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3,508 results for “reprogramming”
RNase-mediated reprogramming of Yersinia virulence
GEO Series GSE249386. Yersinia pseudotuberculosis. 30 samples. Type: Expression profiling by high throughput sequencing.
NANOG metabolically reprograms tumor-initiating stem-like cells in oxidative phosphorylation and fatty acid metabolism
GEO Series GSE68237. Mus musculus. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
N3 induced reprogramming and ShWT1 knock-down in DSRCT cell lines
GEO Series GSE180031. Homo sapiens. 6 samples. Type: Expression profiling by array.
Cachexia-induced reprogramming of visceral organ metabolism by human pancreatic cancer xenografts
<p>We have uploaded the raw MRS data used in this study. The data were obtained through dual-phase extraction from the heart, lungs, kidneys, pancreas, liver, and spleen samples. Each sample was reconstituted in 650 μL of phosphate-buffered D₂O (90:10 D₂O:H₂O, pH 7.4) with trimethylsilylpropanoic acid (TSP), and analyzed by ¹H MRS.All spectra were acquired using a Bruker 750 MHz NMR spectrometer using single pulse sequence under consistent experimental conditions: a spectral width of 15,495.86 Hz, 64K data points, a 90° flip angle, a relaxation delay of 10 seconds, an acquisition time of 2.11 seconds, and 64 scans with 8 dummy scans. The MRS data were analyzed and quantified using TOPSPIN 4.0.6 software. Spectral processing included zero-filling to 64K and applying an exponential line broadening of 0.3 Hz before Fourier transformation. Peak integration was performed using AMIX software, and the area under each peak was used for quantitative analysis after normalization to the internal standard, TSP (trimethylsilylpropanoic acid). <strong> </strong>We identified, for the first time, the profound effects of pancreatic cancer induced cachexia on visceral organ weight and metabolism that can lead to severe consequences in organ function ranging from affecting pathways involved in tissue regeneration and resolving inflammation, to altering IL-4 production in cachectic mice. These results highlight the damaging systemic changes in visceral organ metabolism that occur with cancer induced cachexia that may lead to metabolic interventions to reduce morbidity and mortality. </p> <p> </p> <p> </p>
Metabolomics Data - Integrated multi-omics analysis using MENTOR reveals metabolic reprogramming in the Niemann-Pick type C mouse brain
<p>Mice were euthanized with isoflurane followed by decapitation. Brains were collected rapidly from 7 week old mice from each genotype (WT and Npc1-/-), and the forebrains were quickly dissected and flash frozen in liquid nitrogen. Tissue samples were removed from -80 °C storage and maintained on wet ice throughout the processing steps. Tissues were carefully weighed to 30 mg +/- 2 mg and the extraction solvent (1:1:1:1: Methanol:Acetone:Acetonitrile:Water) containing internal standards was scaled to the tissue weight (30:1). Tissue was disrupted using a probe sonicator at 40% output power, 40% duty cycle for 20 seconds. Samples were allowed to rest on wet ice for 10 min, then centrifuged at 4° C, 14,000 rpm for 10 min. 10 µL of each sample was removed and pooled in a separate autosampler vial for quality control purposes. 200 µL of supernatant was transferred to an autosampler vial and brought to complete dryness using a nitrogen drier in ambient conditions. Samples and pools were reconstituted with 100 µL and 150 µL of water: methanol (8:2 by volume).</p> <p>Analysis was performed on an Infinity Lab II UPLC coupled with a 6545 QTof mass spectrometer (Agilent Technologies) using a JetStream ESI source in negative mode. The following source parameters were used: Gas Temp: 250 °C, Gas Flow: 13 L/min, Nebulizer: 35 psi, Sheath Gas Temp: 325 °C, Sheath Gas Flow: 12 L/min, Capillary: 3500 V, Nozzle Voltage: 1500 V.