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CD8-Positive T-Lymphocytes

CD8-Positive T-Lymphocytes are a subtype of T cells that express the CD8 glycoprotein on their surface.
These cells play a crucial role in cell-mediated immunity, recognizing and destroying virus-infected cells and tumour cells.
They are also involved in the regulation of the immune response.
CD8+ T cells are essential for effective immune surveillance and the clearance of intracellular pathogens.
Understanding the biology and function of CD8+ T cells is crucial for developing new therapies and imprving the treatment of various diseases.

Most cited protocols related to «CD8-Positive T-Lymphocytes»

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
Differential Mutational Analysis of Ipilimumab Response

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Publication 2016
Adenocarcinoma Adenocarcinoma of Lung Carcinoma, Pancreatic Ductal CD8-Positive T-Lymphocytes Cell Lines Cells Cytotoxic T-Lymphocyte Antigen 4 Genes HAVCR2 protein, human Hypernephroid Carcinomas Ipilimumab LINE-1 Elements Melanoma Missense Mutation Mutation PDCD1 protein, human PRDM1 protein, human T-Cell Exhaustion TBX21 protein, human Transcription Factor
3
Comprehensive Tumor Immune Profile Analysis
Multivariate Cox regression, log-rank test and Kaplan–Meier estimators were implemented using the R package survival. The association between CD8 T-cell abundance and tumor status was evaluated using logistic regression corrected for age and clinical stage and was implemented using the R package glm. The same analysis was performed for neutrophil abundance and gender associations, corrected for age and smoking history. Partial correlations of immune cell abundance and gene expression of chemokines and receptors, somatic mutation counts, CT gene expression, as well as immunosuppressive molecule expression were calculated using the R package ppcor. Multiple test correction was performed using the R package qvalue [39 ] and FDR thresholds are applied based on the abundance of signals in the data. In this study, we applied the Pearson correlation to purity and gene expression because it is reasonable to expect that the expression level is linearly associated with tumor purity. For others, we used the Spearman correlation. We applied partial correlation analysis to remove the influence of tumor purity on the involved variables. All other analyses, including linear regression, Fisher’s exact test, Wilcoxon rank sum test, Spearman’s correlation, and hierarchical clustering, were performed using R [40 ]. Of note, in Figs. 2b and 3b, we used the 20 percentile as a cutoff only to help visualize the association of immune infiltration with outcomes and the statistical significance was determined by multivariate Cox regression (Fig. 3a) including all the samples. Our results on survival analysis, neoantigen association, tumor recurrence, and association of checkpoint blockade inhibitory molecules with immune cells are available in Additional file 10: Table S8.
Li B., Severson E., Pignon J.C., Zhao H., Li T., Novak J., Jiang P., Shen H., Aster J.C., Rodig S., Signoretti S., Liu J.S, & Liu X.S. (2016). Comprehensive analyses of tumor immunity: implications for cancer immunotherapy. Genome Biology, 17, 174.
Publication 2016
CD8-Positive T-Lymphocytes Cells Chemokine Diploid Cell Gender Gene Expression Immunosuppressive Agents Inhibitory Checkpoint Molecules Mutation Neoplasms Neutrophil Recurrence
4
Comprehensive Preprocessing of Sequencing Datasets
Full details of each dataset9 (link),10 (link),17 (link),20 ,21 (link),37 (link),42 ,50 (link),55 ,64 (link)–69 (link), including data type, sample type, source, and normalization approach, are available in Supplementary Table 1. Briefly, next generation sequencing datasets were downloaded and analyzed using the authors’ normalization settings unless otherwise specified; these consisted of transcripts per million (TPM), reads per kilobase of transcript per million (RPKM), or fragments per kilobase of transcript per million (FPKM) space. For analyses in log2 space, we added 1 to expression values prior to log2 adjustment. Affymetrix microarray datasets were summarized and normalized as described in ‘Gene expression profiling – Microarrays’ (Supplementary Note 1), using RMA in cases where bulk tissues and ground truth cell subsets were profiled on the same Affymetrix platform, and otherwise using MAS5 normalization. NanoString nCounter data were downloaded from the supplement of Chen et al.20 and analyzed with batch correction in non-log linear space, but without any additional preprocessing.
Two publicly available PBMC datasets from healthy donors profiled by Chromium v2 (5’ and 3’ kits) were downloaded (Supplementary Table 1) and preprocessed as described in ‘Gene expression profiling – Single-cell RNA-seq’ (Supplementary Note 1), with the following minor modifications. During quality control, we excluded cells with >5000 expressed genes for 5’ PBMCs, >4000 expressed genes for 3’ PBMCs, and <200 expressed genes for both datasets. Seurat “FindClusters” was applied on the first 20 principal components, with the resolution parameter set to 0.6. Cell labels were assigned as described above. In addition, myeloid cells were defined by high CD68 expression, megakaryocytes by high PPBP expression, and dendritic cells by high FCER1A expression.
For the 3’ FL signature matrix in Supplementary Figs. 11d, and14a-b, publicly available 10x Chromium v2 scRNA-seq data (3’ kit)70 were downloaded (Supplementary Table 1) and preprocessed as described for the 10x PBMC signature matrices above, but with the following differences. Seurat “FindClusters” was applied on the first 10 principal components, with the resolution parameter set to 0.6. Cell labels were assigned based on the following canonical marker genes (MS4A1 = B cells; CD3E, CD8A and CD8B = CD8 T cells; CD3E and CD4 = CD4 T cells).
Newman A.M., Steen C.B., Liu C.L., Gentles A.J., Chaudhuri A.A., Scherer F., Khodadoust M.S., Esfahani M.S., Luca B.A., Steiner D., Diehn M, & Alizadeh A.A. (2019). Determining cell-type abundance and expression from bulk tissues with digital cytometry. Nature biotechnology, 37(7), 773-782.
Publication 2019
B-Lymphocytes CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes Cells Chromium Dendritic Cells Dietary Fiber Dietary Supplements Donors Figs Genes Megakaryocytes Microarray Analysis Myeloid Cells platelet basic protein, human RNA-Seq Single-Cell RNA-Seq Tissues
5
Hierarchical Annotation of Immune Populations
We define as a pyramid a directed acyclic graph with a root node. Samples of microenvironment purified cells were labeled according to their reported immune or stromal populations, resulting in 63 distinct labels in the MCP discovery series, with an additional 15 labels for the MCP validation series, resulting in a total of 78 labels. We organized these labels in a pyramidal graph (Additional file 2: Figure S1) with nodes representing populations (categories) and directed edges representing relations of inclusion. For instance, the labels “CD8+ T cells”, “CD4+ T cells”, “Tγδ cells”, “Memory T cells”, “Activated T cells”, and “Naïve T cells” and all labels included in them (for instance “Effector-memory CD8 T cells”) form the “T cells” category, which itself is included in the “T/NK lineage” category. Of these 78 sample labels, some correspond to terminal leaves of this pyramid (e.g., “Canonical CD4 Treg cells”), while others correspond to higher level nodes (e.g., peripheral-blood mononuclear cells (“PBMC”)). In addition to these 78 labels, 15 hematopoiesis or immunology-inspired categories that are not directly represented by samples but relevant for their organization in a structured pyramid (for instance “Lymphocytes”) or as a potential cell population (for instance “antigen-experienced B cells”) were added (Additional file 1: Table S13). Categories corresponding to tumor samples were discarded for the identification of TM and only kept as negative controls, resulting in 68 categories available for screening.
Having defined this set of 78 labels and 68 categories (53 categories are directly represented by labels, with 15 additional categories not directly represented in the dataset), we exhaustively encoded the relationships between labels and categories using three possible relationships (Additional file 1: Table S13). Relative to a category, we define three sets of samples:

