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1,360 results for “Microglia”
Fluorescent Microglia Images for Analyzing Morphological Changes due to Injury Duration in the Ischemic Rat Brain
<p>The image data included in this dataset are the confocal microscope images (converted from original .nd2 file to .tiff form) used for the publication: Joseph, A., Liao, R., Zhang, M., Helmbrecht, H., McKenna, M., Filteau, J. R., & Nance, E. (2020). Nanoparticle-microglial interaction in the ischemic brain is modulated by injury duration and treatment. <em>Bioengineering & translational medicine</em>, <em>5</em>(3), e10175. https://doi.org/10.1002/btm2.10175</p> <p>The data is organized by brain slice number, region, and image number. There is also an included excel file 'datadescriptions.xlsx' that provides more information about the metadata of the dataset. </p> <p> </p> <p>The data was procured and processed by the Disease Directed Engineering Lab, PI: Elizabeth Nance, at the University of Washington.</p>
Transcriptomic changes in OIR vs Normoxia microglia
<table> <tbody> <tr> <td>File Name</td> <td>Condition</td> </tr> <tr> <td>L135597_Track-180078_R2.fastq.gz</td> <td>Normoxia</td> </tr> <tr> <td>L135597_Track-180078_R1.fastq.gz</td> <td>Normoxia</td> </tr> <tr> <td>L135598_Track-180079_R1.fastq.gz</td> <td>Normoxia</td> </tr> <tr> <td>L135598_Track-180079_R2.fastq.gz</td> <td>Normoxia</td> </tr> <tr> <td>L135599_Track-180080_R2.fastq.gz</td> <td>Normoxia</td> </tr> <tr> <td>L135599_Track-180080_R1.fastq.gz</td> <td>Normoxia</td> </tr> <tr> <td>L135600_Track-180081_R2.fastq.gz</td> <td>OIR-model</td> </tr> <tr> <td>L135600_Track-180081_R1.fastq.gz</td> <td>OIR-model</td> </tr> <tr> <td>L135601_Track-180082_R1.fastq.gz</td> <td>OIR-model</td> </tr> <tr> <td>L135601_Track-180082_R2.fastq.gz</td> <td>OIR-model</td> </tr> <tr> <td>L135602_Track-180083_R2.fastq.gz</td> <td>OIR-model</td> </tr> <tr> <td>L135602_Track-180083_R1.fastq.gz</td> <td>OIR-model</td> </tr> </tbody> </table>
Fig 2 in Docosahexaenoic Acid (DHA) Reduces LPSInduced Inflammatory Response Via ATF3 Transcription Factor and Stimulates Src/ Syk Signaling-Dependent Phagocytosis in Microglia
<p>Proteome profiler arrays (A and B) and expression of ATF3 gene (C) in microglia. Representative array membranes (A) and the relative levels of cytokines and chemokines (B) in microglia preincubated with 20 μM DHA and next treated with 10 ng/ml LPS.</p>
Fig 1 in Docosahexaenoic Acid (DHA) Reduces LPSInduced Inflammatory Response Via ATF3 Transcription Factor and Stimulates Src/ Syk Signaling-Dependent Phagocytosis in Microglia
<p>Viability of microglia incubated with various concentration of DHA for 12 h (A) and LPS for 2.5 h (B). Viability of microglia incubated with 20 μM DHA followed by 10 ng/ ml LPS treatment (C).</p>
isoMIGA: Expression and Splicing QTL Summary Statistics in Human Microglia
<p>https://github.com/RajLabMSSM/isoMiGA </p> <p>Full associations and top association summary statistics for QTLs mapped in a multi-ethnic meta-analysis of human microglia, as part of the isoform-centric Microglia Genomic Atlas (isoMiGA) project.</p> <p>Sample size = 555 samples from 391 unique donors</p> <p>6 cohorts (Raj MFG, Raj STG, Raj, SVZ, Raj, THA, Roussos, Gaffney) meta-analysed using the linear mixed model random-effects meta-analysis software mmQTL (PMID: 35058635).</p> <p>Each file has the following naming convention:</p> <p>{REFERENCE}_{PHENOTYPE}_{ASSOC}.tsv.gz</p> <p><strong>References</strong></p> <p>The following two transcript references were used for mapping phenotypes:</p> <p>1. GENCODE - GENCODE v38 comprehensive transcripts (https://www.gencodegenes.org/human/release_38.html)</p> <p>2. Union - a union of all GENCODE v38 transcripts with an additional 35,879 novel transcripts identified from long-read RNA-seq in 30 human microglia samples. GTF to be uploaded in Zenodo.