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Memory B Cells

Q: What are the key functions and characteristics of Memory B Cells?

Memory B Cells are a specialized subset of B lymphocytes that retain immunological memory. They play a crucial role in the adaptive immune system by providing long-lasting protection against pathogens. Key functions include rapid and robust response upon re-exposure to an antigen, enabling the body to mount a quicker and more effective immune reaction.

Q: How can PubCompare.ai help optimize Memory B Cell research?

PubCompare.ai is an innovative AI-driven platform that can streamline your Memory B Cell research. It allows you to screen protocol literature more efficiently and leverage AI to pinpoint critical insights. The platform can help researchers identify the most effective protocols related to Memory B Cells for their specific research goals. Its AI-driven analysis can highlight key differences in protocol effectiveness, enabling you to choose the best option for reproducibility and accuracy.

Q: What are some common challenges in working with Memory B Cells?

One of the main challenges in working with Memory B Cells is ensuring reproducibility and accuracy of experimental results. Identifying the most effective protocols from the vast literature can be time-consuming and difficult. Additinally, there can be variations in Memory B Cell populations and responses based on factors like antigen exposure history, which can complicate research efforts.

Q: How can PubCompare.ai help address challenges in Memory B Cell research?

PubCompare.ai can help address key challenges in Memory B Cell research by streamlining the protocol screening process and leveraging AI to pinpoint critical insights. The platform's AI-powered comparisons can enhance reproducibility and accuracy by highlighting the most effective protocols for your specific research goals. This allows you to choose the best option and improve your overall research outcomes.

Q: What are some common applications of Memory B Cells in research and medicine?

Memory B Cells have a wide range of applications in research and medicine. They are widely studied for their role in long-term immunity and vaccine development. Memory B Cells are also important in understanding autoimmune diseases, as dysregulation of their responses can contribute to pathogenesis. Additionally, Memory B Cells are relevant in cancer immunotherapy, as they can be leveraged to enhance the body's anti-tumor immune response.

Memory B cells are a specialized subset of B lymphocytes that retain immunological memory, enabling a rapid and robust response upon re-exposure to an antigen.
These cells play a crucial role in the adaptive immune system, providing long-lasting protection against pathogens.
PubCompare.ai, an innovative AI-driven platform, can optimize your Memory B Cell research by locating the best protocols from literature, pre-prints, and patents with ease.
Leverage AI-powered comparisons to enhance reproducibility and accuracy, and improve your research outcomes with this tool that streamlines your workflow and delivers reliable insights.
Discover how PubCompare.ai can enhance your Memory B Cell studies and drive your research forward.

Most cited protocols related to «Memory B Cells»

Isolation and Characterization of Neutralizing Antibodies

Activated memory B cell supernatants were screened in a high throughput format for neutralization activity using a micro-neutralization assay, as described^{2 (link)}. Heavy and light chain variable regions were isolated from B cell lysates of selected neutralizing hits by reverse transcription from RNA followed by multiplex PCR amplification using family-specific V-gene primer sets. For some antibodies, traditional cloning methods were used for antibody isolation, as described^{2 (link)}. For other antibodies, amplicons from each lysate were uniquely tagged with multiplex identifier (MID) sequences and 454 sequencing regions (Roche). Single round of replication pseudovirus neutralization assays and cell surface binding assays were performed as described previously^{2 (link),27 (link),28 (link)}. Glycan reactivities were profiled on a printed glycan microarray (version 5.0 from the Consortium for Functional Glycomics (CFG)) as described previously^{29 (link)}.

Walker L.M., Huber M., Doores K.J., Falkowska E., Pejchal R., Julien J.P., Wang S.K., Ramos A., Chan-Hui P.Y., Moyle M., Mitcham J.L., Hammond P.W., Olsen O.A., Phung P., Fling S., Wong C.H., Phogat S., Wrin T., Simek M.D., Koff W.C., Wilson I.A., Burton D.R, & Poignard P. (2011). Broad neutralization coverage of HIV by multiple highly potent antibodies. Nature, 477(7365), 466-470.

