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> Anatomy > Body System > Hematopoietic System

Hematopoietic System

The hematopoietic system is the network of tissues and organs responsible for the production, maturation, and regulation of blood cells.
It includes the bone marrow, spleen, lymph nodes, and thymus gland.
This system plays a critical role in immune function, oxygen transport, and blood clotting.
Disorders affecting the hematopoietic system, such as anemias, leukemias, and lymphomas, can have serios implications for an individual's health.
Reseachers studying the hematopoietic system may leverage PubCompare.ai to identfy the most reproducable and effecive protocols from the literature, preprints, and patents, streamlining their workflow and accelerating discoveries.

Most cited protocols related to «Hematopoietic System»

1
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
2
Constructing Customized Immune Cell Signatures
A custom signature matrix can be created using data from purified cell populations. While the process to generate a custom matrix from expression profiles is straightforward, the performance of a custom matrix will depend on the quality of the data used to generate it. Immunophenotyping of leukocytes is a dynamic field with new immune populations continuing to be identified. Care should be taken in determining which immune “cell types” should be included in the signature matrix and which canonical markers should be used to isolate these populations. For example, it is clear that the population of “CD4-expressing T lymphocytes” encompasses heterogeneous populations with diverse functional phenotypes including naïve, memory, Th1, Th2, Th17, T-regulatory cells and T follicular helper cells. Replicates for each purified immune cell type are required to gauge variance in the expression profile (see 5.4 for further details). The platform and methods used to generate data for the signature matrix ideally should be identical to that applied to analysis of the mixture samples. While SVR is robust to unknown cell populations, performance can be adversely affected by genes that are highly expressed in a relevant unknown cell population (e.g., in the malignant cells) but not by any immune components present in the signature matrix. A simple option implemented in CIBERSORT to limit this effect is to remove genes highly expressed in non-hematopoietic cells or tumor cells. If expression data is available from purified tumor cells for the malignancy to be studied, this can be used as a guideline to filter other confounding genes from the signature matrix.
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
Cells Genes Hematopoietic System Leukocytes Malignant Neoplasms Memory Neoplasms Phenotype T Follicular Helper Cells Th17 Cells
3
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
4
Generation of CD11c-Cre Transgenic Mice
To generate the CD11c-Cre transgene, the 160-kb mouse genomic BAC clone RP24-361C4 (BACPAC Resources) was modified by ET recombination, as previously described (43 (link)). The clone contains the entire Itgax (CD11c) gene but lacks the 5′ end of the adjacent Itgam (CD11b) gene, preventing the overexpression of the latter. The recombination cassette containing the Cre recombinase open reading frame, followed by the bovine growth hormone (BGH) polyA signal and the FRT site-flanked prokaryotic Zeocin resistance cassette (ZeoR), replaced the coding part of the first CD11c exon, and the ZeoR cassette was subsequently removed by FLP-mediated recombination. The clone insert was released from the vector backbone using NotI digestion, gel-purified, and microinjected into fertilized oocytes. The founder line containing two copies of the transgene (as determined by quantitative Southern hybridization) was chosen for further analysis. Mice were genotyped by genomic PCR using either generic Cre primers or primers specific for the CD11c-Cre transgene (5′-ACTTGGCAGCTGTCTCCAAG-3′ and 5′-GCGAACATCTTCAGGTTCTG-3′ were specific for the CD11c promoter and Cre, respectively).
The R26-EYFP strain (21 (link)) was provided by F. Costantini (Columbia University, New York, NY). The RBP-Jfl strain (19 (link)) was provided by L. Hennighausen (National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD), with permission from T. Honjo (Kyoto University, Kyoto, Japan). The Mx1-Cre strain was previously described (44 (link)). Cre-negative RBP-Jfl/fl littermates of CKO (RBP-Jfl/fl Cre+) mice were used as controls; in preliminary experiments, wild-type CD11c-Cre+ mice were used as controls and were found indistinguishable from CD11c-Cre− animals. For inducible RBP-J deletion, adult RBP-Jfl/fl Mx1-Cre+ or control RBP-Jfl/fl mice were injected with 0.25 mg poly(I):(C) three times, with 2-d intervals, and analyzed 3 wk later. For hematopoietic reconstitution, 3 × 106 total BM cells per mouse were injected i.v. into lethally irradiated C57BL/6 mice congenic for CD45.1. The recipient mice were analyzed 4–5 wk after reconstitution. Mice were maintained in a specific pathogen-free facility and used according to the protocol approved by the Columbia University's Institutional Animal Care and Use Committee.
Caton M.L., Smith-Raska M.R, & Reizis B. (2007). Notch–RBP-J signaling controls the homeostasis of CD8− dendritic cells in the spleen. The Journal of Experimental Medicine, 204(7), 1653-1664.
Publication 2007
Adult Animals Cells Clone Cells Cloning Vectors Cre recombinase Crossbreeding Deletion Mutation Diabetes Mellitus Digestion Digestive System Exons Generic Drugs Genes Genome growth hormone, bovine Hematopoietic System Institutional Animal Care and Use Committees ITGAM protein, human Kidney Diseases Mice, Inbred C57BL Mice, Laboratory Oligonucleotide Primers Ovum Poly A Poly I-C Prokaryotic Cells RBPJ protein, human Recombination, Genetic Specific Pathogen Free Strains Transgenes Vertebral Column Zeocin
5
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 «Hematopoietic System»

