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Natural Killer T-Cells

Natural Killer T-Cells are a unique subset of T lymphocytes that express markers of both natural killer cells and conventional T cells.
They play a crucial role in the immune system, bridging the gap between innate and adaptive immunity.
These specialized cells can rapidly respond to lipid antigens presented by the MHC class I-like molecule CD1d, and they possess the ability to secrete a wide array of cytokines, making them key regulators of immune responses.
Leverage the power of Natural Killer T-Cells with PubCompare.ai, the AI-driven platform that helps you identify the optimal research protocols.
Effortlessly search through literature, pre-prints, and patents to find the best solutions, using our smart comparisson tools to make informed decisions.
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Most cited protocols related to «Natural Killer T-Cells»

1
Quantifying Immune Cell Infiltration in GC TME
We used the ssGSEA (single-sample gene-set enrichment analysis) algorithm to quantify the relative abundance of each cell infiltration in the GC TME. The gene set for marking each TME infiltration immune cell type was obtained from the study of Charoentong, which stored various human immune cell subtypes including activated CD8 T cell, activated dendritic cell, macrophage, natural killer T cell, regulatory T cell and so on (Table S2) [28 (link), 29 (link)]. The enrichment scores calculated by ssGSEA analysis were utilized to represent the relative abundance of each TME infiltrating cell in each sample.
Zhang B., Wu Q., Li B., Wang D., Wang L, & Zhou Y.L. (2020). m6A regulator-mediated methylation modification patterns and tumor microenvironment infiltration characterization in gastric cancer. Molecular Cancer, 19, 53.
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Publication 2020
Antigen-Presenting Cells CD8-Positive T-Lymphocytes Genes Homo sapiens Macrophage Natural Killer T-Cells Regulatory T-Lymphocytes
2
Flow Cytometric Analysis of Wound Cells

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Publication 2011
Antibodies, Anti-Idiotypic Buffers CD1D protein, human Cells Clone Cells Edetic Acid Flow Cytometry Fluorescein-5-isothiocyanate Glycolipids IgG1 Macrophage Mus Natural Killer T-Cells Neutrophil Phosphates Population Group Proteins Saline Solution Serum Albumin, Bovine Sodium Azide Trees Wounds
3
Comprehensive Tumor Immunophenotyping Protocol
Four-micrometer-thick sequential histologic tumor sections were obtained
from a representative formalin-fixed, paraffin-embedded tumor block and used for
IHC analysis. IHC was performed using an automated staining system (BOND-MAX;
Leica Microsystems) with antibodies against PD-L1 (clone E1L3N, dilution 1:100;
Cell Signaling Technology), CD3 (T-cell lymphocytes; dilution 1:100; Dako), CD4
(helper T cell; Novocastra; clone 4B12, dilution 1:80; Leica Biosystems), CD8
(cytotoxic T cell; clone CD8/144B, dilution 1:20; Thermo Fisher Scientific),
CD57 (natural killer T cell; clone HNK-1, dilution 1:40; BD Biosciences),
granzyme B (cytotoxic lymphocytes; clone F1, ready to use; Leica Biosystems),
CD45RO (memory T cell; clone UCHL1, ready to use; Leica Biosystems), PD-1 (clone
EPR4877-2, dilution 1:250; Abcam), FOXP3 (regulatory T cell; clone 206D,
dilution 1:50; BioLegend), and CD68 (macrophages; clone PG-M1, dilution 1:450;
Dako). Expression of all of the markers in cells was detected using a Novocastra
Bond Polymer Refine Detection kit (Leica Microsystems) with a diaminobenzidine
reaction to detect antibody labeling and hematoxylin counterstaining. Positive
and negative controls were used for PD-L1 IHC expression (human embryonic kidney
293 cell line transfected and nontransfected with PD-L1 gene and human placenta
and tonsil FFPE tissues) during each run IHC staining using autostainers. For
the TAIC IHC expression, human tonsil FFPE tissues with and without primary
antibody were used as positive and negative controls, respectively, with each
run IHC staining.
