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Regulatory T-Lymphocytes

Regulatory T-Lymphocytes (Tregs) are a specialized subpopulation of T cells that play a crucial role in immune regulation and homeostasis.
These cells express the transcription factor Foxp3 and function to suppress excessive or autoreactive immune responses, maintaining tolerance and preventing autoimmunity.
Tregs are essential for the balance of pro- and anti-inflammatory signals, and their dysfunction is implicated in various immune-mediated diseases.
Resaerching Tregs and optimizing protocols for their study can provide valuable insights into the mechanisms of immune regulation and inform the development of therapies targeting this cell type.

Most cited protocols related to «Regulatory T-Lymphocytes»

Comparing RNA and WNN-derived Clustering

In Figures S2N and S2O, we compare the results of differential expression after performing clustering on either the RNA-derived or WNN-derived nearest neighbor graphs for the BMNC dataset. We first cluster cells by the RNA or WNN nearest neighbor graphs respectively, and annotate clusters based on their molecular profiles. While some clusters (for example, regulatory T cells) were only identified in the WNN-derived clusters, we did identify shared populations across both cluster sets including: CD4 Naive T, CD4 Memory T, CD8 Naive T, B Naive, B Memory, HSC, and LMPP subgroups. For both the WNN-derived and RNA-derived cluster sets, we performed four transcriptome-based differential expression tests (HSC versus LMPP, CD8 Naive versus CD4 Naive, CD4 Memory versus CD4 Naive, Naive B versus Memory B) using the Wilcoxon test implemented in Seurat. For genes identified as differentially expressed (adjusted p value < 0.01) in either the WNN-derived or RNA-derived cluster sets, we compared the difference in observed magnitude of log2 fold changes (Figures S2N and S2O).

Hao Y., Hao S., Andersen-Nissen E., Mauck WM I.I.I., Zheng S., Butler A., Lee M.J., Wilk A.J., Darby C., Zager M., Hoffman P., Stoeckius M., Papalexi E., Mimitou E.P., Jain J., Srivastava A., Stuart T., Fleming L.M., Yeung B., Rogers A.J., McElrath J.M., Blish C.A., Gottardo R., Smibert P, & Satija R. (2021). Integrated analysis of multimodal single-cell data. Cell, 184(13), 3573-3587.e29.

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Publication 2021

Cells Genes Memory Population Group Regulatory T-Lymphocytes Transcriptome

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.Fig. 1

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

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

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

Benchmarking Immune Cell Signatures

We considered four external expression data sets from enriched/purified immune cells: two microarray data sets (GEO accession: GSE28490 and GSE2849) [27 (link)], an RNA-seq data set [28 (link)], and a microarray compendium that was used to build the CIBERSORT LM22 signature matrix [17 (link)]. All data sets were preprocessed and normalized as explained in the previous paragraphs. For each gene g specific for a cell type c in the signature matrix, we computed the ratio R_gd between the median expression across all libraries in data set d belonging to the cell type c and the median expression across all libraries in data set d not belonging to the cell type c. For each cell type, the top 30 ranked signature genes (or less, when not available) with median_d(R_gd) ≥ 2 were selected for the final signature matrix. When processing the T_reg signature genes, the data sets belonging to CD4⁺ T cells were not considered. T_reg signature genes were further filtered with a similar approach, but considering the RNA-seq data of circulating CD4⁺ T and T_reg cells from and selecting only the genes with median_d(R_gd) ≥ 1.
The final signature matrix TIL10 (Additional file 1) was built considering the 170 genes satisfying all the criteria reported above. The expression profile of each cell type c was computed as the median of the expression values x_gl over all libraries belonging to that cell type:

x_{gc} = {median}_{lϵc} (x_{gl})

For the analysis of RNA-seq data, quanTIseq further reduces this signature matrix by removing a manually curated list of genes that showed a variable expression in the considered data sets: CD36, CSTA, NRGN, C5AR2, CEP19, CYP4F3, DOCK5, HAL, LRRK2, LY96, NINJ2, PPP1R3B, TECPR2, TLR1, TLR4, TMEM154, and CD248. This default signature considered by quanTIseq for the analysis of RNA-seq data consists of 153 genes and has a lower condition number than the full TIL10 signature (6.73 compared to 7.45), confirming its higher cell specificity. We advise using the full TIL10 matrix (--rmgenes=“none”) for the analysis of microarray data, as they often lack some signature genes, and the reduced matrix (--rmgenes= “default”) for RNA-seq data. Alternatively, the “rmgenes” option allows specifying a custom list of signature genes to be disregarded (see quanTIseq manual).

