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1,973 results for “T cell receptor”
Comparing the effect of TGF-β receptor inhibition on human mesenchymal stem/stromal cells derived from endometrium, bone marrow and adipose tissues
<p><strong>Figure S1: Differences between bmMSC donors. A)</strong> Graph showing two groups of bmMSCs with and without effect of A83-01 treatment on % SUSD2<sup>+</sup> cells. <strong>B)</strong> Graph showing no difference in the number of cells following A83-01 treatment in the two groups of donor cells from <strong>A</strong>. Plots are median for n=3 biological samples per treatment group.</p>
Production and purification of receptor-binding domain (RBD) of the Spike protein from a transiently transfected mammalian cell
<p> </p> <p>Production and purification of recombinant receptor-binding domain (RBD) of the Spike protein from a transiently transfected EXPI293 mammalian cell .</p>
Pre-processed B cell receptor sequences from BioProject PRJNA349143
<p>Processed sequencing data from BioProject PRJNA349143.</p> <p><strong>Study Design</strong></p> <p>Samples were collected from human volunteers as described in Laserson and Vigneault et al, 2014 (1). Briefly, blood samples were collected from three individuals both pre- and post-vaccination for seasonal influenza. Samples were collected for sequencing at time points -8 days, -2 days, -1 hour, +1 hour, +1 day, +3 days, +7 days, +14 days, +21 days and +28 days relative to injection with seasonal influenza vaccine.</p> <p><strong>Library Preparation and Sequencing</strong></p> <p>The original samples from Laserson and Vigneault et al, 2014 (1) were re-sequenced as described in Gupta et al, 2017 (2). Briefly, sequencing libraries were prepared from mRNA using 5'RACE with addition of 17-nucleotide unique molecular identifiers (UMIs). Amplification was performed using constant region primers specific to IGHA, IGHD, IGHE, IGHG, IGHM, IGKC and IGLC. Sequencing was conducted on the Illumina MiSeq platform using the 600 cycle kit with 325 cycles for read 1 and 275 cycles for read 2. A 10% PhiX spike-in was added for sequencing.</p> <p><strong>Data Processing</strong></p> <p>Sequences were processed using the pRESTO (3) and Change-O (4) toolkits as described in Gupta et al, 2017 (2).</p> <p>Note, the provided data has been filtered significantly, including the removal of sequences that fail V(D)J alignment and the exclusion of non-functional sequences.</p> <p><strong>Format</strong></p> <p>Processed sequences are provided in FASTA format annotated using the pRESTO scheme.</p> <p>Annotations included are as follows:</p> <ul> <li><strong>CONSCOUNT:</strong> Raw read count from which UMI consensus sequences were generated, summed over all UMIs for the given unique sequence.</li> <li><strong>DUPCOUNT:</strong> UMI count for the given unique sequence.</li> <li><strong>PRCONS:</strong> Constant region primer (isotype).</li> <li><strong>SUBJECT:</strong> Subject identifier.</li> <li><strong>TIME_POINT:</strong> Time point label.</li> </ul> <p><strong>Citations</strong></p> <ol> <li>Laserson U and Vigneault F, et al. High-resolution antibody dynamics of vaccine-induced immune responses. Proc Natl Acad Sci USA 111, 4928-33 (2014).</li> <li>Gupta NT, et al. Hierarchical Clustering Can Identify B Cell Clones with High Confidence in Ig Repertoire Sequencing Data. J Immunol 1601850 (2017).</li> <li>Vander Heiden JA and Yaari G, et al. pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. Bioinformatics 30, 1930–2 (2014).</li> <li>Gupta NT and Vander Heiden JA, et al. Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. Bioinformatics 31, 3356–8 (2015).</li> </ol>
Enhancing comparative T-cell receptor repertoire analysis in small biological samples through pooling homologous cell samples from multiple mice
<p>All data files used to generate the figures in the paper are shared in this project.</p> <p>Scripts are available on <a href="https://github.com/i3-unit/CRM_24" target="_blank" rel="noopener">GitHub</a>.</p>
Identification of clinically relevant T cell receptors for personalized T cell therapy using combinatorial algorithms