</p> <p>The UPLC was equipped with a 10-port valve configured to allow the column to be either eluted to the mass spectrometer or back-flushed to waste. The chromatographic separation was performed on an Agilent ZORBAX RRHD Extend 80Å C18, 2.1 × 150 mm, 1.8 μm column with an Agilent ZORBAX SB-C8, 2.1 mm × 30 mm, 3.5 μm guard column. The column temperature was 35 °C. Mobile phase A consisted of 97:3 water/ methanol and mobile phase B was 100% methanol; both A and B contained tributylamine and glacial acetic acid at concentrations of 10mM and 15mM, respectively. The column was back-flushed with mobile phase C (100% acetonitrile, no additives) between injections for column cleaning.<br>The LC gradient was as follows: 0-2min, 0%B; 2-12 min, linear ramp to 99%B; 12-17.5 min, 99%B. At 17.5 min, the 10-port valve was switched to reverse flow (back-flush) through the column, and the solvent composition changed to 99%C. From 20.5-21 min the flow rate was ramped to 0.8 mL/min, held until 22.5 min, then reduced to 0.6mL/min. From 22.7-23.5 min the solvent was ramped from 99% to 0% C while flow was simultaneously ramped down from 0.6-0.4mL/min and held until 29.4 min, at which point flow rate was returned to starting conditions at 0.25mL/min. The 10-port valve was returned to restore forward flow through the column at 28.5 min. An isocratic pump was used to introduce reference mass solution through the reference nebulizer for dynamic mass correction. Total run time was 30 min. The injection volume was 5 uL.</p> <p>Data analysis for this platform follows a hybrid targeted/non-targeted approach. Semi-quantitative data for known compounds is obtained by manual integration using Profinder v8.00 (Agilent Technologies, Santa Clara, CA.) Metabolites were identified by matching the retention time (+/- 0.1 min), mass (+/- 10 ppm) and isotope profile (peak height and spacing) to authentic standards. Non-targeted data analysis was performed using Agilent’s MassHunter Find by Molecular Feature workflow (v7.0) with recursion using Agilent’s Mass Profiler Pro (v8.0).</p> <p>A combined feature set was generated by merging untargeted features and named metabolites into a single feature list. The combined feature set underwent data reduction using Binner <em>(M. Kachman et al., 2020)</em>. Briefly, Binner first performs RT-based binning, followed by clustering of features by Pearson’s correlation coefficient, and the assignment of isotopes, adducts or in-source fragments by searching for known mass differences between highly correlated features. After Binner data reduction in-house software was used to search Refmet (https://www.metabolomicsworkbench.org/databases/refmet/index.php) to provide MS1 <em>(L. W. Sumner et al., 2007)</em> Level III identifications, or to an in-house library of authentic standards to provide MS1 Level I identifications.</p> <p>Iterative Data Dependent Acquisition (iDDA) ms/ms analysis was performed on the pooled sample material. iDDA captures ms/ms in stepwise fashion, with rolling excluded precursors. For untargeted platforms, we collect 8 rounds of iDDA at 3 different collision energies. At each collision energy, ~8000 ms/ms spectra are collected, which represent ms/ms spectra for approximately 75-95% of the features obtained by the untargeted data analysis. Analysis of iDDA spectra using NIST2020 was performed to provide MS1 Level II identifications for statistically significant features.</p>
Periodic dietary restriction of animal products induces metabolic reprogramming in humans with effects on health
<p>We conducted metabolomic and proteomic profiling in 411 healthy individuals, of whom almost a half follow a structured dietary pattern of animal product restriction.<br>Metabolite levels were measured on the Nightingale panel. Protein levels were measured on the Olink 1536 panel. Following QC, we conducted differential abundance analysis using limma (statistical software package in R) for metabolites and proteins, as described in our manuscript. Here we have deposited limma output results, annotated for each comparison made (eg. PRT2vsPRT1 for PR individuals at TImepoint 2 vs. Timepoint 1). For more details, please refer to the manuscript</p> <div> </div> <div>For metabolomics the files include:</div> <div>1. Nightingale_LIMMA_777ind_PRT2vsPRT1.txt 2. Nightingale_LIMMA_777ind_NRT2vsNRT1.txt 