1
Measuring Surface Potential of Hematopoietic Cells
Not available on PMC !

Example 3

Testing Surface Potential of Haematopoietic Cells and Neutrophils

Electrophoresis is used to investigate the surface potential variation in haematopoietic cells (e.g. haematopoietic stem cells, and/or precursor cells) and neutrophils by measuring the electrophoretic mobility. The suspended cells are collected from culture, by mechanical detachment and collection from the culture substrate. Collected cells are redistributed in an electrophoresis buffer solution containing 10 mM Tris-HCl and 291 mM glucose, and are introduced into a rectangular glass electrophoresis chamber. 200V DC is applied across the electrophoresis chamber. The electrophoretic velocity of cells, u, is measured by recording the time needed for cells passing a fixed length with 3 mA under a microscope with a CCD camera. The electrophoretic mobility, p, is calculated by μ=ugS/I, where g is the conductivity of medium, S is the cross-sectional area of the electrophoresis chamber, and/is the current. For each condition typically at least 9 readings are performed to calculate cell electrophoretic mobility.

US11878033B2. Cancer-killing cells (2024-01-23). LIFT BIOSCIENCES LTD [GB]. Inventors: Alex Blyth [GB], Nico Bruyniks [GB].
Full text: Click here
Patent 2024
Buffers Cells Electric Conductivity Electrophoresis Glucose Hematopoietic System Malignant Neoplasms Microscopy Neutrophil Range of Motion, Articular Stem Cells, Hematopoietic Tromethamine
2
Immunotherapy for Solid Tumors
Not available on PMC !

Example 7

Use in Patients for Treating Solid Tumours

Stored haematopoietic cells (e.g. haematopoietic stem cells or granulocyte precursor cells obtainable therefrom), and granulocytes (e.g. neutrophils) differentiated therefrom are matched to cancer patients based on their cancer type, blood type (ABO, rhesus and HLA), and/or genetics. Patients may also be matched based on human leukocyte antigen (HLA) similarity.

Patients are treated using:

    • IV infusion of haematopoietic cells (including haematopoietic stem cells, and granulocyte precursor cells) together with granulocyte-colony stimulating factor, human growth hormone, serotonin, and interleukin into the patient; or
    • IV infusion of stimulated granulocyte precursor cells (obtainable from haematopoietic stem cells) into the patient. Without wishing to be bound by theory, it is believed that said cells naturally differentiate into granulocytes (e.g. neutrophils) having a high CKA in a CKA assay in vivo; or
    • direct IV infusion of granulocytes (e.g. neutrophils) having a high CKA in a CKA assay which have been differentiated from haematopoietic cells (e.g. haematopoietic stem cells).

Typically, cells are infused once weekly for 8 weeks with a cell volume of 2×1011 administered per week. Progress of the therapy is monitored and dosing is adapted accordingly.