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Publication 2019

CD4 Positive T Lymphocytes Cells Genes Genes, vif LRRK2 protein, human LY96 protein, human Microarray Analysis Regulatory T-Lymphocytes RNA-Seq

Most recents protocols related to «Regulatory T-Lymphocytes»

MALT1 Inhibition Modulates T Cell Differentiation

Not available on PMC !

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.

US11872220B2. Methods and compositions for treating B-cell malignancies (2024-01-16). Janssen Pharmaceutica NV [BE]. Inventors: Eric Frederik Eldering [NL], Karoline Kielbassa [NL], Ulrike Philippar [BE], Andrew Steele [US], Marco Vincent Haselager [NL], A. P. Kater [NL].

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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

Treg Depletion by Anti-CD25 Antibody

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Example 6

1×10⁵4T1 cells in 200 μl RPMI 1640 media were implanted in the 2^ndthoracic fat pad tissue of Balb/c mice. When tumours reached 50-100 mm³the mice were randomised and a single intraperitoneal flat dose of either 2 μg, 20 μg or 200 μg mouse anti-mouse CD25 (7D4) antibody was administered per mouse. At day 3 and day 9, tumour tissues and whole blood was isolated for immunophenotyping.

Results:

Antibody 7D4 exhibited Treg depleting activity in both whole blood and tumour tissue based on day 3 and day 9 post-dose analysis by immunophenotyping (FIG. 15).

US11879014B2. Method of treating cancer or depleting regulatory T cells in a subject by administering a human IGG1 anti-CD25 antibody (2024-01-23). Tusk Therapeutics Ltd. [GB], Cancer Research Technology Limited [GB]. Inventors: Anne Goubier [GB], Josephine Salimu [GB], Kevin Moulder [GB], Beatriz Goyenechea Corzo [GB], Pascal Merchiers [GB], Sergio Quezada [GB], Karl Peggs [GB], Frederick Arce Vargas [GB], Isabelle Solomon [GB].

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Patent 2024

BLOOD IL2RA protein, human Immunoglobulins Mice, Inbred BALB C Mus Neoplasms Regulatory T-Lymphocytes Tissue, Adipose Tissues

Quantifying Immune Signatures in Tumors

For a pathway, immune signature, or biological process, we quantified its enrichment level in a tumor sample as the single-sample gene-set enrichment analysis (ssGSEA) (52 (link)) score of its marker genes. The marker gene sets representing different immune signatures were from several publications, including CD8+ T cells (36 (link)), CD4+ regulatory T cells (36 (link)), pro-/anti-inflammatory cytokines (36 (link)), and M1/M2 macrophages immune cytolytic activity (36 (link)), and proliferation signature score (53 (link)). These gene sets are listed in Supplementary Table S4.

Li M., Gao X, & Wang X. (2023). Identification of tumor mutation burden-associated molecular and clinical features in cancer by analyzing multi-omics data. Frontiers in Immunology, 14, 1090838.

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Publication 2023

Anti-Inflammatory Agents Biological Processes CD8-Positive T-Lymphocytes Cytokine Genes Genetic Markers Macrophage Neoplasms Regulatory T-Lymphocytes

