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Dataset results
1,326 results for “neutrophil”
Natural killer cells cooperate with neutrophils to suppress pathological angiogenesis in neovascular age-related macular degeneration [scRNA-seq]
GEO Series GSE271262. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Chemotherapy-induced reactive myelopoiesis leads to expansion of immunosuppressive neutrophil-like monocytes in mice and humans [scRNA-Seq]
GEO Series GSE314231. Mus musculus. 5 samples. Type: Expression profiling by high throughput sequencing.
Neutrophil extracellular traps trigger inflammatory bone destruction in periodontitis
GEO Series GSE228021. Mus musculus. 20 samples. Type: Expression profiling by high throughput sequencing.
Neutrophilic inflammation and epithelial barrier disruption in nasal polyps characterize NSAID-exacerbated respiratory disease
GEO Series GSE189690. Homo sapiens. 26 samples. Type: Expression profiling by high throughput sequencing.
Functional reprogramming of neutrophils within tumors by hypoxia-driven histone lactylation [RNA-seq]
GEO Series GSE285509. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Caspase-1-licensed lung epithelial cell pyroptosis drives infiltrating neutrophil necroptosis to dampen immune defense against pulmonary bacterial infection
GEO Series GSE242879. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
The Caudal Hematopoietic Tissue is Differentially Required for Erythrocytes and Neutrophils from Definitive Hematopoiesis in Zebrafish
GEO Series GSE247730. Danio rerio. 4 samples. Type: Expression profiling by high throughput sequencing.
Myeloid EGFR deficiency accelerates recovery from Aki via macrophage efferocytosis and neutrophil apoptosis
GEO Series GSE261530. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Neutrophils Resist Ferroptosis and Promote Breast Cancer Metastasis through Aconitate Decarboxylase 1
GEO Series GSE216425. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Antibodies to FimH in Anti-neutrophil cytoplasmic antibody - associated vasculitis
<p>Peptide microarray containing 4216 unique peptides from 88 different microorganisms represented by validated B-cell epitopes selected from the Immune Epitope Database (<a href="http://www.iedb.org">http://www.iedb.org</a>). A total of 1509 bacterial, 2311 viral, 12 fungal and 384 protozoal epitopes were printed on the arrays. This array was probed with serum from 8 patients with ANCA-associated vasculitis collected at presentation with the disease as well as in remission and analysed for binding by IgM and IgG.</p>
CD40 activation and the effect on Neutrophils
<p>##### CD40 activation and the effect on Neutrophils</p><p> </p><p> </p><p># Load necessary libraries for data manipulation, analysis, and visualization</p><p>library(dplyr)</p><p>library(Seurat)</p><p>library(patchwork)</p><p>library(plyr)</p><p> </p><p># Set the working directory to the folder containing the data</p><p>setwd("C:/Users/ALL/sciebo - Lang, Alexander (allan101@uni-duesseldorf.de)@uni-duesseldorf.sciebo.de/ALL_NGS/scRNAseq/scRNAseq/05_FGK45 Wirkung auf Neutros - scRNAseq/938-1_cellranger_count/outs")</p><p># Read the M0 dataset from the 10X Genomics format</p><p>pbmc.data <- Read10X(data.dir = "filtered_feature_bc_matrix/")</p><p> </p><p>RNA <- pbmc.data$`Gene Expression`</p><p> </p><p>ADT <- pbmc.data$`Antibody Capture`</p><p> </p><p>HST <- pbmc.data$`Multiplexing Capture`</p><p> </p><p> </p><p># Load the Matrix package</p><p>library(Matrix)</p><p> </p><p># Hashtag 1, 2 and 3 are marking the organs (heart, blood, spleen)</p><p># Subset the rows based on row names</p><p>subsetted_rows <- c("TotalSeq-B0301", "TotalSeq-B0302", "TotalSeq-B0303")</p><p>animals_data <- HST[subsetted_rows, , drop = FALSE]</p><p> </p><p># Hashtag 4, 5, 6, 7 are representing IgG_1, IgG_1, FGK45_1 and FGK45_1</p><p>subsetted_rows <- c("TotalSeq-B0304", "TotalSeq-B0305", "TotalSeq-B0306", "TotalSeq-B0307")</p><p>treatment_data <- HST[subsetted_rows, , drop = FALSE]</p><p> </p><p>#Create a Seurat obeject and more assays to combine later</p><p>RNA <- CreateSeuratObject(counts = RNA)</p><p>ADT <- CreateAssayObject(counts = ADT)</p><p>Organ <- CreateAssayObject(counts = animals_data)</p><p>Treatment <- CreateAssayObject(counts = treatment_data)</p><p> </p><p> </p><p>seurat <- RNA</p><p> </p><p>#Add the Assays</p><p>seurat[["ADT"]] <- ADT</p><p> </p><p>seurat[["HST_Mice"]] <- Organ</p><p> </p><p>seurat[["HST_Treatment"]] <- Treatment</p><p> </p><p>#Check for AK Names</p><p>rownames(seurat[["ADT"]])</p><p> </p><p>#Cluster cells on the basis of their scRNA-seq profiles</p><p># perform visualization and clustering steps</p><p>DefaultAssay(seurat) <- "RNA"</p><p>seurat <- NormalizeData(seurat)</p><p>seurat <- FindVariableFeatures(seurat)</p><p>seurat <- ScaleData(seurat)</p><p>seurat <- RunPCA(seurat, verbose = FALSE)</p><p>seurat <- FindNeighbors(seurat, dims = 1:30)</p><p>seurat <- FindClusters(seurat, resolution = 0.8, verbose = FALSE)</p><p>seurat <- RunUMAP(seurat, dims = 1:30)</p><p>DimPlot(seurat, label = TRUE)</p><p> </p><p>FeaturePlot(seurat, features = "S100a9", order = T)</p><p> </p><p># Normalize ADT data,</p><p>DefaultAssay(seurat) <- "ADT"</p><p>seurat <- NormalizeData(seurat, normalization.method = "CLR", margin = 2)</p><p> </p><p>#Demultiplex cells based on Mouse_Hashtag Enrichment</p><p>seurat <- NormalizeData(seurat, assay = "HST_Mice", normalization.method = "CLR")</p><p>seurat <- HTODemux(seurat, assay = "HST_Mice", positive.quantile = 0.99)</p><p> </p><p> </p><p>#Visualize demultiplexing results</p><p># Global classification results</p><p>table(seurat$HST_Mice_classification.global)</p><p> </p><p>DimPlot(seurat, group.by = "HST_Mice_classification")</p><p> </p><p> </p><p>#Demultiplex cells based on Treatment_Hashtag Enrichment</p><p>seurat <- NormalizeData(seurat, assay = "HST_Treatment", normalization.method = "CLR")</p><p>seurat <- HTODemux(seurat, assay = "HST_Treatment", positive.quantile = 0.99)</p><p> </p><p> </p><p>#Visualize demultiplexing results</p><p># Global classification results</p><p>table(seurat$HST_Treatment_classification.global)</p><p> </p><p>DimPlot(seurat, group.by = "HST_Treatment_classification")</p><p> </p><p>Idents(seurat) <- seurat$HST_Treatment_classification</p><p>pbmc.singlet <- subset(seurat, idents = "Negative", invert = T)</p><p>Idents(pbmc.singlet) <- pbmc.singlet$HST_Mice_classification</p><p>pbmc.singlet <- subset(pbmc.singlet, idents = "Negative", invert = T)</p><p> </p><p>DimPlot(pbmc.singlet, group.by = "HST_Treatment_maxID")</p><p> </p><p>#Redo the clssification to remove the doublettes</p><p>pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Treatment", positive.quantile = 0.99)</p><p>table(pbmc.singlet$HST_Treatment_classification.global)</p><p> </p><p>DimPlot(pbmc.singlet, group.by = "HST_Treatment_classification")</p><p>pbmc.singlet <- subset(pbmc.singlet, idents = "Doublet", invert = T)</p><p> </p><p> </p><p>pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Mice", positive.quantile = 0.99)</p><p>table(pbmc.singlet$HST_Mice_classification.global)</p><p>pbmc.singlet <- subset(pbmc.singlet, idents = "Doublet", invert = T)</p><p> </p><p>pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Mice", positive.quantile = 0.60)</p><p>pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Treatment", positive.quantile = 0.60)</p><p>DimPlot(pbmc.singlet, group.by = "HST_Treatment_maxID")</p><p>DimPlot(pbmc.singlet, group.by = "HST_Mice_maxID")</p><p> </p><p> </p><p>seurat <- pbmc.singlet</p><p> </p><p>seurat$organ <- seurat$HST_Mice_maxID</p><p>seurat$mouse <- seurat$HST_Treatment_maxID</p><p>seurat$treatment <- seurat$HST_Treatment_maxID</p><p> </p><p> </p><p>library(plyr)</p><p>seurat$treatment <- revalue(seurat$treatment, c(</p><p> "TotalSeq-B0304" = "IgG",</p><p> "TotalSeq-B0305" = "IgG",</p><p> "TotalSeq-B0306" = "FGK45",</p><p> "TotalSeq-B0307" = "FGK45"</p><p>))</p><p> </p><p>library(plyr)</p><p>seurat$organ <- revalue(seurat$organ, c(</p><p> "TotalSeq-B0301" = "heart",</p><p> "TotalSeq-B0302" = "blood",</p><p> "TotalSeq-B0303" = "spleen"</p><p>))</p><p> </p><p>seurat$mouse <- revalue(seurat$mouse, c(</p><p> "TotalSeq-B0304" = "1",</p><p> "TotalSeq-B0305" = "2",</p><p> "TotalSeq-B0306" = "3",</p><p> "TotalSeq-B0307" = "4"</p><p>))</p><p> </p><p> </p><p>#Cluster cells on the basis of their scRNA-seq profiles without doublettes</p><p># perform visualization and clustering steps</p><p>DefaultAssay(seurat) <- "RNA"</p><p>seurat <- NormalizeData(seurat)</p><p>seurat <- FindVariableFeatures(seurat)</p><p>seurat <- ScaleData(seurat)</p><p>seurat <- RunPCA(seurat, verbose = FALSE)</p><p>seurat <- FindNeighbors(seurat, dims = 1:30)</p><p>seurat <- FindClusters(seurat, resolution = 0.8, verbose = FALSE)</p><p>seurat <- RunUMAP(seurat, dims = 1:30)</p><p>DimPlot(seurat, label = TRUE)</p><p> </p><p> </p><p>DefaultAssay(seurat) <- "ADT"</p><p>seurat <- NormalizeData(seurat, normalization.method = "CLR", margin = 2)</p><p>setwd("C:/Users/ALL/sciebo - Lang, Alexander (allan101@uni-duesseldorf.de)@uni-duesseldorf.sciebo.de/ALL_NGS/scRNAseq/scRNAseq/05_FGK45 Wirkung auf Neutros - scRNAseq/Analyse")</p><p> </p><p> </p><p>saveRDS(seurat, file = "FGK45_heart_blood_spleen.v0.1.RDS")</p><p> </p>
Inhibiting neutrophils activation by long-term administration of 5-amino salicylic acid reduces colitis-associated colorectal tumor burden in ApcMin/+ mice
GEO Series GSE83749. Mus musculus. 16 samples. Type: Expression profiling by array.
Synovial fluid and blood neutrophils from rheumatoid arthritis patients and matched healthy controls
GEO Series GSE116899. Homo sapiens. 30 samples. Type: Expression profiling by high throughput sequencing.
the Arraystar Human m6A-mRNA&lncRNA Epitranscriptomic microarray analysis in the neutrophils of ischemic stroke patients and healthy controls
GEO Series GSE236381. Homo sapiens. 6 samples. Type: Other.
Neutrophil KLF2 regulates inflammasome-dependent neonatal mortality from endotoxemia
GEO Series GSE278604. Mus musculus. 28 samples. Type: Expression profiling by high throughput sequencing.
SPP1+ Neutrophils Mediate Resistance to lmmune Checkpoint Blockade in BAP1-Inactivated Tumors
GEO Series GSE284219. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Analysis of predestined heterogeneity in neutrophil progenitor clones (ER-HoxB8 conditionally immortalized)
GEO Series GSE188683. Mus musculus. 48 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Neutrophil Death Medicates Alveolar Macrophage Proliferation and Phagocytosis
GEO Series GSE212080. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Inflammatory epiCaspase-1 dampens immunogenic cell death by remotely skewing bone marrow hematopoiesis to drive systemic neutrophil-dominant inflammation
GEO Series GSE298319. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Glutamine metabolism suppresses neutrophil recruitment via epigenetic regulation to control inflammatory resolution and skin repair [CtrlGls_RNAseq]
GEO Series GSE289254. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
Understand access before you commit
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.