Publication 2011

Antibodies B-Lymphocytes Biological Assay Cells DNA Replication Genes Immunoglobulins isolation Light Memory B Cells Microarray Analysis Multiplex Polymerase Chain Reaction Polysaccharides Reverse Transcription V-Primer

Isolation and Characterization of Neutralizing Antibodies

Publication 2011

Flow Cytometry Analysis of NSCLC Tumor

Flow cytometry analysis of non-small cell lung cancer tumor (n = 13) specimens was performed as described below, and median fractions of CD4⁺, CD8⁺, CD19⁺, CD56⁺, and CD14⁺ populations were normalized by overall CD45⁺ content (Fig. 3a). For comparison with CIBERSORT, leukocyte signature matrix populations were grouped into the same cluster of differentiation categories: CD14⁺, monocytes, macrophages, and dendritic cells; CD4⁺, all T cell subsets except CD8 and γδ T cells; CD8⁺, CD8 T cells; CD19⁺, naïve and memory B-cells, CD56⁺, resting and activated NK cells. Median CIBERSORT-inferred fractions for lung adenocarcinoma GEPs, shown in Fig. 3a, were determined from two publicly available microarray datasets, GSE7670⁷⁷ and GSE10072⁷⁸.

Gentles A.J., Newman A.M., Liu C.L., Bratman S.V., Feng W., Kim D., Nair V.S., Xu Y., Khuong A., Hoang C.D., Diehn M., West R.B., Plevritis S.K, & Alizadeh A.A. (2015). The prognostic landscape of genes and infiltrating immune cells across human cancers. Nature medicine, 21(8), 938-945.

Publication 2015

Adenocarcinoma of Lung CD8-Positive T-Lymphocytes Dendritic Cells Flow Cytometry Leukocytes Macrophage Memory B Cells Microarray Analysis Monocytes Natural Killer Cells Neoplasms Non-Small Cell Lung Carcinoma T-Lymphocyte T-Lymphocyte Subsets

Benchmarking Immune Cell Deconvolution Methods

All methods were run in R using their original code or R package, except TIMER, which was run from the web interface (https://cistrome.shinyapps.io/timer). All methods were run with their default parameter settings. EPIC was run with the “BRef” signature on PBMC data and with the “Tref” signature on the tumor data. TIMER signatures for COAD, LUAD, and SKCM were used to analyze tumor data from CRC, lung, and melanoma patients, respectively; TIMER was not applied to PBMC data as the web interface only allows the analysis of tumor data. CIBERSORT estimates were aggregated across the major subtypes considered in the benchmarking (e.g., naïve and memory B cells were summed up to obtain total B cell estimates). For EPIC and xCell, T cell estimates were obtained by summing up CD4⁺ and CD8⁺ T cells. xCell “DC” scores were considered for dendritic cells, whereas the MCPcounter estimates from the “Monocytic lineage” were used to quantify monocytes.

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 CD8-Positive T-Lymphocytes Chronic Obstructive Airway Disease Dendritic Cells Lung Melanoma Memory B Cells Monocytes Neoplasms Patients T-Lymphocyte

Isolation of SARS-CoV Memory B Cells

After informed consent was obtained, peripheral blood was collected from a 35-year-old patient who had recovered from infection by SARS-CoV. IgG⁺ memory B cells were isolated by binding to CD22 microbeads (Miltenyi) followed by depletion of cells carrying IgM, IgD and IgA by cell sorting. Memory B cells were seeded at 10 or 50 cells per well in 96 U-bottom microplates in complete medium containing 2.5 μg/ml CpG 2006, in the presence of EBV (30% supernatant of B95-8 cells) and irradiated allogeneic mononuclear cells (50,000 per well). After 2 weeks, the culture supernatants were screened for specific antibodies. Positive cultures were cloned by limiting dilution in the presence of CpG 2006 and irradiated mononuclear cells. Antibody was purified from culture supernatants by affinity chromatography on protein A columns (Amersham).

