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Monocytes

Monocytes are a type of white blood cell that play a crucial role in the immune system.
They originate from hematopoietic stem cells in the bone marrow and are characterized by their large, flexible nucleus and abundant cytoplasm.
Monocytes circulate in the bloodstream and can migrate to tissues, where they differentiate into macrophages or dendritic cells.
As part of the innate immune response, monocytes are responsible for phagocytosing pathogens, presenting antigens, and secreting cytokines that coordinate the immune response.
Monocytes are also involved in the pathogenesis of various inflammatory and autoimmune disorders.
Understanding the biology and function of monocytes is essential for the development of effective therapies targeting these important immune cells.
PubCompare.ai's AI-driven tools can help researchers streamline their monocyte research and identify the most reproducible and accurate approaches from the scientific literature.

Most cited protocols related to «Monocytes»

Reconstructing Developmental Trajectories from scRNA-seq

We check the scree plot to choose ten dimension as the intrinsic dimensions to reconstruct the developmental trajectory for the Paul dataset (cells used in Figure 1 of the original study^{9 (link)}). Five branch points and six terminal lineages (monocytes, neutrophils or eosinophil, basophils, dendritic cells, megakaryocytes, and erythrocytes) are revealed. We ordered the cells using genes Paul et al. used to cluster their data rather than the genes from dpFeature, for the sake of consistency with their clusetering analysis. Similarly, we reconstruct Olsson datasets in four dimensions. The major bifurcation between the granulocyte and monocyte branch (GMP) as well as the intricate branch between GMP and megakaryocyte/erythrocyte (Ery/Meg) are revealed. Top 1, 000 genes from dpFeature based on WT cells are used in both of the WT and full datasets. The distribution (related to confusion matrix) of percentages of cells in each cluster from the original papers over each segment (state in Monocle 2) of the principal graph are calculated and visualized in the heatmap.
We applied BEAM analysis to identify genes significantly bifurcating between Ery/Meg and GMP branch on the Olsson wildtype dataset. We then calculate the instant log ratios (ILRs) of gene expression between Ery/Meg and GMP branch and find genes have mean ILR larger than 0.5. The ILRs are defined as:

{ILR}_{t} = \log (\frac{Y_{1}^{t}}{Y_{2}^{t}})

{ILR}_{t}

is calculated as the log ratio of fitted value at interpolated pseudotime point

t

for the Ery/Meg lineage and that for the GMP lineage. Those genes are used to calculate the lineage score (simply calculated as average expression of those genes in each cell, same as stemness score below) for both of the Olsson and the Paul dataset which is used to color the cells in a tree plot transformed from the high dimensional principal graph (see Supplementary Notes). The same genes are used to create the multi-way heatmap for both of the Paul and Olsson dataset (see plot multiple_branches_heatmap function). Critical functional genes from this procedure are identified. Car1, Car2 (important erythroid functional genes for reversible hydration of carbon dioxide) as well as Elane, Prtn3 (important proteases hydrolyze proteins within specialized neutrophil lysosomes as well as proteins of the extracellular matrix) are randomly chosen as example for creating multi-lineage kinetic curves in both of the Olsson and Paul dataset (see plot_multiple_branches_pseudotime function).
In addition, pseudotime dependent genes for the Ery/Meg and GMP branch are identified in the Olsson wildtype dataset. All genes that always have lower expression from both lineages than the average in the progenitor cells are selected. Those genes are used to calculate the stemness score for both of the Olsson and the Paul dataset which is used to color the cells in the tree plot.

Qiu X., Mao Q., Tang Y., Wang L., Chawla R., Pliner H.A, & Trapnell C. (2017). Reversed graph embedding resolves complex single-cell trajectories. Nature methods, 14(10), 979-982.

Publication 2017

Basophils Carbon dioxide Dendritic Cells Endopeptidases Eosinophil Erythrocytes Extracellular Matrix Proteins Gene Expression Genes Genetic Engineering Granulocyte Kinetics lysosomal proteins Megakaryocytes Monocytes Neutrophil Stem Cells Trees

Reconstructing Developmental Trajectories from scRNA-seq

{ILR}_{t} = \log (\frac{Y_{1}^{t}}{Y_{2}^{t}})

{ILR}_{t}

is calculated as the log ratio of fitted value at interpolated pseudotime point

t

Qiu X., Mao Q., Tang Y., Wang L., Chawla R., Pliner H.A, & Trapnell C. (2017). Reversed graph embedding resolves complex single-cell trajectories. Nature methods, 14(10), 979-982.