C : “positive samples” are those whose label is included in the category (all cells composing a sample which is in C are in the category)

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\begin{document}$$ \overline{C} $$\end{document}C¯ : “negative samples” are those whose label is strictly non-overlapping with the category (all cells of a sample which is in \documentclass[12pt]{minimal}
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-1 : “mixed samples” are those whose label is partly overlapping with the category (some cells of the sample are in C and some are in \documentclass[12pt]{minimal}
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\begin{document}$$ \overline{C} $$\end{document}C¯).

For instance, for CD8+ T cells, C is the set of samples whose label is “CD8 T cells” or “Effector memory CD8 T cells” (Additional file 2: Figure S1; Additional file 1: Table S13), mixed samples are, for instance, CD3+ T cells as they mix CD4+ and CD8+ T cells, or PBMC as they mix CD8+ T cells with, e.g., monocytes. C¯ is defined as all non-positive non-mixed samples.
Note that the relationships represented in Additional file 2: Figure S1 only correspond to the “direct inclusion” relationship, which is transitive (we thus removed for clarity all the arrows which can be inferred by transitivity). Hence, strict exclusion or mixture relationships are not represented but are taken into account during the screening process (the related information is available in Additional file 1: Table S13).
Becht E., Giraldo N.A., Lacroix L., Buttard B., Elarouci N., Petitprez F., Selves J., Laurent-Puig P., Sautès-Fridman C., Fridman W.H, & de Reyniès A. (2016). Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biology, 17, 218.
Publication 2016
Antigens B-Lymphocytes CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes Cells Effector Memory T Cells Hematopoiesis Lymphocyte Memory T Cells Monocytes Neoplasms PBMC Peripheral Blood Mononuclear Cells Plant Roots T-Lymphocyte

Most recents protocols related to «CD8-Positive T-Lymphocytes»

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].
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
BCG Vaccination Route and Immune Response
Not available on PMC !