Parra E.R., Behrens C., Rodriguez-Canales J., Lin H., Mino B., Blando J., Zhang J., Gibbons D.L., Heymach J.V., Sepesi B., Swisher S.G., Weissferdt A., Kalhor N., Izzo J., Kadara H., Moran C., Lee J.J, & Wistuba I.I. (2016). Image Analysis–based Assessment of PD-L1 and Tumor-Associated Immune Cells Density Supports Distinct Intratumoral Microenvironment Groups in Non–small Cell Lung Carcinoma Patients. Clinical cancer research : an official journal of the American Association for Cancer Research, 22(24), 6278-6289.
Publication 2016
Antibodies CD45RO Antigens CD57 Antigens CD274 protein, human Cell Lines Cells Clone Cells Cytotoxic T-Lymphocytes Embryo Formalin Genes GZMB protein, human Helper-Inducer T-Lymphocyte Hematoxylin Homo sapiens Immunoglobulins Lymphocyte Macrophage Memory T Cells Natural Killer T-Cells Neoplasms Palatine Tonsil Paraffin Polymers Regulatory T-Lymphocytes T-Lymphocyte Technique, Dilution Tissues UCHL1 protein, human
4
Comprehensive Tumor Microenvironment Analysis
The gene set variation analysis (GSVA) was performed to identify specific pathways of each subtype (24 (link)). We downloaded Hallmark and KEGG gene sets from the Molecular Signatures Database and further transformed the gene expression matrix into gene set matrix using the GSVA package. Afterwards, we performed gene sets difference analysis using the limma package and the screening threshold were set to |log2 fold change (FC)| >0.2 and adjusted P-value <0.05. Adjusted P-value was obtained from the Benjamini–Hochberg multiple test correction.
Referring to Charoentong et al. study (25 (link)), we obtained the markers of 23 immune cells including: innate immune cells (activated dendritic cells, CD56+ natural killer cells, CD56− natural killer cells, eosinophils, immature dendritic cells, macrophages, mast cells, MDSC, monocytes, natural killer cells, neutrophils, and plasmacytoid dendritic cells) and adaptive immune cells (activated B cells, activated CD4+ T cells, activated CD8+ T cells, Gamma delta T cells, immature B cells, natural killer T cells, Treg cells, follicular helper T cells, Th1 cells, Th2 cells, and Th17 cells). Endothelial cells and fibroblasts, also the important components of TME, played a crucial role in tumor inflammation, angiogenesis, invasion, and metastasis. The markers of endothelial cell and fibroblast were retrieved from the MCP-counter (26 (link)) (Table S3). Based on these markers, we applied the single sample gene set enrichment analysis (ssGSEA) algorithm to evaluate the infiltration abundance of 25 TME cells.
Liu Z., Wang L., Liu L., Lu T., Jiao D., Sun Y, & Han X. (2021). The Identification and Validation of Two Heterogenous Subtypes and a Risk Signature Based on Ferroptosis in Hepatocellular Carcinoma. Frontiers in Oncology, 11, 619242.
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Publication 2021
Acclimatization angiogen B-Lymphocytes CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes Dendritic Cells Endothelial Cells Eosinophil Fibroblasts Gene Expression Genes Genetic Diversity Immature B-Lymphocyte Inflammation Intraepithelial Lymphocytes Macrophage Mast Cell Monocytes Myeloid-Derived Suppressor Cells Natural Killer Cells Natural Killer T-Cells Neoplasm Metastasis Neoplasms Neutrophil Plasmacytoid Dendritic Cells Regulatory T-Lymphocytes T Follicular Helper Cells Th17 Cells Type-2 Helper T Cell Type 1 Helper T Cells
5
Modeling TMPRSS2/SLC6A20 Expression in Lung Cell Types
We used MuSiC (v.0.1.1) (Wang et al., 2019 (link)) to estimate the proportions of lung cell types with >500 cells from our scRNA-seq dataset in lung bulk RNA-seq samples from the GTEx v8 release (GTEx Consortium, 2020 (link)). We combined cell-type labels for capillary (distal and proximal), macrophages (M1 and M2), matrix fibroblasts (1 and 2), and NK/T cells. We modeled the relationship between TMM-normalized TMPRSS2 or SLC6A20 expression as a function of the interaction between genotype and cell-type proportion, while considering the covariates used in the original GTEx data including sex, sequencing platform, PCR, five genotype PCs, and 59 inferred PCs from the expression data. From the original inferred PCs, we excluded inferred PC one because it was highly correlated with AT2 cell-type proportion (Spearman ρ = 0.67). Including additional covariates in the model such as age, body-mass index or smoking status did not have meaningful impact on the results.
