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5,978 results for “Macrophage”
Dynamics of macrophage polarization in Salmonella infection : Raw data
<p>Experimental raw data of the paper "Dynamics of macrophage polarization support <em>Salmonella</em> persistence in a whole living organism", Leiba et al.</p>
RNA sequencing data for bleomycin exposed THP-1 macrophages
<p>This dataset contains normalized counts matrices, from dds_deseq objects, from DeSeq2 analysis of RNA sequencing data, from THP-1 macrophages exposed to multiple doses of bleomycin in the range of 0-100µg/ml for 24H, 48H or 72H.</p>
Fluorescent Macrophages in Drosophila Embryo
<p>This is a data set that contains a time sequence of<strong> fluorescently labelled macrophages</strong> that migrated in a<strong> drosophila embryo</strong>.</p> <p>The macrophages were visualised in the embryo by using the UAS/GAL4 system.We used the srpHemo-Gal4driver,which mediate the expression of genes downstream of a UASsequence specifically in macrophages to express the following UAS fluorescent probes: UAS-RedStinger for the nuclei and UAS-Clip-GFP for the microtubules.</p> <p>Details of the imaging and preparation have been published in:</p> <p> </p> <ul> <li>Evans, I.R.; Zanet, J.; Wood, W.; Stramer, B.M.<em> Live imaging of Drosophila melanogaster embryonic339hemocyte migrations.Journal of visualized experiments:</em> JoVE2010,36.</li> </ul> <p> </p> <p>Details on the segmentation, tracking and analysis of the macrophage migration have been published in:</p> <ul> <li>José Alonso Solís-Lemus, Besaiz J Sánchez-Sánchez, Stefania Marcotti, Mubarik Burki, Brian Stramer, Constantino Carlos Reyes-Aldasoro, <em>Comparative study of contact repulsion in control andmutant macrophages using a novel interaction detection</em>, Journal of Imaging, BioRxiv, https://doi.org/10.1101/2020.03.31.018267</li> </ul> <p>and</p> <ul> <li>Solís-Lemus, J.A.; Stramer, B.; Slabaugh, G.; Reyes-Aldasoro, C.C. <em>Macrosight: A Novel Framework to Analyze the Shape and Movement of Interacting Macrophages Using Matlab</em>.Journal of Imaging 2019,5</li> </ul> <p> </p>
scRNA-seq data for article: Kupffer cell and recruited macrophage heterogeneity orchestrate granuloma maturation and hepatic immunity in visceral leishmaniasis
<p>Single-cell RNA-seq dataset from sorted CD11bInt, F4/80Hi, CD64+ mouse liver cells in naive or Leishmania infantum-infected animals at 42 d.p.i.. Data analyses and results are described in manuscript: "Kupffer cell and recruited macrophage heterogeneity orchestrate granuloma maturation and hepatic immunity in visceral leishmaniasis". Data files are Seurat objects in RDS format. Filtered-out potential doublets, low quality cells and dying cells (excluded cells with <1000 genes detected, cells with >6000 genes detected, cells with mitochondrial gene expression > 10% and cells with <5000 transcript molecules). Data normalization, scaling and integration performed using Seurat.</p> <p>Filtered dataset containing all KCs and macrophages is in the "pessenda_KC_Macro_seurat" file.</p> <p>Our data were then mapped onto a reference dataset published by Remmerie et al. (DOI: 10.1016/j.immuni.2020.08.004) for annotation consistent with the literature. The reference mapped object can be found in the "pessenda_refmap_KC_Macro_seurat" file.</p> <p>Dataset containing the additional analysis of CLEC4F-TIM4+ FACS-sorted KCs can be found in the "pessenda_refmap_KCTimPos_seurat" file.</p>
Multi-omics data for pro-inflammatory and anti-inflammatory exposure to THP-1 macrophages
