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Immature B-Lymphocyte

Immature B-Lymphocytes are undeveloped B cells that have not yet fully matured.
These cells are crucial in the early stages of B-cell development, playing a key role in the immune system's response to pathogens.
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Leverge our intuitive platform to identify the best methodologies and products, ensuring reliable, high-quality findings.
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Most cited protocols related to «Immature B-Lymphocyte»

1
Immune Cell Infiltration Profiling in Cancer
In brief, the infiltration levels of immune cell types were quantified by ssGSEA in R package gsva. The ssGSEA applies gene signatures expressed by immune cell populations (20 (link)) to individual cancer samples (21 (link)). The deconvolution approach used in our study included 27 immune cells that are involved in innate immunity [natural killer (NK) cells, CD56dim NK cells, CD56bright NK cells, plasmacytoid dendritic cells (DCs), immature DCs, activated DCs, neutrophils, monocytes, mast cells, eosinophils, and macrophages] and in adaptive immunity (immature B cells, activated B cells, central memory CD4+ T, effector memory CD4+ T-, activated CD4+ T, central memory CD8+ T, effector memory CD8+ T-, activated CD8+ T-, NK T-, T follicular helper, Tγδ, Th1, Th2, Th17, and Treg). The observation of T-cell infiltration score (TIS) was defined as the average of the standardized values for CD8+ T, central memory CD4+ T, effector memory CD4+ T-, central memory CD8+ T, effector memory CD8+ T-, Th1, Th2, Th17, and Treg cells. The obtained CYT score rule from the data set of Rooney et al. (22 (link)) consisted of cytolytic genes (calculated as geometrical mean of PRF1 and GZMA). The CD8+ T/Treg ratio was the digital ratio of ssGSEA scores for these two cell types. Signaling pathway was evaluated based on ssGSEA (23 (link)) according to previously report (24 (link)). Gene set for “41BB signaling pathway” and “Interferon-a response” was retrieved from MSigDB (25 (link)).
Zhang L., Zhao Y., Dai Y., Cheng J.N., Gong Z., Feng Y., Sun C., Jia Q, & Zhu B. (2018). Immune Landscape of Colorectal Cancer Tumor Microenvironment from Different Primary Tumor Location. Frontiers in Immunology, 9, 1578.
Publication 2018
Adaptive Immunity B-Lymphocytes Cells Dendritic Cells Digit Ratios Eosinophil Genes GZMA protein, human Immature B-Lymphocyte Immunity, Innate Interferons Macrophage Malignant Neoplasms Mast Cell Memory Monocytes Natural Killer Cells Neutrophil Plasmacytoid Dendritic Cells Population Group PRF1 protein, human Regulatory T-Lymphocytes Signal Transduction Pathways T-Lymphocyte Th17 Cells
2
4C-seq protocol for chromatin interactions
Details of publically available datasets downloaded for this study can be found in S1 Table. We used datasets that had more than one replicate available in GEO and processed the FASTQ files using our pipeline. Datasets were further excluded if less than one million reads were available after removal of undigested and self-ligated 4C fragments. Samples were also required to have at least 40% of the reads on the cis chromosome and 40% coverage in the 2Mb region around the bait for 6bp cutters and 200kb for 4bp cutters as this is considered a standard quality control for a good 4C experiment [35 (link)]. Basic statistics for the datasets used can be found in S1 Table.
The following datasets were generated from mouse cells for this study. Cd83, Igh-Cγ1 baits on activated mature B cells, Igκ MiEκ, Tcrb Eβ bait in double negative (DN) T cells and immature B cells. See S1 Table for details of primers and enzymes used for these experiments. All datasets generated for this study can be found GEO (GSE77645).
The 4C-Seq protocol was performed as described previously [14 (link)] and libraries were sequenced using the HiSeq2500 Illumina platform. Splenic mature B cells were isolated and induced to undergo class switch recombination as previously described [14 (link)]. Cells were collected on day 2 of activation. DN T cells, and immature B cells were isolated as described before [29 (link), 36 (link)] and pooled to obtain 10 million cells for each replicate at each developmental stage.