</p> <p><strong>Phenotypes</strong></p> <p>The following phenotypes were tested for genetic association:</p> <ul> <li>expression: total gene expression of each gene following voom normalization of read counts.</li> <li>transcript: transcript usage - each transcript expression normalized by TPM divided by the total TPM for that gene</li> <li>leafcutter: junction usage using the Leafcutter framework to count and cluster intron-splicing junction reads. Each phenotype is the relative usage of the intron against the total count of the introns within that cluster.</li> <li>SUPPA_A3: alternate 3' splice site usage, as identified by SUPPA2 from transcript TPMs</li> <li>SUPPA_A5: alternate 5' splice site usage</li> <li>SUPPA_AF: alternate first exon usage</li> <li>SUPPA_AL: alternate last exon usage</li> <li>SUPPA_RI: intron retention usage</li> <li>SUPPA_SE: exon skipping usage</li> </ul> <p>All phenotype matrices were scaled and centred and then quantile normalized.</p> <p><strong>Associations</strong></p> <p>Top associations (top_assoc.tsv.gz) list the SNP-feature pair with the lowest adjusted P-value (qval) for that feature.</p> <p>Full associations (full_assoc.tsv.gz) list all tested SNP-feature pairs.</p> <p><strong>Data dictionary</strong></p> <p>The columns of the two association files only differ by the presence of the qval column in the top associations.</p> <p>feature: the phenotype being tested</p> <p>variant_id: the genetic variant being tested</p> <p>chr: chromosome</p> <p>pos: position (hg38)</p> <p>ref: reference allele</p> <p>alt: alternate allele</p> <p>Allele: the effect allele that the beta is relative to</p> <p>beta_tissue_0: Gaffney cohort beta</p> <p>sd_tissue_0: Gaffney cohort standard error</p> <p>z_tissue_0: Gafney cohort Z-score</p> <p>beta_tissue_1: Roussos cohort beta</p> <p>sd_tissue_1: Roussos cohort standard error</p> <p>z_tissue_1: Roussos cohort Z-core</p> <p>beta_tissue_2: Raj MFG cohort beta</p> <p>sd_tissue_2: Raj MFG cohort standard error</p> <p>z_tissue_2: Raj MFG cohort Z-score</p> <p>beta_tissue_3: Raj STG cohort beta</p> <p>sd_tissue_3: Raj STG cohort standard error</p> <p>z_tissue_3: Raj STG cohort Z-score</p> <p>beta_tissue_4: Raj SVZ cohort beta</p> <p>sd_tissue_4: Raj SVZ cohort standard error</p> <p>z_tissue_4: Raj SVZ cohort Z-score</p> <p>beta_tissue_5: Raj THA cohort beta</p> <p>sd_tissue_5: Raj THA cohort standard error</p> <p>z_tissue_5: Raj THA cohort Z-score</p> <p>fixed_beta: Fixed effect meta-analysis estimate of the beta</p> <p>fixed_sd: Fixed effect meta-analysis standard error of the beta</p> <p>fixed_z: Fixed effect meta-analysis Z-score</p> <p>Random_Z: Random effect meta-analysis Z-score</p> <p>Fixed_P: Fixed effect meta-analysis P-value</p> <p>Random_P: Random effect meta-analysis P-value</p> <p>Fixed_bonf: Fixed effect meta-analysis P-value adjusted for the number of variants tested in that feature (Bonferroni)</p> <p>Random_bonf: Random effect meta-analysis P-value adjusted for the number of variants tested in that feature (Bonferroni)</p> <p>Fixed_FDR: Fixed effect meta-analysis P-value adjusted for the number of variants tested in that feature (FDR)</p> <p>Random_FDR: Random effect meta-analysis P-value adjusted for the number of variants tested in that feature (FDR)</p> <p>qval: Random effect meta-analysis P-value adjusted for the number of variants tested in that feature (FDR) and for the number of features tested in the dataset (Storey's q value)</p> <p> </p> <p><br> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Supplementary data: The APOE isoforms differentially shape the transcriptomic and epigenomic landscapes of human microglia in a xenotransplantation model of Alzheimer's disease
