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Ependyma

Most cited protocols related to «Ependyma»

Genome-wide gene-based analysis of meta-analysis

A gene-based analysis was applied to the results from our meta-analysis
using Multi-marker Analysis of GenoMic Annotation (MAGMA) 14 (link) to assess the simultaneous effect of multiple genetic
variants on 17,842 genes. To account for LD the European panel of the 1,000
Genomes data (phase 3) 80 (link) was used as a
reference panel. Genetic variants were assigned to genes based on their position
according to the NCBI 37.3 build. To identify those genes that were genome-wide
significant a P-value threshold was calculated by applying a
Bonferroni correction (α = 0.05 / 17,842; P <
2.80 × 10^-6). Regional visualisation plots were produced using
the online LocusZoom platform81 (link).
A gene-set analysis was then performed on our gene-based results using
gene annotation files from the Gene Ontology Consortium (http://geneontology.org/) 82 (link) and the Molecular Signatures Database v5.2 83 . The annotation file includes gene-sets covering three
ontologies; molecular function, cellular components, and biological function and
consisted of 5,917 gene-sets. To correct for multiple testing, we used the MAGMA
default setting of 10,000 permutations, and applied a Bonferroni correction
(α = 0.05 / 5,917; P < 8.45 ×
10^-6). Additional gene-sets were obtained from Skene, et al.
15 (link) providing expression-weighted
enrichment for seven brain cell-types (astrocytes ependymal, endothelial mural,
interneurons, microglia, oligodendrocytes, somatosensory pyramidal neurons, and
hippocampus CA1 pyramidal neurons). These brain cell-types were assessed in
MAGMA using the default setting of 10,000 permutations with a Bonferroni
correction (α = 0.05 / 7; P < 7.14 ×
10^-3) used to assess significance.

Howard D.M., Adams M.J., Clarke T.K., Hafferty J.D., Gibson J., Shirali M., Coleman J.R., Hagenaars S.P., Ward J., Wigmore E.M., Alloza C., Shen X., Barbu M.C., Xu E.Y., Whalley H.C., Marioni R.E., Porteous D.J., Davies G., Deary I.J., Hemani G., Berger K., Teismann H., Rawal R., Arolt V., Baune B.T., Dannlowski U., Domschke K., Tian C., Hinds D.A., Trzaskowski M., Byrne E.M., Ripke S., Smith D.J., Sullivan P.F., Wray N.R., Breen G., Lewis C.M, & McIntosh A.M. (2019). Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nature neuroscience, 22(3), 343-352.

Publication 2019

Astrocytes Biological Processes Brain Cells Cellular Structures Endothelium Ependyma Europeans Gene Annotation Genes Genetic Diversity Genome Interneurons Microglia Oligodendroglia Pyramidal Cells

Systematic Comparison of Cell-Type Expression

To compare average expression across all cell types and only brain cell types, we used TMF (multi-tissue) dataset. For brain-specific expression, 9 cell types with the label of tissue ‘Brain Non_Myeloid’ or ‘Brain Myeloyd’ in the original dataset were extracted. We used six traits which showed significant marginal association with the neurons for the comparison.
To compare average expression across all brain cell types and only neuronal cell types, we used MBA dataset. For neuron-specific expression, 214 neuronal cell types with ‘Neuron’ in the highest class in the original dataset were extracted. For excitatory neuron-specific expression, 60 excitatory neurons which contained a word ‘excitatory’ in the description of the cell type in the original dataset was extracted. To create a dataset with cell types evenly distributed across the highest class of the brain cell types, five-cell types were randomly selected for each of seven classes defined in the MBA dataset (i.e. astrocytes, ependymal, immune, neurons, oligodendrocytes, peripheral glia, and vascular), resulted in 35-cell types including TEGLU4. We used five traits that showed significant marginal P-value with TEGLU4 for the comparison.
To compare average expression across all cell types and only immune cell types, we used TMF dataset. For immune cell-specific expression, 21-cell types from marrow samples were extracted. We used four immunological traits which showed significant marginal P-value with B cell.

