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3,441 results for “Immune cells”
AhR Gene Expression in Immune cell subtypes
<p>Benjamin J. Schmiedel, Divya Singh, Ariel Madrigal, Alan G. Valdovino-Gonzalez, Brandie M. White, Jose Zapardiel-Gonzalo, Brendan Ha, Gokmen Altay, Jason A. Greenbaum, Graham McVicker, Grégory Seumois, Anjana Rao, Mitchell Kronenberg, Bjoern Peters, Pandurangan Vijayanand,<br> Impact of Genetic Polymorphisms on Human Immune Cell Gene Expression,<br> Cell,<br> Volume 175, Issue 6,<br> 2018,<br> Pages 1701-1715.e16,<br> ISSN 0092-8674,<br> https://doi.org/10.1016/j.cell.2018.10.022.<br> (http://www.sciencedirect.com/science/article/pii/S009286741831331X)</p> <p><br> Abstract: Summary<br> While many genetic variants have been associated with risk for human diseases, how these variants affect gene expression in various cell types remains largely unknown. To address this gap, the DICE (database of immune cell expression, expression quantitative trait loci [eQTLs], and epigenomics) project was established. Considering all human immune cell types and conditions studied, we identified cis-eQTLs for a total of 12,254 unique genes, which represent 61% of all protein-coding genes expressed in these cell types. Strikingly, a large fraction (41%) of these genes showed a strong cis-association with genotype only in a single cell type. We also found that biological sex is associated with major differences in immune cell gene expression in a highly cell-specific manner. These datasets will help reveal the effects of disease risk-associated genetic polymorphisms on specific immune cell types, providing mechanistic insights into how they might influence pathogenesis (https://dice-database.org).<br> Keywords: DICE; immunology; GWAS; genetic variants; human immune cells; gene expression; eGenes; eQTLs; sex</p> <p>https://dice-database.org/genes/ahr#boxplot</p>
Data from: Mouse gingival single cell transcriptomic atlas identified a novel fibroblast subpopulation activated to guide oral barrier immunity in periodontitis
<p>Periodontitis, one of the most common non-communicable diseases, is characterized by chronic oral inflammation and uncontrolled tooth supporting alveolar bone resorption. Its underlying mechanism to initiate aberrant oral barrier immunity has yet to be delineated. Here, we report a unique fibroblast subpopulation activated to guide oral inflammation (AG fibroblasts) identified in a single-cell RNA sequencing gingival cell atlas constructed from the mouse periodontitis models. AG fibroblasts localized beneath the gingival epithelium and in the cervical periodontal ligament responded to the ligature placement and to the discrete topical application of Toll-like receptor stimulants to mouse maxillary tissue. The upregulated chemokines and ligands of AG fibroblasts linked to the putative receptors of neutrophils in the early stages of periodontitis. In the established chronic inflammation, neutrophils together with AG fibroblasts appeared to induce type 3 innate lymphoid cells (ILC3s) that were the primary source of interleukin-17 cytokines. The comparative analysis of <em>Rag2-/-</em> and <em>Rag2gc-/-</em> mice suggested that ILC3 contributed to the cervical alveolar bone resorption interfacing the gingival inflammation. We propose that the AG fibroblast–neutrophil–ILC3 axis as a previously unrecognized mechanism which could be involved in the complex interplay between oral barrier immune cells contributing to pathological inflammation in periodontitis.</p>
Multi-omics analysis of innate and adaptive responses to BCG vaccination reveals epigenetic cell states that predict trained immunity
<p>This repository contains personal immune profiles of 323 healthy individuals (300BCG) subjected to Bacillus Calmette-Guérin (BCG) with blood samples collected immediately before (day 0), and 14 and 90 days after the vaccination. The personal immune profiles comprise:</p> <ul> <li>immune cell concentrations measured with flow cytometry and a hematology analyzer</li> <li>plasma concentrations of 73 circulating inflammatory markers</li> <li>30 measurements of cytokine and lactate production capacity of peripheral blood mononuclear cells (PBMCs) in response to four microbial stimuli (Candida albicans, Escherichia coli lipopolysaccharide [LPS], Staphylococcus aureus, Mycobacterium tuberculosis).</li> </ul> <p>Visit <a href="http://300BCG.bocklab.org/">http://300BCG.bocklab.org/</a> to learn more.</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>
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>
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>
