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CD4 Positive T Lymphocytes

CD4 Positive T-Lymphocytes are a subpopulation of T cells that express the CD4 glycoprotein on their surface.
These cells play a central role in the immune response, helping to coordinate and direct other immune cells.
They are involved in a variety of functions, including helper T cell activities, cytokine production, and immune regulation.
Understanding the biology and behavior of CD4+ T cells is crucial for research into immune-related diseases and disorders.
PubCompare.ai leverages AI-driven insights to enhance the accuracy and reproducibility of CD4+ T cell research, helping researchers locate the best protocols from literature, preprints, and patents to improve their outcomes.
Experince the future of CD4+ T cell research todday with PubCompare.ai.

Most cited protocols related to «CD4 Positive T Lymphocytes»

ChIP-Seq data for three factors, NRSF, CTCF, and FoxA1, were used in this study. ChIP-chip and ChIP-Seq (2.2 million ChIP and 2.8 million control uniquely mapped reads, simplified as 'tags') data for NRSF in Jurkat T cells were obtained from Gene Expression Omnibus (GSM210637) and Johnson et al. [8 (link)], respectively. ChIP-Seq (2.9 million ChIP tags) data for CTCF in CD4+ T cells were derived from Barski et al. [5 (link)].
ChIP-chip data for FoxA1 and controls in MCF7 cells were previously published [1 (link)], and their corresponding ChIP-Seq data were generated specifically for this study. Around 3 ng FoxA1 ChIP DNA and 3 ng control DNA were used for library preparation, each consisting of an equimolar mixture of DNA from three independent experiments. Libraries were prepared as described in [8 (link)] using a PCR preamplification step and size selection for DNA fragments between 150 and 400 bp. FoxA1 ChIP and control DNA were each sequenced with two lanes by the Illumina/Solexa 1G Genome Analyzer, and yielded 3.9 million and 5.2 million uniquely mapped tags, respectively.
Publication 2008
CD4 Positive T Lymphocytes ChIP-Chip Chromatin Immunoprecipitation Sequencing CTCF protein, human DNA Chips DNA Library FOXA1 protein, human Gene Expression Genome Jurkat Cells MCF-7 Cells
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).
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
The CIBERSORT web tool was used for inferring proportions using the expression profile (https://cibersort.stanford.edu). CIBERSORT results for activated and resting cell types were combined; B cell and CD4+ T cell percentages are the combination of all their subtypes. t-SNE plots were produced using the Rtsne R package. Purity measurements were obtained from our previous publication [31 (link)]. Correlation plots were generated using the corrplot R package.
Publication 2017
B-Lymphocytes CD4 Positive T Lymphocytes Cells
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).
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
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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: “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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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).
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 «CD4 Positive T Lymphocytes»

Example 7

Impact of IL-2 signalling on Teff responses is characterised in a T cell activation assay, in which intracellular granzyme B (GrB) upregulation and proliferation are examined. Previously frozen primary human Pan T cells (Stemcell Technologies) are labelled with eFluor450 cell proliferation dye (Invitrogen) according to manufacturer's recommendation, and added to 96-U-bottom well plates at 1×105 cells/well in RPMI 1640 (Life Technologies) containing 10% FBS (Sigma), 2 mM L-Glutamine (Life Technologies) and 10,000 U/ml Pen-Strep (Sigma). The cells are then treated with 10 μg/ml anti-CD25 antibodies or control antibodies followed by Human T-Activator CD3/CD28 (20:1 cell to bead ratio; Gibco) and incubated for 72 hrs in a 37° C., 5% CO2 humidified incubator. To assess T cell activation, cells are stained with the eBioscience Fixable Viability Dye efluor780 (Invitrogen), followed by fluorochrome labelled antibodies for surface T cell markers (CD3-PerCP-Cy5.5 clone UCHT1 Biolegend, CD4-BV510 clone SK3 BD Bioscience, CD8-Alexa Fluor 700 clone RPA-T8 Invitrogen, CD45RA-PE-Cy7 clone HI100 Invitrogen, CD25-BUV737 clone 2A3 BD Bioscience) and then fixed and permeabilized with the eBioscience™ Foxp3/Transcription Factor Staining Buffer Set (Invitrogen) before staining for intracellular GrB and intranuclear FoxP3 (Granzyme B-PE clone GB11 BD Bioscience, FoxP3-APC clone 236A/E7). Samples are acquired on the Fortessa LSR X20 Flow Cytometer (BD Bioscience) and analysed using the BD FACSDIVA software. Doublets are excluded using FCS-H versus FCS-A, and lymphocytes defined using SSC-A versus FCS-A parameters. CD4+ and CD8+ T cell subsets gated from the live CD3+ lymphocytes are assessed using a GrB-PE-A versus proliferation eFluor450-A plot. Results are presented as percentage of proliferating GrB positive cells from the whole CD4+ T cell population. Graphs and statistical analysis is performed using GraphPad Prism v7. (results not shown)

Patent 2024
Anti-Antibodies Antibodies Biological Assay Buffers CD4 Positive T Lymphocytes Cell Proliferation Cells Clone Cells CY5.5 cyanine dye Eragrostis Fluorescent Dyes Freezing Glutamine GZMB protein, human Homo sapiens IL2RA protein, human Lymphocyte prisma Protoplasm Stem Cells Streptococcal Infections T-Lymphocyte T-Lymphocyte Subsets Transcriptional Activation Transcription Factor

