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accession-icon GSE55525
Hematopoietic stem cell quiescence attenuates DNA damage repair and response contributing to age-dependent DNA damage accumulation
  • organism-icon Mus musculus
  • sample-icon 71 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

Comprehensive analysis of gene expression in hematopoietic stem and progenitor cells from young and old mice.

Publication Title

Quiescent hematopoietic stem cells accumulate DNA damage during aging that is repaired upon entry into cell cycle.

Sample Metadata Fields

Sex, Age, Specimen part, Time

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accession-icon GSE56928
Global transcriptional profiling reveals distinct functions of thymic stromal subsets and age-related changes during thymic involution
  • organism-icon Mus musculus
  • sample-icon 36 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

The thymic microenvironment is essential for proper differentiation and selection of thymocytes.Thymic involution in aged mice results in decreased T cell output and immune function. Here we use gene expression profiling of FACS sorted thymic stromal subsets to identify molecular mediators of thymocyte: stromal cell interactions, as well as gene expression changes thymic stromal subsets during early stages of thymic involution .

Publication Title

Global transcriptional profiling reveals distinct functions of thymic stromal subsets and age-related changes during thymic involution.

Sample Metadata Fields

Sex, Age, Specimen part, Disease, Disease stage

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accession-icon GSE34723
Gene Expression Commons: an open platform for absolute gene expression profiling
  • organism-icon Mus musculus
  • sample-icon 101 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

Gene expression profiling using microarray has been limited to profiling of differentially expressed genes at comparison setting since probesets for different genes have different sensitivities. We overcome this limitation by using a very large number of varied microarray datasets as a common reference, so that statistical attributes of each probeset, such as dynamic range or a threshold between low and high expression can be reliably discovered through meta-analysis. This strategy is implemented in web-based platform named Gene Expression Commons (http://gexc.stanford.edu/ ) with datasets of 39 distinct highly purified mouse hematopoietic stem/progenitor/functional cell populations covering almost the entire hematopoietic system. Since the Gene Expression Commons is designed as an open platform, any scientist can explore gene expression of any gene, search by expression pattern of interest, submit their own microarray datasets, and design their own working models.

Publication Title

Gene Expression Commons: an open platform for absolute gene expression profiling.

Sample Metadata Fields

Sex, Age

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accession-icon GSE53046
C10ORF10/DEPP, a transcriptional target of FOXO3 regulates ROS-sensitivity by destabilizing peroxisomes in human neuroblastoma
  • organism-icon Homo sapiens
  • sample-icon 9 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

FOXO transcription factors control cellular formation of reactive oxygen species (ROS), which critically contribute to cell survival and cell death in neuroblastoma. Here, we report that C10orf10, also named Decidual Protein induced by Progesterone (DEPP), is a direct transcriptional target of FOXO3 in human neuroblastoma. As FOXO3-mediated apoptosis involves a biphasic ROS accumulation, we analyzed cellular ROS levels in DEPP-knockdown cells by live-cell imaging. Knockdown of DEPP prevented the primary and secondary ROS accumulation during FOXO3 activation and attenuates FOXO3-induced apoptosis, whereas its overexpression raises cellular ROS levels and sensitizes to cell death. In neuronal cells, cellular steady state ROS are mainly detoxified in peroxisomes by the enzyme CAT/catalase. As DEPP contains a peroxisomal-targeting-signal-type-2 (PTS2) sequence at its N-terminus that enables protein import into peroxisomes, we analyzed the effect of DEPP on peroxisomal function by measuring the catalase enzyme activity. Catalase activity was reduced by conditional DEPP overexpression and significantly increased in DEPP-knockdown cells. Using live cell imaging and fluorescent peroxisomal and mitochondrial probes we demonstrate that DEPP localizes to peroxisomes and mitochondria in neuroblastoma cells. The combined data indicate that DEPP reduces peroxisomal activity and thereby impairs the cellular ROS detoxification capacity and contributes to death sensitization.

Publication Title

C10ORF10/DEPP, a transcriptional target of FOXO3, regulates ROS-sensitivity in human neuroblastoma.

Sample Metadata Fields

Cell line, Treatment

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accession-icon GSE77078
Neonatal bone marrow stroma
  • organism-icon Mus musculus
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

Prospectively isolated neonatal bone marrow stroma and endothelium

Publication Title

Hoxb5 marks long-term haematopoietic stem cells and reveals a homogenous perivascular niche.

