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2,285 results for “T-cells”
T-cell And General Immune Response to Seasonal Influenza Vaccine (SLVP018) - Year 1, 2009
ClinicalTrials.gov study NCT01987349. IPD Sharing: NO. Countries: 1. Publications: 3.
A Phase I Trial of Anti-GD2 T-cells (1RG-CART)
ClinicalTrials.gov study NCT02761915. IPD Sharing: NO. Countries: 1. Publications: 2.
Liposomal Doxorubicin Followed By Bexarotene in Treating Patients With Cutaneous T-Cell Lymphoma
ClinicalTrials.gov study NCT00255801. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Spectrum of mutational signatures in T-cell lymphoma reveals a key role for UV radiation in mycosis fungoides and Sezary syndrome
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Data from: Gastrointestinal gd T cells reveal upregulated T-cell transcripts and signaling pathways during peanut oral immunotherapy
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Simulated T-cells Rep-Seq datasets (called SIMTCR) from "Reconstructing antibody repertoires from error-prone immunosequencing datasets" paper
<p>Simulated test datasets used for benchmarking of immunorepertoire construction tools</p>
Multi-Omic Single-Cell Dissection of Leukemic T-Cell Lymphoma Following CAR T-Cell Therapy
<p>This repository contains code used to produce the results in: Till Braun, Michael Rade and Maximilian Merz et al., Multi-Omic Single-Cell Dissection of Leukemic T-Cell Lymphoma Following CAR T-Cell Therapy</p> <p>Contents:<br>- Instructions for using the singularity image and R packages can be found here: <a href="https://github.com/fraunhofer-izi/Braun_et_al_2024/tree/main/singularity">https://github.com/fraunhofer-izi/Braun_et_al_2024/tree/main/singularity</a><br>- "seurat_harmony.Rds" and "seurat_harmony_t.Rds": These Seurat objects were used to produce Figure 2 in this publication. "seurat_tcell_obj.Rds" is a subset of "seurat_harmony.Rds" and contains only T-cells. In addition, the metadata was extended by the output of TCR-Seq (using the R package scRepertoire). The following script uses the objects: <a href="https://github.com/fraunhofer-izi/Braun_et_al_2024/blob/main/publication/figure_scripts/main/fig_02.R">https://github.com/fraunhofer-izi/Braun_et_al_2024/blob/main/publication/figure_scripts/main/fig_02.R</a></p> <p> </p>
Robust detection of SARS-CoV-2 exposure in population using T-cell repertoire profiling
<p>The dataset contains processed T-cell receptor repertoire sequencing data from >1200 individuals of different sex and age. Note that only samples with good sequencing coverage are published (>10^5 reads per file). </p> <p>The main aim of our study is to find TCR sequence biomarkers and develop a bioinformatic pipeline that allows building an accurate and robust classifier that distinguishes COVID-19-convalescent donors from unexposed individuals. We performed immunosequencing of the rearranged TCR α and β regions for PBMCs. For the cohort described in this study (Cohort-I) we sequence both chains of the TCR heterodimer as both of these chains are required to properly predict antigen recognition26. We ran conventional T-cell repertoire data analysis and pre-processed data to remove low-coverage samples. </p> <p>Of samples in Cohort-I which passed read count threshold, 383/377 TCR α/β samples were from healthy donors (SARS-CoV-2 PCR test negative or obtained prior to pandemic) and 890/848 were from COVID-19-positive patients. The majority of samples were accompanied by information on HLA class I and II alleles. Samples were prepared and sequenced in nine batches.</p> <p>The metadata for both TCR alpha and beta repertoires contains the following information:</p> <div> <ul> <li>sequencing_date - date when seguencing was performed</li> <li>batch_name - one of the 9 unique batch identifiers</li> <li>sample_id, patient_id - information on sample identifier and donor identifier</li> <li>COVID_status, COVID_IgG, COVID_IgM, COVID_PCR - information on COVID-19 status</li> <li>HLA-A.1, HLA-A.2, HLA-B.1, HLA-B.2, HLA-C.1, HLA-C.2 - MHC class I alleles</li> <li>HLA-DPB1.1, HLA-DPB1.2, HLA-DQB1.1, HLA-DQB1.2, HLA-DRB1.1, HLA-DRB1.2 - MHC class II alleles</li> <li>file_name - name of the corresponding file in <em>fmba_clonotype_usage_tables.zip </em>archive</li> </ul> </div> <p>Each file in <em>fmba_clonotype_usage_tables.zip </em>archive stores the information on either TCR alpha or beta repertoire. Each line in a file corresponds to the unique clonotype and each clonotype is accompanied with the following information:</p> <ul> <li>count - number of reads where the clonotype was detected</li> <li>freq - count of reads with the clonotype divided by thw whole number of reads in a sample</li> <li>cdr3nt, cdr3aa - nucleotide and amino acid sequences of TCR's CDR3 sequence</li> <li>v, d, j - the V/D/J segment name which was used for the clonotype's rearrangement</li> <li>VEnd, DStart, DEnd, JStart - information on VDJ junction positions </li> </ul> <p>We proceed with selecting a set of CDR3 sequences that can serve as biomarkers and form a feature list for COVID-19 status classifier. We also validate the resulting set of clonotypes in several ways. Co-occurence of specific TCR α and β clonotypes can serve as an independent validation for biomarkers and their co-association with some specific pathogen. Additional information on donor HLAs is provided to filter the set of biomarkers based on HLA restriction: association with donor HLA serves as an additional evidence for TCR specificity to a specific set of antigens presented in a given donor and allows detecting the fingerprint of past and present infection. Furthermore, clonotypes with similar sequences can be aggregated into 'metaclonotype' biomarkers based on clonotype graph analysis.</p> <p>Finally, we train various COVID-19 status classifiers on selected batches from Cohort-I data using different algorithms and incorporating different feature sets. Verification of the robustness of our results was performed using independent batches of the Cohort-I and data from Cohort-II published previously.</p>
Timing of blood sample processing affects the transcriptomic and epigenomic profiles in CD4+ T-cells of atopic subjects
<p><span>Optimal</span><span> pre-analytical conditions for blood sample processing and isolation of selected cell populations for subsequent transcriptomic and epigenomic studies are required to obtain robust and reproducible results. This pilot study was conducted to investigate the potential effects of timing of CD4<sup>+</sup> T-cell processing from peripheral blood of atopic and non-atopic adults on their transcriptomic and epigenetic profiles. Two heparinized blood samples were drawn from each of three atopic and three healthy individuals. For each individual, </span><span>CD4<sup>+</sup></span><span> T-cells were isolated from the first blood sample within 2 hours (immediate) or from the second blood sample after 24 hours storage (delayed). RNA sequencing (RNA-Seq) and histone H3K27 acetylation chromatin immunoprecipitation sequencing (ChIP-Seq) analyses were performed. A multiplicity of genes was shown to be differentially expressed in immediately processed </span><span>CD4<sup>+</sup></span><span> T-cells from atopic versus healthy subjects. These differences disappeared when comparing delayed processed cells due to a drastic change in expression levels of atopy-related genes in delayed processed </span><span>CD4<sup>+</sup></span><span> T-cells from atopic donors. This finding was further validated on the epigenomic level by examining H3K27 acetylation profiles. In contrast, transcriptomic and epigenomic profiles of blood </span><span>CD4<sup>+</sup></span><span> T-cells of healthy donors remained rather unaffected. Taken together, for successful transcriptomics and epigenomics studies, detailed standard operating procedures developed on the basis of samples from both healthy and disease conditions are implicitly recommended.</span></p>
TCRBuilder2+ predictions of the Observed T-cell receptor Space (OTS)
<p>TCRBuilder2+ predictions of over 1.5 million TCR structures from the Observed T-cell receptor Space database. </p>
Summary Statistics for Genetic determinants and phenotypic consequences of blood T-cell proportions in 207,000 diverse individuals
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Supplemental Data: Differential roles of kinetic on- and off-rates in T-cell receptor signal integration revealed with a modified Fab'-DNA ligand