<p>Single-cell RNA (scRNA) and single-cell TCR (scTCR)-sequencing data data for patients number 11, 12 and 14 from the article " Identification of clinically relevant T-cell receptors for personlized T-cell therapy". </p> <p>Each compressed file contains two subfolders. One is for the scRNA-seq (GEX-sequencing) and the other for the scTCR-seq (VDJ-sequencing). </p> <p>In the original article, scRNA-seq and scTCR-seq were aligned to the GRCh38 reference genome using <em>cellranger count </em>(10X Genomics, version 3.0.1) and <em>vdj </em>(10X Genomics, version 3.1.0) respectively. The subsequent data processing was performed using Seurat library (V.4.3.0) on R Statistical Software (V.4.0.3).</p>
The co-inhibitory receptor TIGIT promotes tissue protective functions in T cells
<p>TIGIT KO and wild-type mouse T cells were sorted and collected from the lung and spleen in steady state and LCMV conditions. Briefly, cells were sorted into three sub-populations and subsequently pooled for scRNAseq. The individual subpopulations were CD3+/CD8+, CD3+/CD4+/Foxp3-, and CD3+/CD4+/Foxp3+.</p> <p>tigit-full-dataset.rds: contains the full processed dataset ~198645 cells and 25906 genes formatted as a Seurat (v5) object. </p> <p>Alternatively, the raw data plus the associated metadata can be loaded with the following 3 files:</p> <p>tigit-raw-counts.mtx.gz: A sparse count matrix used in the Seurat object above. The matrix was stored as a matrix market format and was subsequently gzipped. The matrix is expected to have 198645 cells and 25906 features.</p> <p>cell-metadata.csv: Cell metadata found in the seu@meta.data slot of the Seurat object.</p> <p>gene-metadata.csv: gene metadata of the scRNAseq experiment as found in the seu@assays$RNA@meta.features slot of the Seurat object.</p> <p> </p>
Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities: validation cohort meta data and parsed TCR repertoire data
<p>Meta data corresponding the the validation cohort for the paper, "Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities" by Magdalena L Russell, Aisha Souquette, David M Levine, Stefan A Schattgen, E Kaitlynn Allen, Guillermina Kuan, Noah Simon, Angel Balmaseda, Aubree Gordon, Paul G Thomas, Frederick A Matsen IV, and Philip Bradley. These meta data include: </p> <p>(1) SNP genotypes for the two SNPs which overlap with the discovery cohort<br> - (nicaragua_snp_genotypes_ints.tsv) -- SNP genotypes as integers<br> - (nicaragua_snp_genotypes_strings.tsv) -- SNP genotypes as allele strings <br> (2) the ancestry PCs for each individual in the validation cohort (nicaragua_snp_ancestry_PCA.tsv)<br> (3) a file including IMGT genes and sequences used for parsing TCRB repertoire data (human_vj_allele_cdr3_nucseqs.tsv)<br> (4) a file including IMGT genes used for parsing TCRA repertoire data (human_vj_alleles_alpha.tsv)<br> (5) Parsed TCRA repertoire data (nicaragua_parsed_TCRA.tgz)<br> (6) Parsed TCRB repertoire data (nicaragua_parsed_TCRB.tgz) </p> <p><strong>Corresponding raw validation cohort TCR repertoire data is available here:</strong> https://www. ncbi.nlm.nih.gov/bioproject/PRJNA762269 (The BioProject database, accession number: PRJNA762269)</p> <p><strong>Software tools designed to work with these data are available here:</strong> https://github.com/phbradley/tcr-gwas</p>
2014 EPA National Emissions Inventory allocated to the grid cells of InMAP Source-Receptor Matrix
<p>This dataset is the 2014 EPA National Emissions Inventory (NEI) v1 allocated to the individual grid cells of InMAP Source-Receptor Matrix (<a href="https://zenodo.org/record/2589760#.Yds79GjMI2w">ISRM</a>). The source types is classified by EPA Source Classification Codes (SCCs). The dataset includes emissions of both primary and secondary PM<sub>2.5</sub>. Secondary PM<sub>2.5</sub> includes four precursors: NO<sub>x</sub>, SO<sub>x</sub>, NH3, and VOC. The detailed description of emission processing is in <a href="https://doi.org/10.1073/pnas.1818859116">Tessum et al. (2019</a>).</p> <p>Each shapefile in the dataset is in the format of input file of <a href="http://spatialmodel.com/inmap/">InMAP</a>/ISRM, which includes the emission amounts of five pollutants (Primary PM<sub>2.5</sub>, NO<sub>x</sub>, SO<sub>x</sub>, NH3, and VOC), stack information (height, diameter, temperature, and velocity), and SCCs. The unit of emissions is <span class="math-tex">\(\mu g/s\)</span>. (If these emissions are used directly with the ISRM, the resulting outputs will be concentrations, in units of <span class="math-tex">\(\mu g/m^3\)</span>.)</p>
Data sets used to demonstrate the software MadHitter in the manuscript "The Landscape of Receptor-Mediated Precision Cancer Combination Therapy Via a Single-Cell Perspective"