US11878033B2. Cancer-killing cells (2024-01-23). LIFT BIOSCIENCES LTD [GB]. Inventors: Alex Blyth [GB], Nico Bruyniks [GB].
Full text: Click here
Patent 2024
Biological Assay Cells Granulocyte Granulocyte Colony-Stimulating Factor Granulocyte Precursor Cells Hematopoietic System Histocompatibility Antigens Class II Human Growth Hormone Interleukins Intravenous Infusion Macaca mulatta Malignant Neoplasms Neoplasms Neutrophil Patients Serotonin Stem Cells, Hematopoietic Therapeutics
3
Haematopoietic Cell Expansion and Differentiation
Not available on PMC !

Example 5

Expansion and Differentiation of Haematopoietic Cells

The haematopoietic cells (e.g. haematopoietic stem cells) are stimulated using a supernatant growth factor suspension, to either develop more stem cells or differentiate into precursor cells (e.g. myeloid or granulocyte progenitor cells) or granulocytes. Suitable neutrophil synthesis methods are disclosed in Lieber et al, Blood, 2004 Feb. 1; 103(3):852-9, and Choi et al, Nat. Protoc., 2011 March; 6(3):296-313.

The protocol is composed of four major stages:

    • culturing and proliferation of haematopoietic cells;
    • short-term expansion of multipotent myeloid progenitors with a high dose of granulocyte-macrophage colony-stimulating factor (GM-CSF), a granulocyte colony-stimulating factor (G-CSF), a human growth hormone (HGH); serotonin, vitamin C, vitamin D, glutamine (Gln), arachidonic acid, AGE-albumin, interleukin-3 (IL-3), interleukin 8 (IL-8), Interleukin-4 (IL-4), Interleukin-6 (IL-6), interleukin-18 (IL-18), TNF-alpha, Flt-3 ligand, thrombopoietin, foetal bovine serum (FBS), or combinations thereof; and
    • directed differentiation of myeloid progenitors into neutrophils, eosinophils, dendritic cells (DCs), Langerhans cells (LCs), macrophages and osteoclasts.

US11878033B2. Cancer-killing cells (2024-01-23). LIFT BIOSCIENCES LTD [GB]. Inventors: Alex Blyth [GB], Nico Bruyniks [GB].
Full text: Click here
Patent 2024
Albumins Anabolism Arachidonic Acid Ascorbic Acid BLOOD Cell Proliferation Cells Dendritic Cells Eosinophil Ergocalciferol Fetal Bovine Serum flt3 ligand Glutamine Granulocyte Granulocyte-Macrophage Colony-Stimulating Factor Granulocyte Colony-Stimulating Factor Granulocyte Progenitor Cells Growth Factor Hematopoietic System Interleukin-3 Interleukin-18 interleukin 18 protein, human Langerhans Cell Macrophage Malignant Neoplasms Neutrophil Osteoclasts Serotonin Stem Cells Stem Cells, Hematopoietic Thrombopoietin Tumor Necrosis Factor-alpha
4
Isolation and Characterization of High-Density Neutrophils
Not available on PMC !

Example 21

Isolation of High-Density Neutrophils

10 ml of heparinized (20 U/ml) human blood is mixed with an equal volume of 3% Dextran T500 in saline and incubated for 30 minutes at room temperature to sediment erythrocytes. A 50 ml conical polypropylene tube is prepared with 10 ml sucrose 1.077 g/ml and slowly layered with a leukocyte-rich supernatant on top of the 1.077 g/ml sucrose layer prior to centrifuging at 400×g for 30 minutes at room temperature without brake. The high-density neutrophils (HDN) appear in the pellet. Low-density neutrophils (LDN) co-purify with monocytes and lymphocytes at the interface between the 1.077 g/ml sucrose layer and plasma.

The HDNs may be tested in a CKA assay described herein. Haematopoietic cells are suitably obtained from a donor having HDNs.

US11878033B2. Cancer-killing cells (2024-01-23). LIFT BIOSCIENCES LTD [GB]. Inventors: Alex Blyth [GB], Nico Bruyniks [GB].
Full text: Click here
Patent 2024
Biological Assay BLOOD Cells Cell Separation Dextran Erythrocytes Hematopoietic System Homo sapiens Leukocytes Lymphocyte Malignant Neoplasms Monocytes Neutrophil Plasma Polypropylenes Retinal Cone Saline Solution Sucrose Tissue Donors
5
Extracting Hematopoietic Stem Cells from Blood
Not available on PMC !