Watanabe K., Umićević Mirkov M., de Leeuw C.A., van den Heuvel M.P, & Posthuma D. (2019). Genetic mapping of cell type specificity for complex traits. Nature Communications, 10, 3222.

Publication 2019

Astrocytes B-Lymphocytes Blood Vessel Brain Cells Ependyma Histocompatibility Testing Marrow Neuroglia Neurons Oligodendroglia Tissues

Quantifying Neuroanatomical Measures from MRI

Five morphological variables were derived for the current analyses: WMH volume, total relative brain volume (ratio of absolute brain volume to intracranial volume), lateral ventricle volume, hippocampus volume, and entorhinal cortex volume. User operated image analysis was performed on a Sun Microsystems Ultra 5 workstation using the Quantum 6.2 package. Subject identifying information was not available to the operator.
Total brain and WMH volumes were derived on FLAIR-weighted images following a two-step process, as previously described41 (link), 42 (link). First, an operator manually traced the dura mater within the cranial vault, including the middle cranial fossa but not the posterior fossa and cerebellum. Intracranial volume was defined as the number of voxels contained within the manual tracings, multiplied by voxel dimensions and slice thickness. These manual tracings also defined the border between brain and non-brain elements and permitted for the removal of the latter.
Nonuniformities in image intensity were removed 43 (link) and two Gaussian probability functions, representing brain matter and cerebrospinal fluid (CSF), were fitted to the skull-stripped image41 (link), 43 (link). Once brain matter was isolated, a single Gaussian distribution was fitted to image data and a segmentation threshold for WMH was set a priori at 3.5 SDs in pixel intensity above the mean of the fitted distribution of brain matter. Erosion of two exterior image pixels was applied to the brain matter image before modeling to remove partial volume effects and ventricular ependyma on WMH determination. White matter hyperintensity volume was calculated as the sum of voxels greater to or equal to 3.5 SD above the mean intensity value of the image and multiplied by voxel dimensions and slice thickness. Similarly, total brain volume was the sum of voxels designated as brain volume from the segmentation process. Relative brain volume was the ratio of total brain volume to intracranial volume. White matter hyperintensity volumes were also adjusted by intracranial volume.

Brickman A.M., Schupf N., Manly J.J., Luchsinger J.A., Andrews H., Tang M.X., Reitz C., Small S.A., Mayeux R., DeCarli C, & Brown T.R. (2008). Brain morphology in elderly African Americans, Caribbean Hispanics, and Caucasians from Northern Manhattan. Archives of neurology, 65(8), 1053-1061.

Publication 2008

Brain Calvaria Cerebellum Cerebrospinal Fluid Cranium Diencephalon Dura Mater Entorhinal Area Ependyma Fossa, Middle Cranial Heart Ventricle Seahorses Ventricle, Lateral White Matter

Multi-level Cell Type Annotation

We extensively mined clusters obtained in preliminary analyses and found that they largely corresponded to known and putative cell types, broadly consistent with previous data. Some clusters were also clearly derived from doublets, expressing contradictory markers e.g., from neurons and vascular cells.
With any type of clustering the choice of feature space is crucial. For preliminary clustering, we used genes informative across the entire set of cells, projected by PCA. This would be expected to be suitable for finding major cell types, but would not be optimal for finding finer subdivisions among cells of the same kind (e.g., interneurons in a dataset containing both neurons, vascular cells and glia). For example, running Louvain clustering on the full dataset resulted in only 44 clusters, compared to the 265 found by the multi-level, iterative approach described below.
We decided to first split cells by major class. In order to split the data, and to reject many doublets, we trained a classifier to automatically detect the major class of each single cell, as well as classes representing doublets. We first manually annotated clusters to indicate major classes of cells: Neurons, Oligodendrocytes, Astrocytes, Bergman glia, Olfactory ensheathing cells, Satellite glia, Schwann cells, Ependymal, Choroid, Immune, and Vascular. For some of these classes, we distinguished proliferating cells (e.g., Cycling oligodendrocytes, i.e., OPCs). We also manually identified clusters that were clearly doublets between these major classes (e.g., Vascular-Neurons) as well as clusters that were of poor quality.
We then trained a support vector classifier to discriminate all of these labels, using the training set of preliminary clusters manually annotated with class labels. We sampled 100 cells per cluster and used 80% of this dataset to optimize the classifier, and the remaining 20% to assess performance. On average, the classification accuracy was 93% for non-cycling cells. The precision and recall for neurons was 93% and 99%, respectively. That is, 99% of all neurons were classified correctly, and 93% of all cells classified as neurons were actually neurons. The classifier struggled to distinguish cycling cells, presumably because they shared most gene expression with their non-cycling counterparts. For this reason, we always pooled cycling and non-cycling cells after classification. The table below shows the accuracy for all major classes of interest:

	Precision	Recall
astrocyte	87%	96%
astrocyte, cycling	59%	38%
bergmann-glia	100%	97%
blood	77%	65%
ependymal	98%	97%
immune	96%	98%
neurons	93%	99%
neurons, cycling	63%	54%
oec	100%	95%
oligos	91%	97%
oligos, cycling	39%	19%
satellite-glia	90%	95%
satellite-glia, cycling	91%	88%
schwann	100%	100%
choroid	100%	80%
vascular	87%	97%
vascular, cycling	100%	25%

average (non-cycling)	93%	93%

We used this classifier to individually assess the class identity of each cell in each dataset, and to pool cells by major class into new files (with neurons further separated by tissue).

Zeisel A., Hochgerner H., Lönnerberg P., Johnsson A., Memic F., van der Zwan J., Häring M., Braun E., Borm L.E., La Manno G., Codeluppi S., Furlan A., Lee K., Skene N., Harris K.D., Hjerling-Leffler J., Arenas E., Ernfors P., Marklund U, & Linnarsson S. (2018). Molecular Architecture of the Mouse Nervous System. Cell, 174(4), 999-1014.e22.

Publication 2018

2',5'-oligoadenylate Astrocytes Blood Cells Blood Vessel Cells Choroid Cloning Vectors Ependyma Gene Expression Genes Germ Cells Interneurons Mental Recall Neuroglia Neurons Oligodendroglia Satellite Glia Schwann Cells Sense of Smell Tissues

Integration of Mouse Brain Datasets for Batch Correction

We combined two different mouse brain datasets from Saunders et al. [41 (link)] and Rosenberg et al. [42 (link)] to test the batch correction methods on a big dataset [9 (link)]. The two data batches were generated using different technologies, the Drop-seq and SPLiT-seq protocols respectively. There are 691,600 cells in batch 1, and 141,606 cells in batch 2, with 17,745 common genes. This dataset was used to evaluate the removal of batch effects induced by using different scRNA-seq technologies on a big dataset. We downloaded the data by Saunders et al. from http://dropviz.org/ and extracted the Digital Gene Expression (DGE) matrices of cells from the .raw.dge.txt.gz files found under the “DGE By Region” section. Cells were first assigned to clusters and sub-clusters according to the .cell_cluster_outcomes.RDS files also downloaded from the “DGE By Region” section, and cell type annotation was then incorporated based on the assigned clusters and sub-clusters found in the BrainCellAtlas_Saunders_version_2018.04.01.RDS file downloaded from https://storage.googleapis.com/dropviz-downloads/static/annotation.BrainCellAtlas_Saunders_version_2018.04.01.RDS. We removed cells with unknown cell type information and renamed “Endothelial_stalk” and “Endothelial_tip” cells to “Endothelial.” We downloaded the data by Rosenberg et al. from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM3017261. Digital gene expression (DGE) matrices and cluster information were extracted from GSM3017261_150000_CNS_nuclei.mat.gz using the script made available by the author, at https://gist.github.com/Alex-Rosenberg/5ee8b14ea580144facad9c2b87cebf10. We then renamed the clusters with cell type information from Fig. 2 in Rosenberg et al.: clusters 1–54 were renamed to “Neuron,” 55–60 to “Oligodendrocytes,” 61 to “Polydendrocytes,” 62 to “Macrophage,” 63 to “Microglia,” 64 to “Endothelial,” 65 to “Mural,” 66–67 to “Vascular and leptomeningeal cells,” 68–71 to “Astrocyte,” 72 to “Ependymal,” and 73 to “Olfactory ensheathing cells.”

