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Natural Killer Cells

Natural Killer Cells: A subsetof lymphocytes that possess the ability to lyse certain tumor cells and virus-infected cells without prior sensitization.
They represent an important component of the innate immune system.
Thier unique capabilites make them a key focus for immunological research, with PubCompare.ai's innovative platform helping unlock the secrets of these powerful cells and advance your studies to new heights.
Discover the power of natural killer cells and optimize your work with PubCompare.ai's AI-driven tools.

Most cited protocols related to «Natural Killer Cells»

1
Customizing Leukocyte Signature Matrices for Tumor Profiling
In the following two sections, we describe how to create a custom leukocyte signature matrix and apply it to study cellular heterogeneity and TIL survival associations in melanoma tumors profiled by The Cancer Genome Atlas (TCGA). Readers can follow along by creating ‘LM6’, a leukocyte RNA-Seq signature matrix comprised of six peripheral blood immune subsets (B cells, CD8 T cells, CD4 T cells, NK cells, monocytes/macrophages, neutrophils; GSE60424 [20 ]). Key input files are provided on the CIBERSORT website (‘Menu>Download’).
A custom signature file can be created by uploading the Reference sample file and the Phenotype classes file (section 3.3.2) to the online CIBERSORT application (SeeFigure 2) or can be created using the downloadable Java package. To build a custom gene signature matrix with the latter, the user should download the Java package from the CIBERSORT website and place all relevant files under the package folder. To link Java with R, run the following in R:
Within R:

> library(Rserve)

> Rserve(args=“–no-save”)

Command line:

> java -Xmx3g -Xms3g -jar CIBERSORT.jar -M Mixture_file -P Reference_sample_file -c phenotype_class_file -f

The last argument (-f) will eliminate non-hematopoietic genes from the signature matrix and is generally recommended for signature matrices tailored to leukocyte deconvolution. The user can also run this step on the website by choosing the corresponding reference sample file and phenotype class file (seeFigure 2). The CIBERSORT website will generate a gene signature matrix located under ‘Uploaded Files’ for future download.
Following signature matrix creation, quality control measures should be taken to ensure robust performance (see ‘Calibration of in silico TIL profiling methods’ in Newman et al.) [17 (link)]. Factors that can adversely affect signature matrix performance include poor input data quality, significant deviations in gene expression between cell types that reside in different tissue compartments (e.g., blood versus tissue), and cell populations with statistically indistinguishable expression patterns. Manual filtering of poorly performing genes in the signature matrix (e.g., genes expressed highly in the tumor of interest) may improve performance.
To benchmark our custom leukocyte matrix (LM6), we compared it to LM22 using a set of TCGA lung squamous cell carcinoma tumors profiled by RNA-Seq and microarray (n = 130 pairs). Deconvolution results were significantly correlated for all cell subsets shared between the two signature matrices (P < 0.0001). Notably, since LM6 was derived from leukocytes isolated from peripheral blood [20 ,21 (link)], we restricted the CD4 T cell comparison to naïve and resting memory CD4 T cells in LM22. Once validation is complete, a CIBERSORT signature matrix can be broadly applied to mixture samples as described in section 3.3 (e.g., SeeFigure 4).
Chen B., Khodadoust M.S., Liu C.L., Newman A.M, & Alizadeh A.A. (2018). Profiling tumor infiltrating immune cells with CIBERSORT. Methods in molecular biology (Clifton, N.J.), 1711, 243-259.
Publication 2018
B-Lymphocytes BLOOD CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes cDNA Library Cells Genes, vif Genetic Diversity Genetic Heterogeneity Hematopoietic System Leukocytes Lung Neoplasms Macrophage Malignant Neoplasms Melanoma Memory Microarray Analysis Monocytes Natural Killer Cells Neoplasms Neutrophil Phenotype Population Group RNA-Seq RNA Motifs Squamous Cell Carcinoma Strains Tissues
2
RNA-seq Data Preprocessing and Immune Cell Quantification
FASTQ files of RNA-seq reads were pre-processed with Trimmomatic [22 (link)] to remove adapter sequences and read ends with Phred quality scores lower than 20, to discard reads shorter than 36 bp, and to trim long reads to a maximum length of 50 bp. This analysis is implemented in the “Preprocessing” module of quanTIseq (step 1 in Fig. 1c), which also allows selecting different parameters for data preprocessing.