Example 4

Extracting Haematopoietic Stem Cells from Peripheral Blood

Upon giving consent the donors are given a granulocyte-colony stimulating factor (G-CSF) and/or a granulocyte-macrophage colony-stimulating factor (GM-CSF), e.g. Neupogen® (commercially available from Amgen Inc. USA) to help harvest peripheral haematopoietic stem cells with minimal possible discomfort to donors. Cell surface polypeptide markers are used for identifying long-lasting multipotent stem-cells. Suitably markers may include CD 34+, CD59+, Thy1+, CD38low/−, C-kit−/low, and lin−.

US11878033B2. Cancer-killing cells (2024-01-23). LIFT BIOSCIENCES LTD [GB]. Inventors: Alex Blyth [GB], Nico Bruyniks [GB].
Full text: Click here
Patent 2024
Blood Cells CD59 protein, human Cells Donors Granulocyte-Macrophage Colony-Stimulating Factor Granulocyte Colony-Stimulating Factor Hematopoietic System Malignant Neoplasms Multipotent Stem Cells Neupogen Peripheral Blood Stem Cells Polypeptides Proto-Oncogene Protein c-kit Stem Cells, Hematopoietic

Top products related to «Hematopoietic System»

1
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.
2
Penicillin streptomycin by Thermo Fisher Scientific
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Penicillin/streptomycin is a commonly used antibiotic solution for cell culture applications. It contains a combination of penicillin and streptomycin, which are broad-spectrum antibiotics that inhibit the growth of both Gram-positive and Gram-negative bacteria.
3
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The FACSCalibur is a flow cytometry system designed for multi-parameter analysis of cells and other particles. It features a blue (488 nm) and a red (635 nm) laser for excitation of fluorescent dyes. The instrument is capable of detecting forward scatter, side scatter, and up to four fluorescent parameters simultaneously.
4
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.
5
Streptomycin by Thermo Fisher Scientific
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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.
6
Penicillin by Thermo Fisher Scientific
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Penicillin is a type of antibiotic used in laboratory settings. It is a broad-spectrum antimicrobial agent effective against a variety of bacteria. Penicillin functions by disrupting the bacterial cell wall, leading to cell death.
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L glutamine by Thermo Fisher Scientific
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L-glutamine is an amino acid that is commonly used as a dietary supplement and in cell culture media. It serves as a source of nitrogen and supports cellular growth and metabolism.
8
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.
9
Stem cell factor (scf) by Thermo Fisher Scientific
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The SCF is a versatile laboratory instrument designed to perform supercritical fluid extraction and chromatography. It utilizes the unique properties of supercritical fluids, such as adjustable solvent power and low viscosity, to efficiently extract, fractionate, and purify a wide range of compounds. The core function of the SCF is to provide researchers and analysts with a powerful tool for sample preparation, purification, and analysis across various industries and applications.
10
α mem by Thermo Fisher Scientific
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α-MEM is a cell culture medium formulated for the growth and maintenance of mammalian cells. It provides a balanced salt solution, amino acids, vitamins, and other nutrients required for cell proliferation.

More about "Hematopoietic System"

The hematopoietic or blood-forming system is a complex network of tissues and organs responsible for the production, maturation, and regulation of blood cells.
This vital system includes the bone marrow, spleen, lymph nodes, and thymus gland, playing a crucial role in immune function, oxygen transport, and blood clotting.
Disorders affecting the hematopoietic system, such as anemias, leukemias, and lymphomas, can have serious implications for an individual's health.
Researchers studying this system may leverage powerful tools like PubCompare.ai to identify the most reproducible and effective protocols from the literature, preprints, and patents, streamlining their workflow and accelerating their discoveries.
PubCompare.ai is an AI-driven platform that helps researchers optimize their hematopoietic system research.
By utilizing the platform's powerful comparison tools, scientists can locate the best protocols and products from a vast array of sources, including scientific literature, preprints, and patents.
Leveraging resources like fetal bovine serum (FBS), penicillin/streptomycin, FACSCalibur, FACSAria II, streptomycin, penicillin, L-glutamine, FACSAria, and stem cell factor (SCF), researchers can cultivate and maintain healthy hematopoietic cell cultures, enabling them to study this complex system in greater depth.
By streamlining their workflow and accessing the most effective and reproducible protocols, researchers can accelerate their discoveries and drive advancements in hematopoietic system research.