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Lymphoid Cells

Lymphoid Cells: A diverse group of cells that play a central role in the immune system.
Lymphoid cells include T cells, B cells, and natural killer cells, which are responsible for cell-mediated and humoral immune responses.
These cells develop from hematopoietic stem cells in the bone marrow and lymphoid tissues, and are crucial for recognizing and eliminating foreign pathogens, as well as maintaining immunological tolerance.
The study of Lymphoid Cells is essential for understanding the complexities of the immune system and developing therapies for immunological disorders and diseases.

Most cited protocols related to «Lymphoid Cells»

Evaluating PD-L1 Expression in FFPE Tissues

Formalin-fixed, paraffin-embedded (FFPE) tissue sections of 4-mm thickness were stained for PD-L1 with an anti-human PD-L1 rabbit monoclonal antibody (clone SP142; Ventana, Tucson, AZ) on an automated staining platform (Benchmark; Ventana) using a concentration of 4.3 mg ml^{21 (link)}, with signal visualization by diaminobenzidine; sections were counter-stained with haematoxylin. PD-L1 expression was evaluated on tumour cells and tumour-infiltrating immune cells. For tumour cells the proportion of PD-L1-positive cells was estimated as the percentage of total tumour cells; tumour cells typically showed membranous staining with avariably strong component of cytoplasmic staining. The distribution of PD-L1-positive tumour cells within a given tumour sample was typically very focal; in tumours growing as solid aggregates positive tumour cells were more commonly observed at the interface between malignant cells and stroma containing tumour-infiltrating immune cells. For tumour-infiltrating immune cells, the percentage of PD-L1-positive tumour-infiltrating immune cells occupying the tumour was recorded; tumour-infiltrating immune cells with clearly discernible cytoplasm, such as macrophages and dendritic cells, showed a membranous staining pattern for PD-L1—this was more difficult to determine for cells of small lymphoid morphology with scant amounts of cytoplasm. PD-L1-positive tumour-infiltrating immune cells were typically seen as variably-sized aggregates towards the periphery of the tumour mass or in stromal bands dissecting the tumour mass or as single cells scattered in stroma or within tumour-infiltrating immune cell aggregates. Specimens were scored as IHC 0, 1, 2, or 3 if, 1%, $1% but, 5%, $5% but, 10%, or $10% of cells per area were PD-L1 positive, respectively. PD-L1 scores in patients with multiple specimens were based on the highest score. Based on the complexity of our scoring algorithm, we determined concordance between individual reads by different pathologists; in a cohort of >200 NSCLC samples, concordance between two pathologists was >90%. CD8 (clone SP16 (Epitomics)) IHC was performed on a Discovery XT autostainer (Ventana) using CC1 antigen retrieval and OmniMap (Ventana) detection technology.

Herbst R.S., Soria J.C., Kowanetz M., Fine G.D., Hamid O., Gordon M.S., Sosman J.A., McDermott D.F., Powderly J.D., Gettinger S.N., Kohrt H.E., Horn L., Lawrence D.P., Rost S., Leabman M., Xiao Y., Mokatrin A., Koeppen H., Hegde P.S., Mellman I., Chen D.S, & Hodi F.S. (2014). Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature, 515(7528), 563-567.

Publication 2014

Antigens CD274 protein, human Cells Clone Cells Cytoplasm Dendritic Cells Formalin Homo sapiens Lymphoid Cells Macrophage Monoclonal Antibodies Neoplasms Non-Small Cell Lung Carcinoma Paraffin Embedding Pathologists Patients Plasma Membrane Rabbits Tissue, Membrane Vision

Identifying Cell Type-Specific Gene Signatures in AML

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Publication 2019

Cells Diagnosis Epistropheus Gene Expression Genes Genes, Neoplasm Genes, vif Lymphoid Cells Monocytes Patients Promonocyte