quanTIseq method and validation based on blood-cell mixtures. a quanTIseq characterizes the immune contexture of human tumors from expression and imaging data. Cell fractions are estimated from expression data and then scaled to cell densities (cells/mm2) using total cell densities extracted from imaging data. b Heatmap of quanTIseq signature matrix, with z scores computed from log2(TPM+1) expression values of the signature genes. c The quanTIseq pipeline consists of three modules that perform (1) pre-processing of paired- or single-end RNA-seq reads in FASTQ format; (2) quantification of gene expression as transcripts-per-millions (TPM) and gene counts; and (3) deconvolution of cell fractions and scaling to cell densities considering total cells per mm2 derived from imaging data. The analysis can be initiated at any step. Optional files are shown in grey. Validation of quanTIseq with RNA-seq data from blood-derived immune cell mixtures generated in [46 (link)] (d) and in this study (e). Deconvolution performance was assessed with Pearson’s correlation (r) and root-mean-square error (RMSE) using flow cytometry estimates as ground truth. The grey and blue lines represent the linear fit and the “x = y” line, respectively. B, B cells; CD4, non-regulatory CD4+ T cells; CD8, CD8+ T cells; DC, dendritic cells; M1, classically activated macrophages; M2, alternatively activated macrophages; Mono, monocytes; Neu, neutrophils; NK, natural killer cells; T, T cells; Treg, regulatory T cells

Finotello F., Mayer C., Plattner C., Laschober G., Rieder D., Hackl H., Krogsdam A., Loncova Z., Posch W., Wilflingseder D., Sopper S., Ijsselsteijn M., Brouwer T.P., Johnson D., Xu Y., Wang Y., Sanders M.E., Estrada M.V., Ericsson-Gonzalez P., Charoentong P., Balko J., de Miranda N.F, & Trajanoski Z. (2019). Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data. Genome Medicine, 11, 34.
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Publication 2019
B-Lymphocytes Blood Cells CD8-Positive T-Lymphocytes Cells Dendritic Cells Flow Cytometry Gene Expression Genes Homo sapiens Macrophage Monocytes Natural Killer Cells Neoplasms Neutrophil Plant Roots Regulatory T-Lymphocytes RNA-Seq T-Lymphocyte
3
Isolation and Sorting of Hematopoietic Cell Populations