Wang A., Chiou J., Poirion O.B., Buchanan J., Valdez M.J., Verheyden J.M., Hou X., Kudtarkar P., Narendra S., Newsome J.M., Guo M., Faddah D.A., Zhang K., Young R.E., Barr J., Sajti E., Misra R., Huyck H., Rogers L., Poole C., Whitsett J.A., Pryhuber G., Xu Y., Gaulton K.J., Preissl S, & Sun X. (2020). Single-cell multiomic profiling of human lungs reveals cell-type-specific and age-dynamic control of SARS-CoV2 host genes. eLife, 9, e62522.
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Publication 2020
Capillaries Cells Dietary Fiber Fibroblasts Genotype Index, Body Mass Lung Macrophage Natural Killer T-Cells RNA-Seq Single-Cell RNA-Seq TMPRSS2 protein, human

Most recents protocols related to «Natural Killer T-Cells»

1
Comprehensive Immune Landscape of Alzheimer's Disease
The gene sets of 28 immune cells and four classes of immune factors were downloaded from TISIDB database.3 The following 28 types of immune cells were obtained: central memory CD4+ T cells (CD4+ Tcm), central memory CD8+ T cells (CD8+ Tcm), type-2 T helper cells (Th2), CD56dim natural killer cells (CD56− NK), activated CD8+ T cells (CD8+ Ta), activated CD4+ T cells (CD4+ Ta), activated B cells (Ba), effector memory CD8+ T cells (CD8+ Tem), effector memory CD4+ T cells (CD4+ Tem), macrophages, eosinophils, memory B cells (Bm), immature dendritic cells (DCi), gamma delta T cells (γδT), CD56bright natural killer cells (CD56+ NK), monocytes, mast cells, natural killer cells (NK), immature B cells (Bi), type-1 T helper cells (Th1), neutrophils, plasmacytoid dendritic cells (DCp), natural killer T cells (NK T), type-17 T helper cells (Th17), follicular helper T cells (Tfh), regulatory T cells (Tregs), myeloid-derived suppressor cells (MDSC), and activated dendritic cells (DCa). The four classes of immune factors include 41 chemokines, 24 immunosuppressive factors, 46 immunostimulatory factors, and 18 immune receptors.
The ssGSEA algorithm, which classifies gene sets with common biological functions, physiological regulation, and chromosomal localization, was employed via R packages (GSVA 1.42.0) to comprehensively assess the immunologic characteristics of each sample included in the analyses (Hänzelmann et al., 2013 (link)). Normalized data of gene expression profiles were compared with the gene sets to demonstrate the enrichment of immune cells in each AD brain samples. Then, ANOVA was adopted to identify immune cell types with significant differences between the groups with longer lifespan and shorter lifespan. Pearson correlations between the gene expression level of each hub gene and the concentrations of immune cells were carried out using cor.test in R software (version: 4.0.3). The hub genes were identified in 2.4.
The correlations between the gene expression levels of each hub gene and the gene sets of immune factors were also calculated, respectively. Then, the pairs of hub genes and immune-related molecules with |cor| > 0.6 & p value<0.05 were selected to generate a circos plot via Cytoscape.
Zhang J., Li X., Xiao J., Xiang Y, & Ye F. (2023). Analysis of gene expression profiles in Alzheimer’s disease patients with different lifespan: A bioinformatics study focusing on the disease heterogeneity. Frontiers in Aging Neuroscience, 15, 1072184.