<p>This is a zip archive of nine single-cell RNASeq data sets used in the manuscript entitled:</p> <p>"The Landscape of Receptor-Mediated Precision Cancer Combination Therapy Via A Single-Cell Perspective" by Saba Ahmadi, Pattara Sukprasert, Rahulsimham Vegesna, Sanju Sinha, Fiorella Schischlik, Natalie Artzi, Samir Khuller, Alejandro A. Schaffer, Eytan Ruppin,</p> <p>The README.txt describes the data sets in detail.</p> <p>The associated software can be found at https://github.com/ruppinlab/madhitter</p>
Single cell imaging of ERK and Akt activation dynamics and heterogeneity induced by G protein-coupled receptors - Scripts & Source data
<p>Source data and scripts to reproduce the figures that are part of the publication "Single cell imaging of ERK and Akt activation dynamics and heterogeneity induced by G protein-coupled receptors".</p> <p>Journal of Cell Science (2022) 135, jcs259685, DOI: 10.1242/jcs.259685</p> <p> </p> <p>An earlier version of this work is published as a preprint: "Heterogeneity and dynamics of ERK and Akt activation by G protein-coupled receptors depend on the activated heterotrimeric G proteins", DOI: <a href="https://doi.org/10.1101/2021.07.27.453948">10.1101/2021.07.27.453948</a></p>
Hormone receptors AR, ER, PR and growth factor receptor Her-2 expression in oral squamous cell carcinoma: Correlation with overall survival, disease-free survival and 10-year survival in a high-risk population
<p>Oral squamous cell carcinoma (OSCC) comprises most of head and neck neoplasms and is one of the highest-ranking and lethal cancers in Pakistan due to prevailing mouth habits. Growth and hormonal receptors act as prognostic markers and targets for therapy in some cancers, but their application in OSCC is largely unexplored. This study aimed to evaluate the expression of growth and hormonal receptors in OSCC patients and correlate it with 10-year, overall and disease-free survival. To achieve this objective, immunohistochemistry for Her-2, AR, ER and PR was performed on 100 formalin-fixed paraffin-embedded primary OSCC specimens. Receptor expression was correlated with mouth habits and clinicopathological features and patient survival was analyzed using Kaplan-Meier method and Cox regression univariate analysis. We observed that in 100 patients, there were 57 males and 43 females. Immunopositive Her-2 expression was observed in 21% of patients, AR in 13%, ER in 3% and 0% for PR. Patients with betel quid/areca nut mouth habits had significantly absent Her-2 expression (P=0.035). Also, Her-2 negative patients were also negative for AR expression (P=0.002). Her-2 positive patients had poor 10-year survival (P=0.041). A trend of low survival and high recurrence rate was observed in AR positive patients, but this was not significant (P=0.072). No statistically relevant correlations were seen in the case of ER and PR. In conclusion, Her-2 may be a valuable marker for predicting long-term prognosis of OSCC patients.</p>
Molcular Dynamics Data for Therapeutic High Affinity T Cell Receptor Targeting a KRAS G12D Cancer Neoantigen
<p>This folder contains the starting structures and input scripts required to simulate the wild-type and G12D KRAS peptide bound TCR-pHLA complexes, as performed in this study.</p> <p><br> Starting_Structures - This folder contains the amber parameter/topology files used to simulate each system (.prmtop) and the coordinates of the starting structure both as amber coordinate file (.rst) and PDB file (.pdb).<br> MD_Inputs - This folder contains the amber MD inputs used to run the md simulations. <br> MMPBSA_inputs - This folder contains the input files for running MMPBSA with the MMPBSA.py script in amber. The mmpbsa.in script was used for calculating overall binding energy whereas the mmpbsa_decomp.in script was used for calculating the per-residue contribution to binding energy. </p>
Rapid increase in transferrin receptor recycling promotes adhesion during T cell activation