Raviram R., Rocha P.P., Müller C.L., Miraldi E.R., Badri S., Fu Y., Swanzey E., Proudhon C., Snetkova V., Bonneau R, & Skok J.A. (2016). 4C-ker: A Method to Reproducibly Identify Genome-Wide Interactions Captured by 4C-Seq Experiments. PLoS Computational Biology, 12(3), e1004780.
Publication 2016
Antigen T Cell Receptor, beta Chain B-Lymphocytes Cells Chromosomes Division, Cell Enzymes Immature B-Lymphocyte Immunoglobulin Class Switching Mus Oligonucleotide Primers Spleen T-Lymphocyte
3
Transitional B Cell Transfer
Splenic cells from a pool of 8 and, in a second experiment, 20 1-wk-old C57BL/6 mice were depleted of erythrocytes and dead cells by density gradient centrifugation (Ficoll Paque; Pharmacia Biotech). The percentage of transitional type 1 (T1) B cells in the preparation was measured by flow cytometry. Cells were injected into the tail veins of adult RAG-2−/− mice in 200 μl PBS. Each mouse received 2 × 106 B cells.
Sorted transitional type 2 (T2) cells (106) were injected into the tail veins of adult RAG-2−/− mice. The spleens of recipient mice were analyzed by flow cytometry after 24 and 48 h. Three independent experiments were performed.
Loder B.F., Mutschler B., Ray R.J., Paige C.J., Sideras P., Torres R., Lamers M.C, & Carsetti R. (1999). B Cell Development in the Spleen Takes Place in Discrete Steps and Is Determined by the Quality of B Cell Receptor–Derived Signals. The Journal of Experimental Medicine, 190(1), 75-90.
Publication 1999
Adult B-Lymphocytes Cells Centrifugation, Density Gradient Erythrocytes Ficoll Flow Cytometry Immature B-Lymphocyte Mice, Inbred C57BL Mus RAG2 protein, human Tail Transitional Epithelial Cells Veins
4
Comprehensive Tumor Microenvironment Analysis
The gene set variation analysis (GSVA) was performed to identify specific pathways of each subtype (24 (link)). We downloaded Hallmark and KEGG gene sets from the Molecular Signatures Database and further transformed the gene expression matrix into gene set matrix using the GSVA package. Afterwards, we performed gene sets difference analysis using the limma package and the screening threshold were set to |log2 fold change (FC)| >0.2 and adjusted P-value <0.05. Adjusted P-value was obtained from the Benjamini–Hochberg multiple test correction.
Referring to Charoentong et al. study (25 (link)), we obtained the markers of 23 immune cells including: innate immune cells (activated dendritic cells, CD56+ natural killer cells, CD56− natural killer cells, eosinophils, immature dendritic cells, macrophages, mast cells, MDSC, monocytes, natural killer cells, neutrophils, and plasmacytoid dendritic cells) and adaptive immune cells (activated B cells, activated CD4+ T cells, activated CD8+ T cells, Gamma delta T cells, immature B cells, natural killer T cells, Treg cells, follicular helper T cells, Th1 cells, Th2 cells, and Th17 cells). Endothelial cells and fibroblasts, also the important components of TME, played a crucial role in tumor inflammation, angiogenesis, invasion, and metastasis. The markers of endothelial cell and fibroblast were retrieved from the MCP-counter (26 (link)) (Table S3). Based on these markers, we applied the single sample gene set enrichment analysis (ssGSEA) algorithm to evaluate the infiltration abundance of 25 TME cells.
Liu Z., Wang L., Liu L., Lu T., Jiao D., Sun Y, & Han X. (2021). The Identification and Validation of Two Heterogenous Subtypes and a Risk Signature Based on Ferroptosis in Hepatocellular Carcinoma. Frontiers in Oncology, 11, 619242.