<p>Supplementary data for: The APOE isoforms differentially shape the transcriptomic and epigenomic landscapes of human microglia in a xenotransplantation model of Alzheimer’s disease. </p> <p>Supplementary_Table1_QC: Excel sheet containing QC metrics for the RNA-seq data and the other containing QC metrics for the ATAC-seq data. </p> <p>Supplementary_Table2_DEGs: Excel sheet containing DeSeq2 differential expression analysis results for the following comparisons: APOE2 vs APOE3, APOE4 vs APOE3, APOE4 vs APOE2, APOE-KO vs APOE3. </p> <p>Supplementary_Table3_MAGMA_geneset_analysis_res: CSV file containing MAGMA gene set analysis results using the differentially expressed genes (FDR < 0.05) for the comparisons outlined in Supplementary_Table2_DEGs and three independent AD GWAS. </p> <p>Supplementary_Table4_DARs: Excel sheet containing DeSeq2 differential accessibility analysis results for the following comparisons: APOE2 vs APOE3, APOE4 vs APOE3, APOE4 vs APOE2, APOE-KO vs APOE3. </p> <p>Supplementary_Table5_sLDSC_res.csv: CSV file containing s-LDSC results using the consensus set of ATAC-seq peaks with three brain disorder GWAS (Alzheimer's disease, autism spectrum disorder, and amyotrophic lateral sclerosis). </p> <p>Supplementary_Table6_WGCNA_clusterProfiler_pathway_enrichment.csv: CSV file containing pathway enrichment results using two WGCNA-identified modules that were significantly upregulated in APOE2-expressing microglia. </p> <p>Supplementary_Table7_homer_motifEnrichment_res.xlsx: Excel sheet containing Homer motif enrichment analysis results using top 100 peaks with increased and decreased chromatin accessibility for APOE2 vs APOE3, APOE4 vs APOE3, and APOE4 vs APOE2.</p>
Data from: Microglia are necessary to regulate sleep after an immune challenge
<p>Microglia play a critical role in the neuroimmune response, but little is known about the role of microglia in sleep following an inflammatory trigger. Nevertheless, decades of research have been predicated on the assumption that an inflammatory trigger increases sleep through microglial activation. We hypothesized that mice (n = 30) with depleted microglia would sleep less following administration of lipopolysaccharide (LPS) to induce inflammation. Brains were collected and microglial morphology was assessed using quantitative skeletal analyses, and physiological parameters were recorded using non-invasive piezoelectric cages. Mice fed PLX5622 (PLX) diet for 3 weeks had a transient increase in sleep that dissipated by week 2. Subsequently, following a first LPS injection (0.4 mg/kg), mice with depleted microglia slept more than mice on control diet. All mice were returned to normal rodent chow to repopulate microglia in the PLX group (10 days). Nominal differences in sleep existed during the microglia repopulation period. However, following a second LPS injection, mice with repopulated microglia slept similarly to control mice during the dark period but with longer bouts during the light period. Comparing sleep after the first LPS injection to sleep after the second LPS injection, controls exhibited temporal changes in sleep patterns with no change in cumulative minutes slept, whereas in mice with repopulated microglia cumulative sleep decreased during the dark period across all days. Microglia repopulated after PLX also had a reactive morphology with fewer branch endpoints per cell. We conclude that microglia are necessary to regulate sleep after an immune challenge.</p>
Microglia protect against age-associated brain pathologies - all scRNAseq datasets
<p>Zipped cellranger_matrices file contains the filtered_feature_bc_matrices and the raw_feature_bc_matrices obtained from cellranger for the young (E...), middle-aged(S...), old (...vo...) and thalamic (TH...) datasets. </p> <p> The cellinfo csv files give the metadata for each cell kept in the analysis, with a relationship between each barcode and information such as the umi counts or the cell type annotation.</p> <p>The metadata file gives information about each sample, to trace back the sample names used in cellranger to the biological samples. On other tabs it also has qc information such as the thresholds used for each sample.