Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Comparison of bulk RNAseq results of WT and TKO E14 NPCs

ABSTRACT: We investigate the impact of DNA methylation on neural somatic differentiation. To this end, we used in vitro neural progenitor cell (NPC) differentiation on WT and DNA-methylation free TKO murine ESCs. We next performed bulk RNAseq at D0, D2, D4, D6 and D8 of differentiation to study transcriptomic differences.

ORGANISM(S): Mus musculus

PROVIDER: GSE214460 | GEO | 2023/10/09

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

Bulk RNAseq on WT and TKO E14 PGCLCs

Project description:We investigate the impact of DNA methylation on epiblast differentiation. To this end, we used in vitro Epiblast-like cell (EpiLC) differentiation on WT and DNA-methylation free TKO murine ESCs. We next performed bulk RNAseq at D0, D2, D4, D6 and D8 of differentiation to study transcriptomic differences.

2023-10-09 | GSE214461 | GEO

Comparison of bulk RNAseq results of WT and TKO E14 EpiLCs

2023-10-09 | GSE214459 | GEO

Single cell SMART RNAseq on WT and TKO E14 EpiLCs

Project description:We investigate how DNA methylation absence impact transcriptionnal regulation during epiblast differentiation. More specifically, how synchronous is the naïve to primed transitioning in TKO cells compared to WT, and if there is a "germline-competent" TKO subpopulation of EpiLCs. To this end, we used in vitro Epiblast-like cell (EpiLC) differentiation WT and TKO at D0, D1, D2 and D4. We next performed single-cell SMARTseq2 RNA sequencing.

2023-10-09 | GSE214462 | GEO

CUT&RUN against H3K27ac and H3K27me3 on WT and TKO ESCs (D0), and D2, D4 EpiLCs

Project description:We investigate the impact of DNA methylation on chromatin epigenetic marks during epiblast differentiation. To this end, we used in vitro Epiblast-like cell (EpiLC) differentiation on WT and DNA-methylation free TKO murine ESCs. We next performed CUT&RUN against H3K27me3 and H3K27ac at D0, D2 and D4.

2023-10-09 | GSE214465 | GEO

Transcriptome analysis of mRNAs, lncRNAs, and circRNAs during the differentiation of porcine subcutaneous preadipocytes

Project description:Subcutaneous preadipocyte differentiation play a critical role in fat deposition of pigs. However, the action of different RNAs in subcutaneous preadipocyte differentiation, such as messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs), are still largely unclear. In the present study, a transcriptome analysis including mRNAs, lncRNAs, and circRNAs was performed in different stages of D0, D2, D4, and D8 using RNA-seq technology. Our results identified 1,554, 470, 1,344, 1,777, and 676 differentially expressed (DE) mRNAs, 112, 58, 95, 136, and 93 DE lncRNAs, and 902, 787, 710, 932, and 850 DE circRNAs between D2 and D0, between D4 and D2, between D8 and D4, between D4 and D0, and between D8 and D0, respectively. Moreover, functional enrichment analysis showed that the common DE mRNAs during entire differentiation were mainly involved in lipid metabolic and cell differentiation processes. Additionally, co-expression network analysis identified the potential lncRNAs related to adipogenesis, e.g., MSTRG.42239 and MSTRG.146410. Taken together, this study provides a genome-wide landscape of RNA expression profiles during subcutaneous preadipocyte differentiation, thus improving our understanding of molecular mechanism in regulating subcutaneous adipogenesis of pigs.

2018-11-14 | GSE114583 | GEO

Calcineurin signaling regulates neural induction through antagonizing the BMP pathway

Project description:Calcineurin activity is required for neural differentiation of embryonic stem cells (ESC). We assessed gene expression changes at several time points of in vitro ESC to neural differentiation when calcineurin activity is inhibitied by its antagonist FK506 and Cyclosporin(CsA). d4, d6, d8, and d12 EBs of FK506/CsA treatment group (d3-8) and its non-treated control group were harvested from three independent experiments of in vitro differentiation of 46C mouse ESCs. Total RNA of each sample was extracted, reverse transcribed, labeled and hybridized to Affymetric Mouse Gene ST 1.0 Arrays.

2014-04-01 | E-GEOD-54217 | biostudies-arrayexpress

Gene expression analysis in HLHS iPSCs, iPSC-derived cardiac progenitors and cardiomyocytes

Project description:In the present study, genome-wide expression profiling (RNA-Seq) was carried out to identify genes dysregulated during cardiac differentiation of HLHS iPSCs. Transcriptional profiling by RNASeq of three control and three HLHS iPSC lines was performed at various times during differentiation towards cardiac progenitors (CPCs) at D0, D1, D2, D3 and towards cardiomyocytes (CMs) at D6, D8, D14.

2021-11-02 | GSE135411 | GEO

Temporal DNA methylation analysis (RRBS) for differentiating mouse embryonic stem cells deficient for Zeb2

Project description:In this study we performed temporal profiling of DNA methylation by RRBseq of differentiating mouse embryonic stem cells using an embryoid body protocol. Analysis at d0, d4 and d6 revealed that Zeb2 deficient mESC lose their initially acquired DNA methylation at d6.

2016-10-20 | GSE75617 | GEO

Effect of INO80 overexpression on gene expression in adult cardiomyocytes (CMs).

Project description:To capture the swift changes in gene expression upon INO80-OE, we performed time-coursed (D0, D2, D4, and D6) RNA sequencing (RNA-Seq) following INO80 induction.

2024-01-04 | GSE240180 | GEO

Temporal transcriptome analysis of control and Zeb2 knockout mESC in pluripotency and in neural differentiation

Project description:To capture the Zeb2-dependent transcriptional changes in early cell state/fate decisions we performed RNA-seq on Zeb2 control and Zeb2 knockout cells. We chose three stages, which correspond in control ESCs to the naive pluripotent state (d0; very low amounts of Zeb2 mRNA), multipotent progenitors (d4, low Zeb2 mRNA/protein) and early neural progenitors (d6, high Zeb2 mRNA/protein), respectively.

2016-10-20 | GSE75616 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data