logistic regression models trained on the NCCS discovery cohort:</p> <p>PerCellPredictions <CellType> * - Celltype specific models predicting either LTR, R or control group trained on all NCCS samples</p> <p>PerCellPredictions_2Groups_LungOnly <CellType> * - Celltype specific models predicting either LTR or NonResponder group, trained on lung samples only.</p> <p>PerCellPredictions_2Groups_<CellType> * - Celltype specific models predicting either LTR or NonResponder trained on all NCCS samples</p>
Signature of long-lived memory CD8+ T cells in acute SARS-CoV-2 infection
<p>The datasets uploaded in this Zenodo entry were generated in the single cell RNA sequencing part of the project.</p> <p>For the scRNAseq analysis, cells from ten patients and the same time point were pooled together, generating four individual sample sets in total: (1) patients CoV2_T001- CoV2_T010, acute; (2) patients CoV2_T001- CoV2_T010, six months post-infection; (3) patients CoV2_T011- CoV2_T020, acute; (4) patients CoV2_T011- CoV2_T011-20, six months post-infection.</p> <p>We additionally generated two more sample sets: using 5000 unsorted PBMCs from each patient’s sample: (5) patients CoV2_T001- CoV2_T010, six months post-infection unsorted; (6) patients CoV2_T011- CoV2_T020, six months post-infection unsorted. The cells in these two sample sets were hashed.</p> <p>Finally, using PBMCs from four healthy donors, we generated sample set (7) by sorting and pooling 2000 CD8+ T cells from each healthy donor sample.</p> <p>We are here providing the pre-processed sets for each sample set (1-7), i.e. :<br> - "filtered feature bc matrix" files, as output from the ‘cellranger multi’ pipeline (Cell Ranger version 5.0.0), containing cell-RNA count matrices and cell-ADT matrices. ADTs comprise counts for TotalSeq antibodies and dCODE Dextramers.<br> - "filtered_contig_annotations.csv" files, as output from the ‘cellranger multi’ pipeline (Cell Ranger version 5.0.0), containing High-level annotations of each high-confidence, cellular contig for TCR clonal analysis. This file is not present for sets 5 and 6, because we did not perform TCR profiling for these samples.<br> - "clusters.tsv" files, as output from the souporcell SNP analysis (version 2). To cluster cells based on their patient specific genetic variants, we merged sample sets 1, 2 and 5 (comprising sorted cells from both time points of patients CoV2_T001- CoV2_T010 and unsorted cells of the same patients) and sets 3, 4 and 6 (comprising cells from both time points of patients CoV2_T011- CoV2_T020 and unsorted cells of the same patients). Then, we executed the souporcell pipeline with option <em>k=10 </em>(number of clusters to be determined) for each of the two merged sample sets.</p> <p>Together, these files allow to reproduce the analysis as reported in the paper.</p> <p>Additionally, we provide the Seurat Objects "Integrated.h5seurat" and "Integrated_NA_filtered.h5seurat" which can be used to skip the pre-processing steps of the data analysis. See the code provided on https://github.com/Moors-Code/SARS-CoV-2-Tcell-Boyman-collaboration for details.</p> <p> </p>
TCRb sequencing of CD4+ and CD8+ T cells from hematological patients (part 2)
<p>This dataset contains TCRb sequencing of 177 samples from patients with aplastic anemia, myelodysplastic syndrome, immune thrombocytopenia, immunodeficiency or graft-versus-host disease and healthy controls. Samples are separated CD4+ or CD8+ cells from peripheral blood or bone marrow. The data has been produced with immunoSEQ platform (Adaptive Biotechnologies). The details regarding sample processing, sequencing and metadata can be found from the publication <em>Somatic mutations associate with clonal expansion of CD8+ T cells</em> (Lundgren et al, Science Advances, in press).</p> <p>The dataset is divided in 2 parts containing 91 (part 1) and 86 (part 2) files. Data is in immunoSEQ format (v2).</p>
scTCR-seq analysis of CD8+ T cells
<p><span>To investigate whether a higher ratio of KC-derived LMAMs could change T cell population, we conducted single-cell TCR sequencing combined with scRNA-seq on CD8<sup>+</sup> T cells infiltrating liver metastases from <em>Ccr2<sup>GFP/WT</sup></em> background or <em>Ccr2<sup>GFP/GFP</sup></em> background. </span></p>
Supplemental Figures S1-S8: Direct CD137 costimulation of CD8 T cells promotes retention and innate-like function within nascent atherogenic foci
<p>Supplemental Figures and Table S1-S8 for "Direct CD137 costimulation of CD8 T cells promotes retention and innate-like function within nascent atherogenic foci" H-00088-2019, to the American Journal of Physiology-Heart and Circulatory Physiology.</p>
Activation markers plotted against nutrient sensors in CD4+ and CD8+ T-cells.
