refine.bio
  • Search
      • Normalized Compendia
      • RNA-seq Sample Compendia
  • Docs
  • About
  • My Dataset
github link
Showing
of 65 results
Sort by

Filters

Technology

Platform

accession-icon GSE13948
Antagonism of microRNA-122 in mice by systemically administered LNA-antimiR
  • organism-icon Mus musculus
  • sample-icon 21 Downloadable Samples
  • Technology Badge Icon

Description

Antagonism of microRNA-122 in mice by systemically administered LNA-antimiR leads to up-regulation of a large set of predicted target mRNAs in the liver

Publication Title

Antagonism of microRNA-122 in mice by systemically administered LNA-antimiR leads to up-regulation of a large set of predicted target mRNAs in the liver.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE13590
Experimental identification of microRNA-140 targets by silencing and overexpressing miR-140
  • organism-icon Mus musculus
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon

Description

MicroRNAs (miRNAs) are short noncoding RNA molecules regulating the expression of mRNAs. Target identification of miRNAs is computationally difficult due to the relatively low homology between miRNAs and their targets. We present here an experimental approach to target identification where the cartilage-specific miR-140 was overexpressed and silenced in cells it is normally expressed in separate experiments. Expression of mRNAs was profiled in both experiments and the intersection of mRNAs repressed by miR-140 overexpression and derepressed by silencing of miR-140 was identified. The intersection contained only 49 genes, although both treatments affected the accumulation of hundreds of mRNAs. These 49 genes showed a very strong enrichment for the miR-140 seed sequence implying that the approach is efficient and specific. 21 of these 49 genes were predicted to be direct targets based on the presence of the seed sequence. Interestingly, none of these were predicted by the published target prediction methods we used. One of the potential target mRNAs, Cxcl12, was experimentally validated by Northern blot analysis and a luciferase reporter assay.

Publication Title

Experimental identification of microRNA-140 targets by silencing and overexpressing miR-140.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE43928
Expression data from TNF-stimulated mouse glomeruli
  • organism-icon Mus musculus
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon

Description

The specific contribution of the two TNF-receptors Tnfr1 and Tnfr2 to TNF-induced inflammation in the glomerulus is unknown. In mice, TNF exposure induces glomerular expression of inflammatory mediators like adhesion molecules and chemokines in vivo, and glomerular accumulation of leukocytes.

Publication Title

Distinct contributions of TNF receptor 1 and 2 to TNF-induced glomerular inflammation in mice.

Sample Metadata Fields

Specimen part, Treatment

View Samples
accession-icon GSE27975
HL-1 cardiomyocyte response to hypoxia
  • organism-icon Mus musculus
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon

Description

Expression profiling of cultured HL-1 cardiomyocytes subjected to hypoxia for 8 hours.

Publication Title

The VLDL receptor promotes lipotoxicity and increases mortality in mice following an acute myocardial infarction.

Sample Metadata Fields

Cell line

View Samples
accession-icon GSE24291
Expression data from differentiating ES cells expressing Snail during Wnt inhibition
  • organism-icon Mus musculus
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon

Description

ES cells differentiated in the presence of the Wnt inhibitor DKK1 fail to express the transcription factor Snail and undergo EMT. We generated an ES cell line, A2.snail, that induced Snail expression upon addition of doxycycline addition.

Publication Title

Snail and the microRNA-200 family act in opposition to regulate epithelial-to-mesenchymal transition and germ layer fate restriction in differentiating ESCs.

Sample Metadata Fields

Specimen part, Cell line, Time

View Samples
accession-icon GSE12367
Deaf-1 regulated genes in the mouse mammary gland
  • organism-icon Mus musculus
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon

Description

Microarray analysis was used to compare the gene expression profiles of Deaf-1-transduced mouse mammary epithelial cells (MECs) relative to Deaf-1-deficient MECs.

Publication Title

Deaf-1 regulates epithelial cell proliferation and side-branching in the mammary gland.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE12466
Transcriptional signatures of Itk-deficient CD3+, CD4+ and CD8+ T-cells
  • organism-icon Mus musculus
  • sample-icon 23 Downloadable Samples
  • Technology Badge Icon

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Transcriptional signatures of Itk-deficient CD3+, CD4+ and CD8+ T-cells.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE15892
Microarray data from control and pericyte-deficient mouse brain microvascular transcriptomes
  • organism-icon Mus musculus
  • sample-icon 30 Downloadable Samples
  • Technology Badge Icon

