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2,952 results for “CD8”
SARS-CoV-2 antigen exposure history shapes phenotypes and specificity of memory CD8 T cells
<p>This dataset contains aggregated CellRanger output for six 10x Genomics (5'GEX+abTCR+Feature barcoding) experiments from the study by Minervina, Pogorelyy et al (<a href="https://www.medrxiv.org/content/10.1101/2021.07.12.21260227v3">medrxiv</a>). <br> The scripts to process it further are available at github (<a href="https://github.com/pogorely/COVID_vax_CD8">repository</a>). <br> Raw sequencing data is available at SRA (acc. PRJNA744851)</p>
Impact of CD4 T cells on intratumoral CD8 T cell exhaustion and responsiveness to PD-1 blockade therapy in mouse brain tumors
<p>scRNA-seq data (Cellranger filtered feature-barcode matrices) and scVDJ-seq data (Cellranger filtered_contig_annotations.csv files) for publication listed above.</p>
PD1+CD8+ cells are an independent prognostic marker in patients with head and neck cancer
<p><strong>Data open:</strong> file with parametres used for the multivariate evaluation. </p>
CD8+ T cell differentiation status correlates with the feasibility of sustained unresponsiveness following oral immunotherapy
<p>While food allergy oral immunotherapy (OIT) can provide safe and effective desensitization (DS), the immune mechanisms underlying development of sustained unresponsiveness (SU) following a period of avoidance are largely unknown. We compared high dimensional immunophenotypes of innate and adaptive immune cell subsets of participants in a phase 2 randomized, controlled, peanut OIT trial who achieved SU vs. DS (no vs. with allergic reactions upon food challenge after a withdrawal period; n=21 vs. 30 respectively among total 120 intent-to-treat participants). Lower frequencies of naïve CD8<sup>+</sup> T cells and terminally differentiated CD57+ CD8<sup>+</sup> T cell subsets at baseline (pre-OIT) were highly associated with SU. Frequency of naïve CD8<sup>+</sup> T cells showed a significant positive correlation with peanut- and Ara h 2-specific IgE at baseline. Higher frequencies of IL-4<sup>+</sup> and IFNg<sup>+</sup> CD8<sup>+</sup> T cells post-OIT were negatively correlated with SU. Our findings provide compelling evidence that an immune signature consisting of certain CD8<sup>+</sup> T cell subset frequencies is predictive of SU following OIT.</p>
Impact of low pH on glutamine incorporation in CD8+ T cells
<p>OT-I CTLs were cultured for 4 hours with <sup><span>13</span></sup><span>C</span>-glutamine in the presence, or absence, of IL-2 at pH7.4 or pH6.6. Metabolites that incorporated isotopic glutamine were identified by HILIC-HRMS.</p>
Spatial dynamics of CD39⁺CD8⁺ exhausted T cells reveal tertiary lymphoid structures-mediated response to PD-1 blockade in esophageal cancer
<p><strong>Data related to the paper</strong>: <em>"Spatial dynamics of CD39+CD8+ exhausted T cells reveal tertiary lymphoid structures-mediated response to PD-1 blockade in esophageal cancer,”</em> <em>Nature Communications</em> (2024)</p> <p>The repository data consists of two main folders: <strong>IMC_dataset</strong> and <strong>MC_normalized_dataset</strong>.</p> <p><strong>IMC_dataset</strong> includes:</p> <ol> <li> <p><strong>IMC_denoised_dataset</strong>: This folder contains cell mask images and noise-reduced images for each sample.</p> </li> <li> <p><strong>IMC_raw_dataset</strong>: This folder contains raw, unprocessed data.</p> </li> <li> <p><strong>IMC_processed_data</strong>: This folder contains standardized single-cell information and spillover-corrected FCS files, along with the compensation matrix.</p> </li> </ol> <p>The <strong>MC_normalized dataset</strong> includes FCS files that have been sorted by barcode.</p> <p><strong>Please note</strong> that in the IMC dataset, the following mass channels are blank:</p> <ul> <li><strong>Tumor-ROI</strong>: 80Ar, 127I, 131Xe, 145Nd, 146Nd, 149Sm, 160Gd, 171Yb, 174Yb, 176Yb, 190Os</li> <li><strong>SLO-ROI</strong>: 80Ar, 127I, 131Xe, 145Nd, 146Nd, 149Sm, 160Gd, 176Yb, 190Os</li> </ul> <p>The names attached to the file names are IDs.</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>
NGS data produced in 'Rapid selection and identification of functional CD8+ T-cell epitopes from large peptide-coding libraries'; Nature Communications (2019)