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>
Self-adjuvanting Nanovaccines Boost Lung-resident CD4+ T Cell Immune Responses in BCG-primed mice
<p>The "readcount_genename.xls" file and "fpkm_genename.xls" file contain gene expression data for genes (gene list is detailed in "gene.xls") in the panel. All data were obtained from bone marrow derived dendritic cells from WT C57BL/6 mice treated with KFE8 nanofibers or mock. All RNA sequencing and subsequent processing was performed by Novogene Corporation.</p> <p>The remaining .xls files contain the differential expression analysis for KFE8 nanofiber treatment at four hours compared with mock (NF4vsC4) or KFE8 nanofiber treatment at sixteen hours compared with mock (NF16vsC16).</p> <p>The "pathway analysis Rcode.R" file contains the code used to generate Reactome pathway analysis for both time points. </p> <p>The "NF16 volc Rcode.R" file contains the code used to generate a volcano plot for the 16 hour time point. </p>
Single Cell Phenotypic Profiling to Identify a Set of Immune Cell Protein Biomarkers for Relapsed and Refractory Diffuse Large B Cell Lymphoma: A Single-Center Study
<p>Diffuse large B-cell lymphoma (DLBCL) is the most common invasive type of non-Hodgkin lymphoma. Cell-of-origin (COO) classification is related to patients’ prognoses. Primary drug resistance in treatment for DLBCL has been observed. The specific serum biomarkers in these patients who suffer from relapsed and refractory (R/R)-DLBCL remains unclear. In the current study, using single-cell RNA sequencing (scRNA-seq) and mass cytometry (CyTOF), we determined and verified immune cell biomarkers at the mRNA and protein levels in single-cell resolution from 18 diagnostic peripheral blood mononuclear cell (PBMC) specimens collected from patients with R/R DLBCL. As controls, five PBMC specimens from healthy volunteers were obtained. We identified a panel of 35 surface marker genes for the features of R/R DLBCL unique cell cluster by scRNA-seq of eight R/R DLBCL patient samples and validated its efficiency in an external cohort consisting of 10 R/R DLBCL patients by CyTOF. The cell clustering and dimension reduction were compared among R/R DLBCL samples in CyTOF Space with COO as well as the C-MYC expression designation. Immune cells from each patient occupied unique regions in the 32-dimensional phenotypic space with no apparent clustering of samples into discrete subtypes. Significant heterogeneity observed in subgroups was mainly attributed to individual differences among samples and not to expression differences in a single, homogeneous immune cell subpopulation. The marker panel showed reliability in labeling R/R DLBCL without any influence from COO stratification and C-MYC expression designation. Furthermore, we compared all the markers between R/R DLBCL and normal samples. A total of 12 biomarkers were significantly overexpressed in R/R DLBCL relative to the normal samples. Therefore, we further optimized the diagnostic biomarker panel of R/R DLBCL comprising CD82, CD55, CD36, CD63, CD59, IKZF1, CD69, CD163, CD14, CD226, CD84, and CD31. In summary, we developed a novel set of biomarkers for the diagnoses of patients with R/R DLBCL. Detections procedures at single-cell resolution provide precise biomarkers which may substantially overcome intertumoral and intratumoral heterogeneity among primary samples. The findings confirmed that each case was unique and may comprise multiple, genetically distinct subclones.</p> <p>Here we uploaded the dataset of CyTOF for external validation. For more detailed information, please contact Dr zheng (zenki_zheng@163.com)</p> <p> </p>
Immune disease risk variants regulate gene expression dynamics during CD4+ T cell activation
<p>During activation, T cells undergo extensive changes in gene expression which shape the properties of cells to exert their effector function. Therefore, understanding the genetic regulation of gene expression during T cell activation provides essential insights into how genetic variants influence the response to infections and immune diseases. We generated a single-cell map of expression quantitative trait loci (eQTL) across a T cell activation time-course. We profiled 655,349 CD4+ naive and memory T cells, capturing transcriptional states of unstimulated cells and three time points of cell activation in 119 healthy individuals. We identified 38 cell clusters, including stable clusters such as central and effector memory T cells and transient clusters that were only present at individual time points of activation, such as interferon-responding cells. We mapped eQTLs using a T cell activation trajectory and identified 6,407 eQTL genes, of which a third (2,265 genes) were dynamically regulated during T cell activation. We integrated this information with GWAS variants for immune-mediated diseases and observed 127 colocalizations, with significant enrichment in dynamic eQTLs. Immune disease loci colocalized with genes that are involved in the regulation of T cell activation, and genes with similar functions tended to be perturbed in the same direction by disease risk alleles. Our results emphasize the importance of mapping context-specific gene expression regulation, provide insights into the mechanisms of genetic susceptibility of immune diseases, and help prioritize new therapeutic targets.