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5,412 results for “Cell differentiation”
Differential messenger RNA expression in Granulosa Cells from polycystic ovary syndrome with Normoandrogen and Hyperandrogen: Identification of gene sets through bioinformatic Filtering analysis
GEO Series GSE137684. Homo sapiens. 12 samples. Type: Expression profiling by array.
Specific pre-plasma cell states and local proliferation at the dark zone – medulla interface characterize germinal center-derived plasma cell differentiation in lymph node (Figure 4 and Figure 5)
GEO Series GSE275124. Mus musculus. 20 samples. Type: Expression profiling by high throughput sequencing.
Link to dataset related to article "FOXA2 controls the cis-regulatory networks of pancreatic cancer cells in a differentiation grade-specific manner."
<p>Differentiation of normal and tumor cells is controlled by regulatory networks enforced by lineage-determining transcription factors (TFs). Among them, TFs such as FOXA1/2 bind naïve chromatin and induce its accessibility, thus establishing new gene regulatory networks. Pancreatic ductal adenocarcinoma (PDAC) is characterized by the coexistence of well- and poorly differentiated cells at all stages of disease. How the transcriptional networks determining such massive cellular heterogeneity are established remains to be determined. We found that FOXA2, a TF controlling pancreas specification, broadly contributed to the cis-regulatory networks of PDACs. Despite being expressed in both well- and poorly differentiated PDAC cells, FOXA2 displayed extensively different genomic distributions and controlled distinct gene expression programs. Grade-specific functions of FOXA2 depended on its partnership with TFs whose expression varied depending on the differentiation grade. These data suggest that FOXA2 contributes to the regulatory networks of heterogeneous PDAC cells via interactions with alternative partner TFs.</p>
seurat objects for : "scDual-Seq of Toxoplasma gondii-infected mouse bone marrow-derived dendritic cells reveals host cell heterogeneity and differential infection dynamics"
<p><strong>Summary</strong></p> <p>Here, we utilize Dual-scSeq to parse out heterogeneous transcription of bone marrow-derived dendritic cells (BMDCs) infected with T. gondii type I, RH (LDM) or type II, ME49 (PTG) parasites, over multiple time points post-infection (3 and 12h post-infection).</p> <p><strong>Data</strong></p> <p>This repository contains two files, one for each organism investigated (mouse and tgondii), in ".RDS" format generated using Seurat v.4.3.: </p> <p><strong>1. BMDC_infected_mouse.RDS </strong>- object containing normalized read counts (SCT assay) and corresponding metadata for murine BMDCs. </p> <p><strong> metadata columns </strong>describe: </p> <p> - orig.ident: <em>plate identity from smartSeq setup</em></p> <p> - nCount_RNA: <em>UMI count before normalization</em></p> <p> - nFeature_RNA: <em>Gene count before normalization</em></p> <p> - nCount_RNA: <em>UMI count before normalization</em></p> <p> - nUMI: <em>sum of reads per cell for both organisms (mouse + t.gondii) </em></p> <p> - toxo_nUMI:<em> sum of reads per cell for t.gondii</em></p> <p> - mouse_nUMI: <em>sum of reads per cell for mouse</em></p> <p> - nGene: <em>sum of reads per cell for both organisms (mouse + t.gondii) </em></p> <p> - toxo_nGene: <em>sum of genes per cell for t.gondii</em></p> <p> - mouse_nGene: <em>sum of genes per cell for mouse</em></p> <p> - cell_ID: <em>enumerated cells by well</em></p> <p> - well:<em> well_ID of plate used for smartSeq2</em></p> <p> - condition: <em>treatment of cell (one of 8: LDM infection for 3h, LDM infection for 12h, PTG infection for 3h, PTG infection for 12h, LDM Lysate control, PTG Lysate control, uninfected control or LPS control)</em></p> <p> - percent.mt: <em>percentage of transcript mapped to the mitochondrial genome</em></p> <p> - cell_ID: <em>enumerated cells by well</em></p> <p> - nFeature_SCT: <em>Gene count after normalization</em></p> <p> - nCount_SCT: <em>UMI count after normalization</em></p> <p> - nFeature_RNA: <em>Gene count before normalization</em></p> <p> - seurat_clusters: <em>Clusters identified by shared-nearest-neighbor (SNN) inspired graph-based clustering </em></p> <p> - toxo_clusters: <em>Clusters of t.gondii dataset of the corresponding infected cell </em></p> <p> - cell type: <em>Annotated subpopulation of infected cells</em></p> <p> - condition_celltype: <em>condition (see above) combined with celltype (see above)</em></p> <p> - cluster_celltype: <em>seurat_clusters (see above) combined with celltype (see above)</em></p> <p> - cluster_condition: <em>seurat_clusters (see above) combined with condition (see above)</em></p> <p> - UMAP_1: <em>Umap embedding coordinates x-axis</em></p> <p> - UMAP_2: <em>Umap embedding coordinates y-axis</em></p> <p> - cell_cycle_phase: <em>predicted cell cycle phase of murine host cells </em></p> <p> - cycle_phase_t.gondii: <em>predicted cycling phase of t.gondii in the corresponding infected host cell </em></p> <p><strong>2. BMDC_infected_tgondii.RDS </strong>- object containing normalized read counts (SCT assay) and corresponding metadata for murine BMDCs. </p> <p><strong> metadata columns </strong>describe: </p> <p> - orig.ident: <em>plate identity from smartSeq setup</em></p> <p> - nCount_RNA: <em>UMI count before normalization</em></p> <p> - nFeature_RNA: <em>Gene count before normalization</em></p> <p> - nCount_RNA: <em>UMI count