Traggiai E., Becker S., Subbarao K., Kolesnikova L., Uematsu Y., Gismondo M.R., Murphy B.R., Rappuoli R, & Lanzavecchia A. (2004). An efficient method to make human monoclonal antibodies from memory B cells: potent neutralization of SARS coronavirus. Nature Medicine, 10(8), 871-875.

Publication 2004

Allogeneic Cells Antibodies Blood Cells Chromatography, Affinity CpG 2006 Immunoglobulins Infection Memory B Cells Microspheres Patients Severe acute respiratory syndrome-related coronavirus Staphylococcal Protein A Technique, Dilution

Most recents protocols related to «Memory B Cells»

Quantification of LPS-specific B-Cell Response

Activation of PBMC and ELISPOT assay was performed for the measurement of IgG and IgA memory B-cell response to SF2a LPS^{62 (link),63 (link)}. PBMCs were isolated from whole blood using Ficoll-Paque PLUS (GE Healthcare) and incubated for 5 days with the activating reagents 10 ng/mL IL-2 (R&D Systems) and 1 µg/mL R848 (Invivogen) in RPMI-1640 supplemented with 10% Fetal Bovine Serum (BI inc, Rhenium). ELISPOT PVDF microtiter plates (Merck Millipore Corp.) were coated with 5 µg/mL of SF2a LPS or with Goat anti-human IgG-Fc antibody (EMD Millipore Corp.) or Goat anti-human IgA antibody (Bethyl Laborathories Inc.). Activated PBMCs were washed and added in duplicates in two concentrations (3 × 10⁵/100 µL/well and 1.5 × 10⁵/100 µL/well) to LPS coated wells. Activated PBMC at 3 × 10³/100 µL/well were serially 2-fold diluted and added to anti-IgG/IgA coated wells. Plates were incubated in a 37 °C CO₂ humidified incubator for 6 h and washed twice with PBS-Tween and twice with PBS. AP labeled anti-human IgG or anti-human IgA goat antibodies diluted 1:4000 (Bethyl Laborathories Inc.) were added to the wells and incubated ON at 4 °C. Plates were washed twice with PBS-Tween, twice with PBS, once with double distilled water (ddw) and the BCIP/NBT (Merck Millipore Corp.) substrate solution was added and incubated for 20 min. Reaction was stopped by washing twice with ddw and incubating with 200 µL/well ddw for 30 min in RT. The number of spots was counted using a stereoscope attached to a camera (SMZ800N, Nikon). Results are expressed as % LPS-specific antibody-secreting cells/total IgG or IgA secreting cells. An individual positive response after vaccination was defined as an increase in the percentage of SF2a LPS-specific-IgG and IgA secreting cells per total IgG or IgA secreting cells above a cut-off value represented by the mean of the same parameter and two Standard Deviations (SDs) calculated among all placebo recipients at each time point.

Meron-Sudai S., Asato V., Adler A., Bialik A., Goren S., Ariel-Cohen O., Reizis A., Mulard L.A., Phalipon A, & Cohen D. (2023). A Shigella flexneri 2a synthetic glycan-based vaccine induces a long-lasting immune response in adults. NPJ Vaccines, 8, 35.

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Publication 2023

anti-IgA anti-IgG Antibodies Biological Assay BLOOD Cells Enzyme-Linked Immunospot Assay Exanthema Fetal Bovine Serum Ficoll Goat Homo sapiens Immunoglobulin Fc Fragments Immunoglobulin G Immunoglobulins Memory B Cells Placebos polyvinylidene fluoride Rhenium Tweens Vaccination

