> Anatomy > Cell > Neuroglia

Neuroglia

Q: What are the main functions of Neuroglia in the nervous system?

Neuroglia, also known as glial cells, perform a wide range of critical functions in the nervous system. These versatile cells insulate nerve fibers, regulate the brain's immune response, and provide essential support and protection for neurons. Neuroglia are the unsung heroes of the nervous system, playing a crucial role in maintaining its health and proper function.

Q: How can PubCompare.ai help optimize Neuroglia research?

PubCompare.ai is an AI-driven platform that can assist in optimizing your Neuroglia research. The platform allows you to screen protocol literature more efficiently and leverage AI to pinpoint critical insights. PubCompare.ai can help researchers identify the most effective protocols related to Neuroglia, based on their specific research goals. The platform's AI-driven analysis can highlight key differences in protocol effectiveness, enabling you to choose the best option for reproducibility and accuracy.

Q: What are the different types or variations of Neuroglia?

Neuroglia are a diverse group of cells, and there are several different types or variations. The main types include astrocytes, oligodendrocytes, microglia, and ependymal cells. Each type of Neuroglia has its own unique functions and characteristics, contributing to the overall health and function of the nervous system in different ways. Understanding the specificities of these Neuroglia variations is crucial for advancing research in neurological disorders and brain function.

Q: How can PubCompare.ai assist in comparing Neuroglia protocols?

PubCompare.ai's AI-driven analysis can help researchers compare Neuroglia protocols more effectively. The platform can identify key differences in protocol effectiveness, enabling you to choose the best option for your specific research goals. This can enhance the reproducibility and accuracy of your Neuroglia studies, as you'll be able to select the most suitable protocols based on the platform's intelligent comparisons.

Q: What are some practical applications of Neuroglia research?

Neuroglia research has a wide range of practical applications. By understanding the complex roles of these cells, researchers can gain insights into the pathogenesis of neurological disorders, such as multiple sclerosis, Alzheimer's disease, and Parkinson's disease. Additionally, Neuroglia research can lead to the development of novel therapeutic strategies and treatments for brain injuries and other neurological conditions. Thse versatile cells are essential for maintaining the health and function of the nervous system, making them a crucial target for medical research and innovation.

Q: How can PubCompare.ai help overcome common challenges in Neuroglia research?

PubCompare.ai can help overcome common challenges in Neuroglia research by streamlining the process of identifying and comparing relevant protocols. The platform's AI-driven analysis can assist researchers in navigating the vast amount of available literature, pinpointing the most effective protocols, and choosing the best options for their specific research goals. This can enhance reproducibility, accuracy, and overall efficiency in Neuroglia studies, ultimately accelerating the pace of discovery and innovation in this important field of neuroscience.

Neuroglia: The Unsung Heroes of the Nervous System.
Neuroglia, also known as glial cells, are the non-neuronal cells that provide essential support and protection for neurons in the central and peripheral nervous systems.
These versatile cells perform a wide range of critical functions, from insulating nerve fibers to regulating the brain's immune response.
Understanding the complex roles of neuroglia is crucial for advancing research in neurological disorders and brain function.
Explore the latest insights into this fascinating and understudied aspect of the nervous sstem with PubCompare.ai, an AI-driven platform that can help optimize your Neuroglia research.

Most cited protocols related to «Neuroglia»