<p>Microscopy data and associated code for analysis. For more information refer to https://doi.org/10.1101/2024.04.01.587588.</p>
code and datasets for "GP73 reinforces cytotoxic T-cell function by regulating HIF-1α and increasing antitumor efficacy"
<p>We utilized T-cell-specific GP73 knockout mice to establish MC38 and B16 tumor models to investigate the impact of GP73-deficient T cells on tumor growth. Single-cell sequencing was subsequently employed to classify tumor-infiltrating immune cells and assess changes in cytokines and metabolic genes. Through RNA sequencing, real-time quantitative PCR, western blotting, flow cytometry, seahorse analysis, glucose uptake, and lactate secretion assays, we explored how GP73 regulates HIF-1α to influence T-cell antitumor functionality. Furthermore, we established adoptive transfer experiments to study the ability of GP73-overexpressing T cells to combat tumors. Clinical tumor patient blood samples were collected to assess the relationship between immunotherapy efficacy and T-cell GP73 levels.</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>
Image flow cytometry data of T-cells from healthy and Sezary patients
<p>The purpose of our experiments was to investigate the morphology of T-cells using a custom build image flow cytometry device. Blood (T-cells) from healthy donors and Sezary patients was imaged.</p>
Salt-inducible kinase 3 protects tumor cells from cytotoxic T-cell attack by promoting TNF-induced NF-κB activation
<p>WHAT IS ALREADY KNOWN ON THIS TOPIC</p> <ul> <li> <p>Tumor-intrinsic resistance to T cell (TC)-released cytokines, such as tumor necrosis factor (TNF)-α, has recently emerged as a major mechanism of tumor immune evasion. Yet, a deeper characterization of the genes that are responsible for this effect is needed.</p> </li> </ul> <p>WHAT THIS STUDY ADDS</p> <ul> <li> <p>Salt-inducible kinase 3 (SIK3) is a novel regulator of tumor-intrinsic resistance to cytotoxic TC attack.</p> </li> <li> <p>SIK3 confers tumor cell protection from TC-released TNF by sustaining the expression of pro-survival and anti-apoptotic genes under the control of nuclear factor kappa B (NF-κB).</p> </li> <li> <p>A TNF/SIK3/NF-κB-mediated gene signature correlated with significantly reduced patient survival in pancreatic cancer.</p> </li> </ul> <p>HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE AND/OR POLICY</p> <ul> <li> <p>Pharmacological inhibition of SIK3 might be an effective strategy to sensitize cancer cells to TC-based immunotherapies by rewiring tumor cell responses to TC-secreted TNF.</p> </li> </ul>
Benchmark datasets for "Detecting T-cell expansion and quantifying clone survival from deep profiling of immune repertoires"
<p>T-cell receptor repertoire sequencing datasets describing time courses obtained for vaccination, normal aging and blood transplant cases. Datasets reported here were previously published (except for Tem/Tcm data), this is just a compendium of selected samples that is properly pre-processed and formatted.</p>
Movie S2. T-cell migration in a 5 dpf dock11-knockout zebrafish embryo
<p>Representative time-lapse confocal microscopy movie showing the migration of fluorescently labeled T cells in the region around the thymus (anterior region) in a 5 days postfertilization (dpf) <em>dock11</em>-knockout <em>lck:nlsmCherry </em>transgenic zebrafish embryo<em>. </em>Lines correspond to the trajectories of the movement over time of Lck-positive T‑cell progenitors.</p> <p>This movie corresponds to Movie S2 from Supplementary Appendix of Block et al., Systemic Inflammation and Normocytic Anemia in DOCK11 Deficiency. N Engl J Med 2023.</p>
Phase 1b/2a Trial of Allogeneic HSCT From an HLA-partially Matched Related or Unrelated Donor After TCRab+ T-cell/CD19+ B-cell Depletion for Patients With Monogenic and/or Early-onset Medically Refrac
ClinicalTrials.gov study NCT06986382. IPD Sharing: NO. Countries: 1. Publications: 13.
Humanized CD7 CAR T-cell Therapy for r/r CD7+ Acute Leukemia
ClinicalTrials.gov study NCT04762485. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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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.