Evaluating Vaccine Immunogenicity Metrics

Geometric mean (GM), geometric mean titer (GMT) and 95% Confidence Intervals (CIs) were calculated for immunological parameters and summarized by time after vaccination. The percentages of responders were determined by defining a priori cut-off values for each immunological parameter: for serum IgG, IgA and SBA the cut-off value was ≥4-fold rise in titer compared to baseline, for memory B cells the cut-off value was mean + 2 SD of all placebo recipients at each time point.
Differences between GMTs or GMs at follow up versus pre-vaccination and 3 months post-last injection time points were examined by the Wilcoxon signed rank test for repeated measurements. Correlations between immunological parameters were assessed using Spearman’s rank correlation coefficient. Fisher’s exact test was used to assess unadjusted associations of the frequency of specific-IgG or IgA memory B-cells, as categorical variables, with the percentage of responders for serum IgG, IgA and SBA, as well as Student’s t test for the association with mean avidity. All statistical tests were interpreted in a two-tailed fashion using a significance level (α) = 0.05. Data were analyzed using the SPSS version 24 (Armok, N.Y., USA).

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Publication 2023

Memory B Cells Placebos Serum Student Vaccination

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 in Tumors

CIBERSORT method used to quantify infiltrating immune cell ratio (https://cibersort.stanford.edu/) (20 (link)). The proportion of 22 immune cells (B-naive cells, B-cell memory, plasma cells, T-cell CD8, T-cell CD4 naive, T-cell follicular helper cells, T-cell CD4 memory resting, T-cell CD4 memory activation, regulatory T cells (Tregs), γδ cells, monocytes, activation) was calculated by CIBERSORT method NK cells, resting NK cells, macrophage M0, macrophage M1, macrophage M2, resting dendritic cells, activated dendritic cells, resting mast cells, activated mast cells, eosinophils, and neutrophils). Samples with P<0.05 indicated that the proportion of immune cells calculated by CIBERSORT was correct. The tumor purity, stromal score, immune score and ESTIMATE scorewere calculated for each tumor sample by R package “ESTIMATE” (21 (link)). A single sample Gene set enrichment analysis (ssGSEA) algorithm was used to assess the immune infiltration between the two groups based on 28 immune cell types. XCELL, QUANTISEQ, MCPCOUNTER, EPIC and CIBERSORT-ABS software were also used to quantify the relative proportion of immune cell infiltration.

Xu Y., Xia Z., Sun X., Wei B., Fu Y., Shi D, & Zhu Y. (2023). Identification of a glutamine metabolism reprogramming signature for predicting prognosis, immunotherapy efficacy, and drug candidates in bladder cancer. Frontiers in Immunology, 14, 1111319.

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Publication 2023

B-Lymphocytes Dendritic Cells Eosinophil Genes Helper-Inducer T-Lymphocyte Macrophage Mast Cell Memory B Cells Memory T Cells Monocytes Natural Killer Cells Neoplasms Neutrophil Plasma Cells Regulatory T-Lymphocytes T-Lymphocyte

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.

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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

Top products related to «Memory B Cells»

Facsaria 2 by BD

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The FACSAria II is a high-performance cell sorter produced by BD. It is designed for precision cell sorting and analysis. The system utilizes flow cytometry technology to rapidly identify and separate different cell populations within a sample.

Facsaria 3 by BD

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The FACSAria III is a high-performance cell sorter designed for advanced flow cytometry applications. It features a robust and flexible optical system, enabling precise cell sorting and analysis. The core function of the FACSAria III is to provide users with the ability to sort and analyze complex cell samples with high accuracy and efficiency.

Facsaria by BD

Sourced in United States, United Kingdom, Germany, Japan, Belgium, Canada, France, China, Switzerland, Sweden, Australia, Lao People's Democratic Republic, Austria, Uruguay

The FACSAria is a flow cytometry instrument manufactured by BD. It is used for the analysis and sorting of cells and other particles. The FACSAria is designed to provide high-performance cell sorting capabilities, enabling researchers to isolate specific cell populations for further analysis or experimentation.

Memory b cell isolation kit by Miltenyi Biotec

Sourced in Germany, France, United States, United Kingdom

The Memory B Cell Isolation Kit is a laboratory tool used to isolate memory B cells from a sample. The kit contains the necessary reagents and components to selectively separate memory B cells from other cell types present in the sample.

Facscanto 2 by BD

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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.