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Publication 2023
B-Lymphocytes Biological Processes Brain CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes Central Memory T Cells Chemokine Chromosomes Dendritic Cells Effector Memory T Cells Eosinophil Gene Expression Genes Helper-Inducer T-Lymphocyte Immature B-Lymphocyte Immunization Immunologic Factors Immunosuppressive Agents Intraepithelial Lymphocytes Macrophage Mast Cell Memory B Cells Monocytes Myeloid-Derived Suppressor Cells Natural Killer Cells Natural Killer T-Cells neuro-oncological ventral antigen 2, human Neutrophil physiology Plasmacytoid Dendritic Cells Receptors, Immunologic Regulatory T-Lymphocytes Th17 Cells Type-2 Helper T Cell Type 1 Helper T Cells
2
Tumor Immune Landscape Analysis
Tumor-infiltrated cells were estimated by single-sample gene set enrichment analysis (ssGSEA) using the GSVA package (Hänzelmann et al., 2013 (link)). Transcriptional data of tumor-infiltrating cells used for functional analysis were derived from Charoentong et al. (Rooney et al., 2015 (link)). The positive immune regulators were defined as the collection of “effector” cells, active dendritic cells (aDCs), natural killer cells (NKs), and natural killer T cells (NKTs). Negative immune regulators were defined as the collection of regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs). The “effector” cells were defined as active T cells (aCD4+T and aCD8+T) and effector memory T cells (CD4+Tem and CD8+Tem). Cytolytic activity (CYT) was used for evaluating immune activity and calculated as the geometric mean of granzyme A (GZMA) and perforin (PRF1) expression levels as previously defined (Cancer Genome Atlas Research Network, 2012 (link)). Functional enrichment analysis between groups was realized by GSVA based on gene expression data matrices.
Hu Z., Liu Z., Zheng J., Peng Y., Lu X., Li J., Tan K, & Cui H. (2023). Microsatellite instability-related prognostic risk score (MSI-pRS) defines a subset of lung squamous cell carcinoma (LUSC) patients with genomic instability and poor clinical outcome. Frontiers in Genetics, 14, 1061002.
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Publication 2023
Biological Response Modifiers Cells Dendritic Cells Effector Memory T Cells Genes Genome GZMA protein, human Malignant Neoplasms Myeloid-Derived Suppressor Cells Natural Killer Cells Natural Killer T-Cells Neoplasms Perforin Regulatory T-Lymphocytes T-Lymphocyte Transcription, Genetic
3
Retrospective Immunophenotyping Study for HSCT
This study was approved by the Hebei Yanda Lu Daopei Hospital Ethics Committee and informed consent was obtained from all participants before any study procedures were conducted. It was organized as a retrospective review study and Figure 1 illustrates the research design.
A total of 3395 patients who had received allo-HSCT from January 2013 to December 2019 in this hospital were identified. The MRD of these patients was analyzed by two-tube, eight-color MFC panel.
Three groups of patients who underwent bone marrow (BM) or peripheral blood (PB) immunophenotyping for non-malignant diseases at Hebei Yanda Lu Daopei Hospital were also identified. The first group included 5256 participants and the presence of CD16 expression in granulocytes was determined. The second group included 2000 participants who were screened for natural killer (NK) or T-cells for further immunophenotype analysis of NK lymphoma. The third group included 1100 participants over 35 years of age with non-neoplastic diseases who underwent screening of plasma and B cells.
Wang H., Wang A., Chen M., Gong M., Wu X., Zhen J, & Lu Y. (2023). Abnormalities by Multicolor Flow Cytometry for Detection of Minimal Residual Disease in Recipients of Allo-HSCT Originating from Donors: A Cohort Study. Turkish Journal of Hematology, 40(1), 18-27.
Publication 2023
B-Lymphocytes Bone Marrow Ethics Committees, Clinical Granulocyte Hematological Disease Immunophenotyping Lymphoma Natural Killer T-Cells Neoplasms Patients Plasma
4
COVID-19 Vaccine Responses in Mature T/NK-cell Neoplasms
Patients with mature T/NK-cell neoplasms under follow up at the Blood Disorders Center at Aiiku Hospital during the period from May 26, 2021, to August 5, 2022, were included in this study. All of the patients were vaccinated with at least two doses of an mRNA-based COVID-19 vaccine, either BNT162b2 or mRNA-1273. BNT162b2 and mRNA-1273 were administered 21 and 28 days apart, respectively. All disease statuses of patients were determined at the time of the second vaccination. We recruited healthcare workers aged 50 years or older who had received at least two doses of BNT162b2 vaccine as HC. They were unlikely to be transmitted from inpatients since our hospital had not accepted COVID-19 patients. Individuals with a known history of COVID-19 were excluded from both cohorts of patients and HC. This study was a part of a prospective observation study (UMIN 000,045,267, 000,048,764) and it was conducted in compliance with ethical principles based on the Helsinki Declaration and was approved by the institutional review board of Aiiku Hospital. All patients provided written informed consent.