<p>Sharma, G et al. Rapid selection and identification of functional CD8+ T-cell epitopes from large peptide-coding libraries. <em>Nature Communications</em>. Accepted (August 2019)</p> <p><strong>Abstract:</strong></p> <p>Cytotoxic CD8+ T-cells recognize and eliminate infected or malignant cells that present, at their cell surfaces, short peptide epitopes derived from intracellularly processed antigens. However, broadly searching for specific major histocompatibility complex (MHC)-bound peptide epitopes that are naturally processed and capable of eliciting a functional T-cell response has been challenging. Here, we report a method for deep and unbiased T-cell epitope profiling, which is done by using <em>in vitro</em> co-culture of CD8+ T-cells and target cells transduced with high-complexity epitope-encoding minigene libraries. Target cells that are subject to cytotoxic attack from T-cells in co-culture are isolated, before they are lost to apoptosis, by fluorescence-activated cell sorting and characterized by sequencing the minigenes encoded within. In the present study, we validate this highly parallelized method using known murine T-cell receptor/peptide-MHC pairs and diverse minigene-encoded epitope libraries to identify naturally processed and MHC-presented peptide epitopes unambiguously and with high sensitivity.</p>
Single-cell RNA-Seq and TCR-Seq analysis of PD-1+ CD8+ T-cells responding to anti-PD-1 and anti-PD-1/CTLA-4 immunotherapy in melanoma
<p><strong>This dataset details the scRNASeq and TCR-Seq analysis of sorted PD-1+ CD8+ T cells from patients with melanoma treated with checkpoint therapy (anti-PD-1 monotherapy and anti-PD-1 & anti-CTLA-4 combination therapy) at baseline and after the first cycle of therapy. A major publication using this dataset is accessible here: (reference) </strong></p> <p> </p> <p><strong>*experimental design</strong></p> <p> Single-cell RNA sequencing was performed using 10x Genomics with feature barcoding technology to multiplex cell samples from different patients undergoing mono or dual therapy so that they can be loaded on one well to reduce costs and minimize technical variability. Hashtag oligomers (oligos) were obtained as purified and already oligo-conjugated in TotalSeq-C format from BioLegend. Cells were thawed, counted and 20 million cells per patient and time point were used for staining. Cells were stained with barcoded antibodies together with a staining solution containing antibodies against CD3, CD4, CD8, PD-1/IgG4 and fixable viability dye (eBioscience) prior to FACS sorting. Barcoded antibody concentrations used were 0.5 µg per million cells, as recommended by the manufacturer (BioLegend) for flow cytometry applications. After staining, cells were washed twice in PBS containing 2% BSA and 0.01% Tween 20, followed by centrifugation (300 xg 5 min at 4 °C) and supernatant exchange. After the final wash, cells were resuspended in PBS and filtered through 40 µm cell strainers and proceeded for sorting. Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions. Gene expression, hashing and TCR libraries were pooled to desired quantities to obtain the sequencing depths of 15,000 reads per cell for gene expression libraries and 5,000 reads per cell for hashing and TCR libraries. Libraries were sequenced on a NovaSeq 6000 flow cell in a 2X100 paired-end format.</p> <p> </p> <p><strong>*extract protocol</strong></p> <p> PBMCs were thawed, counted and 20 million cells per patient and time point were used for staining. Cells were stained with barcoded antibodies together with a staining solution containing antibodies against CD3, CD4, CD8, PD-1/IgG4 and fixable viability dye (eBioscience) prior to FACS sorting. Barcoded antibody concentrations used were 0.5 µg per million cells, as recommended by the manufacturer (BioLegend) for flow cytometry applications. After staining, cells were washed twice in PBS containing 2% BSA and 0.01% Tween 20, followed by centrifugation (300 xg 5 min at 4 °C) and supernatant exchange. After the final wash, cells were resuspended in PBS and filtered through 40 µm cell strainers and proceeded for sorting. Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions.</p> <p> </p> <p><strong>*library construction protocol</strong></p> <p> Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions. Gene expression, hashing and TCR libraries were pooled to desired quantities to obtain the sequencing depths of 15,000 reads per cell for gene expression libraries and 5,000 reads per cell for hashing and TCR libraries. Libraries were sequenced on a NovaSeq 6000 flow cell in a 2X100 paired-end format.