Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

In-depth transcriptomic analyses investigating molecular mechanisms underlying the osteogenic differentiation of human renal interstitial fibroblasts.

ABSTRACT: In-depth transcriptomic analyses investigating molecular mechanisms underlying the osteogenic differentiation of human renal interstitial fibroblasts.

PROVIDER: PRJNA838590 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR19224422_1.fastq.gz	Fastqsanger.gz
	SRR19224422_2.fastq.gz	Fastqsanger.gz
	SRR19224423_1.fastq.gz	Fastqsanger.gz
	SRR19224423_2.fastq.gz	Fastqsanger.gz
	SRR19224424_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 12

Similar Datasets

In-depth transcriptomic analyses investigating molecular mechanisms underlying the osteogenic differentiation of human renal interstitial fibroblasts.

Project description:Randall’s plaques (RP) are well established as precursor lesions of idiopathic calcium oxalate (CaOx) stones, and the process of biomineralization driven by osteogenic-like cells has been highlighted in RP formation, but the mechanism is poorly understood. Given the potential role of osteogenic-like renal interstitial fibroblasts in biomineralization, the isolated primary human renal interstitial fibroblasts (hRIFs) were either induced with a widely used osteogenic medium or cultured in normal medium for 7 days, and a transcriptomic analysis of LncRNA and mRNA was performed to study molecular mechanisms underlying the osteogenic differentiation of human renal interstitial fibroblasts.

2022-05-19 | GSE203110 | GEO

In-depth transcriptomic analyses investigating molecular mechanisms underlying diabetic retinopathy

Project description:In-depth transcriptomic analyses investigating molecular mechanisms underlying diabetic retinopathy

| PRJNA672925 | ENA

In-depth transcriptomic analyses investigating molecular mechanisms underlying diabetic retinopathy (totalRNA)

Project description:In-depth transcriptomic analyses investigating molecular mechanisms underlying diabetic retinopathy (totalRNA)

| PRJNA672928 | ENA

In-depth transcriptomic analyses investigating molecular mechanisms underlying diabetic retinopathy (smallRNA)

Project description:In-depth transcriptomic analyses investigating molecular mechanisms underlying diabetic retinopathy (smallRNA)

| PRJNA672929 | ENA

Proteomics analysis of physiological effect of ZBTB16 (PLZF) on osteogenic differentiation of human valve interstitial cells (VICs) by DDA PASEF on TimsToF Pro

Project description:During fundamental proteotranscriptomic analysis of human valve interstitial cells (VICs) osteogenic differentiation, we revealed ZBTB16 (PLZF) as one of the most upregulated proteins. Thus, to investigate its physiological role in VICs osteogenic differentiation we performed its overexpression by lentiviral transduction with subsequent proteomics analysis. Despite obvious PLZF overexpression and its reinforcing effect on osteogenic differentiation, we revealed surprisingly small amount of differentially expressed proteins that assumed to be the targets of PLZF.

2022-10-21 | PXD031572 | Pride

Comparative analysis of DDA- and DIA-PASEF methods for proteomics analysis of human valve interstitial cells and osteoblasts during osteogenic differentiation

Project description:Shotgun proteomics is one of the key "omics" methods, the methodology of which is rapidly developing. Mass spectrometers of the TimsToF series (Bruker) with ion mobility are one of the young technological platforms for shotgun proteomics in which both data dependent (DDA) and data independent acquisition (DIA) proteomics methods might be performed. However, only a few comparisons of the effectiveness of DDA and DIA proteomics on TimsToF have been published, carried out mainly on the test samples. From the other hand, peculiarities of osteogenic differentiation of human valve interstitial cells (VICs) are fruitful therapeutic target for calcific aortic valve disease (CAVD) treatment. Still, there is no data whether pathological osteogenic differentiation of VICs similar to normal ossification. Combining this technical and biological tasks we performed comparative proteomics analysis of osteogenic differentiation of human VICs and osteoblasts by DIA- and DDA-PASEF proteomics on TimsToF Pro.

2024-11-12 | PXD036566 | Pride

In-depth transcriptomic analyses investigating molecular mechanisms underlying diabetic retinopathy

Project description:This SuperSeries is composed of the SubSeries listed below.

2021-04-19 | GSE160310 | GEO

Crenigacestat inhibits osteogenic differentiation of human valve interstitial cells and seems promising for treatment of cardiovascular calcification

Project description:Vascular calcification is common pathology various forms of which presented in the half of humans. Calcific aortic valve disease (CAVD) is one of the most dangerous forms of vascular calcification. Despite high mortality there is no target therapy against CAVD. Thus, we tested crenigacestat (LY3039478), inhibitor of Notch-signaling, and shRNA against RBPJk for inhibition of osteogenic differentiation of velve intersticial cells (VICs) in vitro. Both of them effectivelly enhibited osteogenic differentiation and we performed proteomics analysis do describe molecular mechanisms of their effect. Presented dataset contain results of shotgun proteomics analysis with ion mobility in TimsToF Pro instrument (in DDA PASEF mode) of VICs in classic osteogenic medium (OM) and OM supplemented with crenigacestat, DMSO or shCSL.

2022-10-04 | PXD029632 | Pride

Proteomics comparison of human valve interstitial cells from healthy patients and donors with CAVD

Project description:Calcific aortic valve disease (CAVD) is an slowly progressive calcification of heart valve which leads to aortic stenosis. The only existing treatment of CAVD is surgical replacment of calcified valve - development of anti-CAVD treatment is urgent task. For better understanding of molecular mechanisms of CAVD progression we performed proteomics analysis of osteogenic differentiation of human valve interstitial cells isolated from healthy humans or patients with CAVD.

2022-10-21 | PXD032212 | Pride

In-depth transcriptomic analyses investigating molecular mechanisms underlying diabetic retinopathy (smallRNA)

Project description:Diabetic Retinopathy (DR) is among the major global causes for vision loss. With the rise in diabetes prevalence, an increase in DR incidence is expected. Current understanding of both the molecular etiology and pathways involved in the initiation and progression of DR is limited. Here we analyzed mRNA and miRNA expression profiles of 80 human post-mortem retinal samples from 80 patients diagnosed with various stages of DR. We found differentially expressed transcripts to be predominantly associated with late stage DR and pathways such as hippo and gap junction signaling. A multivariate regression model identified transcripts with progressive changes throughout disease stages, which in turn displayed significant overlap with sphingolipid and cGMP−PKG signaling. Combined analysis of miRNA and mRNA expression further uncovered disease-relevant miRNA/mRNA associations as potential mechanisms of post-transcriptional regulation. Finally, integrating human retinal single cell RNA-Sequencing data revealed a continuous loss of retinal ganglion cells, and Müller cell mediated changes in histidine and β-alanine signaling. Our findings offer an unprecedented insight into the development of DR and provide potential avenues for future therapeutic intervention.

2021-04-19 | GSE160308 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

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