</p> <p>This dataset comprises of summary stats for eQTLs identified in the study (parquet files) and the ones which passed significance threshold (tensor_out.tar.gz archive). Files are described by cell subset (CD4 Naive, CD 4 Memory, TEMRA, TCM, etc.), time since activation (16h, 4h, 5days) as described in the publication (preprint https://doi.org/10.1101/2021.12.06.470953)</p>
A B cell actomyosin arc network couples integrin co-stimulation to mechanical force-dependent immune synapse formation
<p>B-cell activation and immune synapse (IS) formation with membrane-bound antigens are actin-dependent processes that scale positively with the strength of antigen-induced signals. Importantly, ligating the B-cell integrin, LFA-1, with ICAM-1 promotes IS formation when antigen is limiting. Whether the actin cytoskeleton plays a specific role in integrin-dependent IS formation is unknown. Here we show using super-resolution imaging of mouse primary B cells that LFA-1: ICAM-1 interactions promote the formation of an actomyosin network that dominates the B-cell IS. This network is created by the formin mDia1, organized into concentric, contractile arcs by myosin 2A, and flows inward at the same rate as B-cell receptor (BCR): antigen clusters. Consistently, individual BCR microclusters are swept inward by individual actomyosin arcs. Under conditions where integrin is required for synapse formation, inhibiting myosin impairs synapse formation, as evidenced by reduced antigen centralization, diminished BCR signaling, and defective signaling protein distribution at the synapse. Together, these results argue that a contractile actomyosin arc network plays a key role in the mechanism by which LFA-1 co-stimulation promotes B-cell activation and IS formation.</p>
Multimodal single cell analysis of the paediatric lower airway reveals novel immune cell phenotypes in early life health and disease
<p>RDS files of SingleCellExperiment objects containing raw single cell RNA-seq count data required to replicate the analyses presented at: <a href="https://oshlacklab.com/paed-cf-cite-seq/">https://oshlacklab.com/paed-cf-cite-seq/</a> and described in the pre-print titled: <em>"</em>Multimodal single cell analysis of the paediatric lower airway reveals novel immune cell phenotypes in early life health and disease<em>"</em>.<br> Instructions for how to incorporate the raw data into the analysis can be found at: <a href="https://oshlacklab.com/paed-cf-cite-seq/gettingStarted.html">https://oshlacklab.com/paed-cf-cite-seq/gettingStarted.html</a> and the complete analysis code and additional data files can be cloned/downloaded from: <a href="https://github.com/Oshlack/paed-cf-cite-seq">https://github.com/Oshlack/paed-cf-cite-seq</a>.</p>
Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19
<p>The biological determinants underlying the range of COVID-19 clinical manifestations are not fully understood. Here, over 1400 plasma proteins and 2600 single-cell immune features comprising cell phenotype, endogenous signaling activity, and signaling responses to inflammatory ligands are cross-sectionally assessed in peripheral blood from 97 patients with mild, moderate, and severe COVID-19 and 40 uninfected patients. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identify and independently validate a multivariate model classifying COVID-19 severity (multi-class AUC<sub>training</sub> = 0.799, p-value = 4.2e-6; multi-class AUC<sub>validation</sub> = 0.773, p-value = 7.7e-6). Examination of informative model features reveals novel biological signatures of COVID-19 severity, including the dysregulation of JAK/STAT, MAPK/mTOR, and NF-κB immune signaling networks in addition to recapitulating known hallmarks of COVID-19. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for prevention and/or treatment of COVID-19 progression.</p>
Do changes in body mass alter white blood cell profiles and immune function in Australian cane toads (Rhinella marina)?