Publication 2017

Customizing Leukocyte Signature Matrices for Tumor Profiling

In the following two sections, we describe how to create a custom leukocyte signature matrix and apply it to study cellular heterogeneity and TIL survival associations in melanoma tumors profiled by The Cancer Genome Atlas (TCGA). Readers can follow along by creating ‘LM6’, a leukocyte RNA-Seq signature matrix comprised of six peripheral blood immune subsets (B cells, CD8 T cells, CD4 T cells, NK cells, monocytes/macrophages, neutrophils; GSE60424 [20 ]). Key input files are provided on the CIBERSORT website (‘Menu>Download’).
A custom signature file can be created by uploading the Reference sample file and the Phenotype classes file (section 3.3.2) to the online CIBERSORT application (SeeFigure 2) or can be created using the downloadable Java package. To build a custom gene signature matrix with the latter, the user should download the Java package from the CIBERSORT website and place all relevant files under the package folder. To link Java with R, run the following in R:
Within R:

> library(Rserve)

> Rserve(args=“–no-save”)

Command line:

> java -Xmx3g -Xms3g -jar CIBERSORT.jar -M Mixture_file -P Reference_sample_file -c phenotype_class_file -f

The last argument (-f) will eliminate non-hematopoietic genes from the signature matrix and is generally recommended for signature matrices tailored to leukocyte deconvolution. The user can also run this step on the website by choosing the corresponding reference sample file and phenotype class file (seeFigure 2). The CIBERSORT website will generate a gene signature matrix located under ‘Uploaded Files’ for future download.
Following signature matrix creation, quality control measures should be taken to ensure robust performance (see ‘Calibration of in silico TIL profiling methods’ in Newman et al.) [17 (link)]. Factors that can adversely affect signature matrix performance include poor input data quality, significant deviations in gene expression between cell types that reside in different tissue compartments (e.g., blood versus tissue), and cell populations with statistically indistinguishable expression patterns. Manual filtering of poorly performing genes in the signature matrix (e.g., genes expressed highly in the tumor of interest) may improve performance.
To benchmark our custom leukocyte matrix (LM6), we compared it to LM22 using a set of TCGA lung squamous cell carcinoma tumors profiled by RNA-Seq and microarray (n = 130 pairs). Deconvolution results were significantly correlated for all cell subsets shared between the two signature matrices (P < 0.0001). Notably, since LM6 was derived from leukocytes isolated from peripheral blood [20 ,21 (link)], we restricted the CD4 T cell comparison to naïve and resting memory CD4 T cells in LM22. Once validation is complete, a CIBERSORT signature matrix can be broadly applied to mixture samples as described in section 3.3 (e.g., SeeFigure 4).

Chen B., Khodadoust M.S., Liu C.L., Newman A.M, & Alizadeh A.A. (2018). Profiling tumor infiltrating immune cells with CIBERSORT. Methods in molecular biology (Clifton, N.J.), 1711, 243-259.

Publication 2018

B-Lymphocytes BLOOD CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes cDNA Library Cells Genes, vif Genetic Diversity Genetic Heterogeneity Hematopoietic System Leukocytes Lung Neoplasms Macrophage Malignant Neoplasms Melanoma Memory Microarray Analysis Monocytes Natural Killer Cells Neoplasms Neutrophil Phenotype Population Group RNA-Seq RNA Motifs Squamous Cell Carcinoma Strains Tissues

Hierarchical Annotation of Immune Populations

We define as a pyramid a directed acyclic graph with a root node. Samples of microenvironment purified cells were labeled according to their reported immune or stromal populations, resulting in 63 distinct labels in the MCP discovery series, with an additional 15 labels for the MCP validation series, resulting in a total of 78 labels. We organized these labels in a pyramidal graph (Additional file 2: Figure S1) with nodes representing populations (categories) and directed edges representing relations of inclusion. For instance, the labels “CD8⁺ T cells”, “CD4⁺ T cells”, “Tγδ cells”, “Memory T cells”, “Activated T cells”, and “Naïve T cells” and all labels included in them (for instance “Effector-memory CD8 T cells”) form the “T cells” category, which itself is included in the “T/NK lineage” category. Of these 78 sample labels, some correspond to terminal leaves of this pyramid (e.g., “Canonical CD4 Treg cells”), while others correspond to higher level nodes (e.g., peripheral-blood mononuclear cells (“PBMC”)). In addition to these 78 labels, 15 hematopoiesis or immunology-inspired categories that are not directly represented by samples but relevant for their organization in a structured pyramid (for instance “Lymphocytes”) or as a potential cell population (for instance “antigen-experienced B cells”) were added (Additional file 1: Table S13). Categories corresponding to tumor samples were discarded for the identification of TM and only kept as negative controls, resulting in 68 categories available for screening.
Having defined this set of 78 labels and 68 categories (53 categories are directly represented by labels, with 15 additional categories not directly represented in the dataset), we exhaustively encoded the relationships between labels and categories using three possible relationships (Additional file 1: Table S13). Relative to a category, we define three sets of samples:

C : “positive samples” are those whose label is included in the category (all cells composing a sample which is in C are in the category)

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\begin{document}$$ \overline{C} $$\end{document}C¯ : “negative samples” are those whose label is strictly non-overlapping with the category (all cells of a sample which is in \documentclass[12pt]{minimal}
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-1 : “mixed samples” are those whose label is partly overlapping with the category (some cells of the sample are in C and some are in \documentclass[12pt]{minimal}
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\begin{document}$$ \overline{C} $$\end{document}C¯).

For instance, for CD8⁺ T cells, C is the set of samples whose label is “CD8 T cells” or “Effector memory CD8 T cells” (Additional file 2: Figure S1; Additional file 1: Table S13), mixed samples are, for instance, CD3⁺ T cells as they mix CD4⁺ and CD8⁺ T cells, or PBMC as they mix CD8⁺ T cells with, e.g., monocytes.

\bar{C}

is defined as all non-positive non-mixed samples.
Note that the relationships represented in Additional file 2: Figure S1 only correspond to the “direct inclusion” relationship, which is transitive (we thus removed for clarity all the arrows which can be inferred by transitivity). Hence, strict exclusion or mixture relationships are not represented but are taken into account during the screening process (the related information is available in Additional file 1: Table S13).

Becht E., Giraldo N.A., Lacroix L., Buttard B., Elarouci N., Petitprez F., Selves J., Laurent-Puig P., Sautès-Fridman C., Fridman W.H, & de Reyniès A. (2016). Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biology, 17, 218.

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Publication 2016

Antigens B-Lymphocytes CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes Cells Effector Memory T Cells Hematopoiesis Lymphocyte Memory T Cells Monocytes Neoplasms PBMC Peripheral Blood Mononuclear Cells Plant Roots T-Lymphocyte

Systematic Evaluation of Trajectory Inference Algorithms

We undertook a systematic evaluation of the performance of Palantir in comparison to widely-used trajectory inference algorithms such as Monocle2, Diffusion Pseudotime (DPT), Partition based Graph Abstraction (PAGA- based on DPT), Slingshot, FateID, and Monocle 2.
We first compared the algorithms by evaluating their setup: the prior biology knowledge required as input and the diversity of outputs provided by each algorithm using the following criteria:
Supplementary Fig. 17a summarizes the characteristics of the different algorithms according to the criteria outline above:
Thus, Palantir uses minimal a priori biological information to (a) automatically determine the different terminal states, (b) generate a unified pseudo-time ordering to compare gene expression trends across lineages and (c) identify continuous branch probabilities and differentiation potential for each cell.
We next used the CD34+ human bone marrow data (replicate 1) as a benchmark to compare the results of the different algorithms. Due to the varied nature of the different outputs, we evaluated the ability of the algorithm to determine known and well established features of human hematopoiesis such as (a) identification of the different lineages represented in the data, with emphasis on less frequent populations such as megakaryocytes, cDCs and pDCs, which are more subtle and challenging to infer (b) recovering known expression trends of key genes across multiple lineages. We choose well-studied canonical genes across the different lineages, whose expression dynamics are known and can thus serve as ground truth. The following canonical genes, representing a broad spectrum of gene expression dynamics, were chosen for this evaluation:
Supplementary Fig. 17b shows the results of this comparison for the different algorithms. Palantir and DPT were able to identify the megakaryocyte lineages, whereas PAGA and Slingshot included these cells to be part of the erythroid lineage. Palantir was the only algorithm able to recover the distinction between the two DC lineages. Comparing the expression trends, all algorithms except Monocle 2 recovered the downregulation of CD34 across all lineages. Palantir recovers the known gene expression trends across all lineages (Fig. 2). While PAGA, DPT and Slingshot identify the trends in the larger lineages, PAGA (and DPT) suffer from a loss in resolution in gene expression trends and Slingshot does not provide a unified ordering of cells to compare gene expression trends across lineages. FateID with the default clustering using RaceID failed to identify any correct lineages and gene expression trends, whereas FateID with a preprocessing procedure and clustering followed in Palantir identifies correct expression trends in only the monocyte and CLP lineages. Monocle 2 could not recover the key hematopoietic lineages or expression trends from the CD34+ bone marrow data. See Supplementary Note 6 for a detailed description of the different algorithms and their performance.