</p> <p> </p> <p><strong>*library strategy</strong></p> <p> scRNA-seq and scTCR-seq</p> <p> </p> <p><strong>*data processing step</strong></p> <p> Pre-processing of sequencing results to generate count matrices (gene expression and HTO barcode counts) was performed using the 10x genomics Cell Ranger pipeline.</p> <p> Further processing was done with Seurat (cell and gene filtering, hashtag identification, clustering, differential gene expression analysis based on gene expression).</p> <p> </p> <p> <strong>*genome build/assembly</strong></p> <p> Alignment was performed using prebuilt Cell Ranger human reference GRCh38.</p> <p> </p> <p><strong>*processed data files format and content</strong></p> <p> RNA counts and HTO counts are in sparse matrix format and TCR clonotypes are in csv format.</p> <p>Datasets were merged and analyzed by Seurat and the analyzed objects are in rds format.</p> <p> </p> <table> <tbody> <tr> <td> <p><strong>file name</strong></p> </td> <td> <p><strong>file checksum</strong></p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>da2e006d2b39485fd8cf8701742c6d77</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>e125fc5031899bba71e1171888d78205</p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_contig_annotations.csv</p> </td> <td> <p>927241805d507204fbe9ef7045d0ccf4</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_contig_annotations.csv</p> </td> <td> <p>8ca544d27f06e66592b567d3ab86551e</p> </td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td> <p><strong>*processed data file </strong></p> </td> <td> <p><strong>antibodies/tags</strong></p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>none</p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>TotalSeq™-C0251 anti-human Hashtag 1 Antibody - (HASH_1) - M1_base_monotherapy<br>TotalSeq™-C0252 anti-human Hashtag 2 Antibody - (HASH_2) - M1_post_monotherapy<br>TotalSeq™-C0253 anti-human Hashtag 3 Antibody - (HASH_3) - C1_base_combined_therapy<br>TotalSeq™-C0254 anti-human Hashtag 4 Antibody - (HASH_4) - C1_post_combined_therapy<br>TotalSeq™-C0255 anti-human Hashtag 5 Antibody - (HASH_5) - C2_base_combined_therapy<br>TotalSeq™-C0256 anti-human Hashtag 6 Antibody - (HASH_6) - C2_post_combined_therapy</p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_contig_annotations.csv</p> </td> <td> <p>none</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>none</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>TotalSeq™-C0251 anti-human Hashtag 1 Antibody - (HASH_1) - M2_base_monotherapy<br>TotalSeq™-C0252 anti-human Hashtag 2 Antibody - (HASH_2) - M2_post_monotherapy<br>TotalSeq™-C0253 anti-human Hashtag 3 Antibody - (HASH_3) - M3_base_monotherapy<br>TotalSeq™-C0254 anti-human Hashtag 4 Antibody - (HASH_4) - M3_post_monotherapy<br>TotalSeq™-C0255 anti-human Hashtag 5 Antibody - (HASH_5) - C3_base_combined_therapy<br>TotalSeq™-C0256 anti-human Hashtag 6 Antibody - (HASH_6) - C3_post_combined_therapy</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_contig_annotations.csv</p> </td> <td> <p>none</p> </td> </tr> </tbody> </table> <p> </p>
Data from: Postnatal administration of S-adenosylmethionine restores developmental AHR activation-induced deficits in CD8+ T cell function during influenza A virus infection
<p>Abstract Developmental exposures can influence life-long health; yet, counteracting negative consequences is challenging due to poor understanding of cellular mechanisms. The aryl hydrocarbon receptor (AHR) binds many small molecules, including numerous pollutants. Developmental exposure to the signature environmental AHR ligand 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) significantly dampens adaptive immune responses to influenza A virus (IAV) in adult offspring. CD8+ cytotoxic T lymphocytes (CTL) are crucial for successful infection resolution, which depends on the number generated and the complexity of their functionality. Prior studies showed developmental AHR activation significantly reduced the number of virus-specific CD8+ T cells, but impact on their functions is less clear. Other studies showed developmental exposure was associated with differences in DNA methylation in CD8+ T cells. Yet, empirical evidence that differences in DNA methylation are causally related to altered CD8+ T cell function is lacking. The two objectives were to ascertain whether developmental AHR