<p><span>Variation in food resources can result in dramatic fluctuations in the body condition of animals dependent on those resources. Decreases in body mass can disrupt patterns of energy allocation and impose stress, thereby altering immune function. In this study we investigated links between changes in body mass of captive cane toads (Rhinella marina), their circulating white blood cell populations, and their performance in immune assays. Captive toads that lost weight over a 3-month period had increased levels of monocytes and heterophils and reduced levels of eosinophils. Basophil and lymphocyte levels were unrelated to changes in mass. Because individuals that lost mass had higher heterophil levels but stable lymphocyte levels, the ratio of these cell types was also higher, partially consistent with a stress response. Phagocytic ability of whole blood was higher in toads that lost mass, due to increased circulating levels of phagocytic cells. Other measures of immune performance were unrelated to mass change. These results highlight the challenges faced by invasive species as they expand their range into novel environments which may impose substantial seasonal changes in food availability that were not present in the native range. Individuals facing energy restrictions may shift their immune function towards more economical and general avenues of combating pathogens. </span></p>
Multi-organ CD45+ immune cell atlas
<p><em>File Description</em></p> <p><strong>MultiOrgan_MetaAtlas_total_level3_qc.rds:</strong> an rds file containing a Seurat object 114275 cells (RNA integrated assays, PCA and UMAP reductions and quality metrics)</p> <p><strong>MultiOrgan_MetaAtlas_total_level3_qc.h5ad: </strong>an h5 file containing the single cell object as the above</p> <p><strong>MultiOrgan_MetaAtlas_total_level3_qc_metadata.csv</strong>: a table containing the metadata for each cell</p> <p> </p> <p><em>Methodology </em></p> <p>162 loom files were downloaded from the 12 different projects of the HCA consortium. In more detail, we focused on 14 different organs: prostate gland, eye, heart, skeletal muscle organ, blood, liver, spleen, brain, kidney, colon, esophagus, lung, thymus, and bone marrow. The files were transformed into Seurat objects with the function as.Seurat() from Seurat package in R. After incorporating metadata information, each object was screened for immune cells based on the expression of the CD45 immune marker. Specifically, cells were considered immune cells if they exhibited at least one read of the PTPRC - CD45 surface marker gene. Finally, the Seurat object was transformed into a .h5 object for Python users.</p> <p> </p> <p><em>Metadata</em></p> <p>All the files contain the following donor metadata variables:</p> <ul> <li>Tissue: Organ/tissues</li> <li>Project: Project of HCA</li> <li>Library_prep: single cell technology</li> <li>Sample_ID: assigned patient identifiers</li> <li>Tissue_part: specific location of the sampling</li> <li>Sex: the donor's sex</li> <li>Age: the donor's age</li> <li>percent.mt: Calculation of mitochondrial proportion</li> <li>percent.ribo: Calculation of ribosomal proportion</li> <li>annotation_level1: characterization of cells into the main immune compartments</li> <li>annotation_level2: focusing on each annotation_level1 category and performing in-depth characterization of the immune</li> <li>cells</li> <li>annotation_level3: focusing on the different subtypes of macrophages and the Tregs compartment</li> <li>The columns scDblFinder.class_rna, scDblFinder.score_rna, scDblFinder.weighted_rna, scDblFinder.cxds_score_rna are related</li> <li>to metrics for doublet detection using scDblFinder</li> <li>The columns S.Score, G2M.Score and Phase are related to cell cycling</li> </ul>
TGF-β neutralization attenuates tumor residency of activated T cells to enhance systemic immunity in mice