<p>Activation markers (CD69, CD25, CD71, and CD38) plotted against nutrient sensors (GLUT1, GLUT4, HK1, HK2, and CD36) in CD4+ T-cells.</p>
IL-2 inside tumor microenvironment is essential and sufficient to reinvigorate CD8+ T cells
<p>The single-cell RNA seq data of day 10 and day 20. </p>
CD4+ and CD8+ T cell responses to peptides covering SARS-CoV-2 Spike in response to mRNA vaccination in persons recovered from SARS-CoV-2 infection
<p>These files contain intracellular cytokine staining flow cytometry data for CD4+ and CD8+ T cells after exposure of serial PBMC to SARS-CoV-2 spike peptides or control antigens. PBMC are from subjects recovered from SARS-CoV-2 infection that subsequently received mRNA vaccination. The data were analyzed and exported from FlowJo version 10 as individual gated events and related Boolean subsets for four functional markers: CD40L, IFN-g, IL-2, and TNF-a. Counts and frequencies are both included.</p> <p> </p> <p><strong>Procedure for obtaining data:</strong></p> <p>Cryopreserved peripheral blood mononuclear cells (PBMC) were thawed and rested overnight. PBMC (1 x 10<sup>6</sup> per well) were stimulated with a SARS-CoV-2 spike overlapping peptide pool (JPT, 1 μg/mL each peptide; 0.4% final DMSO concentration), 0.4% DMSO as a negative control, or PHA-P (Remel; 1.6 μg/mL final concentration) as a positive control, in the presence of anti-CD28 and anti-CD49d (BD Biosciences) antibodies at 37°C for 6 hours. Brefeldin A (Sigma) was added after 2 hours. Cells were stained with Live/Dead Near IR dye (Invitrogen), treated with FACS lyse (BD Biosciences) and frozen at -80<sup>o</sup>C. For staining, the cells were thawed, washed, and permeabilized with Permeabilizing solution 2 (BD Biosciences) then stained with fluorochrome labeled monoclonal antibodies to anti-human CD3 mAb (clone UCHT) conjugated with PE-Texas Red (ECD; Beckman Coulter), anti-human CD4 mAb (clone SK3) conjugated with BV510 (Biolegend), anti-human CD8 mAb (clone SK1) conjugated with PerCP-Cy5.5 (BD Biosciences), and the activation markers anti-human CD40L mAb (clone TRAP1) conjugated with BV421 (BD Biosciences), anti-human IFNγ mAb (clone 4S.B3) conjugated with PE (BD Biosciences), anti-human IL2 mAb (clone MQ1-17H12) conjugated with APC (BD Biosciences), and anti-human TNFα mAb (clone MAb11) conjugated with FITC (BD Biosciences). Events were recorded with BD Fortessa and analyzed with FlowJo (v10 for Mac; BD). </p>
Atlas of virus specific CD8+ Tcells
<p>Code and Data accompanying the manuscript " "</p> <p><strong>TS logistic regression model:</strong></p> <ul> <li>TargetScape_Model_FCS_Input.zip contains the TS data in pickled format to be imported in python</li> <li>targetscape-model-paper-20221001.html contains the python code to perform data analysis, model training, testing and interpretation</li> </ul> <p><strong>TAP logistic regression model:</strong></p> <ul> <li>Generate_Pseudobulk_Markers_ADT_TAP_Cohort.R to generate candidate features by pseudo-bulking surface markers across donors.</li> <li>TAP_ParameterBenchmarking_Model_Training_Evaluation.R to perform model training and parameter benchmark.</li> <li>TAP_DiscoveryCohort_Epitopes.rds file containing a Seurat object of the TAP-Cohort.</li> <li>Cohort_Pseudobulk_ADT_Virus_markers.RDS RDS file containing the candidate markers and their fold-changes/p-values, generated by<em> Generate_Pseudobulk_Markers_ADT_TAP_Cohort.R</em></li> <li><a href="https://zenodo.org/api/files/e8452b8d-5749-4f08-be29-b9c0922a5727/Leave-one-epitope-out-TAP.R">Leave-one-epitope-out-TAP.R</a>: R script to train a model using one epitope as a validation set.