Publication 2021
Acclimatization angiogen B-Lymphocytes CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes Dendritic Cells Endothelial Cells Eosinophil Fibroblasts Gene Expression Genes Genetic Diversity Immature B-Lymphocyte Inflammation Intraepithelial Lymphocytes Macrophage Mast Cell Monocytes Myeloid-Derived Suppressor Cells Natural Killer Cells Natural Killer T-Cells Neoplasm Metastasis Neoplasms Neutrophil Plasmacytoid Dendritic Cells Regulatory T-Lymphocytes T Follicular Helper Cells Th17 Cells Type-2 Helper T Cell Type 1 Helper T Cells
5
Immunophenotyping of B Cell Subsets
Immunofluorescence staining for flow cytometric analysis was performed by incubating PBMCs with excess mAb in PBS/1% BSA on ice for 20 min after blocking with 10 μg human IgG for 20 minutes. Cells were washed in PBS/BSA and in some cases incubated with streptavidin-conjugate. Cells were then washed and fixed in PBS/1% paraformaldehyde before analysis on a dual-laser FACSCalibur (Becton-Dickinson, Mountain View, CA) or three-laser 11-color LSRII (Becton-Dickinson). For some experiments B cells were purified using RosetteSep (Stem Cell Technology, Vancouver, BC, Canada) and enriched for naïve and transitional B cells by CD27 cell depletion, using CD27 microbeads and magnetic sorting (Miltenyi Biotech, Auburn, CA). For purification of distinct transitional cell populations RosetteSep enriched B cells were sorted on a FACSAria as further defined in the Figure legends.
B cells were identified based on CD19 expression and additionally classified by multi-parameter flow cytometry along a developmental pathway based on the expression of defined surface markers as follows: immature (CD38hiCD24hiCD10+IgD−IgM+CD27−), transitional (CD38hiCD24hiIgD+CD27−CD10+/−), naïve (CD38intCD24intIgD+CD27−CD10−), pre-germinal center (CD38hiCD24loIgD+CD27+/−), nonswitched memory (CD27+IgD+), and switched memory (CD27+IgD−) (Table 1) (7 (link), 8 (link), 17 (link), 18 (link)). Distinct transitional cells within the IgD+CD27− compartment were defined based on R123 or mitotracker retention (further described below) and thresholds of expression of CD24 and CD38. FMO controls were used to define negative cell populations. Thresholds of CD24/CD38 expression were defined as follows: bone marrow precursor populations including T1 (CD10+) have the highest expression (+++), T2 cells (also CD10+ and R123+) have intermediate expression (++), and naïve cells (R123 negative) the lowest expression (+). Thus, T1 (R123+CD38+++CD24+++CD10+IgD+CD27−), T2 (R123+CD38++CD24++CD10+IgD+CD27−), and T3 (R123+CD38+CD24+IgD+CD27−) cells were discriminated. In addition to examining a bone marrow control with each experiment, a normal PBMC control was also included in order to further define the CD38/CD24 expression threshold on mature naïve B cells. Transitional cells from multiple tissue sources were additionally examined for the expression of a variety of developmentally regulated and other markers (7 (link), 8 (link), 19 (link)).
Palanichamy A., Barnard J., Zheng B., Owen T., Quach T., Wei C., Looney R.J., Sanz I, & Anolik J.H. (2009). Novel human transitional B cell populations revealed by B cell depletion therapy. Journal of immunology (Baltimore, Md. : 1950), 182(10), 5982-5993.
Publication 2009
B-Lymphocytes Bone Marrow Cells Flow Cytometry Germinal Center Homo sapiens Immature B-Lymphocyte Immunofluorescence Memory Microspheres paraform Population Group Retention (Psychology) Stem Cells Streptavidin Tissues Transitional Epithelial Cells

Most recents protocols related to «Immature B-Lymphocyte»

1
Comprehensive Immune Landscape of Alzheimer's Disease
The gene sets of 28 immune cells and four classes of immune factors were downloaded from TISIDB database.3 The following 28 types of immune cells were obtained: central memory CD4+ T cells (CD4+ Tcm), central memory CD8+ T cells (CD8+ Tcm), type-2 T helper cells (Th2), CD56dim natural killer cells (CD56− NK), activated CD8+ T cells (CD8+ Ta), activated CD4+ T cells (CD4+ Ta), activated B cells (Ba), effector memory CD8+ T cells (CD8+ Tem), effector memory CD4+ T cells (CD4+ Tem), macrophages, eosinophils, memory B cells (Bm), immature dendritic cells (DCi), gamma delta T cells (γδT), CD56bright natural killer cells (CD56+ NK), monocytes, mast cells, natural killer cells (NK), immature B cells (Bi), type-1 T helper cells (Th1), neutrophils, plasmacytoid dendritic cells (DCp), natural killer T cells (NK T), type-17 T helper cells (Th17), follicular helper T cells (Tfh), regulatory T cells (Tregs), myeloid-derived suppressor cells (MDSC), and activated dendritic cells (DCa). The four classes of immune factors include 41 chemokines, 24 immunosuppressive factors, 46 immunostimulatory factors, and 18 immune receptors.
