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accession-icon GSE51540
Effects of TNF-alpha blocking in sorted Th17 cells from co-cultures of human CD4-positive and CD14-positive cells
  • organism-icon Homo sapiens
  • sample-icon 16 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Human CD4+ T cells and CD14+ monocytes from healthy donors were co-cultured with anti-CD3 for three days in the presence or absence of TNF-alpha mAb (Adalimumab). Classical Th17 cells (Th17) or those generated in the presence of the inhibitor (iTh17) were then sorted and analyzed by full transcriptome microarray analysis.

Publication Title

TNF-α blockade induces IL-10 expression in human CD4+ T cells.

Sample Metadata Fields

Specimen part, Treatment, Subject

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accession-icon SRP076926
Analysis of kidney macrophages'' gene expression at steady state
  • organism-icon Mus musculus
  • sample-icon 6 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

Analysis of gene expression (RNAseq) from isolated kidney macrophages injetced i.v. with PBS Overall design: C57BL/6J mice were injected i.v. with PBS. One hour after injection, kidney macrophages were isolated (sorted by FACS) for gene expression analysis.

Publication Title

Immune Monitoring of Trans-endothelial Transport by Kidney-Resident Macrophages.

Sample Metadata Fields

Cell line, Subject

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accession-icon SRP075476
Differentiation and specification of resident tissue macrophages [SMART-Seq2]
  • organism-icon Mus musculus
  • sample-icon 158 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

Tissue resident macrophages are functionally diverse cells that share an embryonic mesodermal origin. However, the mechanism(s) that control their specification remain unclear. We performed transcriptional, molecular and in situ spatio-temporal analyses of macrophage development in mice. We report that Erythro-Myeloid Progenitors generate pre-macrophages (pMacs) that simultaneously colonize the head and caudal embryo from embryonic day (E)9.5 in a chemokine-receptor dependent manner, to further differentiate into tissue F4/80+ macrophages. The core macrophage transcriptional program initiated in pMacs, is rapidly diversified in early macrophages as expression of transcriptional regulators becomes tissue-specific. For example, the preferential expression of the transcriptional regulator Id3 initiated in early fetal liver macrophages appears critical for Kupffer cell differentiation, as inactivation of Id3 causes a selective Kupffer cell deficiency that persists in adults. We propose that colonization of developing tissues by differentiating macrophages is immediately followed by their specification as they establish residence, hereby generating the macrophage diversity observed in post-natal tissues. Overall design: RNA-sequencing of sorted macrophage cell populations (Mac) and progenitors (EMP, pMac) from various tissues and collected at different time points, including technical and biological replicates

Publication Title

Specification of tissue-resident macrophages during organogenesis.

Sample Metadata Fields

Specimen part, Subject, Time

View Samples
accession-icon SRP075553
Differentiation and specification of resident tissue macrophages [MARS-seq]
  • organism-icon Mus musculus
  • sample-icon 1 Downloadable Sample
  • Technology Badge IconIllumina HiSeq 1500

Description

Tissue resident macrophages are functionally diverse cells that share an embryonic mesodermal origin. However, the mechanism(s) that control their specification remain unclear. We performed transcriptional, molecular and in situ spatio-temporal analyses of macrophage development in mice. We report that Erythro-Myeloid Progenitors generate pre-macrophages (pMacs) that simultaneously colonize the head and caudal embryo from embryonic day (E)9.5 in a chemokine-receptor dependent manner, to further differentiate into tissue F4/80+ macrophages. The core macrophage transcriptional program initiated in pMacs, is rapidly diversified in early macrophages as expression of transcriptional regulators becomes tissue-specific. For example, the preferential expression of the transcriptional regulator Id3 initiated in early fetal liver macrophages appears critical for Kupffer cell differentiation, as inactivation of Id3 causes a selective Kupffer cell deficiency that persists in adults. We propose that colonization of developing tissues by differentiating macrophages is immediately followed by their specification as they establish residence, hereby generating the macrophage diversity observed in post-natal tissues. Overall design: RNA-sequencing of sorted macrophage cell populations (Mac) and progenitors (EMP, pMac) from various tissues and collected at different time points, including technical and biological replicates

Publication Title

Specification of tissue-resident macrophages during organogenesis.

Sample Metadata Fields

Specimen part, Subject, Time

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accession-icon GSE90471
Comparison of R1, R2 and R3
  • organism-icon Mus musculus
  • sample-icon 9 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

3 samples of R1, R2 and R3 bone marrow monocytes were compared from 3 biological replicates in 3 separate experiments.

Publication Title

The Heterogeneity of Ly6C<sup>hi</sup> Monocytes Controls Their Differentiation into iNOS<sup>+</sup> Macrophages or Monocyte-Derived Dendritic Cells.

Sample Metadata Fields

Specimen part

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accession-icon SRP067568
Transcriptome profiling of hnRNP A2/B1 and A1 depleted cells
  • organism-icon Homo sapiens
  • sample-icon 10 Downloadable Samples
  • Technology Badge IconIlluminaHiSeq2500

Description

We used NEBNext Ultra Directional RNA Library Prep Kits to prepare RNA-seq libraries of total RNA from hnRNP A2/B1 and A1 depleted A549 cells. Pro-seq libraries were prepared from A549 cells using Illumina adapters Overall design: hnRNP A2/B1 and A1 depleted A549 cells were generated by lentiviral infections of shRNA constructs. RNAs were isolated using Trizol.