Sample Metadata Fields

Specimen part

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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 Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

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

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accession-icon SRP040966
InFusion: advancing discovery of fusion genes and chimeric transcripts from RNA-seq data
  • organism-icon Homo sapiens
  • sample-icon 4 Downloadable Samples
  • Technology Badge IconIlluminaHiSeq2000

Description

Gene fusions and chimeric transcripts occur frequently in cancers and in some cases drive the development of the disease. An accurate detection of these events is crucial for cancer research and in a long-term perspective could be applied for personalized therapy. RNA-seq technology has been established as an efficient approach to investigate transcriptomes and search for gene fusions and chimeric transcripts on a genome-wide scale. A number of computational methods for the detection of gene fusions from RNA-seq data have been developed. However, recent studies demonstrate differences between commonly used approaches in terms of specificity and sensitivity. Moreover their ability to detect gene fusions on the isoform level has not been studied carefully so far. Here we propose a novel computational approach called InFusion for fusion gene detection from deep RNA sequencing data. Validation of InFusion on simulated and on several public RNA-seq datasets demonstrated better detection accuracy compared to other tools. We also performed deep RNA sequencing of two well-established prostate cancer cell lines. Using these data we showed that InFusion is capable of discovering alternatively spliced gene fusion isoforms as well as chimeric transcripts that include non-exonic regions. In addition our method can detect anti-sense transcription in the fusions by incorporating strand specificity of the sequencing library. Overall design: Detection of fusion genes and chimeric transcripts from deep RNA-seq data

Publication Title

InFusion: Advancing Discovery of Fusion Genes and Chimeric Transcripts from Deep RNA-Sequencing Data.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE20100
Expression data from primary MEF lacking either HDAC1, HDAC2 or both
  • organism-icon Mus musculus
  • sample-icon 15 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

Previously published data suggested some redundant functions between HDAC1 and HDAC2 in mouse. To test this hypothesis, we used microarrays to have a genome wide analysis at the transcription level of primary MEFs lacking HDAC1, HDAC2.

Publication Title

Histone deacetylases 1 and 2 act in concert to promote the G1-to-S progression.

Sample Metadata Fields

Sex

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accession-icon GSE30076
Epigenetic repression of cardiac progenitor gene expression by Ezh2 is required for postnatal cardiac homeostasis
  • organism-icon Mus musculus
  • sample-icon 9 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

Adult-onset diseases can be associated with in utero events, but mechanisms for such temporally distant dysregulation of organ function remain unknown. The polycomb histone methyltransferase, Ezh2, stabilizes transcription by depositing repressive histone marks during development that persist into adulthood, but the function of Ezh2-mediated transcriptional stability in postnatal organ homeostasis is not understood. Here, we show that Ezh2 stabilizes the postnatal cardiac gene expression program and prevents cardiac pathology, primarily by repressing the homeodomain transcription factor Six1 in differentiating cardiac progenitors. Loss of Ezh2 in embryonic cardiac progenitors, but not in differentiated cardiomyocytes, resulted in postnatal cardiac pathology, including cardiomyocyte hypertrophy and fibrosis. Loss of Ezh2 caused broad derepression of skeletal muscle genes, including the homeodomain transcription factor Six1, which is expressed in cardiac progenitors but is normally silenced upon cardiac differentiation. Many of the deregulated genes are direct Six1 targets, implying a critical requirement for stable repression of Six1 in cardiac myocytes. Indeed, upon de-repression, Six1 promotes cardiac pathology, as it was sufficient to induce cardiac hypertrophy. Furthermore, genetic reduction of Six1 levels almost completely rescued the pathology of Ezh2-deficient hearts. Thus, repression of a single transcription factor in cardiac progenitors by Ezh2 is essential for stability of the adult heart gene expression program and homeostasis. Our results suggest that epigenetic dysregulation during discrete developmental windows can predispose to adult disease and dysregulated stress responses.

Publication Title

Epigenetic repression of cardiac progenitor gene expression by Ezh2 is required for postnatal cardiac homeostasis.

Sample Metadata Fields

Specimen part

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accession-icon SRP043512
Transcriptional response to stress in serum deprived mouse fibroblasts in the presence of MSK1/2 inhibitor.
  • organism-icon Mus musculus
  • sample-icon 14 Downloadable Samples
  • Technology Badge IconIlluminaHiSeq2000

Description

We have employed gene expression profiling in order to identify targets of transcriptional response to stress in resting mouse Swiss 3T3 fibroblasts, either untreated (control) or treated with anisomycin for 3 or 6 hours to induce the p38/MAP kinase pathway. In order determine transcriptional effects dependent on MSK1/2 kinase activity, H89 inhibitor was used in the study. Overall design: Serum starved (72 h 0.2% FCS) mouse 3T3 cells were treated with anisomycin (188.5 nM) for 3 h or 6h (in duplicates) either with or without 15-min pre-treatment with MSK1/2 inhibitor H89 (10 uM). Untreated, serum-starved cells were used as a control. RNA was collected and gene expression profiling using strand-specific RNA-seq was performed.

Publication Title

H3S28 phosphorylation is a hallmark of the transcriptional response to cellular stress.

Sample Metadata Fields

No sample metadata fields

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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)

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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.

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