Tran H.T., Ang K.S., Chevrier M., Zhang X., Lee N.Y., Goh M, & Chen J. (2020). A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biology, 21, 12.

Publication 2020

Astrocytes Blood Vessel Brain Cell Nucleus Cells Cytosol Endothelial Cells Endothelium Ependyma Fingers Gastrointestinal Stromal Tumors Gene Expression Genes Macrophage Microglia Mus Neurons Oligodendroglia Sense of Smell Single-Cell RNA-Seq Stalking

Most recents protocols related to «Ependyma»

Multimodal Image Analysis Methods

Image analysis was performed in NIS Elements, FIJI (Schindelin et al., 2012 (link)), QuPath (Bankhead et al., 2017 (link)), CellProfiler (Stirling et al., 2021 (link)), or Imaris. All analysis tools have been made available on GitHub (https://github.com/IGC-Advanced-Imaging-Resource/Hall2022_Paper; Murphy, 2022 ). Cerebellum and ventricle area was measured from PAS stained sagittal brain sections in QuPath. The number of cilia in E18.5 ribs was calculated using Batch Pipeline in Imaris, segmenting DAPI and cilia as surfaces. The number of ependymal cells with multiple basal bodies was calculated by segmenting FOP staining and cells in 2D using a CellProfiler pipeline. Briefly, an IdentifyPrimaryObjects module was used to detect the nuclei, followed by an IdentifySecondaryObjects module using the tubulin stain to detect the cell boundaries. Another Identify Primary objects module was used to detect the basal bodies and a RelateObjects module was used to assign parent–child relationships between the cells and basal bodies. The percentage of ciliated ependymal cells, and the number of ependymal cells with rosette-like FOP staining, and elongated FOP-positive structures were counted by eye using NIS Elements Counts Tool. Analysis of cultured ependymal cells (beat frequency, number of cilia, coordinated beat pattern) and beat frequency determination in mTECs and trachea was assessed in FIJI by eye while blinded to genotype. The number of centrioles and cilia in cultured ependymal cells was manually calculated using Imaris. CEP131 and MIB1 intensity at satellites was calculated in FIJI using a macro which segmented basal bodies with Gamma Tubulin, then drew concentric rings, each 0.5 μm wider than the previous and calculated the intensity of MIB1 and CEP131 within these rings. CP110 intensity in MEFs was calculated by manually defining mother and daughter centrioles in FIJI, CP110 intensity in ependyma and tracheas was calculated by segmenting FOP in 3D in Imaris and calculating CP110 intensity within this volume. Image quantification in RPE1 cells were performed using CellProfiler as described previously (Kumar et al., 2021 (link)). Images were prepared for publication using FIJI, Imaris, Adobe Photoshop, Illustrator, and InDesign.

Hall E.A., Kumar D., Prosser S.L., Yeyati P.L., Herranz-Pérez V., García-Verdugo J.M., Rose L., McKie L., Dodd D.O., Tennant P.A., Megaw R., Murphy L.C., Ferreira M.F., Grimes G., Williams L., Quidwai T., Pelletier L., Reiter J.F, & Mill P. (2023). Centriolar satellites expedite mother centriole remodeling to promote ciliogenesis. eLife, 12, e79299.

