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accession-icon SRP144383
Transcriptional profiles of sov mutant ovaries
  • organism-icon Drosophila melanogaster
  • sample-icon 96 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

Stranded, single-end, polyA+, transcriptional profiles were created from ovaries of sterile and fertile sov heteroallelic mutants and Gal4 driven sov RNAi knockdowns. Overall design: 15 ovaries from 4-5 day old post-eclosion females grown in uncrowded conditions were dissected and pooled for each biological replicate for a total of three replicates per genotype. Total RNA was extracted from tissues and polyA RNA was isolated and used to prepare stranded RNAseq libraries. 50 bp single-end sequencing was performed and mapped to Drosophila melanogaster release 6.21 genome.

Publication Title

<i>Drosophila</i> Heterochromatin Stabilization Requires the Zinc-Finger Protein Small Ovary.

Sample Metadata Fields

Sex, Subject

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accession-icon GSE39534
CD1d-restricted NKT cell function prevents insulin resistance in lean mice, and is regulated by adipocytes and is regulated by adipocytes
  • organism-icon Mus musculus
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.1 ST Array (mogene11st)

Description

Lipid overload and adipocyte dysfunction are key to the development of insulin resistance and can be induced by a high-fat diet. CD1d-restricted invariant natural killer T (iNKT) cells have been proposed as mediators between lipid overload and insulin resistance, but recent studies found decreased iNKT cell numbers and marginal effects of iNKT cell depletion on insulin resistance under high-fat diet conditions. Here, we focused on the role of iNKT cells under normal conditions. We showed that iNKT celldeficient mice on a low-fat diet, considered a normal diet for mice, displayed a distinctive insulin resistance phenotype without overt adipose tissue inflammation. Insulin resistance was characterized by adipocyte dysfunction, including adipocyte hypertrophy, increased leptin, and decreased adiponectin levels. The lack of liver abnormalities in CD1d-null mice together with the enrichment of CD1d-restricted iNKT cells in both mouse and human adipose tissue indicated a specific role for adipose tissueresident iNKT cells in the development of insulin resistance. Strikingly, iNKT cell function was directly modulated by adipocytes, which acted as lipid antigen-presenting cells in a CD1d-mediated fashion. Based on these findings, we propose that, especially under low-fat diet conditions, adipose tissueresident iNKT cells maintain healthy adipose tissue through direct interplay with adipocytes and prevent insulin resistance.

Publication Title

Natural killer T cells in adipose tissue prevent insulin resistance.

Sample Metadata Fields

Specimen part

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accession-icon SRP059850
Using single-cell RNA-Seq for unbiased analysis of developmental hierarchies (single cell RNA Seq of GMP)
  • organism-icon Mus musculus
  • sample-icon 123 Downloadable Samples
  • Technology Badge IconIlluminaHiSeq2500

Description

Single cell RNA seq and bioinformatic analysis is used to characterize myeloid differentiation to uncover novel transcriptional networks and key drivers of hematoipoietic development Overall design: Single cell RNA seq of different hematopoietic populations integrated with Chip seq involving multiple markers

Publication Title

Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.

Sample Metadata Fields

No sample metadata fields

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accession-icon SRP059903
Using single-cell RNA-Seq for unbiased analysis of developmental hierarchies (single cell RNA Seq from CMP)
  • organism-icon Mus musculus
  • sample-icon 85 Downloadable Samples
  • Technology Badge IconIlluminaHiSeq2500

Description

Single cell RNA seq and bioinformatic analysis is used to characterize myeloid differentiation to uncover novel transcriptional networks and key drivers of hematoipoietic development Overall design: Single cell RNA seq of different hematopoietic populations integrated with Chip seq involving multiple markers

Publication Title

Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon SRP059844
Using single-cell RNA-Seq for unbiased analysis of developmental hierarchies (single cell RNA Seq of bone marrow lineage-negative Sca1+ CD117+ cells)
  • organism-icon Mus musculus
  • sample-icon 88 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

Single cell RNA seq and bioinformatic analysis is used to characterize myeloid differentiation to uncover novel transcriptional networks and key drivers of hematoipoietic development Overall design: Single cell RNA seq of different hematopoietic populations integrated with Chip seq involving multiple markers

Publication Title

Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon SRP059848
Using single-cell RNA-Seq for unbiased analysis of developmental hierarchies (single cell RNA Seq of Gfi1-/- GMP)
  • organism-icon Mus musculus
  • sample-icon 71 Downloadable Samples
  • Technology Badge IconIlluminaHiSeq2500

Description

Single cell RNA seq and bioinformatic analysis is used to characterize myeloid differentiation to uncover novel transcriptional networks and key drivers of hematoipoietic development Overall design: Single cell RNA seq of different hematopoietic populations integrated with Chip seq involving multiple markers

Publication Title

Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon SRP059873
Using single-cell RNA-Seq for unbiased analysis of developmental hierarchies (single cell RNA Seq of Irf8 KO GMP)
  • organism-icon Mus musculus
  • sample-icon 63 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

Single cell RNA seq and bioinformatic analysis is used to characterize myeloid differentiation to uncover novel transcriptional networks and key drivers of hematoipoietic development Overall design: Single cell RNA seq of different hematopoietic populations integrated with Chip seq involving multiple markers

Publication Title

Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon SRP071150
Using single-cell RNA-Seq for unbiased analysis of developmental hierarchies (single cell RNA Seq of Gfi1-/- Irf8-/- GMP)
  • organism-icon Mus musculus
  • sample-icon 47 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

Single cell RNA seq and bioinformatic analysis is used to characterize myeloid differentiation to uncover novel transcriptional networks and key drivers of hematoipoietic development Overall design: Single cell RNA seq of different hematopoietic populations integrated with Chip seq involving multiple markers

Publication Title

Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.

Sample Metadata Fields

Specimen part, Cell line, Subject

View Samples
accession-icon SRP059847
Using single-cell RNA-Seq for unbiased analysis of developmental hierarchies (single cell RNA Seq of Gfi1-GFP GMP)
  • organism-icon Mus musculus
  • sample-icon 38 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

Single cell RNA seq and bioinformatic analysis is used to characterize myeloid differentiation to uncover novel transcriptional networks and key drivers of hematoipoietic development Overall design: Single cell RNA seq of different hematopoietic populations integrated with Chip seq involving multiple markers

Publication Title

Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon SRP059904
Using single-cell RNA-Seq for unbiased analysis of developmental hierarchies (single cell RNA Seq of Irf8-GFP GMP)
  • organism-icon Mus musculus
  • sample-icon 37 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

Single cell RNA seq and bioinformatic analysis is used to characterize myeloid differentiation to uncover novel transcriptional networks and key drivers of hematoipoietic development Overall design: Single cell RNA seq of different hematopoietic populations integrated with Chip seq involving multiple markers

Publication Title

Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.

Sample Metadata Fields

No sample metadata fields

View Samples
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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)

fund-icon Fund the CCDL

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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