Example 8

Based on the differences in immune responses and protection, several multiple regressions were used to test whether antigen-responsive CD4 or CD8 T cell numbers (BAL) or frequencies (PBMC) after immunization were associated with disease severity (CFU; FIG. 23D). Results indicate that when controlling for all vaccine routes, peak CD4 T cells in the BAL and PBMC, and peak CD8 T cells in the BAL do not have a significant association with total CFU. Of note, in PBMC, higher peak CD8 frequencies are associated with lower total CFU after controlling for route. Overall, these results show that the route of BCG vaccination is the primary determinant of Mtb control with IV being the only route that was significantly protective against TB (FIG. 18F).

US11865168B2. Compositions and methods for treating bacterial infections (2024-01-09). Massachusetts Institute of Technology [US], The United States of America, as represented by The Secretary, Department of Health & Human Services [US], University of Pittsburgh—Of The Commonwealth System of Higher Education [US]. Inventors: Alexander K. Shalek [US], Travis Hughes [US], Marc H. Wadsworth [US], Robert Seder [US], Mario Roederer [US], Joanne L. Flynn [US], Patricia Darrah [US].
Patent 2024
Antigens Bacteria BCG Vaccine CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes Immunization Response, Immune Vaccines
3
SARS-CoV-2 Vaccine CD8+ and CD4+ T-cell Responses
Not available on PMC !

Example 12

CD8+activation by both S and N: CD80+splenocytes from hAd5 S-Fusion+N-ETSD vaccinated mice exposed to S peptide pool 1 (containing RBD and 51) show IFN-γ expression that is significantly higher compared to hAd5 null mice (FIG. 13a); splenocytes from these mice also expressed intracellular IFN-γ in response to the N peptide pool. Evaluation of simultaneous IFN-γ/TNF-α expression from CD8β+splenocytes (FIG. 13c) mirrored those for IFN-γ expression alone. These results indicate that both S and N activate CD8+ T cells.

CD4+activation by N: Although CD8+ cytotoxic T cells mediate killing of virus infected cells, CD4+ T cells are required for sustained cytotoxic T lymphocyte (CTL) activity. Thus, CD4+ T cells in the vaccinated animals was evaluated. In contrast to CD8β+splenocytes, only the N peptide pool stimulated CD4+splenocytes from hAd5 S-Fusion+N-ETSD-inoculated mice to express IFN-γ (FIG. 13b) or IFN-γ/TNF-α (FIG. 13d) at levels that were substantially higher than hAd5 Null control. The contribution by N of CD4+ T-cell responses is vital to an effective immune response to the candidate vaccine.

US11857620B2. Method of inducing immunity against SARS-CoV-2 using spike (s) and nucleocapsid (N)-ETSD immunogens delivered by a replication-defective adenovirus (2024-01-02). ImmunityBio, Inc. [US]. Inventors: Patrick Soon-Shiong [US], Peter Sieling [US], Kayvan Niazi [US], Shahrooz Rabizadeh [US], Lise Geissert [US], Annie Shin [US], Adrian Rice [US], Elizabeth Gabitzsch [US], Jeffrey Safrit [US], Leonard Sender [US].
Patent 2024
Animals CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes Cells Cytotoxic T-Lymphocytes Interferon-alpha Interferon Type II Mice, Knockout Mus Peptides Protoplasm Response, Immune Vaccines Virus
4
Evaluating Combination Immunotherapies
Not available on PMC !

Example 3

Five groups including tucaresol, tucaresol plus PD-1 or PD-L1 antibody, tucaresol plus CTLA-4 antibody, CTLA-4 antibody plus PD-1 or PD-L1 antibody, and tucaresol plus plinabulin are tested to determine the effects on in vitro cytokine production by CD4 and CD8 T cells (e.g, IFN-gamma and IL-2 cells).

The release of pro-inflammatory cytokines (IL-1β, IL-6, IL 12p40) is quantified by ELISA. The assays are performed as described by Martin et al., Cancer Immuno Immunothe (2014) 63(9):925-38. (2014) and Müller et al, Cancer Immunol Res (2014) 2(8), 741-55. Compounds are prepared as a 10 mM stock solution in DMSO and subsequently diluted to the final concentration in cell culture medium for use in the cell line studies and are examined using serial dilution over a concentration range of 1 nM to 10 μM.