RNA-seq Pipeline with Fusion Detection

RNA-seq was performed using TruSeq library preparation and HiSeq 2000 and 2500 sequencers (Illumina). All sequence reads were paired-end, and was performed using (1) total RNA and stranded RNA-seq [75 or 100 base-pair (bp) reads]; (2) polyA-selected mRNA (50, 75 or 100bp reads). Sequencing reads were mapped to the GRCh37 human genome reference by STAR^{44 (link)} (version 2.4.2a) through the suggested two-pass mapping pipeline. Gene annotation downloaded from Ensembl website (see URLs) was used for STAR mapping and the following read-count evaluation. All the samples were sequenced with RefSeq coding region covered with 30-fold coverage ≥15% (median ± standard deviation, 37.2±7.5%). CICERO^{5 (link)} and FusionCatcher^{45 ,46 (link)} were used to detect fusions, and all the reported rearrangements were manually reviewed to keep the reliable ones. Due to the complexity of DUX4 rearrangements, some of the DUX4 fusions were manually rescued by checking the aligned reads within IGV browser^{47 (link)}. RNA extracted from flow sorted normal B lymphoid cells were used for RNA-seq and details were provided in our previous study⁴⁸.

Gu Z., Churchman M.L., Roberts K.G., Moore I., Zhou X., Nakitandwe J., Hagiwara K., Pelletier S., Gingras S., Berns H., Payne-Turner D., Hill A., Iacobucci I., Shi L., Pounds S., Cheng C., Pei D., Qu C., Newman S., Devidas M., Dai Y., Reshmi S.C., Gastier-Foster J., Raetz E.A., Borowitz M.J., Wood B.L., Carroll W.L., Zweidler-McKay P.A., Rabin K.R., Mattano L.A., Maloney K.W., Rambaldi A., Spinelli O., Radich J.P., Minden M.D., Rowe J.M., Luger S., Litzow M.R., Tallman M.S., Racevskis J., Zhang Y., Bhatia R., Kohlschmidt J., Mrózek K., Bloomfield C.D., Stock W., Kornblau S., Kantarjian H.M., Konopleva M., Evans W., Jeha S., Pui C.H., Yang J., Paietta E., Downing J., Relling M.V., Zhang J., Loh M.L., Hunger S.P, & Mullighan C.G. (2019). PAX5-driven Subtypes of B-progenitor Acute Lymphoblastic Leukemia. Nature genetics, 51(2), 296-307.

Publication 2019

Base Pairing DNA Library DUX4 protein, human Gene Annotation Gene Rearrangement Genome, Human Homo sapiens Lymphoid Cells mRNA, Polyadenylated RNA-Seq

Integrating 42 Diverse Cell Datasets

We integrated 42 human datasets covering the major cell types, which means the canonical and well-characterized cell types previously identified without single cell RNA sequencing data and can be usually mapped to the cell type knowledgebase (e.g., EBI Cell Ontology). For 26 of the 42 datasets, we downloaded raw scRNA-seq fastq files, and estimated the gene-level expression abundance with kallisto^{23 (link)} and human genome reference hg19 (downloaded from UCSC).
We curated the cell label by unifying the major cell type annotated by different publications (e.g., considering both “T lymphoid cells” and “T cells” as “T cells”). We neglected the minor cell types, which means those novel subtypes uncovered by scRNA-seq with unsupervised clustering and annotation (e.g., considering both “DC_cluster1” and “DC_cluster2” as DC cells).

Li C., Liu B., Kang B., Liu Z., Liu Y., Chen C., Ren X, & Zhang Z. (2020). SciBet as a portable and fast single cell type identifier. Nature Communications, 11, 1818.