Identifying Active Gene Regulatory Networks in Single-Cell RNA-Seq Using AUCell

AUCell is a new method that allows identifying cells with active gene regulatory networks in single-cell RNA-seq data. The input to AUCell is a gene set, and the output the gene set “activity” (AUC) in each cell. In SCENIC, these gene sets are the regulons, consisting of the TF and their putative targets. AUCell calculates the enrichment of the regulon as an area under the recovery curve (AUC) across the ranking of all genes in a particular cell, whereby genes are ranked by their expression value. This method is therefore independent of the gene expression units and the normalization procedure. In addition, since the cells are evaluated individually, it can easily be applied to bigger datasets (e.g. subsetting the expression matrix if needed). In brief, the scoring method is based on a recovery analysis, where the x-axis (Supplementary Fig. 1c) is the ranking of all genes based on expression level (genes with the same expression value, e.g. '0', are randomly sorted); and the y-axis is the number of genes recovered from the input set. AUCell then uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched at the top of the ranking for each cell. In this way, the AUC represents the proportion of expressed genes in the signature and their relative expression value compared to the other genes within the cell. The output of this step is a matrix with the AUC score for each gene-set in each cell. We use either the AUC scores (across regulons) directly as continuous values to cluster single-cells, or we generate a binary matrix using a cutoff of the AUC score for each regulon. These cutoffs are determined either automatically, or manually adjusted by inspecting the distribution of the AUC scores. Some examples of AUC distributions are provided in Supplementary Fig. 2a. Supplementary Fig. 2b-c shows the validation of AUCell using previously published neuronal and glial gene signatures. The tutorial included in the package, also includes practical explanations and implications of each of the steps of the method.

Aibar S., González-Blas C.B., Moerman T., Huynh-Thu V.A., Imrichova H., Hulselmans G., Rambow F., Marine J.C., Geurts P., Aerts J., van den Oord J., Atak Z.K., Wouters J, & Aerts S. (2017). SCENIC: Single-cell regulatory network inference and clustering. Nature methods, 14(11), 1083-1086.

Publication 2017

Cells Epistropheus Gene Expression Gene Regulatory Networks Genes Genes, vif Neuroglia Neurons Regulon Single-Cell RNA-Seq

Batch Effect Correction in Single-Cell RNA-Seq

For the results shown in this manuscript, we have used the log-transformed total number of UMI assigned to each cell as the dependent variable to model gene-level UMI counts. However, other variables may also be suitable as long as they capture the sampling depth associated with each cell.
Additionally, the model can be flexibly extended to include additional covariates representing nuisance sources of variation, including cell-cycle state, mitochondrial percentage, or experimental batch. In these cases (unlike with sequencing depth), no regularization can be performed for parameters involving these variables, as genes with similar abundances cannot be assumed to (for example) be expressed in a similar pattern across the cell cycle. In these cases, we first learn regularized models using only the sequencing depth covariate, as described above. We next perform a second round of NB regression, including both the depth covariate and additional nuisance parameters as model predictors. In this round, the depth-dependent parameters are fixed to their previously regularized values, while the additional parameters are unconstrained and fit during the regression. The Pearson residuals of this second round of regression represent normalized data.
As a proof-of-concept, we illustrate a potential model extension by including a batch indicator variable when analyzing a dataset of 26,439 murine bipolar cells produced by two experimental batches [32 ], considering all bipolar cells and Müller glia. After running sctransform, either with the inclusion or exclusion of the batch covariate, we performed PCA on all genes and used the first 20 dimensions to compute a UMAP embedding (Additional file 2: Figure S8). We include this example as a demonstration for how additional nuisance parameters can be included in the GLM framework, but note that when cell-type-specific batch effects are present, or there is a shift in the percentage of cell types across experiments, non-linear batch effect correction strategies are needed [18 (link)].

Hafemeister C, & Satija R. (2019). Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biology, 20, 296.

Publication 2019

Cell Cycle Cells Genes Mitochondrial Inheritance Mus Neuroglia

Reproducible Gene Expression in Human and Mouse Brains

All samples from the 18 human and 20 mouse brain data sets were taken from brain tissue and were all run on Affymetrix platforms. Given that we explore the same tissue (albeit from different species) we hypothesized that a subset of genes would show reproducible mean expression levels and network connectivity levels. For example, regardless of the experimental paradigm, there will be a subset of marker genes in neurons and glial cells that will be present, and this subset of genes should be reproducible. In a prior publication we confirmed that both mean expression and connectivity showed a significant amount of reproducibility in all of these data sets, even when comparing across species [3 (link)]. For example, the mean expression (Spearman correlation of R = 0.60, p < E-400) and connectivity (R = 0.27, p < E-70) was highly correlated between human and mouse brains. We and others have used the (Spearman) correlation to assess reproducibility of means and connectivities in pairs of data sets but we should point out that other measures are possible.

Miller J.A., Cai C., Langfelder P., Geschwind D.H., Kurian S.M., Salomon D.R, & Horvath S. (2011). Strategies for aggregating gene expression data: The collapseRows R function. BMC Bioinformatics, 12, 322.