<p>This data characterizes gene expression levels in THP-1 macrophages. The data was generated using RNA sequencing and analyzed with DeSeq2 (version 1.24.0). The analysis included raw count data and normalized count matrices obtained from DESeq2's dds_deseq objects.<br>This data describes the methylation levels of individual CpG sites in THP-1 macrophages. The data was obtained using the Infinium MethylationEPIC v2.0 Kit (Illumina) and analyzed with the minfi package (version 1.46). Specifically, the data underwent quantile normalization using the preprocessQuantile function within minfi. Only CpG sites with a detection p-value less than 0.05 were included to obtain MatrixProcessedGEO.txt file. The beta values (bValues.xlsx) were obtained using the function “getBeta” from the same package, considering each time point individually.<br>The macrophages were exposed to phorbol 12-myristate 13-acetate (PMA) for 48 hours, followed by treatment with either a combination of LPS (10 pg/ml) and interferon-gamma (IFNγ) (20 ng/ml) or a combination of interleukins 13 (IL-13) (20 ng/ml) and 4 (IL-4) (20 ng/ml) for 24, 48, and 72 hours.</p>
ImageInLife20170922IP01_Sipka_Macrophage Recruitment to the Wound
<p>A tail of 3dpf transgenic zebrafish larvae (<em>tg(mpeg1:Gal4/UAS:Kaede))</em> was imaged from 1h to 6h after the tail fin amputation by ANDOR CSU-W1 confocal spinning disk on an inverted NIKON microscope (Ti Eclipse) with ANDOR Neo sCMOS camera (20x air/NA 0.75 objective, mosaic imaging 6x1 with 10% overlap between windows). Laser excitation/emission wavelength: 488/521 nm. Time step is 4min (75 repeats). Z-stack is 132μm with 4μm step (33 steps). Maximum projection is shown as well. Pixel size is 0.326μm.</p> <p>Macrophages expressing green fluorescent protein Kaede in the cytoplasm are moving toward the wound made at the tail fin.</p>
Murine Bone Marrow Derived Macrophages (BMDM's) stimulated with LPS and treated with PBS, Epirubicin and Aclarubicin
<p>This dataset was used in the analysis which composes the GitHub repository:</p> <p><a href="https://github.com/andrebolerbarros/Chora_etal_2022">https://github.com/andrebolerbarros/Chora_etal_2022</a></p> <p>The files presented here correspond to:</p> <p><em>gene_counts.tab:</em> the table for the gene counts produced by the alignment of fastq files using STAR;</p> <p><em>sampleTable.csv:</em> the treatment information for each sample produced.</p>
Data from: Kir2.1 modulation in macrophages sensitises dorsal root ganglion neurons through TNF secretion after nerve injury
<p>This data pertain to the manuscript titled "Kir2.1 modulation in macrophages sensitises dorsal root ganglion neurons through TNF secretion after nerve injury", currently in preprint on BioRxiv (https://doi.org/10.1101/2023.06.21.545843). The name of the data files correspond to the for each figure in the study. The data file in .csv format are organized so that they can easily be opened in R or other analysis language. To understand them and how they are labelled, it is advised to open the figure next to them and find the appropriate panel.</p> <p>Here are included:</p> <ul> <li>Example images of section of mouse dorsal root ganglion (DRG) after spared nerve injury (SNI), labelled for CX3CR1+ cells, Ki67 and MHC class II by immunohistochemistry.</li> <li>LC-MS-MS proteomic data set of CX3CR1+ cells from DRG of mice after SNI.</li> <li>Voltage clamp data of CX3CR1+ cells from DRG of mice after SNI</li> <li>Electrophysiological data sets (multi-electrode array, current clamp and voltage clamp) of dissociated DRG neurons treated with medium conditioned by CX3CR1+ or GFAP+ cells sorted from ipsilateral or contralateral DRG from mice after SNI. In addition, pharmacological treatments were added to the conditioned medium (CM).</li> </ul>
Increased uptake of silica nanoparticles in inflamed macrophages but not upon co-exposure to micron-sized particles