Example 4

Finally, we tested the effect of MALT1 inhibition on T cell differentiation and subset distribution. Stimulation with anti-CD3/CD28 antibodies induced differentiation of naïve T cells and expanded the effector and central memory T cell pool (FIG. 4A). Treatment with increasing concentrations of MALT1 inhibitor reverted the T cell subset distribution back to the phenotype seen in unstimulated cells, suggesting an inhibition of T cell differentiation. Upon investigation of unstimulated cells, MALT1 inhibition did not affect T cell subset distribution (FIG. 4B). Because of the absence of regulatory T cells in MALT1 knockout mice, we investigated the effect of MALT1 inhibition on the regulatory T cell population in CLL or healthy donor PBMCs. Treatment of unstimulated cells with the MALT1 inhibitor resulted in a specific significant decrease of CD4+/CD25+/FoxP3+ T cells in both CLL and healthy donor samples (FIG. 4C). In summary, these observations suggest that MALT1 inhibition partially inhibits T cell differentiation and specifically targets the regulatory T cell compartment.

Patent 2024
Anti-Antibodies Cardiac Arrest CD4 Positive T Lymphocytes Cells Central Memory T Cells IL2RA protein, human MALT1 protein, human Mice, Knockout Phenotype Psychological Inhibition Regulatory T-Lymphocytes T-Lymphocyte T-Lymphocyte Subsets Tissue Donors Vision

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).

Patent 2024
Antigens Bacteria BCG Vaccine CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes Immunization Response, Immune Vaccines
Not available on PMC !

Example 5

GM-BMDCs that were OX40L were also Jagged-1 (FIG. 5A). On the other hand, about half of OX40L+ GM-BMDCs were Jagged-1+ (50.3±0.5%, p<0.02) (FIG. 5A).

To determine if OX40L and Jagged-1 co-expression was required for OX40L+ GM-BMDC-induced expansion of Tregs, the GM-BMDC were sorted into OX40L+ Jagged-1+ and OX40L+ Jagged-1DCs and used them in co-culture with naive CD4+ cells. While total GM-BMDC could induce Treg proliferation (e.g., 8.2%), the OX40L+ Jagged-1+ GM-BMDCs were able to more efficiently expand Tregs (12.5±0.2%). In contrast, OX40L+ Jagged-1 failed to mediate significant expansion of Tregs (1.40.1%, p<0.001) (FIG. 5B). Blocking either ligand with the corresponding blocking antibody caused significant reduction in Treg expansion. However, blocking both ligands (anti-OX40L=10 g/ml, anti-Jagged-1=20 μg/ml) on OX40L+ Jagged-1+ GM-BMDCs abrogated Treg expansion (reduced from 12.5±0.2% to 0.7±0.1%; p<0.01). These results clearly demonstrated that GM-BMDC mediated ex vivo Treg expansion required cell surface expression of both OX40L and Jagged-1.

Patent 2024
Antibodies, Blocking CD4 Positive T Lymphocytes Cells Coculture Techniques Ligands TNFSF4 protein, human Trees

Example 2

FIG. 2 shows a results of a comparison of a mouse population fed a propionic acid diet versus a control group. The propionic acid was administered either on the day of induction (DI) or on the day of onset of disease (OD). It was found that the group given propionic acid on the day the onset of disease occurred (OD) showed a significantly less favorable disease course than the control group.

The influence of propionic acid on the relative axonal density, the demyelination of the white matter, and the number of CD3+-cells is shown in FIG. 3. In general, administration of propionic acid showed a significant improvement compared to the control group.

FIG. 4 shows the effect of administration of propionic acid on the CD4+-CD25+ Foxp3 cells expressed as a significant increase in comparison to the control group.

Patent 2024
Axon Biological Response Modifiers CD4 Positive T Lymphocytes Demyelination Diet Disease Progression IL2RA protein, human Mus propionic acid White Matter

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The CD4+ T cell isolation kit is a laboratory equipment product designed for the isolation and enrichment of CD4+ T cells from biological samples. The kit utilizes magnetic separation technology to effectively separate the CD4+ T cell population from other cell types present in the sample.
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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.
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More about "CD4 Positive T Lymphocytes"

CD4+ T cells, helper T cells, T lymphocytes, CD4 glycoprotein, immune response, cytokine production, immune regulation, T cell biology, T cell behavior, immune-related diseases, CD4+ T cell research, CD4 Positive T Lymphocyte, CD4 T cell isolation, CFSE, Ionomycin, FACSAria, FACSCalibur, FACSCanto II, FACSAria II, IL-2, FBS, RPMI 1640.
CD4+ T cells, also known as helper T cells, are a subpopulation of T lymphocytes that express the CD4 glycoprotein on their surface.
These cells play a crucial role in the immune response, helping to coordinate and direct other immune cells.
They are involved in a variety of functions, including helper T cell activities, cytokine production, and immune regulation.
Understanding the biology and behavior of CD4+ T cells is crucial for research into immune-related diseases and disorders.
Researchers often use techniques such as CD4+ T cell isolation, CFSE labeling, and flow cytometry (using instruments like FACSAria, FACSCalibur, FACSCanto II, and FACSAria II) to study these cells.
Culturing CD4+ T cells in the presence of IL-2 and FBS-supplemented RPMI 1640 medium is also common.
PubCompare.ai leverages AI-driven insights to enhance the accuracy and reproducibility of CD4+ T cell research, helping researchers locate the best protocols from literature, preprints, and patents to improve their outcomes.
Experince the future of CD4+ T cell research todday with PubCompare.ai.