Publication 2020

Cells Gene Expression Genome, Human Homo sapiens Lymphoid Cells Single-Cell RNA-Seq T-Lymphocyte

Profiling Immune Cell Subsets and Serum Ig Levels

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Publication 2016

Biopharmaceuticals Cells Enzyme Immunoassay Europeans Flow Cytometry Fluorescence Lymphoid Cells Population Group Serum

Most recents protocols related to «Lymphoid Cells»

Validating Epimap Gene-Peak Predictions

We retrieved the Epimap peak-gene link predictions for the PBMC matching cell-types (CD14 MONOCYTE, B CELL, NK CELL) from https://personal.broadinstitute.org/cboix/epimap/links/links_corr_only/. We chose these cell-types because their cluster showed greater homogeneity and boundaries in the PBMC multiome dataset (in contrast to the lymphoid cells, see Fig. S1). For each Epimap cell-type, we kept peak-gene links found in all replicates to insure reproducibility. We recovered the hg38 positions using the AnnotationHub package (Annotationhub chain: hg19ToHg38.over.chain.gz). For these analyses, peak-gene links from the PBMC multiomic dataset were considered positive when the ATACseq peak overlapped at least partly the Epimap enhancer position and the linked gene was the same. ROC curves were calculated with the ROCR package by increasing the thresholds of the model’s statistic. For the 15,113 peak-gene links with |Pearson R|> 0.1, 1630, 984 and 1538 were considered positive (found in Epimap) for CD14 MONOCYTE, B CELL, NK CELL respectively. For the 590,842 peak-gene links with |Pearson R|> 0.01, 12,848, 7562 and 13,084 were considered positive (found in Epimap) for CD14 MONOCYTE, B CELL, NK CELL respectively.

Leblanc F.J, & Lettre G. (2023). Major cell-types in multiomic single-nucleus datasets impact statistical modeling of links between regulatory sequences and target genes. Scientific Reports, 13, 3924.

Publication 2023

B-Lymphocytes Cells Genes Lymphoid Cells Monocytes Natural Killer Cells

Immune Response Analysis in Mice

For immunization studies, mice were subcutaneously immunized with 50 µg KLH (Sigma-Aldrich) or 100 µg NP-OVA (Biosearch Technologies) emulsified in CFA (Sigma-Aldrich), and lymphoid cells from the draining LNs were stained and analyzed by flow cytometry on day 8 to 10. For viral infection experiments, mice were intraperitoneally injected with LCMV-Armstrong (2 × 10⁵ pfu), and lymphoid cells in the spleen were analyzed on day 8. In some experiments, mice were intraperitoneally treated with either Dimethyl sulfoxide (DMSO) vehicle or 20 mg/kg GW3965 (Tocris).
For BM chimera studies, 8- to 10-wk-old sublethally-irradiated Rag1^−/− mice (9 Gy; X-RAD IR160, Precision X-Ray, USA) were i.v. injected 1:1 mixture of WT and Nr1h2^−/− BM cells before 6 wk of reconstitution. The recipients were s.c. immunized with KLH in CFA or infected with LCMV, and lymphoid cells from the draining LNs or spleen were analyzed as indicated (46 (link)).
For OT-II T cell cotransfer studies, 1:1 mixture of flow-sorted WT and Nr1h2^−/− naïve OT-II T cells (2 × 10⁶ cells) were i.v. transferred into sex-matched B6.SJL congenic recipient mice. One day later, the recipients were s.c. injected with 100 µg OVA (Sigma-Aldrich) emulsified in CFA and lymphoid cells from the draining LNs were analyzed on day 8.

Kim J., Lee H., Lee J.E., Choi G., Chung H., Kim D., Park M.J., Gye Y.S., Shin K.S., Kang C.Y., Kwok S.K, & Chung Y. (2023). Liver X receptor controls follicular helper T cell differentiation via repression of TCF-1. Proceedings of the National Academy of Sciences of the United States of America, 120(9), e2213793120.