Publication 2011

Brain Genes Genes, vif Homo sapiens Mice, Laboratory Neuroglia Neurons Tissues

Detailed Cell-Type Specific Brain Protocols

TRAP data were generated as described (1 (link),2 (link)), and are available for download from GEO: GSE13379. Etv1 data were not plotted because of known contamination with endothelial or lymphoblast cells (1 (link)). Other cell types and drivers are listed in Table 1. This dataset contains samples representing a variety of pure and mixed cell types from different structures of the mouse brain, as well as samples from the corresponding whole tissue. The purified samples are referred to as immunoprecipitates (IP). In parallel, RNA which did not bind to the antibody was also harvested to provide an assessment of the gene expression of the tissue as a whole. These samples are referred to as unbound RNA. Microarray analysis, as traditionally applied to the nervous system, results in samples that are most similar to unbound samples. As the immunoprecipitation does not lead to significant depletion of cell-specific RNAs, here we use the unbound samples as a measure for the total tissue homogenate RNA (referred to as Total).

Table 1.

List of the cell populations, relevant drivers and abbreviations

Cell populations	Driver	Abbreviations used*
Drd1+ medium spiney neurons of neostriatum	Drd1	CS.Drd1
Drd2+ medium spiney neurons of neostriatum	Drd2	CS.Drd2
Cholinergic Interneurons of corpus striatum	Chat	CS.Chat
Motor neurons of brain stem	Chat	BS.Chat
Cholinergic neurons of basal forebrain	Chat	BF.Chat
Mature oligodendrocytes of cerebellum	Cmtm5	Cb.Cmtm5
Astroglia of cerebellum	Aldh1l1	Cb.Aldh1L1
Golgi neurons of cerebellum	Grm2	Cb.Grm2
Unipolar brush cells and Bergman glia of cerebellum	Grp	Cb.Grp
Stellate and basket cells of cerebellum	Lypd6	Cb.Lypd6
Granule cells of cerebellum	Neurod1	Cb.Neurod1
Oligodendroglia of cerebellum	Olig2	Cb.Olig2
Purkinje cells of cerebellum	Pcp2	Cb.Pcp2
Bergman glia and mature oligos. of cerebellum	Sept4	Cb.Sept4
Cck+ neurons of cortex	Cck	Ctx.Cck
Mature oligodendrocytes of cortex	Cmtm5	Ctx.Cmtm5
Cort+ interneurons of cortex	Cort	Ctx.Cort
Astrocytes of cortex	Aldh1l1	Ctx.AldhL1
Corticospinal, corticopontine neurons	Glt25d2	Ctx.Glt25d2
Corticothalamic neurons	Ntsr1	Ctx.Ntsr1
Oligodendroglia of cortex	Olig2	Ctx.Olig2
Pnoc+ neurons of cortex	Pnoc	Ctx.Pnoc
Motor neurons of the spinal cord	Chat	SC.Chat

*Abbreviations used for Figures 4, 5, 7 and Supplementary Figures 7 and 8

Dougherty J.D., Schmidt E.F., Nakajima M, & Heintz N. (2010). Analytical approaches to RNA profiling data for the identification of genes enriched in specific cells. Nucleic Acids Research, 38(13), 4218-4230.

Publication 2010

2',5'-oligoadenylate Brain Cells DRD1 protein, human Endothelium Gene Expression Immunoglobulins Immunoprecipitation Interneurons Microarray Analysis Mus Neuroglia Neurons Oligodendroglia Population Group Systems, Nervous Tissues