3. Nightingale_LIMMA_777ind_PRT1vsNRT1.txt 4. Nightingale_LIMMA_777ind_PRT2vsNRT2.txt</div> <div> </div> <div>For proteomics the files include:</div> <div>1. Olink_LIMMA_793ind_NRT2vsNRT1.txt 2. Olink_LIMMA_793ind_PRT1vsNRT1.txt 3. Olink_LIMMA_793ind_PRT2vsNRT2.txt 4. Olink_LIMMA_793ind_PRT2vsPRT1.txt</div> <div> </div> <div>The columns in the files refer to:</div> <div><strong>logFC:</strong> Log-fold change, indicating the magnitude of change in gene expression between conditions. <strong>CI.L and CI.R:</strong> Confidence intervals for the log-fold change. <strong>AveExpr:</strong> Average expression level of the gene across conditions. <strong>t:</strong> t-statistic, measuring the significance of the difference in expression between conditions. <strong>P.Value: </strong>P-value, indicating the probability of observing the data if the null hypothesis (no difference in expression) is true. <strong>adj.P.Val:</strong> Adjusted P-value, considering multiple testing corrections. <strong>B:</strong> B-statistic, a measure of the strength of evidence for differential expression. <strong>metabolite:</strong> metabolite that was measured.<strong>OlinkID: </strong>protein ID provided by Olink <strong>UniProt: </strong>Uniprot ID <strong>Assay: </strong>gene name</div> <p> </p>
Sustained macrophage reprogramming is required for CD8 T cell-dependent long-term tumor resolution
Open the record for dataset details and reuse information.
Th Tl Xb Prescription on Reprogramming of Lipid Metabolism and Endothelial Injury for Cerebral Infarction Patients
ClinicalTrials.gov study NCT06549582. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Intestinal Remodeling And Reprogramming of Glucose Metabolism Following Laparoscopic Roux-en-Y Gastric Bypass
ClinicalTrials.gov study NCT02288351. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Reprogramming of Human Fibroblasts into Hematopoietic Progenitors by Fusion With Human Fetal Liver CD34+ Cells (HuGene-1_0-st)
GEO Series GSE23413. Homo sapiens. 5 samples. Type: Expression profiling by array.
Environmentally Induced Transgenerational Epigenetic Reprogramming of Primordial Germ Cells and Subsequent Germline [Affymetrix]
GEO Series GSE43559. Rattus norvegicus. 8 samples. Type: Expression profiling by array.
Transcriptional and epigenetic reprogramming mediated by chronic IL1β exposure drives self-renewal ability and myeloid priming in TET2 deficient stem and progenitor cells [Bisulfite-Seq]
GEO Series GSE210025. Mus musculus. 48 samples. Type: Methylation profiling by high throughput sequencing.
Transcriptome analysis during somatic cell reprogramming
GEO Series GSE46149. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
N-Myc-mediated epigenetic reprogramming drives lineage plasticity in advanced prostate cancer [ChIP-seq]
GEO Series GSE117304. Homo sapiens. 54 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
UHRF2 mediates resistance to DNA methylation reprogramming in primordial germ cells [RNAseq_E8.5]
GEO Series GSE240968. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Comparative analysis of the cardiomyocyte differentiation potential of induced pluripotent stem cells reprogrammed from human atrial or ventricular fibroblasts
GEO Series GSE221268. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Diet-Induced Obesity Reprograms the Inflammatory Response of the Murine Lung to Inhaled Endotoxin
GEO Series GSE38092. Mus musculus. 32 samples. Type: Expression profiling by array.
The Sin3a/HDAC co-repressor complex cooperates with Nanog in promoting stem cell pluripotency and somatic cell reprogramming
GEO Series GSE91022. Mus musculus. 18 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by array.
HIV reprograms host m6Am RNA methylome by viral Vpr protein-mediated degradation of PCIF1
GEO Series GSE171800. Homo sapiens. 12 samples. Type: Methylation profiling by high throughput sequencing.
KLF4 Recruits SWI/SNF to Increase Chromatin Accessibility and Reprogram the Endothelial Enhancer Landscape under Laminar Shear Stress [RNA-seq siDUSP5]
GEO Series GSE152895. Homo sapiens. 5 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.