activation affects CTL function, and whether differences in methylation contribute to reduced CD8+ T cell responses to infection. Developmental AHR triggering significantly reduced CTL polyfunctionality, and modified the transcriptional program of CD8+ T cells. S-adenosylmethionine (SAM), which increases DNA methylation, but not Zebularine, which diminishes DNA methylation, restored polyfunctionality and boosted the number of virus-specific CD8+ T cells. These findings suggest that diminished methylation, initiated by developmental exposure to an AHR-binding chemical, contributes to durable changes in antiviral CD8+ CTL functions later in life. Thus, deleterious consequence of development exposure to environmental chemicals are not permanently fixed, opening the door for interventional strategies to improve health.</p>
Analysis on CD8+/FOXP3+Ratio and Its Association with Primary Endocrine Therapy Resistance in Indonesian Luminal B Her-2 Negative BC Patients
<p>Analysis on CD8+/FOXP3+Ratio and Its Association with Primary Endocrine Therapy Resistance in Indonesian Luminal B Her-2 Negative BC Patients</p>
High CD8 + / FOXP3+ Ratio is Significantly Associated with Primary Endocrine Therapy Resistance Occurrence in Locally Advanced Luminal B Her-2 Negative Breast Cancer Patients
<p>High CD8 + / FOXP3+ Ratio is Significantly Associated with Primary Endocrine Therapy Resistance Occurrence in Locally Advanced Luminal B Her-2 Negative Breast Cancer Patients</p>
Prospective Randomized Comparative Study of Cell Transfer Therapy Using CD8+-Enriched Short-Term Cultured Anti-Tumor Autologous Lymphocytes Following a Non-Myeloablative Lymphocyte Depleting Chemother
ClinicalTrials.gov study NCT01118091. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Cell Therapy for Metastatic Melanoma Using CD8 Enriched Tumor Infiltrating Lymphocytes
ClinicalTrials.gov study NCT01236573. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Data from: Postnatal administration of S-adenosylmethionine restores developmental AHR activation-induced deficits in CD8+ T cell function during influenza A virus infection
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Interleukin-17A signaling promotes CD8+ T cell cytotoxicity against West Nile virus infection through enhancing PI3K-mTOR-mediated metabolism
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Data from: Coordinated ARP2/3 and glycolytic activities regulate the morphological and functional fitness of human CD8+ T cells
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CD8+ T cell differentiation status correlates with the feasibility of sustained unresponsiveness following oral immunotherapy
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Commensal bacteria maintain a Qa-1b-restricted unconventional CD8+ T population in gut epithelium
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IL-32 producing CD8+ memory T cells and Tregs define the IDO1/PD-L1 niche in human cutaneous leishmaniasis skin lesions.
<p>https://github.com/NidhiSDey/leish-ME/</p> <p>Human cutaneous leishmaniasis (CL) is characterised by chronic skin pathology. Experimental and clinical data suggest that immune checkpoints (ICs) play a crucial role in disease outcome but the cellular and molecular niches that facilitate IC expression during leishmaniasis are ill-defined. We previously showed that in Sri Lankan patients with CL two ICs, indoleamine 2,3-dioxygenase 1 (IDO1) and programmed death-ligand 1 (PD-L1) are enriched in lesional skin and that reduced PD-L1 expression early after treatment onset predicts cure rate following antimonial therapy. Here, we use spatial cell interaction mapping to identify IL-32-expressing CD8+ memory cells and regulatory T cells as key components of the IDO1 / PD-L1 niche in a cohort of Sri Lankan CL patients. This finding was confirmed in patients with distinct forms of dermal leishmaniasis in Brazil and India. Furthermore, in our Sri Lankan cohort the abundance of IL-32+ cells and IL-32+CD8+ T cells at treatment onset was prognostic for rate of cure. This study provides a unique spatial perspective on the expression of key ICs in these important skin diseases and a novel route to identify biomarkers of treatment response.</p>
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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.
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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.