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Publication 2011
Antibodies B-Lymphocytes Basophils Cells Dendritic Cells Eosinophil Ficoll Flow Cytometry Granulocyte Progenitor Cells Hypaque Immunophenotyping ITGAM protein, human Lymph Megakaryocyte-Erythroid Progenitor Cells Megakaryocytes Monocytes Myeloid Progenitor Cells Natural Killer Cells Neutrophil Population Group Stem Cells, Hematopoietic T-Lymphocyte trizol
4
Multimodal analysis of IFNβ response
We chose to use the first 20 aligned canonical correlation vectors to calculate the cell-cell distance matrix and subsequent SNN. We ran FindClusters with a resolution parameter of 0.6, resulting in 13 distinct clusters of cells. These clusters corresponded to CD14+ and CD16+ monocytes, CD4+ memory and naive T cells, CD8+ T cells, B cells, NK cells, dendritic cells, and erythrocyte populations which all showed significant enrichment for canonical cell type markers after running a likelihood-ratio test for differential expression implemented in Seurat as FindAllMarkers. The same 20 CCs were used as input for visualization via tSNE.
To understand global correlations between IFNβ responses for each cell type (Figure 2G), we first placed cells into 26 bins (based on the 13 immune clusters, but also grouped stimulated and resting cells within each cluster separately), and calculated the average expression for each gene within each group. The difference between the average expression of stimulated and resting cells for each cluster represents its transcriptional response to IFNβ stimulation. We then calculated the Pearson correlation of these responses between all pairs of clusters, using 431 genes that exhibited at least a two-fold change in response to stimulation for at least one of the 13 clusters.
Butler A., Hoffman P., Smibert P., Papalexi E, & Satija R. (2018). Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature biotechnology, 36(5), 411-420.
Publication 2018
B-Lymphocytes CD8-Positive T-Lymphocytes Cells Cloning Vectors Cytosol Dendritic Cells Erythrocytes Gene Expression Genes Germ Cells Memory Monocytes Natural Killer Cells T-Lymphocyte Transcription, Genetic
5
Immune Cell Signature Scoring Protocol
Signature scoring: Signature estimates were constructed as the median of z-scored (log2) expression values of each signature gene component except for the NK markers (see below).
TCD8 (CD8+ T cells): (CD8A, CD8B) Source: Mining of immune signatures in tumors using CD8A as sentinel marker. Reciprocal-Mutual-Rank methods were used to identify transcripts most intimately associated with sentinel markers. Caveats: CD8A is also expressed in a fraction of dendritic cells, some NK cells, and occasionally (rarely) in tumors.
Treg (Regulatory T Cells): (FOXP3, CCR8) Source: Mining of immune signatures in tumors using FOXP3 as sentinel marker. Reciprocal-Mutual-Rank methods were used to identify transcripts most intimately associated with sentinel markers. Caveats: Although CCR4 and CCR8 seem to be most predominantly co-expressed with FOXP3 in tumors, in sorted immune cells these receptors can also be seen in activated populations of CD4+ and CD8+ T cells.
Tcell (Pan T-Cell): (CD3D, CD3E, CD2) Mining of immune signatures in tumors using CD3 family members as sentinel markers. Reciprocal-Mutual-Rank methods were used to identify transcripts most intimately associated with CD3 epsilon (CD3E).
Bcell (B-cell): (CD19, CD79A, MS4A1) Source: Mining of immune signatures in tumors using CD19 as sentinel marker. Reciprocal-Mutual-Rank methods were used to identify transcripts most intimately associated with sentinel markers.
Mono (Monocyte lineage): (CD86, CSF1R, C3AR1) Source: Examination of correlation between antigen presenting cell-related genes across TCGA. Caveats: may not discriminate well between monocytes, macrophages, and other related members of the lineage.
M2mf (M2 Macrophage): (CD163, VSIG4, MS4A4A) Source: cross-referencing of Fantom/Hacohen/Rooney macrophage marker sets with mutual rank distance measures across TCGA[21 (link)]. The initial set was expanded with neighboring genes, cross-referenced with the literature and Mouse Immunological Genome Project (http://immgen.org) expression profiles to reduce to a small list of macrophage markers.
NK (Natural Killer cells): (KIR2DL1, KIR2DL3, KIR2DL4, KIR3DL1, KIR3DL2, KIR3DL3, KIR2DS4) Source: Mutual-rank correlation analysis of Natural Killer Group (NKG) and Killer-Cell Immmunoglobulin-Like Receptor (KIR) receptor families in TCGA tumor data revealed co-regulation of multiple members of the KIR family. However, any specific KIR gene was often observed to be at the lower limit of detection set by the TCGA RNA-seq pipeline. Compared to other cellular signatures, a larger collection of (KIR) markers was selected, a mean instead of median summarization was used to estimate NK cell content, and a small Gaussian noise component was added (mean 0.16, standard deviation 0.08) to improve the normality of the NK signature score distribution.
TregCD8 and NKCD8 signatures were constructed by subtracting the TCD8 estimate from Treg estimate, or the TCD8 from the NK estimate, respectively.
Siemers N.O., Holloway J.L., Chang H., Chasalow S.D., Ross-MacDonald P.B., Voliva C.F, & Szustakowski J.D. (2017). Genome-wide association analysis identifies genetic correlates of immune infiltrates in solid tumors. PLoS ONE, 12(7), e0179726.
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Publication 2017
Antigen-Presenting Cells B-Lymphocytes C3AR1 protein, human CCR8 protein, human CD3E protein, human CD8-Positive T-Lymphocytes CD79A protein, human CD94 Antigen CD163 protein, human Cells Dendritic Cells Family Member Gene, c-fms Genes Genome KIR2DL1 protein, human KIR3DL1 protein, human Macrophage Monocytes Mus Natural Killer Cells Neoplasms Regulatory T-Lymphocytes RNA-Seq T-Lymphocyte Vision