The ssGSEA algorithm, which classifies gene sets with common biological functions, physiological regulation, and chromosomal localization, was employed via R packages (GSVA 1.42.0) to comprehensively assess the immunologic characteristics of each sample included in the analyses (Hänzelmann et al., 2013 (link)). Normalized data of gene expression profiles were compared with the gene sets to demonstrate the enrichment of immune cells in each AD brain samples. Then, ANOVA was adopted to identify immune cell types with significant differences between the groups with longer lifespan and shorter lifespan. Pearson correlations between the gene expression level of each hub gene and the concentrations of immune cells were carried out using cor.test in R software (version: 4.0.3). The hub genes were identified in 2.4.
The correlations between the gene expression levels of each hub gene and the gene sets of immune factors were also calculated, respectively. Then, the pairs of hub genes and immune-related molecules with |cor| > 0.6 & p value<0.05 were selected to generate a circos plot via Cytoscape.
Zhang J., Li X., Xiao J., Xiang Y, & Ye F. (2023). Analysis of gene expression profiles in Alzheimer’s disease patients with different lifespan: A bioinformatics study focusing on the disease heterogeneity. Frontiers in Aging Neuroscience, 15, 1072184.
Publication 2023
B-Lymphocytes Biological Processes Brain CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes Central Memory T Cells Chemokine Chromosomes Dendritic Cells Effector Memory T Cells Eosinophil Gene Expression Genes Helper-Inducer T-Lymphocyte Immature B-Lymphocyte Immunization Immunologic Factors Immunosuppressive Agents Intraepithelial Lymphocytes Macrophage Mast Cell Memory B Cells Monocytes Myeloid-Derived Suppressor Cells Natural Killer Cells Natural Killer T-Cells neuro-oncological ventral antigen 2, human Neutrophil physiology Plasmacytoid Dendritic Cells Receptors, Immunologic Regulatory T-Lymphocytes Th17 Cells Type-2 Helper T Cell Type 1 Helper T Cells
2
Immune Landscape and Diagnostic Potential of BUB1B and RRM2 in LIHC
R software, version 4.0.5, was used to perform two-sided Spearman correlation analyses between BUB1B and RRM2 genes and immune infiltrating cells in TCGA LIHC samples, which were first analyzed using the xCELL algorithm. For performing the ssGSEA correlation between BUB1B and RRM2 gene expression with memory B cells, immature B cells, effector memory CD4+ T cells, central memory CD4+ T cell immune markers, and T helper 2 cell immune markers gene sets, the “GSVA” package was used. The results of spearman correlation and ssGSEA were visualized using the “tidyverse”, “broom”, “fs” and “lubridate” packages, the area under the ROC curve (AUC) was computed and utilized to compare the diagnostic value of these hub genes using timeROC and survival packages.
By using GraphPad Prism (version 8.0.1), the difference in BUB1B and RRM2 gene expression between aflatoxin B1 and their control samples, fibrotic and non-fibrotic samples, and liver tumors and normal samples were compared using an independent Student’s t-test. Clinically, the difference between high and low BUB1B and RRM2 gene expression and their clinical phenotype data in the TCGA LIHC was analyzed using chi- square test. p < 0.05 was considered statistically significant. * p < 0.05, ** p < 0.01, *** p < 0.001.