Advancing White Matter Imaging Techniques

Though the vast majority of recent MRI studies of white matter have focused on diffusion, MT or relaxometry, there are other techniques that may provide complementary information. One of the oldest methods is MR spectroscopy, which may be used to characterize specific metabolites in the tissue including NAA (N-acetylaspartate), creatine, choline and neurotransmitters like GABA and glutamine/glutamate. Each of these metabolites reflects different physiological processes and have unique spectral signatures. Of significant interest in white matter is NAA, which is a marker of the presence, density and health of neurons including the axonal processes. In fact, NAA may be one of the most specific markers of healthy axons and, as such, it is surprising that it is not used more widely for the investigation of white matter in the brain. This may be due in part to the fact that MR spectroscopy is extremely sensitive to the homogeneity of the magnetic field, which makes it challenging to apply in areas near air or bone interfaces. The concentrations of the metabolites are also in the micromolar range (compare with multiple molar for water), thus, large voxels must be used and the acquisition speed is slow. Therefore, MR spectroscopy studies are often limited by poor coverage, poor resolution, and long scan times.
The recent push towards ever higher magnetic fields makes quantitative MRI methods more challenging. Imaging distortions in DTI studies increase proportional to the field strength. The RF power deposition (SAR – specific absorption rate) increases quadratically with the magnetic field strength, which limits the application of MT pulses and can also limit the flip angles used in steady state imaging. However, susceptibility weighted imaging is one method that greatly benefits from higher magnetic field strengths. Recent studies have observed interesting contrast in white matter tracts as a function of orientation and degree of myelination (Liu et al., 2011 ). Stunning images of white matter tracts have recently been obtained in ex vivo brain specimens (Sati et al., 2011 ). Techniques for characterizing white matter in the human brain are only beginning to be developed.
Other white matter cellular components are the glia, which include oligodendrocytes, astrocytes, and microglia. In general, there are no specific markers of changes in either oligodendrocytes or astrocytes. Recent evidence suggests that hypointense white matter lesions on T1w imaging may indicate reactive astrocytes (Sibson et al., 2008 (link)). Increases in microglia often accompany inflammation, which can be detected using contrast agents, either gadolinium or superparamagnetic iron oxide (SPIO) particles. Recent studies have suggested that SPIO particles are preferentially taken up by macrophages in inflammatory regions. The impact of these contrast agents on other quantitative MRI measures have not (Oweida et al., 2004 (link)) been widely studied, thus multimodal imaging studies must be designed carefully.

Alexander A.L., Hurley S.A., Samsonov A.A., Adluru N., Hosseinbor A.P., Mossahebi P., Tromp D.P., Zakszewski E, & Field A.S. (2011). Characterization of Cerebral White Matter Properties Using Quantitative Magnetic Resonance Imaging Stains. Brain Connectivity, 1(6), 423-446.

Publication 2011

Astrocytes Axon Bones Brain Cellular Structures Choline Contrast Media Creatine Diffusion ferric oxide Gadolinium gamma Aminobutyric Acid Glutamate Glutamine Homo sapiens Inflammation Macrophage Magnetic Fields Magnetic Resonance Spectroscopy Microglia Molar Myelin Sheath N-acetylaspartate Neuroglia Neurons Neurotransmitters Oligodendroglia Physiological Processes Pulses Radionuclide Imaging Susceptibility, Disease Tissues White Matter

Most recents protocols related to «Neuroglia»

Comparative Analysis of Actin Regulators in Single-Cell RNA-seq

Mouse and human brain single cell RNA-seq data was downloaded from the Allen Brain Map data portal (2010 Allen Institute for Brain Science. Allen Brain Map [Sunkin et al., 2013 (link)]; available from: https://portal.brain-map.org/atlases-and-data/rnaseq). In our analyses, we used read counts derived from the 2019 Smart-Seq method. The 2019 Smart-Seq data was selected due to having an overall greater read depth than the 2020 10x Genomics data. To directly compare the gene expression of Evl, Enah, and Vasp within mouse glutamatergic neurons, we first selected the top 1,000 glutamatergic neurons that had the greatest number of read counts. Next, we performed TPM normalization on the expression matrix using the R function “calculateTPM” from the R library “scater” (McCarthy et al., 2017 (link)). The gene lengths used in the calculateTPM function were calculated from the GTF2LengthGC.R script provided by the github user dpryan79. For the GTF and FASTA input files, gencode.vM25.annotation.gtf and GRCm38.p6.genome.fa files were downloaded from GENCODE (Frankish et al., 2019 (link)). The same pipeline was used to calculate gene expression of EVL, ENAH, and VASP in human glutamatergic cells, except the GENCODE files gencode.v34.annotation.gtf and GRCh38.p13.genome.fa were used to calculate gene lengths.
To compare the gene expression of Evl, Enah, and Vasp between glutamatergic neurons and non-neuronal cells in mice, we first selected the top 250 glutamatergic neurons and the top 250 non-neuronal cells that had the highest read counts. Next, the SCnorm pipeline was used to normalize gene expression across samples (Bacher et al., 2017 (link)). All default settings were used except the parameter “ditherCounts” was set to TRUE. The same pipeline was utilized for the comparison of EVL, ENAH, and VASP between human glutamatergic neurons and non-neuronal cells.