Publication 2023

Basal Bodies Brain Cell Nucleus Cells Centrioles Cerebellum Cerebral Ventricles Cilia Cultured Cells DAPI Daughter Ependyma gamma-Tubulin Genotype Mothers Ribs Satellite Viruses Stains Trachea Tubulin

Isolation and culture of various cell types

MEFs were maintained as previously published (Hall et al., 2013 (link)). SNAP labeling was performed as previously described (Quidwai et al., 2021 (link)). Ependymal cells were isolated and cultured as published in Delgehyr et al., 2015 (link). mTECs were isolated and cultured as described in Eenjes et al., 2018 (link); You et al., 2002 (link). RPE1-hTERT (female, human epithelial cells immortalized with hTERT, Cat. No. CRL-4000) from ATCC were grown in Dulbecco's Modified Eagle Medium (DMEM, Life Technologies) or DMEM/F12 (Thermo Fisher Scientific, 10565042) supplemented with 10% fetal bovine serum at 37°C with 5% CO₂. For live imaging, the membrane was cut out and placed cilia down on a glass dish (Nest, 801002) in a drop of media. PCM1^−/− RPE1 cells were generated as described previously (Kumar et al., 2021 (link)) (all figures except for Figure 8—figure supplement 1, in which case they were generated as in Gheiratmand et al., 2019 (link)). hTERT-RPE1: Source ATCC, confirmed mycoplasma negative and verified by STR profiling. Two PCM1^−/− RPE1 cell lines were generated using single guide RNAs (Supplementary file 1). Loss of PCM1 was confirmed by genotyping, immunoblotting, and immunofluorescence. Monoclonal PCM1^−/− RPE1 cell lines stably expressing eGFP or eYFP-PCM1 (plasmid a gift from Bryan Dynlacht; Wang et al., 2016 (link)) were generated using lentiviruses and manually selected based on fluorescence. To synchronize cells in G1/S aphidicolin (Sigma) was added to the culture medium at 2 μg/ml for 16 hr. To arrest cells in mitosis, taxol (paclitaxel; Millipore-Sigma) was added to the culture medium at 5 μM for 16 hr prior to rounded up cells being collected by mitotic shake-off. For arrest in G0, cells were washed 2× with phosphate-buffered saline (PBS; Gibco) and 1× with DMEM (without serum) before being cultured in serum-free DMEM for 16 hr. To disrupt cytoplasmic microtubules, cells were treated with 20 μM nocodozole (Sigma, SML1665) for 1–2 hr prior to fixation.

Publication 2023

Aphidicolin Cardiac Arrest Cell Lines Cells Cilia Culture Media Cytoplasm Dietary Supplements Eagle Ependyma Epithelial Cells Females Fetal Bovine Serum Fluorescence Homo sapiens Hyperostosis, Diffuse Idiopathic Skeletal Immunofluorescence Lentivirus Microtubules Mitosis Mycoplasma Paclitaxel Phosphates Plasmids RNA, Single Guide Saline Solution Serum Taxol Tissue, Membrane Tremor

Quantifying Brain Vascular Density

ROIs were manually outlined around each anatomical subregion according to the Allen Brain Atlas, and using visible anatomical landmarks. Due to differences in fluorescent labeling intensity, each region was thresholded individually to isolate labeled blood vessels from the background, so each region measured from 0.023 to 4.67 mm² within each hemisphere/section. Area fraction of blood vessels above the threshold was measured for each ROI. In total, mean vascular density was calculated from multiple subregions for 17 ROI (olfactory area - OLF; cingulate cortex - CA; retrosplenial cortex - RSP; primary visual - V1; primary somatosensory - S1; primary motor area - M1; auditory - AUD; hippocampus - HIP; perirhinal - PERI; insular - INS; thalamus - TH; habenula - HAB; hypothalamus - HY; caudate putamen - CP; white matter - WM; pericisternal - PCS; ependymal around lateral ventricles - EPD) in 6 KO and 6 WT animals. Further statistical comparison was performed assuming inhomogeneous signal distribution properties between different ROI (similarly as for DWI). Hence, considering independent measurements of vascular densities among ROI analyzed and due to small group size, nonparametric Mann-Whitney U-test was employed to compare the vessel densities from KO and WT animals ROI-wise.
For ROI-wise correlation analysis, a mean value of AQP4 expressions as well as vascular densities at ROI was calculated from all respective animals strain-wise.