</p> <p>The raw fastq files and RDS files are available at GEO: GSE267545 and GEO:GSE215440 with the same metadata as here as well as the SingleCellExperiment in form of RDS objects (with information such as normalised reads and dimensional reduction embedings) available at GEO: GSE267545</p> <p>The code used to do the analysis is available in Anna-Williams GitHub and also in ZENODO: </p> <p> https://github.com/Anna-Williams/David-young (10.5281/zenodo.11199128) for the young dataset</p> <p> https://github.com/Anna-Williams/David-old (10.5281/zenodo.11199278) for the middle-aged dataset</p> <p>https://github.com/Anna-Williams/David-vold (10.5281/zenodo.11199322) for the old dataset<br>https://github.com/Anna-Williams/David-Thalamus (10.5281/zenodo.11199349) for the thalamic dataset<br>https://github.com/Anna-Williams/David-AgeIntegration (10.5281/zenodo.11199367) for the integration between the young, middle-aged and old datasets.</p> <p> </p> <p>Microglia are brain-resident macrophages that contribute to central nervous system development, maturation, and preservation. Here, we examine the consequences of lifelong absence of microglia on ageing using the Csf1rΔFIRE/ΔFIRE mouse model. In juvenile Csf1rΔFIRE/ΔFIRE mice, we show that microglia are largely dispensable for the transcriptomic maturation of other brain cell types. In contrast, with advancing age, multiple pathologies accumulate in Csf1rΔFIRE/ΔFIRE brains, astrocytes and oligodendrocyte-lineage cells become increasingly dysregulated, and white matter integrity declines, mimicking many of the pathological features of human CSF1R-related leukoencephalopathy. The thalamus is particularly sensitive to neuropathological changes in the absence of microglia, with atrophy, neuron loss, vascular disturbances, macroglial dysregulation, and severe calcifications all detected in this region. Thalamic calcification formation, which often occurs with normal ageing, is dramatically accelerated in Csf1rΔFIRE/ΔFIRE brains but can be prevented via transplantation of wild-type microglia. Our results indicate that lifelong absence of microglia results in an age-related neurodegenerative condition that can be prevented by the transplantation of healthy microglia.</p>
The Regulation of Microglia Activity and the Production of IL-1α and IL-6 in the Degenerated Retina by Mesenchymal Stem Cells
<p><span>Activation of immune response and production of proinflammatory factors plays an important role in the development and progression of retinal degenerative diseases (RDD). For this reason, focusing on immunomodulation is potential option for the efficient ophthalmological therapy. Mesenchymal stem cells (MSCs) have been study in the treatment of RDD mainly due to their regenerative and neuroprotective actions. Nevertheless, MSCs also possess several immunomodulatory properties. Our study shows that NaIO<sub>3</sub>-induced degeneration increased <em>in vitro</em> and <em>in vivo</em> expression of genes for Interleukin (IL)-1α and IL-6 in the mouse retinal tissue. In addition, intraperitoneal application of NaIO<sub>3</sub> increased expression of gene for Iba-1 and infiltration of CD45<sup>+</sup>CD11b<sup>+</sup> cells into the retina. CD45<sup>+</sup> population was also responsible for the production of IL-1α while IL-6 was produced by CD45<sup>-</sup> cells. Cocultivation of degenerated retina in the presence of MSCs decreased the expression and production of both studied cytokines and expression of gene for Iba-1 in the retinal tissue. On contrary, it was observed that MSCs treated with supernatant from degenerated retina increased the expression of genes for cyclooxygenase-2, transforming growth factor-β, programmed-death ligand 1 and nerve growth factor. These results show that MSCs are able to regulate immune reaction in the degenerated retinal environment.</span></p>
The analysis of composition and abundance of the raft proteome of microglia using a tandem mass tag (TMT)-based quantitative proteomic analysis.