Parker S.S., Ly K.T., Grant A.D., Sweetland J., Wang A.M., Parker J.D., Roman M.R., Saboda K., Roe D.J., Padi M., Wolgemuth C.W., Langlais P, & Mouneimne G. (2023). EVL and MIM/MTSS1 regulate actin cytoskeletal remodeling to promote dendritic filopodia in neurons. The Journal of Cell Biology, 222(5), e202106081.

Publication 2023

Brain Brain Mapping Cells DNA Library Gene Expression Genes Genome Homo sapiens Mice, Laboratory Neuroglia Neurons Single-Cell RNA-Seq VASP protein, human

Identifying cell-type specific markers with MAST

Marker genes for glial and dentate gyrus (DG) granule neurons sub-clusters were found using the MAST test^{110 (link)} (using batch and sex as latent variables for glial cells in the brain to account for technical effects), which was run using the FindMarkers function in Seurat v.4 (using the assay set to RNA to use the normalized UMI counts values per gene). To find diet-dependent differentially expressed signatures, we used the same scheme as above using the MAST algorithms with batch and sex as latent variables, within each glial cell type, comparing all cells of HFD-fed and CD-fed 5xFAD mice; similarly, WT mice were compared between diet groups. P-values were adjusted for multiple hypothesis testing using Benjamini–Hochberg’s correction (FDR). Adjusted P-value threshold of 0.010 was used to report significant changes and a fold change threshold of 0.250.

Suzzi S., Croese T., Ravid A., Gold O., Clark A.R., Medina S., Kitsberg D., Adam M., Vernon K.A., Kohnert E., Shapira I., Malitsky S., Itkin M., Brandis A., Mehlman T., Salame T.M., Colaiuta S.P., Cahalon L., Slyper M., Greka A., Habib N, & Schwartz M. (2023). N-acetylneuraminic acid links immune exhaustion and accelerated memory deficit in diet-induced obese Alzheimer’s disease mouse model. Nature Communications, 14, 1293.

Publication 2023

Biological Assay Brain Cells Cytoplasmic Granules Diet Genes Genetic Markers Gyrus, Dentate Mus Neuroglia Neurons

Gene Expression Analysis of Glial Transcripts in Alzheimer's Mouse Models

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2023

Biological Assay Brain Crossbreeding Gene Expression Gene Expression Profiling Genes Genes, Housekeeping Mice, Laboratory Neuroglia POU3F2 protein, human RNA Probes Tissues