<p>Deep TCR sequencing was performed using the TCR Profiling Kit from MiLaboratories (Mouse α/β TCR RNA; Kit MiLaboratories; TMMR-001). Deep TCR sequencing was analyzed using the MiXCR software from MiLaboratories per manufacturer's recommendations. The files correspond to the TCR-beta sequences.<br>The files are named as follows:</p> <p> </p> <table> <tbody> <tr> <td>file_name</td> <td>cell type sequenced</td> <td>tissue of origin</td> <td>treatment</td> <td>mouse_id</td> </tr> <tr> <td>21BA1dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>bintrafusp alpha</td> <td>1</td> </tr> <tr> <td>23BA2dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>bintrafusp alpha</td> <td>2</td> </tr> <tr> <td>25BA3dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>bintrafusp alpha</td> <td>3</td> </tr> <tr> <td>27BA4dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>bintrafusp alpha</td> <td>4</td> </tr> <tr> <td>29BA5dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>bintrafusp alpha</td> <td>5</td> </tr> <tr> <td>22BA1SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>bintrafusp alpha</td> <td>1</td> </tr> <tr> <td>24BA2SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>bintrafusp alpha</td> <td>2</td> </tr> <tr> <td>26BA3SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>bintrafusp alpha</td> <td>3</td> </tr> <tr> <td>28BA4SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>bintrafusp alpha</td> <td>4</td> </tr> <tr> <td>30BA5SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>bintrafusp alpha</td> <td>5</td> </tr> <tr> <td>31CON1dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>control</td> <td>6</td> </tr> <tr> <td>33CON2dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>control</td> <td>7</td> </tr> <tr> <td>35CON3dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>control</td> <td>8</td> </tr> <tr> <td>37CON4dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>control</td> <td>9</td> </tr> <tr> <td>39CON5dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>control</td> <td>10</td> </tr> <tr> <td>32CON1SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>control</td> <td>6</td> </tr> <tr> <td>34CON2SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>control</td> <td>7</td> </tr> <tr> <td>36CON3SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>control</td> <td>8</td> </tr> <tr> <td>38CON4SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>control</td> <td>9</td> </tr> <tr> <td>40CON5SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>control</td> <td>10</td> </tr> <tr> <td>1aPDL11dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-PDL1 antibody</td> <td>11</td> </tr> <tr> <td>3aPDL12dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-PDL1 antibody</td> <td>12</td> </tr> <tr> <td>5aPDL13dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-PDL1 antibody</td> <td>13</td> </tr> <tr> <td>7aPDL14dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-PDL1 antibody</td> <td>14</td> </tr> <tr> <td>9aPDL15dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-PDL1 antibody</td> <td>15</td> </tr> <tr> <td>2aPDL11SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-PDL1 antibody</td> <td>11</td> </tr> <tr> <td>4aPDL12SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-PDL1 antibody</td> <td>12</td> </tr> <tr> <td>6aPDL13SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-PDL1 antibody</td> <td>13</td> </tr> <tr> <td>8aPDL14SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-PDL1 antibody</td> <td>14</td> </tr> <tr> <td>10aPDL15SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-PDL1 antibody</td> <td>15</td> </tr> <tr> <td>11aTGFB1dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-TGF-beta antibody</td> <td>16</td> </tr> <tr> <td>13aTGFB2dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-TGF-beta antibody</td> <td>17</td> </tr> <tr> <td>15aTGFB3dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-TGF-beta antibody</td> <td>18</td> </tr> <tr> <td>17aTGFB4dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-TGF-beta antibody</td> <td>19</td> </tr> <tr> <td>19aTGFB5dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-TGF-beta antibody</td> <td>20</td> </tr> <tr> <td>12aTGFB1SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-TGF-beta antibody</td> <td>16</td> </tr> <tr> <td>14aTGFB2SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-TGF-beta antibody</td> <td>17</td> </tr> <tr> <td>16aTGFB3SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-TGF-beta antibody</td> <td>18</td> </tr> <tr> <td>18aTGFB4SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-TGF-beta antibody</td> <td>19</td> </tr> <tr> <td>20aTGFB5SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-TGF-beta antibody</td> <td>20</td> </tr> </tbody> </table>
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>
Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single cell resolution
<p>Supplementary Tables for article "Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single cell resolution"<br> </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.