E-MTAB-5230
Ontology highlight
ABSTRACT:
PROVIDER: E-MTAB-5230 | ExpressionAtlas | 2019-05-14
REPOSITORIES: ExpressionAtlas
Similar Datasets
Project description:This experiment was designed to monitor the gene expression changes induced by the knockdown of WTAP in human pluripotent stem cells (hPSCs) during the exit from pluripotency induced by the inhibition of Activin/Nodal signalling. hPSCs carrying a tetracycline-inducible short hairpin RNA (shRNA) against WTAP were cultured in control conditions or in presence of tetracycline (TET) to drive gene knockdown. Cells were monitored by RNA-seq in the pluripotent state (culture conditions containing Activin), or at various time points after inhibition of Activin/Nodal signalling with the receptor antagonist SB-431542 (SB). Samples were collected after 2h, 8h and 32h.
2018-01-04 | E-MTAB-5230 | biostudies-arrayexpress
Project description:mouse embryonic fibroblasts, embryonic stem cells, and induced pluripotent stem cells were compared via metabolomic analysis
2018-08-02 | MSV000082680 | GNPS
Project description:RNA-seq of WTAP knockdown human pluripotent stem cells during the exit from pluripotency induced by Activin/Nodal inhibition
| PRJEB17778 | ENA
Project description:<p>Variability in induced pluripotent stem cell (iPSC) lines remains a roadblock for disease modeling and regenerative medicine. Through linear mixed models we have described different sources of gene expression variability from RNA sequencing data in 317 human iPSC lines from 101 individuals. We found that ~50% of genome-wide expression variability is explained by variation across individuals and identified a set of expression quantitative trait loci that contribute to this variation. These analyses coupled with allele specific expression show that iPSCs retain a subject-specific gene expression pattern. Pathway enrichment and key driver analyses, based on predictive causal gene networks, found that Polycomb targets explain a significant part of the non-genetic variability present in iPSCs within and across individuals. These publically available iPSC lines and genetic datasets will be a resource to the scientific community and will open new avenues to reduce variability in iPSCs and improve their utility in disease modeling.</p> <p>SNP array data from individuals included in RNA-seq transcriptome profiling study of human induced pluripotent stem cells to characterize gene expression variation across individuals and within multiple iPSC lines from the same individual. Genotyping was performed on patient blood.</p> Data availability: <ul> <li>SNP-genotyping: dbGaP - current study</li> <li>RNA-seq counts: <a href="http://www.ncbi.nlm.nih.gov/geo/">GEO</a> - GSE79636</li> <li>FASTQ files: <a href="http://www.ncbi.nlm.nih.gov/sra">SRA</a> - SRP072417</li> </ul>
| phs001139 | dbGaP
Project description:Integrative epigenomic and transcriptomic characterization of hepatocyte-like cells differentiated in vitro from human induced pluripotent stem cells in comparison with primary human hepatocytes. This study comprises single cell RNA-seq, bulk mRNA-seq, ATAC-seq and RRBS.
| EGAS00001004201 | EGA
Project description:Modeling Preeclampsia using human induced pluripotent stem cells [RNA-seq]
| PRJNA668942 | ENA
Project description:There are a total of four samples each for this analysis. Each sample consists of the cells grown on three 10 cm culture plates. Each plate should have 2x106 cells for a total of 6x106 cells per sample when all three plates are combined. The first sample is undifferentiated human embryonic stem cells, the second sample is human glutamatergic neurons derived from those human embryonic stem cells, the third sample is undifferentiated human induced pluripotent stem cells and the fourth sample is human glutamatergic neurons derived from those human induced pluripotent stem cells.
2017-06-19 | ST000768 | MetabolomicsWorkbench
Project description:Metabolism is vital to cellular function and tissue homeostasis during human lung development. In utero, embryonic pluripotent stem cells undergo endodermal differentiation towards a lung progenitor cell fate that can be mimicked in vitro using induced human pluripotent stem cells (hiPSCs) to study genetic mutations. To identify differences between wild type and surfactant protein B (SFTPB)-deficient cell lines during endoderm specification towards lung, we used an untargeted metabolomics approach to evaluate the developmental changes in metabolites. We found that the metabolites most enriched during the differentiation from pluripotent stem cell to lung progenitor cell, regardless of cell line, were sphingomyelins and phosphatidylcholines, two important lipid classes in fetal lung development. The SFTPB mutation had no metabolic impact on early endodermal lung development. The identified metabolite signatures during lung progenitor cell differentiation may be utilized as biomarkers for normal embryonic lung development.
2021-06-29 | ST002055 | MetabolomicsWorkbench
Project description:<p>The NeuroLINCS Center is part of the NIH Common Fund's Library of Integrated Network-based Cellular Signatures (LINCS) program, which aims to characterize how a variety of human cells, tissues and the entire organism respond to perturbations by drugs and other molecular factors.</p> <p>As Part of the LINCS program, the NeuroLINCS study concentrates on human brain cells, which are far less understood than other cells in the body. Our initial focus is to produce diseased motor neurons from patients by utilizing high-quality induced pluripotent stem cell (iPSC) lines from Amyotrophic Lateral Sclerosis (ALS) and Spinal Muscular Atrophy (SMA) patients in addition to unaffected normal healthy controls. Using state-of-the-art OMICS methods (genomics, epigenomics, transcriptomics, and proteomics), we intend to create a wealth of cellular data that is patient-specific in the context of their baseline genetic perturbations and in the presence of other genetic and environmental perturbagens (e.g. endoplasmic reticulum stress). The primary data will be used to build cell signatures that convey the key features that distinguish the state of a cell and determine its behavior. Ultimately, the analysis of these datasets will lead to the identification of a network of unique signatures relevant to each of these motor neuron diseases. The datasets represented in this study are generated from assays interrogating RNA expression (RNA-seq), chromatin accessibility (ATAC-seq) and whole genome sequencing. </p>
| phs001231 | dbGaP