Hamdy H., Yang Y., Cheng C, & Liu Q. (2023). Identification of Potential Hub Genes Related to Aflatoxin B1, Liver Fibrosis and Hepatocellular Carcinoma via Integrated Bioinformatics Analysis. Biology, 12(2), 205.
Publication 2023
Aflatoxin B1 BUB1B protein, human Cells Central Memory T Cells Cytisus Diagnosis Effector Memory T Cells Fibrosis Gene Expression Genes Immature B-Lymphocyte Memory B Cells Neoplasms, Liver Phenotype prisma RRM2 protein, human Student Type-2 Helper T Cell
3
Comprehensive Flow Cytometry Gating for Lymphocyte Subsets
Blood samples were taken prior to surgery and flow cytometry was performed within 24 h at our local laboratory using an FC 500 flow cytometer from Beckman Coulter (Brea, CA, USA). The gating strategy was used as previously published by our study group [12 (link),15 (link),26 (link),27 (link),28 (link)] and is displayed in detail in Supplementary Figure S1. For cell staining, antibodies from Beckman Coulter (Brea, CA, USA) and Biolegend (San Diego, CA, USA) were applied. Absolute values of lymphocytes were calculated using leukocyte counts measured with Stem-Count (Stem-Kit, Beckman Coulter). Initial lymphocyte values were reported as percentages. Detailed information regarding the used antibodies and gating strategy is displayed in Supplementary Figure S1 and Table S1. In brief, lymphocytes were identified by using forward and side scatter. B lymphocytes were defined by CD19 positivity and further subdivided into naïve, class-switched memory, non-class-switched memory, and transitional B cells. NK-like T cells were primarily identified (CD3+ CD56+) [29 (link),30 (link)]. NK lymphocytes, which were of special interest in this study, were detected as CD56+ cells and subdivided into three functional NK subsets, namely CD56+ CD16+, CD56dim CD16bright, and CD56bright CD16dim. T cells were identified by CD4 or CD8 positivity, and both were further subdivided into memory, naïve, central memory, effector memory, and effector memory RA (herein called “EMRA”) cells. To further analyze CD8+ cells, they were divided into early, intermediate, late, or exhausted and terminal effector cells. CD4+ T helper cells were classified into Th1, Th2, and Th17/22. Moreover, CD4+ and CD8+ were subdivided into activated and regulatory cells.
Reitsam N.G., Märkl B., Dintner S., Sipos E., Grochowski P., Grosser B., Sommer F., Eser S., Nerlinger P., Jordan F., Rank A., Löhr P, & Waidhauser J. (2023). Alterations in Natural Killer Cells in Colorectal Cancer Patients with Stroma AReactive Invasion Front Areas (SARIFA). Cancers, 15(3), 994.
Publication 2023
Antibodies B-Lymphocytes BLOOD CD8-Positive T-Lymphocytes Cells Flow Cytometry Helper-Inducer T-Lymphocyte Immature B-Lymphocyte Leukocyte Count Lymphocyte Memory Natural Killer T-Cells Operative Surgical Procedures Stem, Plant T-Lymphocyte Th17 Cells
4
Immune Cell Signatures in Tumor Microenvironment
The immune-related signatures of 28 TILs [activated B cell; activated CD4+ T cell; activated CD8+ T cell; central memory CD4+ T cell; central memory CD8+ T cell; effector memory CD4+ T cell; effector memory CD8+ T cell; gamma delta T cell; immature B cell; memory B cell; regulatory T cell; T follicular helper cell; type 1 T helper cell; type 17 T helper cell; type 2 T helper cell; activated dendritic cell; CD56bright natural killer (NK) cell; CD56dim NK cell; eosinophil; immature dendritic cell; macrophage; mast cell; monocyte; myeloid-derived suppressor cell; NK cell; NK T cell; neutrophil; plasmacytoid dendritic cell] were analyzed in TISIDB website (12 (link)). The 28 TILs, gene expression, copy number, methylation, and mutation were viewed and downloaded. For each cancer type, the relative abundance of TILs was inferred from gene set variation analysis based on the gene expression profile.