<p>Silica nanoparticles (NPs) are widely used in various industrial and biomedical applications. Little is known about the cellular uptake of co-exposed silica particles, as can be expected in our daily life. In addition, an inflamed microenvironment might affect a NP’s uptake and a cell’s physiological response. Herein, prestimulated mouse J774A.1 macrophages with bacterial lipopolysaccharide were post-exposed to micron- and nanosized silica particles, either alone or together, i.e., simultaneously or sequentially, for different time points. The results indicated a morphological change and increased expression of tumor necrosis factor alpha in lipopolysaccharide prestimulated cells, suggesting a M1-polarization phenotype. Confocal laser scanning microscopy revealed the intracellular accumulation and uptake of both particle types for all exposure conditions. A flow cytometry analysis showed an increased particle uptake in lipopolysaccharide prestimulated macrophages. However, no differences were observed in particle uptakes between single- and co-exposure conditions. We did not observe any colocalization between the two silica (SiO<sub>2</sub>) particles. However, there was a positive colocalization between lysosomes and nanosized silica but only a few colocalized events with micro-sized silica particles. This suggests differential intracellular localizations of silica particles in macrophages and a possible activation of distinct endocytic pathways. The results demonstrate that the cellular uptake of NPs is modulated in inflamed macrophages but not in the presence of micron-sized particles.</p>
Dataset related to article "The soluble glycoprotein NMB (GPNMB) produced by macrophages induces cancer stemness and metastasis via CD44 and IL-33"
<p>This record contains data related to article “The soluble glycoprotein NMB (GPNMB) produced by macrophages induces cancer stemness and metastasis via CD44 and IL-33"</p> <p> </p> <p>Abstract</p> <p>In cancer, myeloid cells have tumor-supporting roles. We reported that the protein GPNMB (glycoprotein nonmetastatic B) was profoundly upregulated in macrophages interacting with tumor cells. Here, using mouse tumor models, we show that macrophage-derived soluble GPNMB increases tumor growth and metastasis in Gpnmb-mutant mice (DBA/2J). GPNMB triggers in the cancer cells the formation of self-renewing spheroids, which are characterized by the expression of cancer stem cell markers, prolonged cell survival and increased tumor-forming ability. Through the CD44 receptor, GPNMB mechanistically activates tumor cells to express the cytokine IL-33 and its receptor IL-1R1L. We also determined that recombinant IL-33 binding to IL-1R1L is sufficient to induce tumor spheroid formation with features of cancer stem cells. Overall, our results reveal a new paracrine axis, GPNMB and IL-33, which is activated during the cross talk of macrophages with tumor cells and eventually promotes cancer cell survival, the expansion of cancer stem cells and the acquisition of a metastatic phenotype.</p> <p> </p>
Articles citing HIMF as marker for alternatively activated macrophages
<p>These articles offer good evidence that HIMF is a marker for alternatively activated macrophages.</p>
Differentiation of human monocytes into macrophages (RNA-seq, Salmon 1.4.0, GENCODE 36)
<p>RNA-seq of differentiation of human monocytes into macrophages as described in:</p> <p>Phanstiel et al "Static and Dynamic DNA Loops form AP-1-Bound Activation Hubs during Macrophage Development" Molecular Cell, Volume 67, Issue 6, 2017, Pages 1037-1048.e6.</p> <p>https://doi.org/10.1016/j.molcel.2017.08.006</p> <p>See publication for full author list.</p> <p>Data from publication was reprocessed by Michael Love. Paired end reads were quantified with Salmon 1.4.0 and GENCODE 36 human transcripts.</p>
RNA-Seq read counts from monocyte- and ips-derived macrophages