Publication 2023

Cells Chimera Flow Cytometry GW 3965 Lymphocytic choriomeningitis virus Lymphoid Cells Mice, Congenic Mus Radiography RAG-1 Gene Spleen Sulfoxide, Dimethyl T-Lymphocyte Vaccination Virus Diseases

Immunization and Serum Transfer in Mice

In some experiments mice were subcutaneously (s.c.) immunized with 50 µg SA-DEL in Ribi, reimmunized with 10 µg SA, or with SA-OVA or SA-NucPrs (with 10 µg of SA in each conjugate prep) in Ribi at day 8 after immunization. In some experiments serum from mice immunized with 50 µg SA in Ribi at day 8 after immunization were collected and injected into unimmunized mice followed by immunization with 10 µg SA or SA-NucPrs (containing 10 µg SA). In some experiments naïve mice were immunized with 10 µg SA or SA-NucPrs (containing 10 µg SA) directly. Lymphoid cells from the draining lymph nodes and the spleens of immunized mice were analyzed at the indicated time points. Blood was collected into Microvette CB 300 tube via cardiac puncture when the mice are under deep anesthesia. About 0.5–1 ml blood was obtained from one mouse. Serum recovered after centrifugation at 10,000 × g 5 min 20°C.

Ke F., Benet Z.L., Maz M.P., Liu J., Dent A.L., Kahlenberg J.M, & Grigorova I.L. (2023). Germinal center B cells that acquire nuclear proteins are specifically suppressed by follicular regulatory T cells. eLife, 12, e83908.

Publication 2023

Anesthesia BLOOD Centrifugation Heart Lymphoid Cells Mus Punctures Serum Vaccination

Isolation and Analysis of Liver Immune Cells

We isolated primary hepatocytes and NPCs as previously described (20 (link), 49 (link)). Briefly, we anesthetized mice and digested livers by perfusion of EGTA buffer and collagenase buffer (MilliporeSigma, C5138) through the inferior vena cava, purified hepatocytes with Percoll, and concentrated the remaining NPCs by Nycodenz density centrifugation. We analyzed NPCs by multicolor flow cytometry using an LSRFortessa (BD Biosciences). Briefly, we centrifuged isolated cells at 450g for 5 minutes at 4°C, washed in cold staining buffer (PBS, 2% BSA), resuspended 1 × 10⁶ to 10 × 10⁶ NPCs in Zombie Aqua Fixable Viability Dye (BioLegend, 423101) diluted 1:1,000 in PBS, and then incubated for 15–30 minutes at room temperature in the dark. After another wash, we incubated NPCs with TruStain FcX Fc receptor blocker (BioLegend, 101319) for 5 minutes, then with fluorochrome-conjugated antibodies against mouse CD45 (BioLegend, 103157), CD11b (BioLegend, 101239), CD11c (BioLegend, 117329), Ly6C (BioLegend, 128011), Ly6G (BioLegend, 127617), F4/80 (BioLegend, 123130), CD3 (BioLegend, 100236), B220 (BioLegend, 103224), and NK1.1 (BioLegend, 156508) diluted at 1:200 for 20 minutes at 4°C in staining buffer. Gating strategy is shown in Supplemental Figure 1. After staining, we fixed cells with 4% paraformaldehyde for 15 minutes at room temperature, washed, and then resuspended in staining buffer prior to sample acquisition. Total NPCs were further fractionated by FACS, using vitamin A fluorescence of HSCs as previously described (49 (link)), or antibody-based cell sorting of lymphoid cells with CD45-APC (BD Biosciences, 559864), myeloid cells with CD11b-FITC (BD Biosciences, 553310), and cholangiocytes with EpCAM-PE (Invitrogen, 12579182). We analyzed data using FCS Express7 (De Novo Software).

Kang J., Postigo-Fernandez J., Kim K., Zhu C., Yu J., Meroni M., Mayfield B., Bartolomé A., Dapito D.H., Ferrante AW J.r., Dongiovanni P., Valenti L., Creusot R.J, & Pajvani U.B. (2023). Notch-mediated hepatocyte MCP-1 secretion causes liver fibrosis. JCI Insight, 8(3), e165369.