Xu S., Ma B., Feng X., Yao C., Jian Y., Chen Y., Wang X., Xie H, & Li L. (2023). EZH2-regulated immune risk score prognostic model predicts outcome of clear cell renal cell carcinoma. Translational Andrology and Urology, 12(1), 71-82.
Publication 2023
B-Lymphocytes CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes Central Memory T Cells Dendritic Cells Effector Memory T Cells Eosinophil Gene Expression Genes Helper-Inducer T-Lymphocyte Immature B-Lymphocyte Intraepithelial Lymphocytes Lymphocytes, Tumor-Infiltrating Macrophage Malignant Neoplasms Mast Cell Memory B Cells Methylation Monocytes Mutation Myeloid-Derived Suppressor Cells Natural Killer Cells Natural Killer T-Cells Neutrophil Plasmacytoid Dendritic Cells Regulatory T-Lymphocytes T Follicular Helper Cells Type-2 Helper T Cell Type 1 Helper T Cells
5
Profiling Immune Cell Infiltration in Alzheimer's Disease
Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised algorithm. GSVA R package was applied in ssGSEA for the analysis of immune cell infiltration in AD and control samples21 (link), which including activated CD4 T cell, activated B cell, activated CD8 T cell, activated dendritic cell, CD56bright natural killer cell, CD56dim natural killer cell, central memory CD4 T cell, effector memeory CD4 T cell, central memory CD8 T cell, effector memeory CD8 T cell, gamma delta T cell, macrophage, eosinophil, immature B cell, immature dendritic cell, mast cell, MDSC, memory B cell, monocyte, neutrophil, plasmacytoid dendritic cell, regulatory T cell, T follicular helper cell, natural killer cell, natural killer T cell, Type 1 T helper cell, Type 2 T helper cell, and Type 17 T helper cell. Metagene of 28 immune cell subtypes was obtained from https://www.cell.com/cms/10.1016/j.celrep.2016.12.019/attachment/f353dac9-4bf5-4a52-bb9a-775e74d5e968/mmc3.xlsx22 (link). We compared the difference in proportion of immune cells in AD and control samples via Wilcoxon tests. A P-value ≤ 0.05 was considered statistically significant.
Li S., Xiao J., Huang C, & Sun J. (2023). Identification and validation of oxidative stress and immune-related hub genes in Alzheimer’s disease through bioinformatics analysis. Scientific Reports, 13, 657.
Publication 2023
Antigen-Presenting Cells B-Lymphocytes CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes Cell-Matrix Junction Cells Central Memory T Cells Eosinophil Genetic Diversity Helper-Inducer T-Lymphocyte Immature B-Lymphocyte Intraepithelial Lymphocytes Macrophage Mast Cell Memory B Cells Monocytes Myeloid-Derived Suppressor Cells Natural Killer Cells Natural Killer T-Cells Neutrophil Plasmacytoid Dendritic Cells Regulatory T-Lymphocytes Somatostatin-Secreting Cells T Follicular Helper Cells Type-2 Helper T Cell Type 1 Helper T Cells

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More about "Immature B-Lymphocyte"

Immature B cells, B-lymphocytes, pre-B cells, undeveloped B cells, B-cell precursors, B-cell development, B-cell maturation, B-cell differentiation, immune system response, pathogen recognition, flow cytometry, LSRFortessa, FACSAria III, FACSAria, FACSCalibur, FACSAria II, FACSCalibur flow cytometer, Streptomycin, Cytofix, BrdU Flow Kit, Anti-B220-PerCP.
Immature B-lymphocytes are crucial in the early stages of B-cell development, playing a key role in the immune system's response to pathogens.
These undeveloped B cells have not yet fully matured and are essential for generating a robust antibody response.
Optimize your research on Immature B-Lymphocytes with PubCompare.ai, our AI-driven protocol comparison tool.
Leverage our intuitive platform to identify the best methodologies and products, ensuring reliable, high-quality findings.
Expereince the power of AI-assisted Immature B-Lymphocyte research with PubCompare.ai.