Description

The blood-brain barrier (BBB) consists of specific physical barriers, enzymes and transporters, which together maintain the necessary extracellular environment of the central nervous system (CNS). The main physical barrier is found in the CNS endothelial cell, and depends on continuous complexes of tight junctions combined with reduced vesicular transport. Other possible constituents of the BBB include extracellular matrix, astrocytes and pericytes, but the relative contribution of these different components to the BBB remains largely unknown. Here we demonstrate a direct role of pericytes at the BBB in vivo. Using a set of adult viable pericyte-deficient mouse mutants we show that pericyte deficiency increases the permeability of the BBB to water and a range of low-molecular-mass and high-molecular-mass tracers. The increased permeability occurs by endothelial transcytosis, a process that is rapidly arrested by the drug imatinib. Furthermore, we show that pericytes function at the BBB in at least two ways: by regulating BBB-specific gene expression patterns in endothelial cells, and by inducing polarization of astrocyte end-feet surrounding CNS blood vessels. Our results indicate a novel and critical role for pericytes in the integration of endothelial and astrocyte functions at the neurovascular unit, and in the regulation of the BBB.

Publication Title

Pericytes regulate the blood-brain barrier.

Sample Metadata Fields

Sex, Age, Specimen part

View Samples
accession-icon GSE20244
A comprehensive methylome map of lineage commitment from hematopoietic progenitors
  • organism-icon Mus musculus
  • sample-icon 26 Downloadable Samples
  • Technology Badge Icon

Description

Epigenetic modifications must underlie lineage-specific differentiation since terminally differentiated cells express tissue-specific genes, but their DNA sequence is unchanged. Hematopoiesis provides a well-defined model of progressive differentiation in which to study the role of epigenetic modifications in cell fate decisions. Multi-potent progenitors (MPPs) can differentiate into all blood cell lineages, while downstream progenitors commit to either myeloerythroid or lymphoid lineages. While DNA methylation is critical for myeloid versus lymphoid differentiation, as demonstrated by the myeloerythroid bias in Dnmt1 hypomorphs {Broske, 2009 #6}, a comprehensive DNA methylation map of hematopoietic progenitors, or of any cell lineage, does not exist. Here we have generated a mouse DNA methylation map, examining 4.6 million CpG sites throughout the genome including all CpG islands and shores, examining MPPs, all lymphoid progenitors (ALPs), common myeloid progenitors (CMPs), granulocyte/macrophage progenitors (GMPs), and thymocyte progenitors (DN1, DN2, DN3). Interestingly, differentiation towards the myeloid lineage corresponds with a net decrease in DNA methylation, while lymphoid commitment involves a net increase in DNA methylation, but both show substantial dynamic changes consistent with epigenetic plasticity during development. By comparing lineage-specific DNA methylation to gene expression array data, we find many examples of genes and pathways not previously known to be involved in lymphoid/myeloid differentiation, such as Gcnt2, Arl4c, Gadd45, and Jdp2. Several transcription factors, including Meis1 and Prdm16 were methylated and silenced during differentiation, suggesting a role in maintaining an undifferentiated state. Additionally, epigenetic modification of modifiers of the epigenome appears to be important in hematopoietic differentiation. Our results directly demonstrate that modulation of DNA methylation occurs during lineage-specific differentiation, often correlating with gene expression changes, and define a comprehensive map of the methylation and transcriptional changes that accompany myeloid versus lymphoid fate decisions.

Publication Title

Comprehensive methylome map of lineage commitment from haematopoietic progenitors.

Sample Metadata Fields

Sex, Age

View Samples
accession-icon GSE27159
Expression profiling of the murine neural crest precursor cell line, JoMa1
  • organism-icon Mus musculus
  • sample-icon 7 Downloadable Samples
  • Technology Badge Icon

Description

JoMa1 cells are pluripotent precursor cells, derived from the neural crest of mice transgenic for tamoxifen-inducible c-Myc. Following transfection with a cDNA encoding for MYCN, cells become immortlized even in the absence of tamoxifen.

Publication Title

MYCN and ALKF1174L are sufficient to drive neuroblastoma development from neural crest progenitor cells.

Sample Metadata Fields

Specimen part, Cell line

View Samples
...

refine.bio is a repository of uniformly processed and normalized, ready-to-use transcriptome data from publicly available sources. refine.bio is a project of the Childhood Cancer Data Lab (CCDL)

fund-icon Fund the CCDL

Developed by the Childhood Cancer Data Lab

Powered by Alex's Lemonade Stand Foundation

Cite refine.bio

Casey S. Greene, Dongbo Hu, Richard W. W. Jones, Stephanie Liu, David S. Mejia, Rob Patro, Stephen R. Piccolo, Ariel Rodriguez Romero, Hirak Sarkar, Candace L. Savonen, Jaclyn N. Taroni, William E. Vauclain, Deepashree Venkatesh Prasad, Kurt G. Wheeler. refine.bio: a resource of uniformly processed publicly available gene expression datasets.
URL: https://www.refine.bio

Note that the contributor list is in alphabetical order as we prepare a manuscript for submission.

BSD 3-Clause LicensePrivacyTerms of UseContact