Most recents protocols related to «Natural Killer Cells»

1
Bioactivity Validation of Compounds 3 and 7
Not available on PMC !

Example 19

To confirm bioactivity of 3 and 7, experiments were performed with the HH cell line, a mature T cell line derived from peripheral blood of a patient with aggressive cutaneous T cell leukemia/lymphoma (ATCC® CRL-2105™) which been demonstrated to only express the IL-2Rβ/γ. One of the earliest events in cytokine mediated activation of lymphocytes such as CD8+ T cells and NK cells is Janus Associated Kinase mediated phosphorylation and activation of Signal transducer and activator of transcription (pSTAT5). Thus, pSTAT5 was used to measure biological activity of 3 and 7 alongside 12. 3 demonstrated clear bioactivity in IL-2Rβ/γ expressing HH cells (EC50: 773 ng/ml) that was approximately 3.5 fold lower than 12 (EC50: 233 ng/ml). Additionally, 7 induced bioactivity (EC50: 756 ng/ml) very similar to 3, demonstrating that 7 retains bioactivity after being released from prodrug 5 even after accelerated (stress) conditions.

US11879001B2. Conjugate comprising an IL-2 moiety (2024-01-23). ASCENDIS PHARMA ONCOLOGY DIVISION A/S [DK]. Inventors: Nina Gunnarsson [DK], Matiss Maleckis [DK], David B. Rosen [US].
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Patent 2024
Biopharmaceuticals BLOOD CD8-Positive T-Lymphocytes Cell Lines Cells Cytokine IL19 protein, human Kinase, Janus Leukemia Lymphocyte Activation Lymphoma, T-Cell, Cutaneous Natural Killer Cells Patients Phosphorylation Prodrugs Transcription, Genetic Transducers
2
Anti-PD-L1 Enhances NK Cell Activation
Not available on PMC !

Example 5

This example provides a showing of an effect of anti-PD-L1 on NK cell activation. Disclosed anti-PD-L1 antibodies were assayed for their ability to promote the activation of lymphocytes. Peripheral blood mononuclear cells or purified populations of lymphocyte subsets isolated by cell sorting were cultured with IL-2 (100 U/ml) in the presence or absence of added anti-PD-L1 antibodies (10 μg/ml). After five days of culture, cells were stained for expression of CD25 as a measure of cell activation and analyzed by flow cytometry. The results shown in FIG. 5 reveal that H6 and H10 enhance cell activation and that the responsive lymphocyte population is the NK cell.

US11878058B2. Antigen binding proteins that bind PD-L1 (2024-01-23). Sorrento Therapeutics, Inc. [US]. Inventors: Heyue Zhou [US], Randy Gastwirt [US], Barbara A. Swanson [US], John Dixon Gray [US], Gunnar F. Kaufmann [US].
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Patent 2024
Anti-Antibodies Antigens Binding Proteins CD274 protein, human Cells Flow Cytometry IL2RA protein, human Lymphocyte Activation Natural Killer Cells PBMC Peripheral Blood Mononuclear Cells
3
Binding of Anti-PD-L1 Antibodies to Lymphocytes
Not available on PMC !