Setty M., Kiseliovas V., Levine J., Gayoso A., Mazutis L, & Pe’er D. (2019). Characterization of cell fate probabilities in single-cell data with Palantir. Nature biotechnology, 37(4), 451-460.

Publication 2019

Biopharmaceuticals Bone Marrow Cells chenodeoxycholate sulfate conjugate Differentiations, Cell Diffusion DNA Replication Down-Regulation Gene Expression Genes Hematopoiesis Hematopoietic System Homo sapiens Megakaryocytes Monocytes PDC protein, human

Most recents protocols related to «Monocytes»

Modulating Immune Responses with Anti-PD-L1 Antibodies

Not available on PMC !

Example 14

The ability of anti-PD-L1 antibodies to modulate immune responsiveness was assessed using a mixed lymphocyte reaction (MLR). With this assay, the effects anti-PD-L1 antibodies on cell activation and the production of IL-2 were measured. The MLR was performed by culturing 10⁵purified human CD4+ cells from one donor with 10⁴monocyte derived dendritic cells prepared from another donor. To prepare the dendritic cells, purified monocytes were cultured with GM-CSF (1,000 U/ml) and IL-4 (500 U/ml) for seven days. Anti-PD-L1 or control antibodies were added to the allogeneic MLR cultures at 10 μg/ml unless stated otherwise. Parallel plates were set up to allow collection of supernatants at day 3 and at day 5 to measure IL-2 using a commercial ELISA kit (Biolegend). The antibodies used were the disclosed H6B1L, RSA1, RA3, RC5, SH1E2, SH1E4, SH1B11, and SH1C8 as compared to prior disclosed antibodies 10A5 (Bristol-Myers-Squibb/Medarex) and YW243.55S70 (Roche/Genentech) that were obtained via in-house production from prior-disclosed antibody sequences (U.S. Patent Application 2009/0055944 and U.S. Patent Application US 2010/0203056; the disclosure of which are incorporated by reference herein).

Production of IL-2 was enhanced by the addition of the anti-PD-L1 antibodies.

US11878058B2. Antigen binding proteins that bind PD-L1 (2024-01-23). Sorrento Therapeutics, Inc. [US]. Inventors: Heyue Zhou [US], Randy Gastwirt [US], Barbara A. Swanson [US], John Dixon Gray [US], Gunnar F. Kaufmann [US].

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Patent 2024

Anti-Antibodies Antibodies Antigens Binding Proteins Biological Assay CD274 protein, human Cells Dendritic Cells Enzyme-Linked Immunosorbent Assay Granulocyte-Macrophage Colony-Stimulating Factor Homo sapiens Immunoglobulins Lymphocyte Culture Test, Mixed Monocytes Tissue Donors

Evaluating AST Cytotoxicity Compared to As(III)

Not available on PMC !

Example 6

The AST cytotoxicity was evaluated and compared with that of inorganic As(III) using five different types of human cell lines from major organs/tissues: HEK293, immortalized embryonic kidney cells; THP-1, monocytes derived from an acute monocytic leukemia patient; macrophage, macrophage-like cells differentiated from THP-1; HepG2, immortalized cells isolated from a hepatocellular carcinoma; and Caco-2, immortalized cell line derived from a colorectal adenocarcinoma patient (FIG. 5). The results show that AST has much lower cytotoxicity in human cells than As(III). The LC₅₀values of AST on all the tested cell lines except Caco2 were greater than 250 μM. Caco-2 was relatively more sensitive to AST with a lower LC₅₀value (150-200 μM). In contrast, the LC₅₀values of As(III) on all the tested cell lines except macrophage were lower than 25 μM, while that of macrophage was higher (100 μM), suggesting that AST is >10 times less cytotoxic than As(III). AST at 100 μM completely inhibits PfGS-I activity (FIG. 2C), P. falciparum proliferation in blood (FIG. 3) and transmission to mosquitoes (FIG. 4A), but had little effect on most of the tested human cell lines (FIG. 5). Thus, AST is effective against the malaria parasite with limited effect on human cells.