Publication 2023

Antibodies Buffers Cells Centrifugation Cold Temperature Collagenase Egtazic Acid Fc Receptor Flow Cytometry Fluorescein-5-isothiocyanate Fluorescence Fluorescent Dyes Hepatocyte Immunoglobulins ITGAM protein, human Liver Lymphoid Cells Mus Myeloid Cells Nycodenz paraform Percoll Perfusion Stem Cells, Hematopoietic TACSTD1 protein, human Vena Cavas, Inferior Vitamin A

Empirical Gene Set Significance Test

To calculate the significance of differential expression of any given gene set M of size N, empirical distribution was determined. Specifically, differential expression of the set M was calculated as the average of the differential expression of genes of the set M, where the differential expression of each gene was quantified as: , which accounts for both fold change and P value of the differential expression. Then, a random gene set R of size N was sampled, and (i.e., average differential expression of genes in the set R) was calculated. Repeating this procedure 1E7 times, we constructed the empirical distribution of . P value of the significance of upregulation of M was calculated as , and significance of downregulation of M was calculated as
We compared cells of lymphoid lineage (i.e., CD4⁺ T, CD8⁺ T, B, and NK cells) between ESRRAi treatment and control (vehicle). The significance of the differential expression of a marker set is determined by the empirical distribution procedure described above.

Sahu A., Wang X., Munson P., Klomp J.P., Wang X., Gu S.S., Han Y., Qian G., Nicol P., Zeng Z., Wang C., Tokheim C., Zhang W., Fu J., Wang J., Nair N.U., Rens J.A., Bourajjaj M., Jansen B., Leenders I., Lemmers J., Musters M., van Zanten S., van Zelst L., Worthington J., Liu J.S., Juric D., Meyer C.A., Oubrie A., Liu X.S., Fisher D.E, & Flaherty K.T. (2023). Discovery of Targets for Immune–Metabolic Antitumor Drugs Identifies Estrogen-Related Receptor Alpha. Cancer Discovery, 13(3), 672-701.

Publication 2023

Down-Regulation Gene Expression Genes Lymphoid Cells Natural Killer Cells Transcriptional Activation

Top products related to «Lymphoid Cells»

Dnase 1 by Roche

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DNase I is a lab equipment product that serves as an enzyme used for cleaving DNA molecules. It functions by catalyzing the hydrolytic cleavage of phosphodiester bonds in the DNA backbone, effectively breaking down DNA strands.

Dnase 1 by Merck Group

Sourced in United States, Germany, Switzerland, United Kingdom, Italy, Japan, Macao, Canada, Sao Tome and Principe, China, France, Australia, Spain, Belgium, Netherlands, Israel, Sweden, India

DNase I is a laboratory enzyme that functions to degrade DNA molecules. It catalyzes the hydrolytic cleavage of phosphodiester linkages in the DNA backbone, effectively breaking down DNA strands.

Collagenase d by Roche

Sourced in Switzerland, United States, Germany, United Kingdom, France, Japan, Canada, Australia, Ireland

Collagenase D is an enzyme solution used for the dissociation and isolation of cells from various tissues. It is a mixture of proteolytic enzymes that cleave the collagen present in the extracellular matrix, allowing for the release of individual cells.

Ionomycin by Merck Group

Sourced in United States, Germany, United Kingdom, Macao, France, Italy, China, Canada, Switzerland, Sao Tome and Principe, Australia, Japan, Belgium, Denmark, Netherlands, Israel, Chile, Spain

Ionomycin is a laboratory reagent used in cell biology research. It functions as a calcium ionophore, facilitating the transport of calcium ions across cell membranes. Ionomycin is commonly used to study calcium-dependent signaling pathways and cellular processes.

Fetal bovine serum (fbs) by Thermo Fisher Scientific

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Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.

Facsaria by BD

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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.

Golgistop by BD

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GolgiStop is a cell culture reagent that inhibits protein transport from the endoplasmic reticulum to the Golgi apparatus, thereby preventing the secretion of newly synthesized proteins. It is a useful tool for investigating protein trafficking and localization in cells.