US11857522B2. Compositions containing tucaresol or its analogs (2024-01-02). BeyondSpring Pharmaceuticals, Inc. [US]. Inventors: Ramon Mohanlal [US], Lan Huang [US], George Kenneth Lloyd [US].
Patent 2024
Biological Assay CD8-Positive T-Lymphocytes CD274 protein, human Cell Culture Techniques Cell Lines Cells Culture Media Cytokine Cytotoxic T-Lymphocyte Antigen 4 Enzyme-Linked Immunosorbent Assay Gamma Rays GZMB protein, human Immunoglobulins Inflammation Interleukin-1 beta Interleukin-12 Subunit p40 Malignant Neoplasms plinabulin Sulfoxide, Dimethyl Technique, Dilution tucaresol
5
Targeted Delivery of TNF Reduces Glioblastoma
Not available on PMC !

Example 1

Three patients with recurrent glioblastoma were treated with L19-TNFα at a dose level of 10 μg/kg. Already twenty-four hours after the infusion, a decrease in overall tumor perfusion and an emerging tumor necrosis was detected, as shown in FIG. 1A. One patient had progressive disease after three months and two patients still have stable disease with an increasing area of necrosis in the tumor region at six months after treatment. This is surprising considering that the Progression Free Survival (PFS) for recurrent glioblastoma is 1.5 months.

The patient with progressive disease underwent re-section and the tissue from this surgery, i.e. after treatment with L19-TNFα, was compared with the tissue obtained during first surgery. By immunohistochemistry, a significant increase in tumor-infiltrating CD4 and CD8 T-cells in the tumor after L19-TNFα treatment was detected. Furthermore, increased levels of cleaved caspase-3 were found suggesting a higher number of dead tumor cells, as shown in FIG. 1B. These data demonstrate the in situ activation due to the targeted delivery of TNF.

US11872288B2. TNF-alpha immunoconjugate therapy for the treatment of brain tumors (2024-01-16). PHILOGEN S.P.A [IT]. Inventors: Theresa Hemmerle [CH].
Patent 2024
Aftercare Caspase 3 CD8-Positive T-Lymphocytes Glioblastoma Immunohistochemistry Necrosis Neoplasms Obstetric Delivery Operative Surgical Procedures Patients Perfusion Recurrent Brain Tumors Tissues Tumor Necrosis Factor-alpha

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1
Carboxyfluorescein succinimidyl ester (cfse) by Thermo Fisher Scientific
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CFSE (Carboxyfluorescein succinimidyl ester) is a fluorescent dye used for cell proliferation and tracking assays. It binds to cellular proteins, allowing the labeling and monitoring of cell division in various cell types.
2
Cd8 t cell isolation kit by Miltenyi Biotec
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The CD8+ T cell isolation kit from Miltenyi Biotec is designed to isolate CD8+ T cells from a variety of cell samples. The kit utilizes a magnetic bead-based separation technology to selectively enrich for the CD8+ T cell population.
3
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The EasySep Mouse CD8+ T Cell Isolation Kit is a laboratory product designed to quickly and efficiently isolate CD8+ T cells from mouse splenocytes or lymph node cells. The kit utilizes a magnetic separation process to negatively select the desired cell population.

More about "CD8-Positive T-Lymphocytes"

CD8+ T cells, cytotoxic T cells, killer T cells, and Tc cells are all terms used to refer to CD8-Positive T-Lymphocytes.
These specialized immune cells play a crucial role in cell-mediated immunity, recognizing and destroying virus-infected cells, tumor cells, and other intracellular pathogens.
CD8+ T cells are essential for effective immune surveillance and the clearance of intracellular threats.
In research settings, various techniques are used to isolate, analyze, and manipulate CD8+ T cells.
CFSE (Carboxyfluorescein succinimidyl ester) is a common dye used to track cell division and proliferation in CD8+ T cells.
The CD8+ T cell isolation kit is a valuable tool for purifying these cells from complex samples, such as whole blood or splenocytes.
Flow cytometry instruments like the FACSCanto II, FACSCalibur, FACSAria, FACSAria II, and LSRFortessa are widely used to phenotype and quantify CD8+ T cell populations.
To study cytokine production and other functional aspects of CD8+ T cells, researchers often utilize reagents like GolgiPlug, which inhibits protein transport and allows for the detection of intracellular cytokines such as IL-2.
The EasySep Mouse CD8+ T Cell Isolation Kit provides a convenient method for isolating high-purity CD8+ T cells from murine samples.
Understanding the biology and function of CD8+ T cells is crucial for developing new therapies and improving the treatment of various diseases, including viral infections, cancer, and autoimmune disorders.
Leveraging the latest tools and techniques, researchers can unlock the full potential of these versatile and powerful immune cells.