Neuronal and Glial Cell Culture and Inflammasome Activation

SH-SY5Y human neuroblastoma cell line (ECACC, 94030304) and the mouse spontaneously immortalized microglia-9 (SIM-A9) cell line (ATCC-CRL-3265) [30 (link)] were cultured in Gibco™ DMEM/ F-12 with GlutaMAX™ (ThermoFisher, Waltham, MA, 31331028), supplemented with 10% fetal bovine serum (ThermoFisher, Waltham, MA, 10100139) and 5 μg/ml plasmocin™ (InvivoGen, San Diego, CA, ant-mpt). The culture medium of SIM-A9 cells was supplemented with 5% horse serum (Sigma-Aldrich, Saint Louis, MO, H1270). Embryonic cortical-hippocampal neurons from wild-type (WT) and SREBF-2 mice (B6;SJL-Tg(rPEPCKSREBF2)788Reh/J, RRID:IMSR_JAX:003311) were isolated on day 16–17 of pregnancy by trypsin digestion following a standard protocol [31 (link)]. Dissociated cells were grown in Neurobasal™ medium (ThermoFisher, 21103–049) supplemented with 2.5% (v/v) B27 supplement (ThermoFisher, 17504–001), 0.5 mM L-glutamine (Sigma-Aldrich, G7513) and 5 μg/ml plasmocinTM (InvivoGen, ant-mpt), and plated onto poly-D-lysine (Sigma-Aldrich, P6407)- and laminin (Sigma-Aldrich, L2020)-coated plates at a density of 2 × 10⁵ cells/cm². Half of the culture medium was changed every 3 or 4 days. Over 95% of neuronal purity was confirmed by immunochemistry using antibodies targeting neuronal and glial markers. Experiments were performed at 7 to 10 days in vitro (DIV). All procedures involving animals and their care were approved by the ethics committee of the University of Barcelona and were conducted in accordance with institutional guidelines in compliance with national and international laws and policies.
Cell cholesterol enrichment was achieved by incubation with a cholesterol:methyl-β-cyclodextrin complex (CHO:MCD; containing 50 μg/ml of cholesterol) (Sigma-Aldrich, C4951) for 1 h followed by 4-h recovery. To induce inflammasome activation, cells were treated with 10 μg/ml lipopolysaccharide (LPS) from Escherichia coli O111:B4 (Sigma-Aldrich, L4391), 10 μg/ml N-acetylmuramyl-L-alanyl-D-isoglutamine hydrate (also known as muramyl dipeptide, MDP; Sigma-Aldrich, A9519), 5 mM ATP (Sigma-Aldrich, A2383), 150 μg/ml monosodium urate crystals (MSU; Santa Cruz Biotech., sc-202711), and oligomeric Aβ at the indicated times. Preincubation with 4 mM glutathione ethyl ester (GSHee) or with the cell-permeable caspase 1 inhibitor I (10 μM; Bachem, 4095744) was performed 30 min before treatment when indicated.

de Dios C., Abadin X., Roca-Agujetas V., Jimenez-Martinez M., Morales A., Trullas R., Mari M, & Colell A. (2023). Inflammasome activation under high cholesterol load triggers a protective microglial phenotype while promoting neuronal pyroptosis. Translational Neurodegeneration, 12, 10.

Publication 2023

Acetylmuramyl-Alanyl-Isoglutamine Animals Antibodies Cell Lines Cells Cholesterol Cortex, Cerebral Culture Media Cyclodextrins Dietary Supplements Digestion Embryo Equus caballus Escherichia coli Ethics Committees Fetal Bovine Serum Glutamine Homo sapiens Inflammasomes interleukin-1beta-converting enzyme inhibitor isoglutamine Laminin Lipopolysaccharides Lysine Microglia Mus Neuroblastoma Neuroglia Neurons Permeability plasmocin Poly A Pregnancy S-ethyl glutathione Serum Sodium Urate Monohydrate Trypsin

Neuronal and Glial Cell Culture and Inflammasome Activation

Publication 2023

Top products related to «Neuroglia»

Fetal bovine serum (fbs) by Thermo Fisher Scientific

Sourced in United States, China, United Kingdom, Germany, Australia, Japan, Canada, Italy, France, Switzerland, New Zealand, Brazil, Belgium, India, Spain, Israel, Austria, Poland, Ireland, Sweden, Macao, Netherlands, Denmark, Cameroon, Singapore, Portugal, Argentina, Holy See (Vatican City State), Morocco, Uruguay, Mexico, Thailand, Sao Tome and Principe, Hungary, Panama, Hong Kong, Norway, United Arab Emirates, Czechia, Russian Federation, Chile, Moldova, Republic of, Gabon, Palestine, State of, Saudi Arabia, Senegal

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.

Glutamax by Thermo Fisher Scientific

Sourced in United States, Germany, United Kingdom, France, Switzerland, Canada, Japan, Australia, China, Belgium, Italy, Denmark, Spain, Austria, Netherlands, Sweden, Ireland, New Zealand, Israel, Gabon, India, Poland, Argentina, Macao, Finland, Hungary, Brazil, Slovenia, Sao Tome and Principe, Singapore, Holy See (Vatican City State)

GlutaMAX is a chemically defined, L-glutamine substitute for cell culture media. It is a stable source of L-glutamine that does not degrade over time like L-glutamine. GlutaMAX helps maintain consistent cell growth and performance in cell culture applications.