<p>This file contains raw RNA-Seq read counts from monocyte-derived macrophages (MDMs) and induced pluripotent stem cell-derived macrophages (IPSDMs) before and after 6 hours stimulation after LPS.</p> <p>The file was created using featureCounts. The first two columns are:</p> <ul> <li>gene_id: Ensembl 74 gene id.</li> <li>length: Length of the gene in bp.</li> </ul> <p>The MDM samples are:</p> <ul> <li>B1_ctrl</li> <li>B1_LPS</li> <li>B4_ctrl</li> <li>B4_LPS</li> <li>B5_ctrl</li> <li>B5_LPS</li> <li>B2_ctrl</li> <li>B2_LPS</li> <li>B3_ctrl</li> <li>B3_LPS</li> </ul> <p>The IPSDM samples are:</p> <ul> <li>CRL1_ctrl</li> <li>CRL1_LPS</li> <li>S7RE_ctrl</li> <li>S7RE_LPS</li> <li>FSPS10C_ctrl</li> <li>FSPS10C_LPS</li> <li>FSPS11B_ctrl</li> <li>FSPS11B_LPS</li> </ul> <p> </p>
Flow cytometry data from human iPSC-derived macrophages
<p>Human induced pluripotent cells (iPSCs) were obtained from the HipSci project (http://www.hipsci.org) and differentiated into macrophages using an established protocol (van Wilgenburg, 2013). The genotype_id column of the flow_sample_metadata.txt file contains the canonical HipSci iPSC line name from which the macrophages were differentiated.</p> <p><strong>Data acquisition</strong></p> <p>We used flow cytometry to measure the cell surface expression of three canonical macrophage markers: CD14, CD16 (FCGR3A/FCGR3B) and CD206 (MRC1). Macrophages were cultured in 10 cm tissue-culture treated plates and detached from the plates by incubation in 6 mg/ml lidocaine-PBS solution (Sigma L5647) for 30 minutes followed by gentle scraping. From each cell line we harvested between 300,000-500,000 cells. Detached cells were washed in media, centrifuged at 1200 rpm for 5 minutes and resuspended in flow cytometry buffer (2% BSA, 0.001% EDTA in D-PBS) and split into two wells of a 96-well plate. Nonspecific antibody binding sites were blocked by incubating cells with Human TruStain FcX (Biolegend) for 45 minutes and washing with flow cytometry buffer. Half of the cells were stained for 1 hour with the PE-isotype control (BD 555749) antibody. The other half of the cells were co-stained for 1 hour with following three antibodies: CD14-Pacific Blue (BD 558121), CD16-PE (BD 555407), CD206-APC (BD 550889). After staining, the cells were washed three times. Resuspended cells were filtered through cell-strainer cap tubes (BD 352235) and measured on the BD LSRFortessa Cell Analyzer.</p>
Macrophage phagocytosis assay of Acinetobacter baumannii AB074, passaged 15 days in antibiotics or transferrin.
<p>Macrophage phagocytosis assay of Acinetobacter baumannii AB074, passaged 15 days in antibiotics or transferrin. Experiment #1, replica #1-2, from March 7th, 2017 and Experiment #2, replica #1-2 from March 16th, 2017</p> <p>MICs for AB074: transferrin = 4 mcg/ml; ciprofloxacin = 1 mcg/ml; meropenem = 0,5 mcg/ml</p> <p>6 groups of treatment:</p> <p>1. No drug</p> <p>2. Transferrin only, last passage dose = 16 mcg/ml </p> <p>3. Ciprofloxacin only, last passage dose = 5 mcg/ml </p> <p>4. Ciprofloxacin + transferrin, last passage dose = 0,5 mcg/ml of cipro + 16 mcg/ml of transferrin</p> <p>5. Meropenem only, last passage dose = 2,5 mcg/ml </p> <p>6. Meropenem + transferrin, last passage dose = 2,5 mcg/ml + 16 mcg/ml of transferrin</p> <p>The file name structure: group_picture#_strain_timeOfPassage_ experiment#_replica#</p>
Differential gene expression in iPSC-derived macrophages after IFNg stimulation and Salmonella infection
<p>We used likelihood ratio test implemented in DESeq2 v1.10.0 (test = “LRT”) to test if a model that allowed different mean expression in each condition explained the data better than a null model assuming the same mean expression across conditions. See the manuscript for more details: http://www.biorxiv.org/content/early/2017/05/18/102392 .</p> <p>We used the following commands in DESeq2:<br> #Run DESeq2<br> dds = DESeq2::DESeqDataSetFromMatrix(combined_expression_data_filtered$counts, design, ~condition_name) <br> dds = DESeq2::DESeq(dds, test = "LRT", reduced = ~ 1)</p> <p>#Extract differentially expressed genes in each condition<br> ifng_genes = results(dds, contrast=c("condition_name","IFNg","naive")) <br> sl1344_genes = results(dds, contrast=c("condition_name","SL1344","naive")) <br> ifng_sl1344_genes = results(dds, contrast=c("condition_name","IFNg_SL1344","naive"))</p>