</li> </ul> <p><strong>Figures related to Machine Learning approaches:</strong></p> <ul> <li>Model_Analysis_Figure_Table_Generation.R script to generate Figures 3 and Figure 4 of the main manuscript as well as corresponding Supplementary Figures</li> <li>TargetScapeModel_Feature_Importance.csv contains the regression coefficients of the model learned on TargetScape (TS) data.</li> <li>TargetScape_Cohort_Logicle.csv contains the actual data of the TS cohort after logicle transformation.</li> <li>TargetScape_Validation_Cohort_Logicle.csv contains the actual data of the TS validation cohort after logicle transformation</li> <li>TargetScape_Model_Predictions.csv contains predictions of the TS model made on the TS validation cohort</li> <li>Performance_Benchmarking_TAP.zip contains all parameter benchmarking performance files generated by the TAP model above while executing TAP_ParameterBenchmarking_Model_Training_Evaluation<em>.R.</em></li> <li>Performance_Selected_Model.txt contains model performance information on the selected TAP model</li> <li>TAP_Model_MultinomialElaNetLogRegModel_minCellsPerPatient_30_minCellsPerVirus_200_ResourcePaper.RDS contains the best model trained on TAP data.</li> <li>TAP-Validation.RDS contains the Seurat object of the TAP validation cohort.</li> </ul>
Injection of CD4 and CD8 + T Cells Anti-Cytomegalovirus (CMV) or Anti-adenovirus
ClinicalTrials.gov study NCT01325636. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Is There an Association Between Innate CD8+ T Cells and the Evolution of Tyrosine Kinase Inhibitor Resistance Mutations in Phi+ Hematological Malignancies.
ClinicalTrials.gov study NCT04965649. IPD Sharing: Not stated. Countries: 1. Publications: 3.
CD8 Reactivity to Microorganisms in Blood and Breast Milk
ClinicalTrials.gov study NCT03084614. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Randomized Clinical Trial, Open, Multicenter Parallel, no Suspension Inferiority Prophylactic Treatment With Valganciclovir in Kidney Transplant CMV-seropositive Cellular Immunity to Develop CD8 + CMV
ClinicalTrials.gov study NCT03123627. IPD Sharing: YES. Countries: 1. Publications: 1.
ImmunoPET With an Anti-CD8 Imaging Agent
ClinicalTrials.gov study NCT04029181. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Identification and Characterization of SARS-CoV-2 Specific CD8 T Cells in Humans
ClinicalTrials.gov study NCT04565067. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Clinical Safty and Efficacy Study of Infusion of iNKT Cells and CD8+T Cells in Patients With Advanced Solid Tumor
ClinicalTrials.gov study NCT03093688. IPD Sharing: NO. Countries: 1. Publications: 2.
A PET Imaging Agent to Assess the Level of Tumor Tissue-infiltrating CD8 + T Cells in Patients With Solid Tumors
ClinicalTrials.gov study NCT05126927. IPD Sharing: NO. Countries: 1. Publications: 2.
A Multi-center, Single-arm, Open, Phase I/IIa Clinical Trial to Evaluate the Efficacy and Safety of EBViNT Cell (EBV Specific Autologous CD8+ T Cell) in Patients With Treatment Failed Epstein Barr Vir
ClinicalTrials.gov study NCT03789617. IPD Sharing: NO. Countries: 1. Publications: 2.
Study of the T CD8 Immune Response in Horton's Disease
ClinicalTrials.gov study NCT02158208. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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OpenNeuro
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