Rpmi 1640 medium by Thermo Fisher Scientific

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RPMI 1640 medium is a commonly used cell culture medium developed at Roswell Park Memorial Institute. It is a balanced salt solution that provides essential nutrients, vitamins, and amino acids to support the growth and maintenance of a variety of cell types in vitro.

Lsrfortessa by BD

Sourced in United States, United Kingdom, Germany, France, Canada, Australia, Belgium, China, Uruguay, Japan, Sweden, Switzerland, Cameroon

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.

Facsaria 2 by BD

Sourced in United States, Germany, United Kingdom, Japan, Belgium, China, Canada, Italy, France, South Sudan, Singapore, Australia, Denmark, Uruguay

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.

What are the main types of lymphoid cells and their functions?

The main types of lymphoid cells are T cells, B cells, and natural killer (NK) cells. T cells are responsible for cell-mediated immunity and help coordinate the immune response. B cells produce antibodies and are crucial for humoral immunity. NK cells recognize and kill infected or cancerous cells. These lymphoid cells develop from hematopoietic stem cells in the bone marrow and lymphoid tissues, and play a central role in the body's defense against pathogens and maintenance of immunological tolerance.

How can I ensure I'm using the most effective protocols for my Lymphoid Cells research?

PubCompare.ai can help you screen protocol literature more efficiently and leverage AI to pinpoint critical insights. The platform's AI-driven analysis can highlight key differences in protocol effectiveness, enabling you to choose the best option for reproducibility and accuracy. By using PubCompare.ai, you can identify the most effective protocols related to Lymphoid Cells for your specific research goals, improving the reliability and quality of your work.

What are some common challenges in working with Lymphoid Cells?

Some common challenges in working with Lymphoid Cells include maintaining cell viability, accurately identifying and separating different subsets, and ensuring consistent activation and differentiation. Proper sample handling, culture conditions, and flow cytometry techniques are critical. PubCompare.ai can assist by helping you identify the most robust and reliable protocols to address these challenges and optimize your Lymphoid Cells reserach.

How can I use PubCompare.ai to compare different Lymphoid Cells protocols?

PubCompare.ai allows you to easily compare Lymphoid Cells protocols from the literature, pre-prints, and patents. The platform's AI-driven analysis can highlight key differences in factors like cell yield, purity, activation status, and functional outcomes. This enables you to make informed decisions about the most effective protocols for your specific research needs, improving reproducibility and accuracy. The platform's user-friendly interface and customizable comparison tools make it easy to identify the best protocols for your Lymphoid Cells experiments.

More about "Lymphoid Cells"

Lymphoid cells, also known as leukocytes or white blood cells, are a diverse group of cells that play a central role in the immune system.
These cells include T cells, B cells, and natural killer (NK) cells, which are responsible for both cell-mediated and humoral immune responses.
Lymphoid cells develop from hematopoietic stem cells in the bone marrow and lymphoid tissues, such as the thymus, spleen, and lymph nodes.
Lymphoid cells are crucial for recognizing and eliminating foreign pathogens, including viruses, bacteria, and parasites.
They are also essential for maintaining immunological tolerance, preventing autoimmune diseases.
The study of lymphoid cells is fundamental to understanding the complexities of the immune system and developing therapies for a wide range of immunological disorders and diseases.
To study lymphoid cells, researchers often use various techniques and reagents, such as DNase I for tissue dissociation, Collagenase D for enzymatic digestion, Ionomycin for cell activation, and fetal bovine serum (FBS) for cell culture.
Flow cytometry instruments, such as the FACSAria and LSRFortessa, are commonly used to isolate and analyze different subsets of lymphoid cells.
Additionally, reagents like GolgiStop are used to block protein secretion, allowing for the detection of intracellular cytokines and other molecules.
By leveraging the power of PubCompare.ai, an AI-driven platform, researchers can optimize their lymphoid cells research by easily locating the best protocols from literature, pre-prints, and patents.
The platform's AI-driven comparisons can help improve the reproducibility and accuracy of their studies, leading to more reliable insights into the complexities of the immune system and the development of effective therapies.