Example 2

This example provides the results from binding the disclosed anti-PD-L1 antibodies to human lymphocytes. Anti-PD-L1 antibodies were assayed for binding to non-activated lymphocytes. Peripheral blood mononuclear cells were incubated with anti-PD-L1 antibodies (1 μg/ml) followed by washing. Binding of the anti-PD-L1 antibody was detected by staining with a phycoerythrin conjugated and human Ig reagent. To identify the stained populations the cells were co-stained with an anti-CD3 FITC or an anti-CD56 APC reagent. Since the anti-human Ig reagent reacts with immunoglobulin on B lymphocytes the cells were also stained with an anti-human CD19 APC-Cy5 reagent. The data in FIG. 2 were derived from the CD19 negative lymphocytes following analysis using a FACSAria (Becton Dickinson, San Jose, CA). The results show that CD56 positive NK cells, but not CD3+ T cells, react with the anti-PD-L1 antibodies.

US11878058B2. Antigen binding proteins that bind PD-L1 (2024-01-23). Sorrento Therapeutics, Inc. [US]. Inventors: Heyue Zhou [US], Randy Gastwirt [US], Barbara A. Swanson [US], John Dixon Gray [US], Gunnar F. Kaufmann [US].
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Patent 2024
Anti-Antibodies Antibodies, Anti-Idiotypic Antigens B-Lymphocytes Binding Proteins CD274 protein, human Cells Fluorescein-5-isothiocyanate Homo sapiens Immunoglobulins Lymphocyte Muromonab-CD3 Natural Killer Cells PBMC Peripheral Blood Mononuclear Cells Phycoerythrin Population Group T-Lymphocyte
4
NK Cell Modulation of Anti-PD-L1 Antibody Effects
Not available on PMC !

Example 4

This example provides a showing of the effect of NK cells on the disclosed anti-PD-L1 antibodies on mediated inhibition of proliferation. With the anti-PD-L1 antibodies showing a preferential binding to NK cells, the significance of this in the inhibition of proliferation was tested. By cell sorting using a FACS Aria (Becton Dickinson, San Jose, CA) purified population of CD4+, CD8+, CD56+ (NK) and monocytes were obtained. As a base culture, 1.5×105 CD4+ cells and 3×104 monocytes were stimulated with anti-CD3 (1 ng/ml) with or without H10 anti-PD-L1 antibody (10 μg/ml). In separate cultures, either CD8+ cells or NK cells (both at 3×104) were added to this base culture. After three days of culture, cells were stained for expression of CD25 as a measure of lymphocyte activation as measured by flow cytometry. The results shown in FIG. 4 were compared to those obtained using whole, unfractionated PBMC (1.5×105). The anti-PD-L1 antibody inhibited the activation of lymphocytes in the cultures containing whole PBMC and those where NK cells were added, but not in the absence of NK cells.

US11878058B2. Antigen binding proteins that bind PD-L1 (2024-01-23). Sorrento Therapeutics, Inc. [US]. Inventors: Heyue Zhou [US], Randy Gastwirt [US], Barbara A. Swanson [US], John Dixon Gray [US], Gunnar F. Kaufmann [US].
Full text: Click here
Patent 2024
Anti-Antibodies Antibodies, Anti-Idiotypic Antigens Binding Proteins CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes CD274 protein, human Cells Flow Cytometry IL2RA protein, human Lymphocyte Activation Monocytes Muromonab-CD3 Natural Killer Cells NRG1 protein, human Psychological Inhibition
5
PDL1-CD3-Fc Induces NK Cell Killing
Not available on PMC !

Example 9

Experiments are performed to assess the ability of PDL1-CD3-Fc constructs to induce NK cell-mediated killing of target cells. Briefly, U251 cells are labelled with cell membrane dye PKH67 green, and then seeded and allowed to adhere to wells over night (FIG. 32). Primary NK cells (StemCell Technologies, Inc.) are then added to each well at an effector to target ratio of 1:1, along with varying amounts of virally produced PDL1-CD3-Fc protein. Effector/target cell co-culture are incubated at 37° C. for 6 hours prior to live/dead analysis by 7-AAD staining. Stained cells are analyzed by flow cytometry on a BD LSR Fortesa cytometer.