Kobayashi M., Mori A., Onozawa M., Tsukamoto S., Senjo H., Ishio T., Yokoyama E., Kanaya M., Izumiyama K., Saito M., Muraki H., Morioka M., Teshima T, & Kondo T. (2023). Humoral response to mRNA-based COVID-19 vaccine and booster effect of a third dose in patients with mature T cell and NK-cell neoplasms. Annals of Hematology, 102(4), 819-827.
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Publication 2023
2019-nCoV Vaccine mRNA-1273 BNT162B2 COVID 19 CVnCoV COVID-19 vaccine Ethics Committees, Research Health Personnel Hematological Disease Inpatient Natural Killer T-Cells Neoplasms Patients Secondary Immunization Vaccines
5
Immune Cell Profiling of the TME
The gene set of TME infiltration immune cell has been described in the study of Charoentong et al. (35 (link)), which harbors numerous human immune cell subtypes such as activated CD8 T cell, activated dendritic cell, activated B cell, macrophage, mast cells, monocyte, natural killer T cell, and regulatory T cells. The relative abundance of each TME cell infiltration in each cluster was calculated by the single-sample gene-set enrichment analysis (ssGSEA) algorithm and represented by enrichment scores (29 (link), 36 (link)).
Jing Q., Yao H., Li H., Yuan C., Hu J., Zhang P., Wu Y., Zhou Y., Ren X., Yang C., Lei G., Du J., Ke X., Xia J, & Tong X. (2023). A novel RNA modification prognostic signature for predicting the characteristics of the tumor microenvironment in gastric cancer. Frontiers in Oncology, 13, 905139.
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Publication 2023
B-Lymphocytes CD8-Positive T-Lymphocytes Dendritic Cells Genes Homo sapiens Macrophage Mast Cell Monocytes Natural Killer T-Cells Regulatory T-Lymphocytes

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More about "Natural Killer T-Cells"

Natural killer T-cells (NKT cells) are a unique subset of T lymphocytes that exhibit characteristics of both natural killer cells and conventional T cells.
These specialized immune cells play a crucial role in bridging the gap between innate and adaptive immunity.
NKT cells can rapidly respond to lipid antigens presented by the MHC class I-like molecule CD1d, and they possess the ability to secrete a wide array of cytokines, making them key regulators of immune responses.
NKT cells are identified by the expression of markers typically associated with natural killer cells, such as CD56 and CD161, in addition to the T cell receptor (TCR).
These cells are further classified into distinct subsets based on their TCR usage and cytokine production profiles.
The study and analysis of NKT cells often involves the use of flow cytometry techniques, such as FACSCanto II, FACSCalibur, FACSAria II, FACSDiva software, and the FACSCalibur flow cytometer.
These powerful tools allow for the precise identification, enumeration, and characterization of NKT cell populations within various biological samples.
The potential of NKT cells in immunotherapeutic applications has been the focus of extensive research.
Leveraging the unique properties of these cells, PubCompare.ai, an AI-driven platform, can help researchers identify optimal research protocols and find the best solutions by effortlessly searching through literature, preprints, and patents.
By using PubCompare.ai's smart comparison tools, researchers can make informed decisions and unlock the full potential of their NKT cell-related studies.
Whether you're investigating the role of NKT cells in disease pathogenesis, exploring their potential in cancer immunotherapy, or studying their broader immunoregulatory functions, PubCompare.ai can be a valuable resource to support your research endeavors.
Unlock the power of NKT cells and drive your research forward with the help of this innovative AI-driven platform.