Neurobasal medium by Thermo Fisher Scientific

Sourced in United States, Germany, United Kingdom, Japan, Italy, Canada, Spain, France, Switzerland, China, Australia, Israel, Denmark, Ireland, Sweden, Austria

Neurobasal medium is a cell culture medium designed for the maintenance and growth of primary neuronal cells. It provides a defined, serum-free environment that supports the survival and differentiation of neurons. The medium is optimized to maintain the phenotypic characteristics of neurons and minimizes the growth of non-neuronal cells.

Dulbecco modified eagle medium (dmem) by Thermo Fisher Scientific

Sourced in United States, China, United Kingdom, Germany, France, Australia, Canada, Japan, Italy, Switzerland, Belgium, Austria, Spain, Israel, New Zealand, Ireland, Denmark, India, Poland, Sweden, Argentina, Netherlands, Brazil, Macao, Singapore, Sao Tome and Principe, Cameroon, Hong Kong, Portugal, Morocco, Hungary, Finland, Puerto Rico, Holy See (Vatican City State), Gabon, Bulgaria, Norway, Jamaica

DMEM (Dulbecco's Modified Eagle's Medium) is a cell culture medium formulated to support the growth and maintenance of a variety of cell types, including mammalian cells. It provides essential nutrients, amino acids, vitamins, and other components necessary for cell proliferation and survival in an in vitro environment.

Penicillin streptomycin by Thermo Fisher Scientific

Sourced in United States, Germany, United Kingdom, China, Canada, France, Japan, Australia, Switzerland, Israel, Italy, Belgium, Austria, Spain, Gabon, Ireland, New Zealand, Sweden, Netherlands, Denmark, Brazil, Macao, India, Singapore, Poland, Argentina, Cameroon, Uruguay, Morocco, Panama, Colombia, Holy See (Vatican City State), Hungary, Norway, Portugal, Mexico, Thailand, Palestine, State of, Finland, Moldova, Republic of, Jamaica, Czechia

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.

B27 supplement by Thermo Fisher Scientific

Sourced in United States, United Kingdom, Germany, Japan, Canada, China, Italy, France, Switzerland, Spain, Israel, Australia, Austria, Poland, Denmark, Palestine, State of

B27 supplement is a serum-free and animal component-free cell culture supplement developed by Thermo Fisher Scientific. It is designed to promote the growth and survival of diverse cell types, including neurons, embryonic stem cells, and other sensitive cell lines. The core function of B27 supplement is to provide a defined, optimized combination of vitamins, antioxidants, and other essential components to support cell culture applications.

Poly l lysine (pll) by Merck Group

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

Poly-L-lysine is a synthetic polymer composed of the amino acid L-lysine. It is commonly used as a coating agent for various laboratory applications, such as cell culture and microscopy. Poly-L-lysine enhances the attachment and growth of cells on surfaces by providing a positively charged substrate.

L glutamine by Thermo Fisher Scientific

Sourced in United States, United Kingdom, Germany, France, Canada, Switzerland, Italy, Australia, Belgium, China, Japan, Austria, Spain, Brazil, Israel, Sweden, Ireland, Netherlands, Gabon, Macao, New Zealand, Holy See (Vatican City State), Portugal, Poland, Argentina, Colombia, India, Denmark, Singapore, Panama, Finland, Cameroon

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.

Poly d lysine by Merck Group

Sourced in United States, Germany, United Kingdom, Macao, Canada, Sao Tome and Principe, Australia, Italy, France, China, Japan, Israel, Netherlands, Austria

Poly-D-lysine is a synthetic polymer commonly used as a coating for cell culture surfaces. It enhances cell attachment and promotes cell growth by providing a positively charged substrate that facilitates cell adhesion.

Penicillin by Thermo Fisher Scientific

Sourced in United States, United Kingdom, Germany, China, France, Canada, Japan, Australia, Switzerland, Italy, Israel, Belgium, Austria, Spain, Brazil, Netherlands, Gabon, Denmark, Poland, Ireland, New Zealand, Sweden, Argentina, India, Macao, Uruguay, Portugal, Holy See (Vatican City State), Czechia, Singapore, Panama, Thailand, Moldova, Republic of, Finland, Morocco

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.