<p>This Dataset contains primary data used for the publication "Rapid increase in transferrin receptor recycling promotes adhesion during T cell activation" accepted for publication in BMC Biology on 15. July 2022</p> <p><strong>Abstract:</strong></p> <p>Background<br> T cell activation leads to increased expression of the receptor for the iron transporter transferrin (TfR) to provide iron required for the cell differentiation and clonal expansion that takes place during the days after encounter with a cognate antigen. However, T cells mobilise TfR to their surface within minutes after activation, although the reason and mechanism driving this process remain unclear.</p> <p>Results<br> Here we show that T cells transiently increase endocytic uptake and recycling of TfR upon activation, thereby boosting their capacity to import iron. We demonstrate that increased TfR recycling is powered by a fast endocytic sorting pathway relying on the membrane proteins flotillins, Rab5 and Rab11a-positive endosomes. Our data further reveal that iron import is required for a non-canonical signalling pathway involving the kinases Zap70 and PAK, which controls adhesion of the integrin LFA-1 and eventually leads to conjugation with antigen-presenting cells.</p> <p>Conclusions<br> Altogether, our data suggest that T cells boost their iron importing capacity immediately upon activation to promote adhesion to antigen-presenting cells.</p> <p> </p> <p>Data are organised in compressed (.zip) folders entitled as the corresponding Figures in the publication.</p> <p>Programs we recommend to view the files are:<br> .fcs files: FlowJo software v10 (Tree Star, Ashland, OR, USA)<br> .lsm and .czi files: ZEN 2012 SP1 and higher (Carl Zeiss, Jena, Germany)<br> .lif files: LAS X v3 (Leica Microsystems, Wetzlar, Germany)<br> .pzfx files: Prism v7 software (GraphPad, San Diego, CA, USA)</p>
T-cell receptor Vβ (TCRVB) deep sequencing on human T cells isolated from humanized mice engrafted with human PBMC and treated or not with PTCy post-transplantation
<p>Nucleotide sequence data of TCRVB sequencing performed on T cells isolated from the pre-transplantation hPBMC (donor T cells) or from mice organs at day 21 post-transplantation (injected or not with PTCy at day 3) to determine the impact of PTCy on the T cell V beta (TCRVB) receptor repertoire diversity.</p> <p>NSG mice were engrafted with human PBMC to develop xeno-GVHD and treated or not with 100 mg/kg PTCy. Spleens and lungs from 10 NSG mice per group were pooled and stained to sorted human CD4+ and CD8+ . DNA of human CD4+ and CD8+ T cells sorted from each organ and from the PBMC donor was extracted. One hundred fifty µg from each sample were used for T-cell receptor Vβ (TCRVB) deep sequencing performed by Adaptive Biotechnologies.<br> </p>
Membrane Fluctuation Model to Understand the Effect of the Receptor Nanoclustering on the Activation of Natural Killer Cells through Biomechanical Feedback
<p>Data for all the plots from original paper "Membrane Fluctuation Model to Understand the Effect of the Receptor Nanoclustering on the Activation of Natural Killer Cells through Biomechanical Feedback".</p>
Supporting dataset for the publication 'Human T cell receptor occurrence patterns encode immune history, genetic background, and receptor specificity'
<p>This dataset accompanies the publication "Human T cell receptor occurrence patterns encode immune history, genetic background, and receptor specificity" by William S DeWitt III, Anajane Smith, Gary Schoch, John A Hansen, Frederick A Matsen IV and Philip Bradley, accepted for publication in the journal eLife. It contains data on T cell receptor occurrence patterns and repertoire features that can be processed with the software tools provided in the github repository https://github.com/phbradley/pubtcrs in order to replicate the findings of the manuscript.</p> <p> </p>
Partis post-processed B cell receptor repertoires from BioProject PRJNA349143
<p>These files correspond to partis annotations of several datasets found in BioProject PRJNA349143. (DOI: 10.5281/zenodo.821659).</p>
Cdc42 couples T cell receptor endocytosis to GRAF1-mediated tubular invaginations of the plasma membrane