Lsrfortessa by BD

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The LSRFortessa is a flow cytometer designed for multiparameter analysis of cells and other particles. It features a compact design and offers a range of configurations to meet various research needs. The LSRFortessa provides high-resolution data acquisition and analysis capabilities.

Il 21 by Thermo Fisher Scientific

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IL-21 is a recombinant human cytokine that is a member of the common gamma chain receptor cytokine family. It functions as a regulator of T and B cell proliferation and differentiation.

Facsaria fusion by BD

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The FACSAria Fusion is a high-performance cell sorter designed for advanced flow cytometry applications. It features a compact, modular design and offers a range of configuration options to meet the specific needs of researchers.

Facsdiva software by BD

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FACSDiva software is a user-friendly flow cytometry analysis and data management platform. It provides intuitive tools for data acquisition, analysis, and reporting. The software enables researchers to efficiently process and interpret flow cytometry data.

Rnaseout by Thermo Fisher Scientific

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RNaseOUT is a recombinant ribonuclease inhibitor protein that protects RNA from degradation by RNase enzymes. It is a highly effective inhibitor of RNase A, RNase B, and RNase C.

What are the key functions and characteristics of Memory B Cells?

How can PubCompare.ai help optimize Memory B Cell research?

PubCompare.ai is an innovative AI-driven platform that can streamline your Memory B Cell research. It allows you to screen protocol literature more efficiently and leverage AI to pinpoint critical insights. The platform can help researchers identify the most effective protocols related to Memory B Cells for their specific research goals. Its AI-driven analysis can highlight key differences in protocol effectiveness, enabling you to choose the best option for reproducibility and accuracy.

What are some common challenges in working with Memory B Cells?

One of the main challenges in working with Memory B Cells is ensuring reproducibility and accuracy of experimental results. Identifying the most effective protocols from the vast literature can be time-consuming and difficult. Additinally, there can be variations in Memory B Cell populations and responses based on factors like antigen exposure history, which can complicate research efforts.

How can PubCompare.ai help address challenges in Memory B Cell research?

PubCompare.ai can help address key challenges in Memory B Cell research by streamlining the protocol screening process and leveraging AI to pinpoint critical insights. The platform's AI-powered comparisons can enhance reproducibility and accuracy by highlighting the most effective protocols for your specific research goals. This allows you to choose the best option and improve your overall research outcomes.

What are some common applications of Memory B Cells in research and medicine?

Memory B Cells have a wide range of applications in research and medicine. They are widely studied for their role in long-term immunity and vaccine development. Memory B Cells are also important in understanding autoimmune diseases, as dysregulation of their responses can contribute to pathogenesis. Additionally, Memory B Cells are relevant in cancer immunotherapy, as they can be leveraged to enhance the body's anti-tumor immune response.

More about "Memory B Cells"

Memory B cells, also known as MBCs, are a specialized subset of B lymphocytes that retain immunological memory.
These cells play a crucial role in the adaptive immune system, providing long-lasting protection against pathogens.
MBCs are able to rapidly and robustly respond upon re-exposure to an antigen, making them a key component in the body's defense against infection.
The FACSAria II, FACSAria III, and FACSAria Fusion are flow cytometry instruments that can be used to isolate and analyze Memory B cells.
The Memory B Cell Isolation Kit is a tool that allows for the specific purification of these cells from a sample.
The FACSCanto II and LSRFortessa are additional flow cytometry systems that can be utilized in Memory B cell research.
Interleukin-21 (IL-21) is a cytokine that has been shown to play an important role in the differentiation and maintenance of Memory B cells.
The FACSDiva software is often used in conjunction with flow cytometry instruments to facilitate data acquisition and analysis related to Memory B cell populations.
By leveraging the innovative AI-driven platform PubCompare.ai, researchers can optimize their Memory B cell studies.
This tool can help locate the best protocols from literature, pre-prints, and patents, while also providing AI-powered comparisons to enhance reproducibility and accuracy.
Utilyzing PubCompare.ai can streamline the research workflow and deliver reliable insights, ultimately improving research outcomes for Memroy B cell studies.