What are the main functions of Neuroglia in the nervous system?

Neuroglia, also known as glial cells, perform a wide range of critical functions in the nervous system. These versatile cells insulate nerve fibers, regulate the brain's immune response, and provide essential support and protection for neurons. Neuroglia are the unsung heroes of the nervous system, playing a crucial role in maintaining its health and proper function.

How can PubCompare.ai help optimize Neuroglia research?

PubCompare.ai is an AI-driven platform that can assist in optimizing your Neuroglia research. The platform allows you to screen protocol literature more efficiently and leverage AI to pinpoint critical insights. PubCompare.ai can help researchers identify the most effective protocols related to Neuroglia, based on their specific research goals. The platform's AI-driven analysis can highlight key differences in protocol effectiveness, enabling you to choose the best option for reproducibility and accuracy.

What are the different types or variations of Neuroglia?

Neuroglia are a diverse group of cells, and there are several different types or variations. The main types include astrocytes, oligodendrocytes, microglia, and ependymal cells. Each type of Neuroglia has its own unique functions and characteristics, contributing to the overall health and function of the nervous system in different ways. Understanding the specificities of these Neuroglia variations is crucial for advancing research in neurological disorders and brain function.

How can PubCompare.ai assist in comparing Neuroglia protocols?

PubCompare.ai's AI-driven analysis can help researchers compare Neuroglia protocols more effectively. The platform can identify key differences in protocol effectiveness, enabling you to choose the best option for your specific research goals. This can enhance the reproducibility and accuracy of your Neuroglia studies, as you'll be able to select the most suitable protocols based on the platform's intelligent comparisons.

What are some practical applications of Neuroglia research?

Neuroglia research has a wide range of practical applications. By understanding the complex roles of these cells, researchers can gain insights into the pathogenesis of neurological disorders, such as multiple sclerosis, Alzheimer's disease, and Parkinson's disease. Additionally, Neuroglia research can lead to the development of novel therapeutic strategies and treatments for brain injuries and other neurological conditions. Thse versatile cells are essential for maintaining the health and function of the nervous system, making them a crucial target for medical research and innovation.

How can PubCompare.ai help overcome common challenges in Neuroglia research?

PubCompare.ai can help overcome common challenges in Neuroglia research by streamlining the process of identifying and comparing relevant protocols. The platform's AI-driven analysis can assist researchers in navigating the vast amount of available literature, pinpointing the most effective protocols, and choosing the best options for their specific research goals. This can enhance reproducibility, accuracy, and overall efficiency in Neuroglia studies, ultimately accelerating the pace of discovery and innovation in this important field of neuroscience.

More about "Neuroglia"

Neuroglia, also known as glial cells, are the non-neuronal cells that play a vital role in the central and peripheral nervous systems.
These versatile cells perform a wide range of critical functions, from insulating nerve fibers to regulating the brain's immune response.
Understanding the complex roles of neuroglia is crucial for advancing research in neurological disorders and brain function.
Neuroglia can be further classified into several subtypes, including astrocytes, oligodendrocytes, microglia, and ependymal cells.
Astrocytes provide structural and metabolic support for neurons, while oligodendrocytes produce myelin, which insulates nerve fibers and facilitates efficient signal transmission.
Microglia act as the immune cells of the central nervous system, and ependymal cells line the ventricles and central canal of the spinal cord.
In cell culture, neuroglia research often utilizes specialized media and supplements such as FBS (Fetal Bovine Serum), GlutaMAX, Neurobasal medium, DMEM (Dulbecco's Modified Eagle Medium), Penicillin/Streptomycin, B27 supplement, Poly-L-lysine, L-glutamine, and Poly-D-lysine.
These components support the growth and maintenance of neuroglia in vitro, enabling researchers to study their diverse functions and interactions.
Exploring the latest insights into neuroglia can be facilitated by AI-driven platforms like PubCompare.ai, which can help optimize your research by locating the best protocols from literature, preprints, and patents.
This intelligent comparison tool can enhance the reproducibility and accuracy of your neuroglia studies, empowering you to make groundbreaking discoveries in this fascinating and understudied aspect of the nervous system.