<p>This Dataset contains primary data used for the publication "Cdc42 couples T cell receptor endocytosis to GRAF1-mediated tubular invaginations of the plasma membrane" published online on 04. November 2019<br> doi:10.3390/cells8111388</p> <p><strong>Abstract:</strong> T cell activation is immediately followed by internalization of the T cell receptor (TCR).<br> TCR endocytosis is required for T cell activation, but the mechanisms supporting removal of TCR<br> from the cell surface remain incompletely understood. Here we report that TCR endocytosis is<br> linked to the clathrin-independent carrier (CLIC) and GPI-enriched endocytic compartments<br> (GEEC) endocytic pathway. We show that unlike the canonical clathrin cargo transferrin or the<br> adaptor protein Lat, internalized TCR accumulates in tubules shaped by the small GTPase Cdc42<br> and the Bin/amphiphysin/Rvs (BAR) domain containing protein GRAF1 in T cells. Preventing<br> GRAF1-positive tubules to mature into endocytic vesicles by expressing a constitutively active<br> Cdc42 impairs the endocytosis of TCR, while having no consequence on the uptake of transferrin.<br> Together, our data reveal a link between TCR internalization and the CLIC/GEEC endocytic route<br> supported by Cdc42 and GRAF1.</p> <p> </p> <p>Data are organised in compressed (.zip) folders entitled as the corresponding Figures in the publication.</p> <p>Programs we recommend to view the files are:<br> .fcs files: FlowJo software v10 (Tree Star, Ashland, OR, USA)<br> .lif files: LAS X v3 (Leica Microsystems, Wetzlar, Germany)<br> .pzfx files: Prism v7 software (GraphPad, San Diego, CA, USA)</p> <p> </p> <p>In case this Dataset is updated, new version will be available with doi:10.5281/zenodo.3545842</p>
Cell selectivity in succinate receptor SUCNR1/GPR91 signaling in skeletal muscle
<p>Succinate is released by skeletal muscle during exercise and activates <em>SUCNR1</em>/GPR91. Signaling of SUCNR1 is involved in cell-cell communication in skeletal muscle. However, the specific cell types responding to succinate and the directionality of communication are unclear. <em>De novo</em> analysis of transcriptomic datasets demonstrated that <em>SUCNR1</em> mRNA is expressed in immune, adipose, and liver tissues, but scarce in skeletal muscle. In human tissues, <em>SUCNR1</em> mRNA was associated with macrophage markers. Single-cell RNA sequencing and fluorescent RNAscope demonstrated that in human skeletal muscle, <em>SUCNR1</em> mRNA is not expressed in muscle fibers but coincided with macrophage populations. Human M2-polarized macrophages exhibit high levels of <em>SUCNR1</em> mRNA and stimulation with selective agonists of SUCNR1 triggered Gq- and Gi-coupled signaling. Primary human skeletal muscle cells were unresponsive to <em>SUCNR1</em> agonists. In conclusion, SUCNR1 is not expressed in muscle cells and its role in the adaptive response of skeletal muscle to exercise is most likely mediated via paracrine mechanisms involving M2-like macrophages within the muscle.</p>
Combined in vitro and cell-based selection display method producing specific binders against IL-9 receptor in high yields
<p>The FEBS Journal FJ-22-0728</p> <p>Combined in vitro and cell-based selection display method producing specific binders against IL-9 receptor in high yields</p> <p>Maroš Huličiak, Lada Biedermanová, Daniel Berdár, Štěpán Herynek, Lucie Kolářová,<br> Jakub Tomala, Pavel Mikulecký, and Bohdan Schneider</p> <p><em>Institute of Biotechnology of the Czech Academy of Sciences,<br> Průmyslová 595, 252 50 Vestec, Czech Republic</em></p> <p>Correspondence: Bohdan Schneider, Institute of Biotechnology of the Czech Academy of Sciences,<br> BIOCEV, CZ-252 50 Vestec, Czech Republic,<br> Tel: +420 728 303 566, e-mail: bohdan.schneider@ibt.cas.cz</p> <p>Abstract</p> <p>We combined cell-free ribosome display and cell-based yeast display selection to build specific protein binders to the extracellular domain of the human interleukin 9 receptor alpha (IL-9Rα). The target, IL-9Rα, is the receptor involved in the signaling pathway of IL-9, a pro-inflammatory cytokine medically important for its involvement in respiratory diseases. The successive use of modified protocols of ribosome and yeast displays allowed us to combine their strengths - the virtually infinite selection power of ribosome display and the production of (mostly) properly folded and soluble proteins in yeast display. The described experimental protocol is optimized to produce binders highly specific to the target, including selectivity to common proteins such as BSA, and proteins potentially competing for the binder such as receptors of other cytokines. The binders were trained from DNA libraries of two protein scaffolds called 57aBi and 57bBi developed in our laboratory. We show that the described unconventional combination of ribosome and yeast displays is effective in developing selective small protein binders to the medically relevant molecular target.</p>
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