Gomolka R.S., Hablitz L.M., Mestre H., Giannetto M., Du T., Hauglund N.L., Xie L., Peng W., Martinez P.M., Nedergaard M, & Mori Y. (2023). Loss of aquaporin-4 results in glymphatic system dysfunction via brain-wide interstitial fluid stagnation. eLife, 12, e82232.

Publication 2023

Anatomic Landmarks Animals Auditory Perception Blood Vessel Brain Cingulate Cortex Ependyma Habenula Hypothalamus Motor Cortex, Primary Neostriatum Retrosplenial Cortex Seahorses Sense of Smell Strains Thalamus Ventricle, Lateral White Matter

Lineage Analysis of Subependymal Zone Cells

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Publication 2023

Alexa Fluor 647 Antibodies Astrocytes Biological Assay Buffers Calcium Cell Proliferation Cells Edetic Acid EGFR protein, human Endothelium Ependyma Erythrocytes Euthanasia Flow Cytometry Fluorescent Antibody Technique Glucose Hemoglobin, Sickle HEPES Magnesium Microglia Molecular Probes Mus Myelin Neurogenesis Neurons Oligodendroglia Population Group

Ultrastructural Analysis of Choroid Plexus

Dissected pieces of CP tissue were immediately fixed in 2.5% glutaraldehyde buffered in 0.1 M cacodylate (pH 7.4) for 2 h. Samples were post-fixed in 1% osmium tetroxide in PBS (pH 7.4) for 1 h, subsequently dehydrated in a graded ethanol series and acetone and embedded in epoxy resin (Sigma Aldrich, Darmstadt, Germany). Semithin sections (1.5 µm) were taken and stained with methylene blue to identify regions of interest (i.e., the ependymal–CP transition area) before cutting ultrathin sections (50 nm). Ultrathin sections were analyzed, and images were recorded on a LEO 912AB transmission electron microscope (Zeiss, Oberkochen, Germany).

Bihlmaier R., Deffner F., Mattheus U., Neckel P.H., Hirt B, & Mack A.F. (2023). Aquaporin-1 and Aquaporin-4 Expression in Ependyma, Choroid Plexus and Surrounding Transition Zones in the Human Brain. Biomolecules, 13(2), 212.

Publication 2023

Acetone Cacodylate Ependyma Epoxy Resins Ethanol Glutaral Osmium Tetroxide Tissues Transmission Electron Microscopy

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More about "Ependyma"

The ependyma is a specialized epithelial lining that covers the ventricles of the brain and the central canal of the spinal cord.
It plays a crucial role in the production and circulation of cerebrospinal fluid (CSF), as well as in the maintenance of the blood-brain barrier.
Ependymal cells also have sensory and secretory functions, and may be involved in the regeneration of neural tissue.
Optimizing research on the ependyma can provide valuable insights into brain and nervous system physiology and pathology.
Researchers can utilize PubCompare.ai's AI-driven protocol comparison tool to easily locate the most reproducible and accurate ependyma research protocols from the literature, pre-prints, and patents.
This can help improve the accuracy and efficiency of ependyma studies.
When studying the ependyma, researchers may use FBS (Fetal Bovine Serum) and Poly-L-lysine to support cell growth and attachment.
DNase I can be used to dissociate ependymal cells.
Stereological techniques like StereoInvestigator and the MO-10 micromanipulator can be employed to quantify ependymal cell populations.
Poly-D-lysine may also be utilized as a substrate for ependymal cell cultures.
Imaging techniques like the Axioskop microscope and Neurolucida Explorer software can be used to visualize and analyze the morphology and distribution of ependymal cells.
Fluorescent dyes such as Alexa 594 can be employed to label and track ependymal cells.
Additionally, transfection reagents like JetPRIME may be used to introduce genetic constructs into ependymal cells for functional studies.
By leveraging these tools and techniques, researchers can optimize their investigations of the ependyma and uncover valuable insights into its role in brain and nervous system physiology and pathology.