US11877996B1. Arsinothricin as a multi-stage antimalarial (2024-01-23). None [US], None [US], None [US], None [US]. Inventors: Masafumi Yoshinaga [US], Barry P. Rosen [US], Jun Li [US], Guodong Niu [US].

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Patent 2024

Acute Monocytic Leukemia Adenocarcinoma BLOOD Cardiac Arrest Cell Lines Cells Culicidae Cytotoxin Embryo Hepatocellular Carcinomas Homo sapiens Kidney Macrophage Malaria Monocytes Parasites Patients Tissues Transmission, Communicable Disease

NK Cell Modulation of Anti-PD-L1 Antibody Effects

Not available on PMC !

Example 4

This example provides a showing of the effect of NK cells on the disclosed anti-PD-L1 antibodies on mediated inhibition of proliferation. With the anti-PD-L1 antibodies showing a preferential binding to NK cells, the significance of this in the inhibition of proliferation was tested. By cell sorting using a FACS Aria (Becton Dickinson, San Jose, CA) purified population of CD4+, CD8+, CD56+ (NK) and monocytes were obtained. As a base culture, 1.5×10⁵CD4+ cells and 3×10⁴monocytes were stimulated with anti-CD3 (1 ng/ml) with or without H10 anti-PD-L1 antibody (10 μg/ml). In separate cultures, either CD8+ cells or NK cells (both at 3×10⁴) were added to this base culture. After three days of culture, cells were stained for expression of CD25 as a measure of lymphocyte activation as measured by flow cytometry. The results shown in FIG. 4 were compared to those obtained using whole, unfractionated PBMC (1.5×10⁵). The anti-PD-L1 antibody inhibited the activation of lymphocytes in the cultures containing whole PBMC and those where NK cells were added, but not in the absence of NK cells.

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Patent 2024

Anti-Antibodies Antibodies, Anti-Idiotypic Antigens Binding Proteins CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes CD274 protein, human Cells Flow Cytometry IL2RA protein, human Lymphocyte Activation Monocytes Muromonab-CD3 Natural Killer Cells NRG1 protein, human Psychological Inhibition

Engineered CAR Spacer Mutations

Not available on PMC !

Example 3

The overall data from the above Examples suggested that the Fc spacer performs best overall. However the Fc domain in vivo may lead to non-specific activation from cells which express Fc receptors. To abrogate this effect, mutations were introduced into the Fc region as shown in FIG. 5(a). These mutations had no deleterious effects on CAR expression, as shown in FIG. 5(b).

In addition, it was shown that these mutations had no effect on CAR killing function (FIG. 5(c)). Finally, it was shown that these mutations had the desired effect in terms of non-specific killing of FcR expressing targets (a monocytoid line called THP1), and IL-1Beta release by these monocytes (FIG. 5e).

US11879016B2. Chimeric antigen receptor (2024-01-23). AUTOLUS LIMITED [GB]. Inventors: Martin Pulé [GB], John Anderson [GB], Simon Thomas [GB].

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Patent 2024

Cells Interleukin-1 beta Monocytes Mutation

Transfection of Hematopoietic Cells

Not available on PMC !

Example 26

Blood cells, such as mature peripheral blood T lymphocytes, monocytes, macrophages, T cell progenitors, macrophage-monocyte progenitor cells, and/or pluripotent hematopoietic stem cells (such as those found in umbilical cord blood and occupying bone marrow spaces) as well as other stem/progenitor cells can be transfected using the therapeutic vector(s) in vitro. Appropriate concentrations of the therapeutic vector(s) may be those consistent with Browning et al., 1999. Subsequently, cells are expanded (propagated) in vitro, and are then transferred to the host via introduction of the cells to the venous or arterial circulation using an intravenous needle or catheter. Subsequently, cells transfected with the therapeutic vectors are able to “home” to the bone marrow and other tissues.

It is understood that the examples and embodiments described herein are for illustrative purposes only and that various modifications or changes in light thereof will be suggested to persons skilled in the art and are to be included within the spirit and purview of this application and scope of the appended claims. All publications, patents, and patent applications cited herein are hereby incorporated by reference in their entirety for all purposes.