<p>To determine the proteins in the membrane raft, we used the TMT-labeling and nano-liquid chromatography mass spectrometry (nano-LC-MS/MS) analysis by Creative Proteomics (NY, USA; https://www.creative-proteomics.com/). Rat primary microglia were treated with IL-6 (25 ng/ml) for 15 min. Membrane rafts were obtained by flotation assay. Samples were prepared from three independent experiments. Proteins in equal volumes of raft fractions were digested with trypsin, desalted, and labeled with a TMT reagent (Thermo Fisher Science). The TMT-labeled peptides were fractionated and analyzed by nano-LC-MS/MS. The resulting MS/MS data were analyzed and searched against the rat protein database using Proteome Discoverer 2.1.</p>
database/Fornix volumetric increase and microglia morphology contribute to spatial and recognition-like memory decline during ageing
<p>Published along with the journal paper: Fornix volumetric increase and microglia morphology contribute to spatial and recognition-like memory decline during ageing</p>
Microglia-mediated T cell Infiltration Drives Neurodegeneration in Tauopathy
<p>Extracellular amyloid-β (Aβ) deposition as neuritic plaques and intracellular accumulation of hyperphosphorylated, aggregated tau as neurofibrillary tangles (NFT) are two of the characteristic hallmarks in Alzheimer’s disease (AD). The regional progression of brain atrophy in AD highly correlates with tau accumulation but not amyloid deposition and the mechanisms of tau-mediated neurodegeneration remain elusive. Innate immune responses represent a common pathway for the initiation and progression of some neurodegenerative diseases. To date, little is known about the extent or role of the adaptive immune response in the presence of Aβ or tau pathology. We systematically compared the immunological milieus in the brain of mice with amyloid deposition or tau aggregation and neurodegeneration. We found that mice with tauopathy but not amyloid, developed a unique innate and adaptive immune response and that depletion of microglia or T-cells blocked tau-mediated neurodegeneration. T cells, especially cytotoxic T cells, were markedly increased in areas with tau pathology in mice with tauopathy and in the AD brain. T cell numbers correlated with the extent of neuronal loss, and dynamically transformed their cellular characteristics from activated to exhausted states along with unique TCR clonal expansion. Inhibition of IFN-γ and PD-1signaling both significantly ameliorated brain atrophy. Our results thus reveal a tauopathy and neurodegeneration-related immune hub involving activated microglia and T cell responses, which could serve as therapeutic targets for preventing neurodegeneration in AD and primary tauopathies.</p>
Microglia modulate TNFα‐mediated synaptic plasticity
<p>The pro-inflammatory cytokine tumor necrosis factor α (TNFα) tunes the capacity of neurons to express synaptic plasticity. It remains, however, unclear how TNFα mediates synaptic positive (=change) and negative (=stability) feedback mechanisms. We assessed effects of TNFα on microglia activation and synaptic transmission onto CA1 pyramidal neurons of mouse organotypic entorhino–hippocampal tissue cultures. TNFα mediated changes in excitatory and inhibitory neurotransmission in a concentration-dependent manner, where low concentration strengthened glutamatergic neurotransmission via synaptic accumulation of GluA1-only-containing AMPA receptors and higher concentration increased inhibition. The latter induced the synaptic accumulation of GluA1-only-containing AMPA receptors as well. However, activated, pro-inflammatory microglia mediated a homeostatic adjustment of excitatory synapses, that is, an initial increase in excitatory synaptic strength at 3 h returned to baseline within 24 h, while inhibitory neurotransmission increased. In microglia-depleted tissue cultures, synaptic strengthening triggered by high levels of TNFα persisted and the impact of TNFα on inhibitory neurotransmission was still observed and dependent on its concentration. These findings underscore the essential role of microglia in TNFα-mediated synaptic plasticity. They suggest that pro-inflammatory microglia mediate synaptic homeostasis, that is, negative feedback mechanisms, which may affect the ability of neurons to express further plasticity, thereby emphasizing the importance of microglia as gatekeepers of synaptic change and stability.</p>
A Pilot Study for the Evaluation of Minocycline as a Microglia Inhibitor in the Treatment of Diabetic Macular Edema
ClinicalTrials.gov study NCT01120899. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Effects of Botanical Microglia Modulators in Gulf War Illness
ClinicalTrials.gov study NCT02909686. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Single-cell spatial transcriptomics of an inducible destabilized-domain Cre mouse line to target disease associated microglia
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Microglia modulate TNFα‐mediated synaptic plasticity
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Lactate receptor HCAR1 regulates neurogenesis and microglia activation after neonatal hypoxia-ischemia
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Electrophysiology of neurons adjacent and away from microglia in control and TBI conditions
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Data from: Microglia are necessary to regulate sleep after an immune challenge
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