Immune cell deconvolution in colorectal cancer

The immunedeconv package in R (https://www.aclbi.com/static/index.html#/immunoassay), which integrates CIBERSORT (17 (link)), is a deconvolution algorithm based on gene expression that is able to evaluate changes in the expression of one set of genes relative to all other genes in the sample. This package was used to analyze the levels of tumor-infiltrating immune cells. Among 478 COAD samples based on TCGA-COAD data, samples with the top 25% and the lowest 25% levels of GIPC2 expression were classified into the high- and low-expression groups, respectively. The abundance of 22 types of immune cells [naïve B cells, memory B cells, plasma B cells, CD8⁺ T cells, naïve CD4⁺ T cells, resting CD4⁺ memory T cells, activated CD4⁺ memory T cells, follicular helper T cells, regulatory T cells, γδ T cells, resting natural killer (NK) cells, activated NK cells, monocytes, M0 macrophages, M1 macrophages, M2 macrophages, resting myeloid dendritic cells, activated myeloid dendritic cells, activated mast cells, resting mast cells, eosinophils and neutrophils] were estimated using the CIBERSORT algorithm. Briefly, gene expression datasets from TCGA were uploaded to the Xiantao bioinformatics analysis tool, and after standard annotation, the immunedeconv R package was used to estimate the P-values for deconvolution via the CIBERSORT algorithm. This tool was then used to compare the expression of immune checkpoint-associated genes, including CD274, CTLA4, HAVCR2, LAG3, PDCD1, PDCD1LG2, TIGIT and SIGLEC15, between patients with COAD in the high and low GIPC2 expression groups, respectively. The aforementioned analyses and R package were implemented using R foundation for statistical computing (2020) version 4.0.3 (18 ) and the software packages ggplot2 (https://cran.r-project.org/web/packages/ggplot2/index.html) and pheatmap (https://cran.r-project.org/web/packages/pheatmap/index.html) were used for generating images.

Kang M, & Su Z. (2023). Increased expression of GIPC2 in colon adenocarcinoma is associated with a favorable prognosis and high levels of immune cell infiltration. Oncology Reports, 49(4), 66.

Publication 2023

B-Lymphocytes CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes CD274 protein, human Cell Cycle Checkpoints Cells Chronic Obstructive Airway Disease CTLA4 protein, human Dendrites Dendritic Cells Eosinophil Gene Expression Genes HAVCR2 protein, human Immunoassay Macrophage Mast Cell Memory B Cells Memory T Cells Monocytes Myeloid Cells Natural Killer Cells Neoplasms Neutrophil Patients PDCD1 protein, human Plasma Cells Regulatory T-Lymphocytes T-Lymphocyte T Follicular Helper Cells TIGIT protein, human

Comprehensive Immune Cell Profiling

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Publication 2023

Anti-Antibodies Antibodies, Anti-Idiotypic CD4 Positive T Lymphocytes Cells CY5.5 cyanine dye Cytokine Fluorescein-5-isothiocyanate Fluorescent Antibody Technique Helper-Inducer T-Lymphocyte IFNG protein, mouse IL2RA protein, human Ionomycin ITGAM protein, human Mus Proteins Protoplasm Regulatory T-Lymphocytes Tetradecanoylphorbol Acetate Th17 Cells Type-2 Helper T Cell

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More about "Regulatory T-Lymphocytes"

Regulatory T-Lymphocytes (Tregs) are a specialized subpopulation of T cells that play a crucial role in immune regulation and homeostasis.
These cells, also known as suppressor T cells, express the transcription factor Forkhead box P3 (Foxp3) and function to suppress excessive or autoreactive immune responses, maintaining tolerance and preventing autoimmunity.
Tregs are essential for the balance of pro- and anti-inflammatory signals, and their dysfunction is implicated in various immune-mediated diseases, such as autoimmune disorders, cancer, and chronic infections.
Researching Tregs and optimizing protocols for their study can provide valuable insights into the mechanisms of immune regulation and inform the development of therapies targeting this cell type.
Flow cytometry techniques, such as the FACSCalibur, FACSCanto II, and FACSAria platforms, are commonly used to identify and isolate Tregs.
The Mouse Regulatory T Cell Staining Kit and the CD4+CD25+ Regulatory T Cell Isolation Kit are examples of tools that facilitate the detection and purification of these cells.
Additionally, compounds like Ionomycin, CFSE, and CellTrace Violet can be used to stimulate and track Tregs in various experimental settings.
By leveraging AI-powered tools like PubCompare.ai, researchers can streamline their Treg studies by locating the best protocols from literature, preprints, and patents, while benefiting from AI-driven comparisons to identify the optimal solutions for their research needs.
This approach can help accelerate the understanding of Treg biology and inform the development of targeted immunotherapies.