US11859168B2. Electroporation, developmentally-activated cells, pluripotent-like cells, cell reprogramming and regenerative medicine (2024-01-02). None [US]. Inventors: Christopher B. Reid [US], Melissa Braga [US], Lilian N. Santamaria [US], Ashley N. Wickrema [US], Marinne D. Wickrema [US], Anthony Hao Dinh [US], Yaman Eksioglu [US], Mat Hoang Ho [US].

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Patent 2024

Arteries BLOOD Blood Cells Bone Marrow Catheters Cells Cloning Vectors Light Macrophage Monocytes Needles Stem Cells Stem Cells, Hematopoietic T-Lymphocyte Therapeutics Tissues Umbilical Cord Blood Veins

Top products related to «Monocytes»

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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.

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Streptomycin is a broad-spectrum antibiotic used in laboratory settings. It functions as a protein synthesis inhibitor, targeting the 30S subunit of bacterial ribosomes, which plays a crucial role in the translation of genetic information into proteins. Streptomycin is commonly used in microbiological research and applications that require selective inhibition of bacterial growth.

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What are the main functions and roles of monocytes in the immune system?

Monocytes are a key part of the innate immune response, responsible for phagocytosing pathogens, presenting antigens, and secreting cytokines to coordinate the overall immune reaction. They can migrate from the bloodstream into tissues and differentiate into macrophages or dendritic cells, which have additional specialized functions in the immune system. Monocytes are also implicated in the development of inflammatory and autoimmune disorders, making them an important target for therapeutic intervention.

Are there different types or subtypes of monocytes, and how do they differ in their characteristics and functions?

Yes, there are several recognized monocyte subsets based on the expression of specific cell surface markers. The main subtypes include classical, intermediate, and non-classical monocytes, which exhibit distinct patterns of gene expression, cytokine production, and migratory behavior. These monocyte subpopulations play differing roles in immune surveillance, inflammation, and tissue repair, and their relative proportions can shift in disease states.

How can PubCompare.ai assist researchers working with monocytes?

PubCompare.ai's AI-driven tools can help streamline monocyte research in several ways. First, the platform allows you to more efficiently screen the published literature on monocyte-related protocols and experiments. Second, the platform's intelligent analysis can pinpoint critical insights, such as key differences in the effectiveness of various monocyte isolation, culture, and manipulation techniques. This can enable researchers to identify the most reproducible and accurate approaches for their specific research goals involving monocytes. By leveraging PubCompare.ai, you can save time, optimize your monocyte studies, and achieve more reliable, high-quality results.

What are some common challenges or considerations when working with monocytes in the lab?

One key consideration when working with monocytes is their inherent plasticity and responsiveness to environmental cues. Monocytes can rapidly change their phenotype and functional properties depending on the specific in vitro or in vivo conditions they encounter. This can make it challenging to maintain consistent monocyte populations and behaviors across different experimental setings. Additionally, monocyte isolation from blood or tissue samples requires careful optimization to avoid inadvertent activation or differentiation of the cells. Proper handling and culture conditions are critical to preserve the native characteristics of monocytes for meaningful research.

More about "Monocytes"

Monocytes are a crucial component of the immune system, playing a vital role in the body's defense against various pathogens.
These white blood cells, also known as mononuclear phagocytes, originate from hematopoietic stem cells in the bone marrow and are characterized by their large, flexible nuclei and abundant cytoplasm.
Monocytes circulate in the bloodstream and can migrate to tissues, where they differentiate into macrophages or dendritic cells.
As part of the innate immune response, monocytes are responsible for phagocytosing (engulfing and digesting) pathogens, presenting antigens, and secreting cytokines that coordinate the overall immune response.
Monocytes play a crucial role in the pathogenesis of various inflammatory and autoimmune disorders, such as atherosclerosis, rheumatoid arthritis, and multiple sclerosis.
Understanding the biology and function of monocytes is essential for the development of effective therapies targeting these important immune cells.
Researchers often utilize cell culture media, such as RPMI 1640 and fetal bovine serum (FBS), to maintain and study monocytes in the lab.
Additionally, they may use antibiotics like penicillin and streptomycin to prevent bacterial contamination, and lipopolysaccharide (LPS) to activate and stimulate monocyte responses.
CD14 MicroBeads, a magnetic bead-based separation technique, can be used to isolate and purify monocytes from blood samples.
Phorbol 12-myristate 13-acetate (PMA) is another commonly used agent for differentiating monocytes into macrophages or dendritic cells in vitro.
L-glutamine is an essential nutrient required for the growth and proliferation of monocytes and other immune cells.
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