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Saccharomyces cerevisiae
25 Downloadable Samples
Affymetrix Yeast Genome S98 Array (ygs98)
Description
These Affymetrix data were used to determine the role of each non-essential subunit of the conserved Ccr4-Not complex in the control of gene expression in the yeast S. cerevisiae. The study was performed with cells growing exponentially in high glucose and with cells grown to glucose depletion. Specific patterns of gene de-regulation were observed upon deletion of any given subunit, revealing the specificity of each subunits function. Consistently, the purification of the Ccr4-Not complex through Caf40p by tandem affinity purification from wild-type cells or cells lacking individual subunits of the Ccr4-Not complex revealed that each subunit had a particular impact on complex integrity. Furthermore, the micro-arrays revealed that the role of each subunit was specific to the growth conditions. From the study of only two different growth conditions, revealing an impact of the Ccr4-Not complex on more than 85% of all studied genes, we can infer that the Ccr4-Not complex is important for expression of most of the yeast genome.
Publication Title
Specific roles for the Ccr4-Not complex subunits in expression of the genome.
Sample Metadata Fields
No sample metadata fields
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GSE1428- Homo sapiens
- 22 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
This series includes the global gene expression profile of the vastus lateralis muscle for 10 young (19-25 years old) and 12 older (70-80 years old) male subjects.
Publication Title
Identification of a molecular signature of sarcopenia.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 21 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
We isolated hematopoietic stem and progenitor cells from AML patients by FACS.
Publication Title
Cellular origin of prognostic chromosomal aberrations in AML patients.
Sample Metadata Fields
Specimen part
View Samples- Homo sapiens
- 33 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
We used microarray to create a normal cell landscape for the myeloid arm of the hematopoietic system.
Publication Title
Comparing cancer vs normal gene expression profiles identifies new disease entities and common transcriptional programs in AML patients.
Sample Metadata Fields
Specimen part
View Samples- Mus musculus
- 12 Downloadable Samples
- Affymetrix Mouse Gene 1.0 ST Array (mogene10st)
Description
To investigate the role of the transcription factor ERG in hematopoiesis we generated Erg heterozygous knockout and conditional Erg knockout mice. We found that several hematopoietic cell types were decreased in these mice. To define Erg downstream target genes in hematopoietic stem cells, we sorted Lineage-, Sca-1+, c-kit+, CD150+, CD48- cells from Erg +/- mice for gene expression analysis. To define Erg downstream target genes in hematopoietic progenitors, we sorted multipotent progenitors (Lineage-, Sca-1+, c-kit+, CD150-) from Erg -/- mice for gene expression analysis.
Publication Title
ERG promotes the maintenance of hematopoietic stem cells by restricting their differentiation.
Sample Metadata Fields
Sex, Specimen part
View Samples- Homo sapiens
- 12 Downloadable Samples
IlluminaHiSeq2500
Description
Pancreatic cancers (PCs) are highly metastatic with poor prognosis, mainly due to delayed detection. We hypothesized that intercellular communication is critical for metastatic progression. Here, we show that PC-derived exosomes induce liver pre-metastatic niche formation in naïve mice and consequently increase liver metastatic burden. Uptake of PC-derived exosomes by Kupffer cells caused transforming growth factor ß secretion and upregulation of fibronectin production by hepatic stellate cells. This fibrotic microenvironment enhanced recruitment of bone marrow-derived macrophages. We found that macrophage migration inhibitory factor (MIF) was highly expressed in PC-derived exosomes, and its blockade prevented liver pre-metastatic niche formation and metastasis. Compared to patients whose pancreatic tumors did not progress, MIF was markedly higher in exosomes from stage I PC patients who later developed liver metastasis. These findings suggest that exosomal MIF primes the liver for metastasis and may be a prognostic marker for the development of PC liver metastasis. Overall design: Normal pancreas and Pancreatic cancer exosomes education of human von Kupffer cells in vitro
Publication Title
Pancreatic cancer exosomes initiate pre-metastatic niche formation in the liver.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 2 Downloadable Samples
- Affymetrix Human Gene 2.0 ST Array (hugene20st)
Description
Breast cancer is the most common cancer in women worldwide and metastatic dissemination is the principal factor related to death by this disease. Breast cancer stem cells, are thought to be responsible for metastasis and chemoresistance.. In this study, based on whole transcriptome analysis from putative breast CSCs and reverse-engineering of transcription control networks, we were able to identify two networks associated to this phenotype.
Publication Title
Transcription Factor Networks derived from Breast Cancer Stem Cells control the immune response in the Basal subtype.
Sample Metadata Fields
Age, Disease stage
View Samples- Homo sapiens
- 12 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
This dataset was created to study M-CSF dependent in vitro differentiation of human monocytes to macrophages as a model process to demonstrate that independent component analysis (ICA) is a useful tool to support and extend knowledge-based strategies and to identify complex regulatory networks or novel regulatory candidate genes.
Publication Title
Analyzing M-CSF dependent monocyte/macrophage differentiation: expression modes and meta-modes derived from an independent component analysis.
Sample Metadata Fields
Specimen part
View Samples- Mus musculus
- 12 Downloadable Samples
- Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
External stimulations of cells by hormones, growth factors or cytokines activate signal transduction pathways that subsequently induce a rearrangement of cellular gene expression. The representation and analysis of changes in the gene response is complicated, and essentially consists of multiple layered temporal responses. In such situations, matrix factorization techniques may provide efficient tools for the detailed temporal analysis. Related methods applied in bioinformatics intentionally do not take prior knowledge into account. In signal processing, factorization techniques incorporating data properties like second-order spatial and temporal structures have shown a robust performance. However, large-scale biological data rarely imply a natural order that allows the definition of an autocorrelation function. We therefore develop the concept of graph-autocorrelation. We encode prior knowledge like transcriptional regulation, protein interactions or metabolic pathways as a weighted directed graph. By linking features along this underlying graph, we introduce a partial ordering of the samples to define an autocorrelation function. Using this framework as constraint to the matrix factorization task allows us to set up the fast and robust graph decorrelation (GraDe) algorithm. To analyze the alterations in the gene response in IL-6 stimulated primary mouse hepatocytes by GraDe, a time-course microarray experiment was performed. Extracted gene expression profiles show that IL-6 activates genes involved in cell cycle progression and cell division in a time-resolved manner. On the contrary, genes linked to metabolic and apoptotic processes are down-regulated indicating that IL-6 mediated priming rendered hepatocytes more responsive towards cell proliferation and reduces expenses for the energy household.
Publication Title
Knowledge-based matrix factorization temporally resolves the cellular responses to IL-6 stimulation.
Sample Metadata Fields
Specimen part, Treatment, Time
View Samples- Mus musculus
- 129 Downloadable Samples
- Illumina HiSeq 2000
Description
We performed single-cell RNA sequencing (RNA-seq) during the in vitro transition of mouse ESCs (mESCs) from a naïve pluripotent state into epiblast-like cells (EpiLCs), a primed pluripotent state. We derived pseudotime expression trajectories to investigate transcript dynamics of key metabolic regulators, with the aim to identify metabolic pathways that potentially impact on early embryonic cell state transitions. Overall design: Single-cell RNA-seq during the in vitro differentiation of mouse embryonic stem cells (ESCs) in 2i culture conditions (time point t=0h) into epiblast-like cells (EpiLCs) at time points t=24h and t=48h.
Publication Title
Metabolic regulation of pluripotency and germ cell fate through α-ketoglutarate.
Sample Metadata Fields
Specimen part, Cell line, Subject
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