Publication Title

A widespread sequence-specific mRNA decay pathway mediated by hnRNPs A1 and A2/B1.

Sample Metadata Fields

No sample metadata fields

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accession-icon SRP067554
Initial seeding of the embryonic thymus by an immune-restricted lympho-myeloid progenitor independently of Notch signaling
  • organism-icon Mus musculus
  • sample-icon 35 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 4000, Illumina HiSeq 2000

Description

Gene expression analysis of purified thymopoiesis-initiating progenitors/early thymic progenitors, lymphoid primed multipotent progenitors (LMPP) and hematopoietic stem/progenitor cells from E11.5, E12.5, E13.5 embryos, neonatal (1 week old) and adult (8 weeks old) mice Overall design: Differentially expressed genes analysis

Publication Title

Initial seeding of the embryonic thymus by immune-restricted lympho-myeloid progenitors.

Sample Metadata Fields

Specimen part, Cell line, Subject

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accession-icon SRP052999
Lympho-myeloid contribution of an immune-restricted progenitor emerging prior to definitive hematopoietic stem cells
  • organism-icon Mus musculus
  • sample-icon 26 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000

Description

An immune-restricted lymphomyeloid-primed progenitor with the capacity to contribute to both myeloid and lymphoid lineages in the developing embryo emerges prior to definitive HSCs. Overall design: Examination of fetal sorted lymphoid primed progentors and adult progenitors The fastq files are not provided at this time due to further analyses.

Publication Title

Lymphomyeloid contribution of an immune-restricted progenitor emerging prior to definitive hematopoietic stem cells.

Sample Metadata Fields

No sample metadata fields

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accession-icon SRP187754
RNA sequencing profiling of the retina in C57BL/6J and DBA/2J mice: enhancing the retinal microarray datasets from GeneNetwork
  • organism-icon Mus musculus
  • sample-icon 12 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000

Description

Purpose: The goal of the present study is to provide an independent assessment of the retinal transcriptome signatures of the C57BL/6J (B6) and DBA/2J (D2) mice and to enhance existing microarray datasets for accurately defining the allelic differences in the BXD recombinant inbred strains. Methods: Retinas from both B6 and D2 mice (3 of each) were used for the RNA-seq analysis. Transcriptome features were examined for both strains. Differentially expressed genes between the 2 strains were identified and bioinformatic analysis was performed to analyze the transcriptome differences between B6 and D2 strains, including Gene ontology (GO) analysis, Phenotype and Reactome enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The RNA-seq data were then directly compared with one of the microarray datasets (DoD Retina Normal Affy MoGene 2.0 ST RMA Gene Level Microarray Database) hosted on GeneNetwork (www.genenetwork.org). Results: RNA-seq provided an in-depth analysis of the transcriptome of the B6 and D2 retina with a total of more than 30,000,000 reads per sample. Over 70% of the reads were uniquely mapped, resulting in a total of 18,100 gene counts for all 6 samples. 1,665 genes were differentially expressed, with 858 of these more highly expressed in B6 and 807 more highly expressed in D2. Several molecular pathways were differentially active between the two strains, including the retinoic acid metabolic process, endoplasmic reticulum lumen, extracellular matrix organization, and PI3K-Akt signaling pathway. The most enriched KEGG pathways were the pentose and glucuronate interconversions pathway, the cytochrome P450 pathway, protein digestion and absorption pathway and the ECM-receptor interaction pathway. Each of these pathways had a more than 4-fold enrichment. The DoD normal retina microarray database provided expression profiling for 26,191 annotated transcripts for B6 mouse, D2 mouse and 53 BXD strains. A total of 13,793 genes in this microarray dataset were comparable to the RNA-seq dataset. For both B6 and D2, the RNA-seq data and microarray data were highly correlated with each other (Pearson's r = 0.780 for B6 and 0.784 for D2). Our results suggest that the microarray dataset can reliably detect differentially expressed genes between the B6 and D2 retinas, with a positive predictive value of 45.6%, and a negative predictive value of 93.6%. Examples of true positive and false positive genes are provided. Conclusions: Retinal transcriptome features of B6 and D2 mouse strains provide a useful reference for a better understanding of the mouse retina. Generally, the microarray database presented on GeneNetwork shows good agreement with the RNA-seq data, while we note that any allelic difference between B6 and D2 should be verified with the latter. Overall design: Retinal mRNA profiles of 2 strains of mice, C57BL/6J and DBA/2J, were generated by deep sequencing, in triplicate, using Illumina TruSeq Stranded Total RNA kit.

Publication Title

RNA sequencing profiling of the retina in C57BL/6J and DBA/2J mice: Enhancing the retinal microarray data sets from GeneNetwork.

Sample Metadata Fields

Specimen part, Cell line, Subject

View Samples
accession-icon GSE11783
Gene expression profile of bladder tissue of patients with ulcerative interstitial cystitis
  • organism-icon Homo sapiens
  • sample-icon 16 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Interstitial cystitis (IC), a chronic bladder disease with an increasing incidence, is diagnosed using subjective symptoms in combination with cystoscopic and histological evidence. The ultimate goal is the development of a diagnostic assay for IC on a molecular level.

Publication Title

Gene expression profile of bladder tissue of patients with ulcerative interstitial cystitis.

Sample Metadata Fields

Specimen part, Disease, Disease stage

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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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