Mural cells sustain a homeostatic vascular macrophage niche limiting chronic inflammation
<p>If you use any of these data, please cite the corresponding publication:</p><p><a href="https://doi.org/10.1016/j.immuni.2023.08.002">Mural cell-derived chemokines provide a protective niche to safeguard vascular macrophages and limit chronic inflammation</a><br><a href="https://doi.org/10.1016/j.immuni.2023.08.002">Pekayvaz et al., Immunity, 2023</a> . ( <a href="https://doi.org/10.1016/j.immuni.2023.08.002">https://doi.org/10.1016/j.immuni.2023.08.002</a> )</p><p><strong>scRNA-seq data</strong></p><p><i>processed:</i></p><p>processed_kidney_seurat.Rds<br>processed_lung_seurat.Rds</p><p><i>count matrices:</i></p><p>sample_M1_raw_feature_bc_matrix.h5<br>sample_M2_raw_feature_bc_matrix.h5</p><p><i>script:</i></p><p>process_sctransform.R</p><p>functions.R with helper functions (mainly for plotting) used in the process-script.</p><p><strong>RNA-seq</strong></p><p><i>macrophages:</i></p><p>macs_control_vs_knockout.hisat2.DirectDESeq2.tsv|xlsx (count data and DESeq2 results)</p><p><i>aorta:</i></p><p>aorta_control_vs_knockout.hisat2.DirectDESeq2.tsv|xlsx (count data and DESeq2 results)</p><p><i>Dead vs. Living:</i></p><p>peri_Dead_vs_Living.exon_all.DirectDESeq2.tsv|xlsx (count data and DESeq2 results)</p><p> </p>
RNA sequencing of macrophages co-cultured with MSCs and RNA sequencing of alveolar macrophages from mice with lung injury treated with MSCs
<p>RNA sequencing of macrophages co-cultured with MSCS Table 5</p> <p>RNA sequencing of alveolar macrophages from mice with lung injury treated with MSCS Table 8</p>
Proteome analysis of Corynebacterium diphtheriae - macrophage interaction
<p>Contact of <em>Corynebacterium diphtheriae</em> with macrophages induce adaptations on both bacterial and cellular sides. Using an experimental design involving gentamicin protection and liquid chromagraphy followed by mass-spectrometry, a multi-species proteomic dataset was analyzed at different time points of the infection assay. Several previously undescribed <em>Corynebacterium </em>proteins were differentially regulated, as well as key macrophage components of the phagolysosome. Overall, Bacteria responded to phagocytosis by changes in DNA repair, transcription and cell wall synthesis proteins, while macrophages showed changes in components of the innate immune system.</p> <p>This dataset consists of:</p> <ul> <li> Raw protein abundance data for Macrophage THP-1 cells (M0) obtained by LC-MS/MS followed by peptide sequencing using Proteome Discoverer (ThermoFisher) -see .zip folder.</li> <li>Raw protein abundance data for Macrophage <em>C. diphtheriae </em>ISS3319 (CD) obtained by LC-MS/MS followed by peptide sequencing using Proteome Discoverer (ThermoFisher) - see .zip folder.</li> <li>Differential protein abundance analysis for CD and M0 using LIMMA. </li> <li>Data underlying the growth curves observed in CD in RPMI + 10% FBS conditions (infection assay conditions).</li> <li>BLASTP searches, PFAM clans, and InterPro annotations for CD.</li> <li>STRING-based PPI network (baseline) and APSPs between differentially abundant proteins in CD. </li> <li>Related R scripts.</li> </ul>
Fig. 2 in Macrophages And Pigment Cells In The Liver Of Pelophylax Ridibundus (Anura)
Fig. 2. Mitosis in the precursor cell of the macrophage line (A) and macrophages of varying degrees of maturity (B, C, D, E, F) on the smears of the frog lake liver. Coloring according to Pappenheim, x900.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.