These results will demonstrate that virally produced PDL1-CD3-Fc compounds are able to stimulate NK cell-mediated death of target cells such as U251.

US11865081B2. Oncolytic viral delivery of therapeutic polypeptides (2024-01-09). Virogin Biotech Canada Ltd. [CA]. Inventors: Mitchell H. Finer [US], Luke Evnin [US].
Full text: Click here
Patent 2024
7-aminoactinomycin D CD274 protein, human Cell Culture Techniques Cell Death Cells Dental Occlusion Flow Cytometry Natural Killer Cells PKH67 Plasma Membrane Proteins Stem Cells

Top products related to «Natural Killer Cells»

1
Golgistop by BD
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GolgiStop is a cell culture reagent that inhibits protein transport from the endoplasmic reticulum to the Golgi apparatus, thereby preventing the secretion of newly synthesized proteins. It is a useful tool for investigating protein trafficking and localization in cells.
2
Nk cell isolation kit by Miltenyi Biotec
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The NK cell isolation kit is a laboratory product designed to isolate natural killer (NK) cells from biological samples. It utilizes magnetic bead-based separation technology to selectively capture and enrich NK cells. The core function of this kit is to provide a reliable and efficient method for the isolation of NK cells for various research and experimental applications.
3
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The FACSCanto II is a flow cytometer instrument designed for multi-parameter analysis of single cells. It features a solid-state diode laser and up to four fluorescence detectors for simultaneous measurement of multiple cellular parameters.
4
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The FACSCalibur is a flow cytometry system designed for multi-parameter analysis of cells and other particles. It features a blue (488 nm) and a red (635 nm) laser for excitation of fluorescent dyes. The instrument is capable of detecting forward scatter, side scatter, and up to four fluorescent parameters simultaneously.
5
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8
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IL-2 is a cytokine that plays a crucial role in the regulation of the immune system. It is a protein produced by T-cells and natural killer cells, and it is essential for the activation, proliferation, and differentiation of these cells. IL-2 is an important component in various immunological processes, including the promotion of T-cell growth and the enhancement of the cytolytic activity of natural killer cells.
9
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More about "Natural Killer Cells"

Natural killer (NK) cells are a type of lymphocyte, a white blood cell, that plays a crucial role in the body's innate immune response.
These powerful cells possess the unique ability to recognize and eliminate certain tumor cells and virus-infected cells without requiring prior sensitization.
NK cells are an essential component of the body's first line of defense, making them a key focus for immunological research.
Researchers studying NK cells often utilize specialized equipment and reagents such as the GolgiStop and GolgiPlug, which help maintain the structure and function of these cells during experimentation.
The NK cell isolation kit is another valuable tool, allowing for the efficient separation and purification of NK cells from blood or tissue samples.
Flow cytometry instruments, like the FACSCanto II and FACSCalibur, are commonly used to analyze the phenotype and function of NK cells.
These advanced instruments, paired with FACSDiva software, enable researchers to precisely measure and quantify various NK cell characteristics, such as their expression of surface markers and their cytotoxic capabilities.
The culture medium RPMI 1640, supplemented with fetal bovine serum (FBS), provides a nurturing environment for NK cells in vitro.
Additionally, the cytokine interleukin-2 (IL-2) is often used to stimulate and expand NK cell populations, further enhancing their research potential.
The LSRFortessa is another powerful flow cytometry platform that can be employed to delve deeper into the complexities of NK cell biology, unlocking the secrets of these remarkable immune cells and advancing our understanding of their role in health and disease.
By leveraging the latest technologies and research tools, scientists can optimize their studies of natural killer cells, leading to breakthroughs that have the potential to transform the field of immunology and open new avenues for therapeutic interventions.