before normalization</em></p> <p> - nUMI: <em>sum of reads per cell for both organisms (mouse + t.gondii) </em></p> <p> - toxo_nUMI:<em> sum of reads per cell for t.gondii</em></p> <p> - mouse_nUMI: <em>sum of reads per cell for mouse</em></p> <p> - nGene: <em>sum of reads per cell for both organisms (mouse + t.gondii) </em></p> <p> - toxo_nGene: <em>sum of genes per cell for t.gondii</em></p> <p> - mouse_nGene: <em>sum of genes per cell for mouse</em></p> <p> - cell_ID: <em>enumerated cells by well</em></p> <p> - well:<em> well_ID of plate used for smartSeq2</em></p> <p> - condition: <em>treatment of cell (one of 8: LDM infection for 3h, LDM infection for 12h, PTG infection for 3h, PTG infection for 12h, LDM Lysate control, PTG Lysate control, uninfected control or LPS control)</em></p> <p> - percent.mt: <em>percentage of transcript mapped to the mitochondrial genome</em></p> <p> - cell_ID: <em>enumerated cells by well</em></p> <p> - nFeature_SCT: <em>Gene count after normalization</em></p> <p> - nCount_SCT: <em>UMI count after normalization</em></p> <p> - nFeature_RNA: <em>Gene count before normalization</em></p> <p> - seurat_clusters: <em>Clusters identified by shared-nearest-neighbor (SNN) inspired graph-based clustering </em></p> <p> - mouse_clusters: <em>Clusters of mouse dataset of the corresponding infected cell </em></p> <p> - mouse_celltype: <em>Annotated subpopulation if infected cells</em></p> <p> - condition_celltype: <em>condition (see above) combined with mouse_celltype (see above)</em></p> <p> - cluster_celltype: <em>seurat_clusters (see above) combined with mouse_celltype (see above)</em></p> <p> - cluster_condition: <em>seurat_clusters (see above) combined with condition (see above)</em></p> <p> - UMAP_1: <em>Umap embedding coordinates x-axis</em></p> <p> - UMAP_2: <em>Umap embedding coordinates y-axis</em></p> <p> - cell_cycle_phase_mouse: <em>predicted cell cycle phase of murine host cells </em></p> <p> - cycle_phase_t.gondii: <em>predicted cycling phase of t.gondii in the corresponding infected host cell </em></p>
Supplementary Material to Dissertation Thesis Characterization of Nipah virus in differentiated bronchial epithelial cells cultured at the air-liquid interface and Cedar virus reverse genetics
<p>Supplementary Material to Dissertation Thesis "Characterization of Nipah virus in differentiated bronchial epithelial cells cultured at the air-liquid interface and Cedar virus reverse genetics" of Martin Müller</p>
Identification of differentially expressed genes in cisplatin-resistant and parental esophageal squamous cell carcinoma cells
GEO Series GSE169337. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Differential gene expression in murine neural stem cells (NS5) and neural stem cell-derived astrocytes
GEO Series GSE10721. Mus musculus. 8 samples. Type: Expression profiling by array.
Effects of Hikeshi Deficiency on Neural Stem Cell Proliferation and Differentiation
GEO Series GSE300274. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Differentially expressed genes in HNRNPC knockdown MHCC97H cells
GEO Series GSE180789. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
TWIST1 direct targets during embryonic stem cell differentiation
GEO Series GSE130253. Mus musculus. 30 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Expression profiling of human mesoangioblasts indicates that S1P induces differentiation towards smooth muscle cells
GEO Series GSE17596. Homo sapiens. 4 samples. Type: Expression profiling by array.
Identification of Differentially Expressed Circular RNAs between the RNA of Drug Resistant HCT-116 Colorectal Cancer cells and its Extracellular Vesicle- Derivative
GEO Series GSE120985. Homo sapiens. 6 samples. Type: Non-coding RNA profiling by array.
Satb1 directs the differentiation of TH17 cells through suppression of IL-2 expression
GEO Series GSE250579. Mus musculus. 20 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Analysis of differentially expressed genes between control and shTAK1 pancreatic cancer cells
GEO Series GSE137265. Homo sapiens. 6 samples. Type: Expression profiling by array.
Tumor-Induced Erythroid Precursor-Differentiated Myeloid Cells Mediate Immunosuppression and Curtail Anti-PD-1/PD-L1 Treatment Efficacy
GEO Series GSE201715. Homo sapiens; Mus musculus. 7 samples. Type: Expression profiling by high throughput sequencing.
IL-27, PXN, CXCR4, GZMA, PRF1 and Foxp3 genes are differentially expressed in CD4+ T cells of HTLV-1-infected individuals.
GEO Series GSE38537. Homo sapiens. 12 samples. Type: Expression profiling by array.
MicroRNA expression profiles in pigeon(Columba livia) skeletal muscle satellite cells during differentiation
GEO Series GSE146603. Columba livia. 2 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Bulk RNA seq of mouse epiblast stem cells with Mixl1 differential timing expression
GEO Series GSE279960. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
TWIST1 direct targets during embryonic stem cell differentiation [RNA-seq]
GEO Series GSE130252. Mus musculus. 21 samples. Type: Expression profiling by high throughput sequencing.
Isolation of biologically active small peptide from KSHV LANA protein sequence, which induces CHD4 degradation to promote cell differentiation and inhibits leukemic cell growth (SLAM-seq)
GEO Series GSE228656. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing; Other.
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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.