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accession-icon GSE9801
Human Monocytes to M-CSF differentiated Macrophages
  • organism-icon Homo sapiens
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon 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
accession-icon GSE21031
Time-series of IL-6 stimulated primary mouse hepatocytes
  • organism-icon Mus musculus
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon 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
accession-icon SRP126246
Single-cell transcriptome profiling during the in vitro differentiation of mouse ESCs (mESCs) into epiblast-like cells (EpiLCs).
  • organism-icon Mus musculus
  • sample-icon 129 Downloadable Samples
  • Technology Badge IconIllumina 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

View Samples
accession-icon GSE75012
Microarray expression data from monocytes, Mo-DCs and CD24 DCs
  • organism-icon Mus musculus
  • sample-icon 9 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

Cross-presentation of cell-associated antigens is carried out by classical DCs (cDCs) and monocyte-derived DCs (Mo-DCs), but whether a similar or distinct program exists for this process is unknown. In examining this issue, we discovered that only Ly-6ChiTremL4 monocytes, but not Ly-6ChiTremL4+ monocytes, can differentiate into Zbtb46+ Mo-DCs in response to GM-CSF and IL-4. However, Ly-6ChiTremL4+ monocytes were committed to Nur77-dependent development of Ly-6CloTremL4+ monocytes. Further, differentiation of monocytes with GM-CSF required addition of IL-4 to generate Zbtb46+ Mo-DCs that cross-presented as efficiently as CD24+ cDCs, which was accompanied by increased Batf3 and Irf4 expression. Unlike cDCs, Mo-DCs required only IRF4, and not Batf3, for cross-presentation. Further, Irf4/ monocytes failed to develop into Zbtb46+ Mo-DCs, and instead developed into macrophages. Thus, cDCs and Mo-DCs use distinct transcriptional programs for cross-presentation that may drive different antigen-processing pathways. These differences may influence development of therapeutic DC vaccines based on Mo-DCs.

Publication Title

Distinct Transcriptional Programs Control Cross-Priming in Classical and Monocyte-Derived Dendritic Cells.

Sample Metadata Fields

Sex, Specimen part, Treatment

View Samples
accession-icon GSE75014
Microarray expression data from WT and IRF4 KO Sirp-a+ DCs
  • organism-icon Mus musculus
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

Cross-presentation of cell-associated antigens is carried out by classical DCs (cDCs) and monocyte-derived DCs (Mo-DCs), but whether a similar or distinct program exists for this process is unknown. In examining this issue, we discovered that only Ly-6ChiTremL4 monocytes, but not Ly-6ChiTremL4+ monocytes, can differentiate into Zbtb46+ Mo-DCs in response to GM-CSF and IL-4. However, Ly-6ChiTremL4+ monocytes were committed to Nur77-dependent development of Ly-6CloTremL4+ monocytes. Further, differentiation of monocytes with GM-CSF required addition of IL-4 to generate Zbtb46+ Mo-DCs that cross-presented as efficiently as CD24+ cDCs, which was accompanied by increased Batf3 and Irf4 expression. Unlike cDCs, Mo-DCs required only IRF4, and not Batf3, for cross-presentation. Further, Irf4/ monocytes failed to develop into Zbtb46+ Mo-DCs, and instead developed into macrophages. Thus, cDCs and Mo-DCs use distinct transcriptional programs for cross-presentation that may drive different antigen-processing pathways. These differences may influence development of therapeutic DC vaccines based on Mo-DCs.

Publication Title

Distinct Transcriptional Programs Control Cross-Priming in Classical and Monocyte-Derived Dendritic Cells.

Sample Metadata Fields

Sex, Specimen part, Treatment

View Samples
accession-icon GSE75013
Microarray expression data from circulating blood monocytes
  • organism-icon Mus musculus
  • sample-icon 3 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

Cross-presentation of cell-associated antigens is carried out by classical DCs (cDCs) and monocyte-derived DCs (Mo-DCs), but whether a similar or distinct program exists for this process is unknown. In examining this issue, we discovered that only Ly-6ChiTremL4 monocytes, but not Ly-6ChiTremL4+ monocytes, can differentiate into Zbtb46+ Mo-DCs in response to GM-CSF and IL-4. However, Ly-6ChiTremL4+ monocytes were committed to Nur77-dependent development of Ly-6CloTremL4+ monocytes. Further, differentiation of monocytes with GM-CSF required addition of IL-4 to generate Zbtb46+ Mo-DCs that cross-presented as efficiently as CD24+ cDCs, which was accompanied by increased Batf3 and Irf4 expression. Unlike cDCs, Mo-DCs required only IRF4, and not Batf3, for cross-presentation. Further, Irf4/ monocytes failed to develop into Zbtb46+ Mo-DCs, and instead developed into macrophages. Thus, cDCs and Mo-DCs use distinct transcriptional programs for cross-presentation that may drive different antigen-processing pathways. These differences may influence development of therapeutic DC vaccines based on Mo-DCs.

Publication Title

Distinct Transcriptional Programs Control Cross-Priming in Classical and Monocyte-Derived Dendritic Cells.

Sample Metadata Fields

Sex, Specimen part, Treatment

View Samples
accession-icon GSE75015
Distinct transcriptional programs control cross-presentation in classical- and monocyte-derived dendritic cells
  • organism-icon Mus musculus
  • sample-icon 1 Downloadable Sample
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Distinct Transcriptional Programs Control Cross-Priming in Classical and Monocyte-Derived Dendritic Cells.

Sample Metadata Fields

Sex, Specimen part, Treatment

View Samples
accession-icon GSE66856
The transcriptional landscape of early pancreatic development
  • organism-icon Mus musculus
  • sample-icon 21 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

Pancreas organogenesis is a highly dynamic process where neighbouring tissue interactions lead to dynamic changes in gene regulatory networks that orchestrates endocrine, exocrine and ductal lineage formation. To understand the spatio-temporal regulatory logic we have used the Forkhead transcription factor Foxa2-Venus fusion (FVF) knock-in reporter mouse to separate the FVF+ pancreatic epithelium from the FVF- surrounding mesenchyme and blood vessels to perform a whole genome-wide mRNA expression profiling at embryonic day (E)12.5-15.5. This allowed us to annotate genes and molecular processes differentially regulated in these cell types and compartments of the pancreas to generate a dynamic transcriptional landscape.

Publication Title

The global gene expression profile of the secondary transition during pancreatic development.

Sample Metadata Fields

Specimen part

View Samples
accession-icon GSE110812
Altered compensatory cytokine signaling underlies the discrepancy between Flt3-/- and Flt3l-/- mice
  • organism-icon Mus musculus
  • sample-icon 16 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Altered compensatory cytokine signaling underlies the discrepancy between <i>Flt3<sup>-/-</sup></i> and <i>Flt3l<sup>-/-</sup></i> mice.

Sample Metadata Fields

Specimen part

View Samples
accession-icon GSE110789
Gene expression in WT, Flt3 KO, and Flt3L KO cDC1s and cDC2s
  • organism-icon Mus musculus
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

The goal of this study was to determine whether there are any gene expression changes in cDC1s and cDC2s from WT, Flt3 KO, or Flt3L KO mice. Specifically whether developing in the absence of Flt3 signaling had any effects on the gene expression of the cDCs

Publication Title

Altered compensatory cytokine signaling underlies the discrepancy between <i>Flt3<sup>-/-</sup></i> and <i>Flt3l<sup>-/-</sup></i> mice.

Sample Metadata Fields

Specimen part

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)

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Developed by the Childhood Cancer Data Lab

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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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